Hubbry Logo
Systems biologySystems biologyMain
Open search
Systems biology
Community hub
Systems biology
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Systems biology
Systems biology
Systems biology
View on Wikipedia
from Wikipedia

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research.[1] This multifaceted research domain necessitates the collaborative efforts of chemists, biologists, mathematicians, physicists, and engineers to decipher the biology of intricate living systems by merging various quantitative molecular measurements with carefully constructed mathematical models. It represents a comprehensive method for comprehending the complex relationships within biological systems. In contrast to conventional biological studies that typically center on isolated elements, systems biology seeks to combine different biological data to create models that illustrate and elucidate the dynamic interactions within a system. This methodology is essential for understanding the complex networks of genes, proteins, and metabolites that influence cellular activities and the traits of organisms.[2][3]  One of the aims of systems biology is to model and discover emergent properties, of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques of systems biology.[1][4] By exploring how function emerges from dynamic interactions, systems biology bridges the gaps that exist between molecules and physiological processes.

As a paradigm, systems biology is usually defined in antithesis to the so-called reductionist paradigm (biological organisation), although it is consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations: "the reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge ... the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." (Sauer et al.)[5] "Systems biology ... is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different. ... It means changing our philosophy, in the full sense of the term." (Denis Noble)[6]

The central flow of biological information and the corresponding omics fields, emphasizing the systems biology approach of integrating genomics, transcriptomics, proteomics, and metabolomics to link genotype to phenotype.

As a series of operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory.[7] Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models.[8]

A comprehensive systems biology approach necessitates: (i) a thorough characterization of an organism concerning its molecular components, the interactions among these molecules, and how these interactions contribute to cellular functions; (ii) a detailed spatio-temporal molecular characterization of a cell (for example, component dynamics, compartmentalization, and vesicle transport); and (iii) an extensive systems analysis of the cell's 'molecular response' to both external and internal perturbations. Furthermore, the data from (i) and (ii) should be synthesized into mathematical models to test knowledge by generating predictions (hypotheses), uncovering new biological mechanisms, assessing the system's behavior derived from (iii), and ultimately formulating rational strategies for controlling and manipulating cells. To tackle these challenges, systems biology must incorporate methods and approaches from various disciplines that have not traditionally interfaced with one another.[9] The emergence of multi-omics technologies has transformed systems biology by providing extensive datasets that cover different biological layers, including genomics, transcriptomics, proteomics, and metabolomics. These technologies enable the large-scale measurement of biomolecules, leading to a more profound comprehension of biological processes and interactions.[10] Increasingly, methods such as network analysis, machine learning, and pathway enrichment are utilized to integrate and interpret multi-omics data, thereby improving our understanding of biological functions and disease mechanisms.[11]

History

[edit]

Holism vs. Reductionism

It is challenging to trace the origins and beginnings of systems biology. A comprehensive perspective on the human body was central to the medical practices of Greek, Roman, and East Asian traditions, where physicians and thinkers like Hippocrates believed that health and illness were linked to the equilibrium or disruption of bodily fluids known as humors. This holistic perspective persisted in the Western world throughout the 19th and 20th centuries, with prominent physiologists viewing the body as controlled by various systems, including the nervous system, the gastrointestinal system, and the cardiovascular system. In the latter half of the 20th century, however, this way of thinking was largely supplanted by reductionism:[12][13] To grasp how the body functions properly, one needed to comprehend the role of each component, from tissues and cells to the complete set of intracellular molecular building blocks.[14]

In the 17th century, the triumphs of physics and the advancement of mechanical clockwork prompted a reductionist viewpoint in biology, interpreting organisms as intricate machines made up of simpler elements.[15]

Jan Smuts (1870–1950), naturalist/philosopher and twice Prime Minister of South Africa, coined the commonly used term holism. Whole systems such as cells, tissues, organisms, and populations were proposed to have unique (emergent) properties. It was impossible to try and reassemble the behavior of the whole from the properties of the individual components, and new technologies were necessary to define and understand the behavior of systems.[15]

Even though reductionism and holism are often contrasted with one another, they can be synthesized. One must understand how organisms are built (reductionism), while it is just as important to understand why they are so arranged (systems; holism). Each provides useful insights and answers different questions. However, the study of biological systems requires knowledge about control and design paradigms, as well as principles of structural stability, resilience, and robustness that are not directly inferred from mechanistic information. More profound insight will be gained by employing computer modeling to overcome the complexity in biological systems.[15]

Nevertheless, this perspective was consistently balanced by thinkers who underscored the significance of organization and emergent traits in living systems. This reductionist perspective has achieved remarkable success, and our understanding of biological processes has expanded with incredible speed and intensity. However, alongside these extraordinary advancements, science gradually came to understand that possessing complete information about molecular components alone would not suffice to elucidate the workings of life: the individual components rarely illustrate the function of a complex system. It is now commonly recognized that we need approaches for reconstructing integrated systems from their constituent parts and processes if we are to comprehend biological phenomena and manipulate them in a thoughtful, focused way.[16]

Origin of systems biology as a field

Shows trends in systems biology research. From 1992 to 2013 database development articles increased. Articles about algorithms have fluctuated but remained fairly steady. Network properties articles and software development articles have remained low but experienced an increased about halfway through the time period 1992–2013. The articles on metabolic flux analysis decreased from 1992 to 2013. In 1992 algorithms, equations, modeling and simulation articles were most cited. In 2012 the most cited were database development articles.
Shows trends in systems biology research by presenting the number of articles out of the top 30 cited systems biology papers during that time which include a specific topic[17]

In 1968, the term "systems biology" was first introduced at a conference.[18] Those within the discipline soon recognized—and this understanding gradually became known to the wider public—that computational approaches were necessary to fully articulate the concepts and potential of systems biology. Specifically, these techniques needed to view biological phenomena as complex, multi-layered, adaptive, and dynamic systems. They had to account for transformations and intricate nonlinearities, thereby allowing for the smooth integration of smaller models ("modules") into larger, well-organized assemblies of models within complex settings. It became clear that mathematics and computation were vital for these methods.[19][20][21][22] An acceleration of systems understanding came with the publication of the first ground-breaking text compiling molecular, physiological, and anatomical individuality in animals,[23] which has been described as a revolution.[24]

Initially, the wider scientific community was reluctant to accept the integration of computational methods and control theory in the exploration of living systems, believing that "biology was too complex to apply mathematics." However, as the new millennium neared, this viewpoint underwent a significant and lasting transformation.[14] More scientists started working on integration of mathematical concepts to understand and solve biological problems. Now, systems biology has been widely applied in several fields including agriculture and medicine.

Approaches to systems biology

[edit]

Top-down approach

[edit]

Top-down systems biology identifies molecular interaction networks by analyzing the correlated behaviors observed in large-scale 'omics' studies. With the advent of 'omics', this top-down strategy has become a leading approach. It begins with an overarching perspective of the system's behavior – examining everything at once – by gathering genome-wide experimental data and seeks to unveil and understand biological mechanisms at a more granular level – specifically, the individual components and their interactions. In this framework of 'top-down' systems biology, the primary goal is to uncover novel molecular mechanisms through a cyclical process that initiates with experimental data, transitions into data analysis and integration to identify correlations among molecule concentrations and concludes with the development of hypotheses regarding the co- and inter-regulation of molecular groups. These hypotheses then generate new predictions of correlations, which can be explored in subsequent experiments or through additional biochemical investigations.[25] The notable advantages of top-down systems biology lie in its potential to provide comprehensive (i.e., genome-wide) insights and its focus on the metabolome, fluxome, transcriptome, and/or proteome. Top-down methods prioritize overall system states as influencing factors in models and the computational (or optimality) principles that govern the dynamics of the global system. For instance, while the dynamics of motor control (neuro) emerge from the interactions of millions of neurons, one can also characterize the neural motor system as a sort of feedback control system, which directs a 'plant' (the body) and guides movement by minimizing 'cost functions' (e.g., achieving trajectories with minimal jerk).[26]

Bottom-up approach

[edit]

Bottom-up systems biology infers the functional characteristics that may arise from a subsystem characterized with a high degree of mechanistic detail using molecular techniques. This approach begins with the foundational elements by developing the interactive behavior (rate equation) of each component process (e.g., enzymatic processes) within a manageable portion of the system. It examines the mechanisms through which functional properties arise in the interactions of known components. Subsequently, these formulations are combined to understand the behavior of the system. The primary goal of this method is to integrate the pathway models into a comprehensive model representing the entire system - the top or whole. As research and understanding advance, these models are often expanded by incorporating additional processes with high mechanistic detail.[26]

The bottom-up approach facilitates the integration and translation of drug-specific in vitro findings to the in vivo human context. This encompasses data collected during the early phases of drug development, such as safety evaluations. When assessing cardiac safety, a purely bottom-up modeling and simulation method entails reconstructing the processes that determine exposure, which includes the plasma (or heart tissue) concentration-time profiles and their electrophysiological implications, ideally incorporating hemodynamic effects and changes in contractility. Achieving this necessitates various models, ranging from single-cell to advanced three-dimensional (3D) multiphase models. Information from multiple in vitro systems that serve as stand-ins for the in vivo absorption, distribution, metabolism, and excretion (ADME) processes enables predictions of drug exposure, while in vitro data on drug-ion channel interactions support the translation of exposure to body surface potentials and the calculation of important electrophysiological endpoints. The separation of data related to the drug, system, and trial design, which is characteristic of the bottom-up approach, allows for predictions of exposure-response relationships considering both inter- and intra-individual variability, making it a valuable tool for evaluating drug effects at a population level. Numerous successful instances of applying physiologically based pharmacokinetic (PBPK) modeling in drug discovery and development have been documented in the literature.[27]

Associated disciplines

[edit]
Overview of signal transduction pathways

According to the interpretation of systems biology as using large data sets using interdisciplinary tools, a typical application is metabolomics, which is the complete set of all the metabolic products, metabolites, in the system at the organism, cell, or tissue level.[28]

Items that may be a computer database include: phenomics, organismal variation in phenotype as it changes during its life span; genomics, organismal deoxyribonucleic acid (DNA) sequence, including intra-organismal cell specific variation. (i.e., telomere length variation); epigenomics/epigenetics, organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., DNA methylation, Histone acetylation and deacetylation, etc.); transcriptomics, organismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression; interferomics, organismal, tissue, or cell-level transcript correcting factors (i.e., RNA interference), proteomics, organismal, tissue, or cell level measurements of proteins and peptides via two-dimensional gel electrophoresis, mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry). Sub disciplines include phosphoproteomics, glycoproteomics and other methods to detect chemically modified proteins; glycomics, organismal, tissue, or cell-level measurements of carbohydrates; lipidomics, organismal, tissue, or cell level measurements of lipids.[citation needed]

The molecular interactions within the cell are also studied, this is called interactomics.[29] A discipline in this field of study is protein–protein interactions, although interactomics includes the interactions of other molecules.[30] Neuroelectrodynamics, where the computer's or a brain's computing function as a dynamic system is studied along with its (bio)physical mechanisms;[31] and fluxomics, measurements of the rates of metabolic reactions in a biological system (cell, tissue, or organism).[28]

In approaching a systems biology problem there are two main approaches. These are the top down and bottom up approach. The top down approach takes as much of the system into account as possible and relies largely on experimental results. The RNA-Seq technique is an example of an experimental top down approach. Conversely, the bottom up approach is used to create detailed models while also incorporating experimental data. An example of the bottom up approach is the use of circuit models to describe a simple gene network.[32]

Various technologies utilized to capture dynamic changes in mRNA, proteins, and post-translational modifications. Mechanobiology, forces and physical properties at all scales, their interplay with other regulatory mechanisms;[33] biosemiotics, analysis of the system of sign relations of an organism or other biosystems; Physiomics, a systematic study of physiome in biology.

Cancer systems biology is an example of the systems biology approach, which can be distinguished by the specific object of study (tumorigenesis and treatment of cancer). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing cancer genome in patient tumour samples) and tools (immortalized cancer cell lines, mouse models of tumorigenesis, xenograft models, high-throughput sequencing methods, siRNA-based gene knocking down high-throughput screenings, computational modeling of the consequences of somatic mutations and genome instability).[34] The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for personalized cancer medicine and virtual cancer patient in more distant prospective. Significant efforts in computational systems biology of cancer have been made in creating realistic multi-scale in silico models of various tumours.[35]

The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks.[36][37][38][39] For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics[40] and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., flux balance analysis).[38][40]

Other aspects of computer science, informatics, and statistics are also used in systems biology. These include new forms of computational models, such as the use of process calculi to model biological processes (notable approaches include stochastic π-calculus, BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and constraint-based modeling; integration of information from the literature, using techniques of information extraction and text mining;[41] development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via loose coupling of software, websites and databases, or commercial suits; network-based approaches for analyzing high dimensional genomic data sets. For example, weighted correlation network analysis is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data analysis, e.g. approaches to identify and score pathways with differential activity of their gene, protein, or metabolite members.[42] Much of the analysis of genomic data sets also include identifying correlations. Additionally, as much of the information comes from different fields, the development of syntactically and semantically sound ways of representing biological models is needed.[43]

Model and its types

[edit]

Definition

[edit]

A model serves as a conceptual depiction of objects or processes, highlighting certain characteristics of these items or activities. A model captures only certain facets of reality; however, when created correctly, this limited scope is adequate because the primary goal of modeling is to address specific inquiries.[44] The saying, "essentially, all models are wrong, but some are useful," attributed to the statistician George Box, is a suitable principle for constructing models.[45]

Types of models

[edit]
  • Boolean Models: These models are also known as logical models and represent biological systems using binary states, allowing for the analysis of gene regulatory networks and signaling pathways. They are advantageous for their simplicity and ability to capture qualitative behaviors.[46][47][48]
    This figure deals with the tool BooleSim which is used for simulating and manipulating Boolean models. The given figure deals with a simple synthetic repressilator (A&C) and the concerned output (time series) is obtained using the tool BooleSim (B & D). The boxes represent nodes and the arrow shows the relationship between them. Pointed and blunt arrows indicate promotion and repression of the gene. Yellow coloured boxes indicate the switched on status of the gene and blue colour denotes its switched off state.
  • Petri nets (PN):  A unique type of bipartite graph consisting of two types of nodes: places and transitions. When a transition is activated, a token is transferred from the input places to the output places; the process is asynchronous and non-deterministic.[49][50]  
  • Polynomial dynamical systems (PDS)- An algebraically based approach that represents a specific type of sequential FDS (Finite Dynamical System) operating over a finite field. Each transition function is an element within a polynomial ring defined over the finite field. It employs advanced rapid techniques from computer algebra and computational algebraic geometry, originating from the Buchberger algorithm, to compute the Gröbner bases of ideals in these rings. An ideal consists of a set of polynomials that remain closed under polynomial combinations.[51][52]
  • Differential equation models (ODE and PDE)- Ordinary Differential Equations (ODEs) are commonly utilized to represent the temporal dynamics of networks, while Partial Differential Equations (PDEs) are employed to describe behaviors occurring in both space and time, enabling the modeling of pattern formation. These spatiotemporal Diffusion-Reaction Systems demonstrate the emergence of self-organizing patterns, typically articulated by the general local activity principle, which elucidates the factors contributing to complexity and self-organization observed in nature.[53][54]
  • Bayesian models: This kind of model is commonly referred to as dynamic models. It utilizes a probabilistic approach that enables the integration of prior knowledge through Bayes' Theorem. A challenge can arise when determining the direction of an interaction.[55][56]
  • Finite State Linear Model (FSML): This model integrates continuous variables (such as protein concentration) with discrete elements (like promoter regions that have a limited number of states) in modeling.[57]
  • Agent-based models (ABM): Initially created within the fields of social sciences and economics, it models the behavior of individual agents (such as genes, mRNAs (siRNA, miRNA, lncRNA), proteins, and transcription factors) and examines how their interactions influence the larger system, which in this case is the cell.[58][59]
  • Rule – based models: In this approach, molecular interactions are simulated using local rules that can be utilized even in the absence of a specific network structure, meaning that the step to infer the network is not required, allowing these network-free methods to avoid the complex challenges associated with network inference.[60]
  • Piecewise-linear differential equation models (PLDE): The model is composed of a piecewise-linear representation of differential equations using step functions, along with a collection of inequality restrictions for the parameter values.[61]
A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michaelis–Menten reaction.
  • Stochastic models: Models utilizing the Gillespie algorithm for addressing the chemical master equation provide the likelihood that a particular molecular species will possess a defined molecular population or concentration at a specified future point in time.[62] The Gillespie method is the most computationally intensive option available. In cases where the number of molecules is low or when modeling the effects of molecular crowding is desired, the stochastic approach is preferred.[63][64][65]
    The graph demonstrates the enzymatic conversion of cellulose to glucose over time where red line denoted cellulose and green line denotes glucose, with key enzymes facilitating the process and their concentrations changing as the reaction progresses (Time course run in COPASI). This is a typical kinetic profile for a multi-enzyme hydrolysis system.
  • State Space Model (SSM): Linear or non-linear modeling techniques that utilize an abstract state space along with various algorithms, which include Bayesian and other statistical methods, autoregressive models, and Kalman filtering.[66][67]

Creating biological models

[edit]

Researchers begin by choosing a biological pathway and diagramming all of the protein, gene, and/or metabolic pathways. After determining all of the interactions, mass action kinetics or enzyme kinetic rate laws are used to describe the speed of the reactions in the system. Using mass-conservation, the differential equations for the biological system can be constructed. Experiments or parameter fitting can be done to determine the parameter values to use in the differential equations.[68] These parameter values will be the various kinetic constants required to fully describe the model. This model determines the behavior of species in biological systems and bring new insight to the specific activities of systems. Sometimes it is not possible to gather all reaction rates of a system. Unknown reaction rates are determined by simulating the model of known parameters and target behavior which provides possible parameter values.[69][70]

The use of constraint-based reconstruction and analysis (COBRA) methods has become popular among systems biologists to simulate and predict the metabolic phenotypes, using genome-scale models. One of the methods is the flux balance analysis (FBA) approach, by which one can study the biochemical networks and analyze the flow of metabolites through a particular metabolic network, by optimizing the objective function of interest (e.g. maximizing biomass production to predict growth).[27]

Applications in system biology

[edit]

Systems biology, an interdisciplinary field that combines biology, data analysis, and mathematical modeling, has revolutionized various sectors, including medicine, agriculture, and environmental science. By integrating omics data (genomics, proteomics, metabolomics, etc.), systems biology provides a holistic understanding of complex biological systems, enabling advancements in drug discovery, crop improvement, and environmental impact assessment. This response explores the applications of systems biology across these domains, highlighting both industrial and academic research contributions. System biology is used in agriculture to identify the genetic and metabolic components of complex characteristics through trait dissection.[71] It aids in the comprehension of plant-pathogen interactions in disease resistance.[72] It is utilized in nutritional quality to enhance nutritional content through metabolic engineering.[73]

Cancer

[edit]

Approaches to cancer systems biology have made it possible to effectively combine experimental data with computer algorithms and, as an exception, to apply actionable targeted medicines for the treatment of cancer. In order to apply innovative cancer systems biology techniques and boost their effectiveness for customizing new, individualized cancer treatment modalities, comprehensive multi-omics data acquired through the sequencing of tumor samples and experimental model systems will be crucial.[74]

Cancer systems biology has the potential to provide insights into intratumor heterogeneity and identify therapeutic options. In particular, enhanced cancer systems biology methods that incorporate not only multi-omics data from tumors, but also extensive experimental models derived from patients can assist clinicians in their decision-making processes, ultimately aiming to address treatment failures in cancer.[74]

Drug development

[edit]

Before the 1990s, phenotypic drug discovery formed the foundation of most research in drug discovery, utilizing cellular and animal disease models to find drugs without focusing on a specific molecular target. However, following the completion of the human genome project, target-based drug discovery has become the predominant approach in contemporary pharmaceutical research for various reasons. Gene knockout and transgenic models enable researchers to investigate and gain insights into the function of targets and the mechanisms by which drugs operate on a molecular level. Target-based assays lend themselves better to high-throughput screening, which simplifies the process of identifying second-generation drugs—those that improve upon first-in-class drugs in aspects such as potency, selectivity, and half-life, especially when combined with structure-based drug design. To do this, researchers utilize the three-dimensional structure of target proteins and computational models of interactions between small molecules and those targets to aid in the identification of superior compounds.[75]

Food safety and quality

[edit]

The multi-omics technologies in system biology can be also be used in aspects of food quality and safety. High-throughput omics techniques, including genomics, proteomics, and metabolomics, offer valuable insights into the molecular composition of food products, facilitating the identification of critical elements that affect food quality and safety. For example, integrating omics data can enhance the understanding of the metabolic pathways and associated functional gene patterns that contribute to both the nutritional value and safety of food crops. This comprehensive approach guarantees the creation of food products that are both nutritious and safe, capable of satisfying the increasing global demand.[76][77]

Environmental system biology

Genomics examines all genes as an evolving system over time, aiming to understand their interactions and effects on biological pathways, networks, and physiology in a broader context compared to genetics.[78] As a result, genomics holds significant potential for discovering clusters of genes associated with complex disorders, aiding in the comprehension and management of diseases induced by environmental factors.[79]

When exploring the interactions between the environment and the genome as contributors to complex diseases, it is clear that the genome itself cannot be altered for the time being. However, once these interactions are recognized, it is feasible to minimize exposure or adjust lifestyle factors related to the environmental aspect of the disease.[80][81] Gene-environment interactions can occur through direct associations with active metabolites at certain locations within the genome, potentially leading to mutations that could cause human diseases. Indirect interactions with the human genome can take place through intracellular receptors that function as ligand-activated transcription factors, which modulate gene expression and maintain cellular balance, or with an environmental factor that may produce detrimental effects.[82] This type of environmental-gene interaction could be more straightforward to investigate than direct interactions since there are numerous markers of this kind of interaction that are readily measurable before the disease manifests. Examples of this include the expression of cytochrome P450 genes following exposure to environmental substances, such as the polycyclic aromatic hydrocarbon benzo[a]pyrene, which binds to the Ah receptor.[83][84][85]

Technical challenges

[edit]

One of the main challenges in systems biology is the connection between experimental descriptions, observations, data, models, and the assumptions that stem from them. In essence, systems biology must be understood within an information management framework that significantly encompasses experimental life sciences. Models are created using various languages or representation schemes, each suitable for conveying and reasoning about distinct sets of characteristics. There is no single universal language for systems biology that can adequately cover the diverse phenomena we aim to investigate. However, this intricate scenario overlooks two important aspects. Models can be developed in multiple versions over time and by different research teams. Conflicts can occur, and observations may be disputed. Various researchers might produce models in different versions and configurations. The unpredictable elements suggest that systems biology is not likely to yield a definitive collection of established models. Instead, we can expect a rich ecosystem of models to develop within a structure that fosters discussion and cooperation among participants. Challenges also exist in verifying the constraints and creating modeling frameworks with robust compositional strategies. This may create a need  to handle models that may conflict with one another, whether between schemes or across different scales.  In the end, the goal could involve the creation of personalized models that reflect differences in physiology, as opposed to universal models of biological processes.[86]

Other challenges include the massive amount of data created by high-throughput omics technologies which presents considerable challenges in terms of computation and storage. Each analysis in omics can result in data files ranging from terabytes to petabytes, which requires strong computational systems and ample storage solutions to manage and process these datasets effectively.[87] The computational requirements are made more difficult by the necessity for advanced algorithms that can integrate and analyze diverse, high-dimensional data. Approaches like deep learning and network-based methods have displayed potential in tackling these issues, but they also demand significant computational power.[88]

Artificial intelligence (AI) in systems biology

[edit]

Utilizing AI in Systems Biology enables scientists to uncover novel insights into the intricate relationships present within biological systems, such as those among genes, proteins, and cells. A significant focus within Systems Biology is the application of AI for the analysis of expansive and complex datasets, including multi-omics data produced by high-throughput methods like next-generation sequencing and proteomics. Approaches powered by AI can be employed to detect patterns and correlations within these datasets and to anticipate the behavior of biological systems under varying conditions.[89]

For instance, artificial intelligence can identify genes that are expressed differently across various cancer types or detect small molecules linked to particular disease states.[90] A key difficulty in analyzing multi-omics data is the integration of information from multiple sources. AI can create integrative models that consider the intricate interactions between different types of molecular data. These models may be utilized to uncover new biomarkers or therapeutic targets for diseases, as well as to enhance our understanding of fundamental biological processes. By significantly speeding up our comprehension of complex biological systems, AI has the potential to lead to new treatments and therapies for a range of diseases.[89]

Structural systems biology is a multidisciplinary field that merges systems biology with structural biology to investigate biological systems at the molecular scale. This domain strives for a thorough understanding of how biological molecules interact and function within cells, tissues, and organisms. The integration of AI in structural systems biology has become increasingly vital for examining extensive and complex datasets and modeling the behavior of biological systems. AI facilitates the analysis of protein–protein interaction networks within structural systems biology. These networks can be explored using graph theory and various mathematical methods, uncovering key characteristics such as hubs and modules.[91] AI can also assist in the discovery of new drugs or therapies by predicting the effect of a drug on a particular biological component or pathway.[92]

See also

[edit]

References

[edit]

Sources

[edit]

Further reading

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Systems biology

View on Grokipedia
from Grokipedia
Systems biology is an interdisciplinary field that integrates computational modeling, , and experimental to study complex biological systems at multiple scales, from molecules to organisms, aiming to understand emergent properties and predict system behaviors through holistic analysis rather than isolated components. Emerging as a response to the limitations of reductionist approaches in traditional , systems biology gained prominence following the in the early 2000s, which provided vast datasets necessitating integrative analysis. Its roots trace back to mid-20th-century developments in general by and by , which emphasized interconnected networks and feedback loops in living systems. Key milestones include the establishment of dedicated research programs, such as the NIH's Laboratory of Systems Biology in 2011, focusing on dynamics. At its core, systems biology employs iterative cycles of data collection from high-throughput technologies like , , and —collectively known as multi-omics—combined with mathematical modeling to simulate interactions and identify such as toggle switches or repressilators. This approach reveals how molecular diversity and regulatory networks produce system-level phenomena, including and emergent behaviors, often using predictive tools like digital twins for applications in disease modeling and . Unlike bioinformatics, which primarily handles and analysis, systems biology emphasizes hypothesis-driven experimentation and model validation to redesign or engineer biological circuits, bridging to . It relies on collaborative teams spanning , physics, engineering, and to tackle challenges in , , and environmental .

Definition and Fundamentals

Core Definition

Systems biology is an interdisciplinary field that seeks to understand the structure and dynamics of complex biological systems through the integration of experimental data, computational modeling, and theoretical analysis, aiming to elucidate emergent properties across scales from molecules to organisms. This approach emphasizes the holistic study of biological processes, where the behavior of the whole system cannot be fully predicted from its individual parts alone. At its core, systems biology relies on high-throughput data generation techniques, such as and , to capture comprehensive profiles of biological states, combined with network analysis to map interactions like those in gene regulatory networks and predictive modeling to simulate system responses. For instance, gene regulatory networks illustrate how transcription factors and their targets interact dynamically to control cellular functions, revealing emergent behaviors such as robustness or adaptability in response to perturbations. Unlike traditional reductionist biology, which dissects systems into isolated components, systems biology prioritizes the quantitative analysis of interactions and feedback loops to explain system-level phenomena. This shift enables predictive insights into complex processes, such as disease mechanisms or evolutionary adaptations. Systems biology emerged in the late 1990s as a response to the challenges of the post-genomic era, where vast datasets demanded integrative frameworks beyond classical molecular approaches. Approaches like top-down and bottom-up methods serve as key strategies to achieve this holistic perspective.

Key Principles

Systems biology is grounded in the principle of , where complex system-level behaviors arise from the interactions of simpler components rather than being predictable from individual parts alone. For instance, in metabolic pathways, the collective dynamics of enzymes and substrates can lead to emergent cellular responses such as oscillations or that are not evident from isolated reactions. This principle underscores the need to study biological systems holistically to uncover properties like robustness and adaptability that emerge at higher levels of organization. Central to systems biology are feedback loops within dynamic biological networks, which regulate processes through positive and negative mechanisms. loops maintain by counteracting perturbations, while loops amplify signals to drive decisions like . These loops are modeled as dynamical systems using ordinary differential equations, such as dxdt=f(x)\frac{dx}{dt} = f(x), where xx represents state variables like concentrations and f(x)f(x) captures interaction rules, enabling predictions of stability and oscillations. Scale integration is a core principle, bridging molecular interactions—such as protein-protein binding—to systems-level outcomes like organ function, through concepts of and robustness. Modularity allows biological systems to be decomposed into reusable modules that function semi-independently, enhancing evolvability, while robustness ensures functionality persists despite perturbations via redundant pathways or feedback. This multi-scale view, from genes to tissues, facilitates understanding how local molecular events propagate to global phenotypes. Quantitative rigor in systems biology relies on mathematical frameworks to describe and simulate processes precisely, moving beyond qualitative descriptions. For example, are often modeled with differential equations like d[S]dt=k1[E][S]−k2[ES]\frac{d[S]}{dt} = k_1 [E][S] - k_2 [ES], where [S][S] is substrate concentration, [E][E] enzyme, [ES][ES] complex, and k1,k2k_1, k_2 rate constants, allowing derivation of steady-state behaviors such as Michaelis-Menten kinetics. This approach, exemplified in early computational models of physiological systems by figures like , provides testable predictions and reveals design principles underlying biological complexity.

Historical Development

Early Foundations

The early foundations of systems biology trace back to ancient and classical philosophies that emphasized holistic and purposeful interpretations of . In Eastern traditions, particularly (TCM), practitioners viewed the human body as an interconnected whole governed by dynamic balances, such as the interplay of forces and the circulation of through meridians, treating illness as disruptions in systemic harmony rather than isolated events. This holistic perspective anticipated modern systems approaches by prioritizing emergent properties and regulatory interactions across organs and physiological processes. Similarly, in , Aristotle's teleological framework in biology described organisms as structured for specific ends, with parts serving the function of the entire entity, as elaborated in works like De Anima and Parts of Animals, influencing later conceptions of and . In the mid-20th century, emerged as a pivotal influence, bridging and through concepts of feedback and control. Norbert Wiener's 1948 book Cybernetics: Or Control and Communication in the Animal and the Machine formalized the study of regulatory mechanisms in , drawing parallels between neural signaling, , and mechanical servosystems to model adaptive behaviors in organisms. Wiener's ideas profoundly shaped biological modeling by introducing quantitative tools for analyzing communication and stability in , inspiring applications in and . Parallel to cybernetics, Ludwig von Bertalanffy's general in the mid-20th century provided a framework for understanding open systems in biology, emphasizing organization and wholeness beyond , as outlined in his 1968 book General System Theory. Physiological modeling advanced these foundations in the with computational simulations of cellular dynamics. Denis Noble's 1962 work introduced the first of the in , modifying the Hodgkin-Huxley squid equations to incorporate heart-specific ionic currents, including the time-dependent conductance and inward , collectively termed the Noble equations. This ionic model simulated the action potential and pacemaker activity, demonstrating how differential equations could predict emergent electrical behaviors in excitable cells and paving the way for integrative simulations of organ-level function. Systems theory extended to ecology in the 1970s, where network-based approaches analyzed population interactions. Robert May's 1972 analysis of random ecological networks revealed that stability in large, complex systems declines with increasing connectance and species diversity, using linear stability criteria to show that random interactions often lead to chaotic oscillations or collapse. These findings underscored the importance of structural properties in maintaining biological resilience, influencing later holistic models of ecosystems as interconnected dynamic systems. These pre-1990s developments laid essential theoretical groundwork, bridging to the genomic era's emphasis on molecular networks.

Modern Emergence

The term "systems biology" was first coined by Mihajlo Mesarović in 1968, but gained modern prominence in the through Leroy Hood's work to describe an integrative approach that combines high-throughput technologies with computational modeling to understand biological systems as interconnected networks rather than isolated parts. This conceptualization emerged amid rapid advances in , emphasizing the need to move beyond sequencing individual genes toward analyzing their dynamic interactions. Building briefly on early cybernetic influences from the mid-20th century, which viewed biological processes through feedback loops, the marked a formal shift toward data-intensive, holistic studies. The completion of the in 2003 catalyzed this transition, redirecting focus from gene discovery to elucidating functions within complex regulatory networks. Post-project efforts highlighted how genomic data revealed multilayered interactions among , proteins, and metabolites, necessitating systems-level analyses to interpret emergent properties like disease mechanisms. A pivotal milestone was the founding of the Institute for Systems Biology (ISB) in 2000 by , along with Alan Aderem and Ruedi Aebersold, as the first dedicated institution to pioneer this interdisciplinary field through collaborative, technology-driven research. In 2007, the (NIH) further institutionalized systems biology via its Roadmap for Medical Research, funding Centers of Excellence to integrate computational and experimental approaches for studying human health and disease. In 2011, the NIH established the Laboratory of Systems Biology within the National Institute of and Infectious Diseases, focusing on systems . The rise of technologies profoundly influenced this emergence, with transcriptomics and enabling the generation of vast datasets on and protein interactions, thus facilitating data-driven models of biological networks. These high-throughput methods, such as microarray-based transcript profiling and for , shifted biology from hypothesis-driven to empirical, integrative exploration of system-wide dynamics. In the 2010s, systems biology advanced through integration with , exemplified by the project's 2012 release of comprehensive maps of functional genomic elements, including transcription factors and states across cell types. This resource supported network-based analyses by providing empirical data on regulatory interactions, enhancing predictive modeling of cellular responses and disease pathways. Such initiatives solidified systems biology as a core paradigm, bridging genomics with computational tools to uncover systemic principles in biology.

Methodological Approaches

Top-Down Approach

The top-down approach in systems biology involves a holistic analysis that begins with system-level observations, such as phenotypic data or high-level 'omics measurements, and works backward to elucidate underlying molecular mechanisms. This methodology emphasizes reverse-engineering biological processes by integrating large-scale datasets to infer interactions and functions within complex systems. For instance, imaging techniques applied to whole organisms or tissues provide initial phenotypic insights that guide subsequent dissection of regulatory networks. Key techniques in the top-down approach include methods like (RNAi) to systematically perturb functions and observe system-wide effects, perturbation experiments that alter biological conditions to reveal network responses, and strategies that link phenotypes to specific genetic modifications. These tools generate comprehensive datasets, such as transcriptomic profiles from DNA microarrays or RNA sequencing, enabling the reconstruction of metabolic or signaling pathways through statistical and bioinformatics analyses. A representative example is the modeling of immune responses, where microarray data from blood samples are used to identify co-expression modules that infer activation of specific pathways, such as interferon signaling or B-cell differentiation, during infection or vaccination. This approach has revealed transcriptional signatures associated with immune cell activation, facilitating the prediction of response dynamics without prior knowledge of individual components. The top-down approach excels at capturing emergent properties, such as nonlinear interactions that arise only at the systems level, providing a genome-wide perspective that complements bottom-up methods focused on molecular details. However, it faces limitations in for very large systems due to challenges in and interpretation, often requiring sophisticated computational tools to avoid incomplete or biased inferences.

Bottom-Up Approach

The bottom-up approach in systems biology involves constructing detailed models of biological systems by assembling fundamental components, such as genes, proteins, and metabolites, based on established molecular interactions and biochemical knowledge to simulate and predict emergent system-level behaviors. This method starts from constitutive elements like individual reactions and pathways, integrating them iteratively to explain higher-level properties, contrasting with data-driven inference methods. It relies on prior knowledge from literature, databases, and experimental data to build mechanistically grounded representations of cellular processes. Key techniques in the bottom-up approach include kinetic modeling, which describes reaction rates using differential equations derived from enzyme mechanisms, and pathway reconstruction, which assembles networks from curated biochemical data. For instance, are often modeled with the Michaelis-Menten equation, which approximates the rate of substrate conversion under steady-state assumptions: v=Vmax⁡[S]Km+[S]v = \frac{V_{\max} [S]}{K_m + [S]} where vv is the reaction velocity, Vmax⁡V_{\max} is the maximum rate, [S][S] is the substrate concentration, and KmK_m is the Michaelis constant reflecting enzyme-substrate affinity. This equation enables simulation of dynamic behaviors in metabolic or signaling pathways by incorporating measured parameters, though approximations like quasi-steady-state are used when full mechanistic details are unavailable. Pathway reconstruction begins with draft models generated from genomic annotations and databases (e.g., ), followed by manual curation to resolve gaps using organism-specific literature, resulting in stoichiometric matrices for further analysis. A prominent example is the reconstruction of metabolic networks in , where bottom-up modeling employs (FBA) to predict steady-state fluxes through the genome-scale network. FBA optimizes an objective function, such as biomass production, subject to constraints represented by the steady-state condition: ∑jSijvj=0\sum_j S_{ij} v_j = 0 for each ii, where SS is the stoichiometric matrix and vjv_j are reaction fluxes. Early applications, such as the iJR904 model, integrated over 900 reactions from biochemical literature to simulate growth phenotypes and gene deletion effects, achieving predictions that matched experimental yields within 10-20% accuracy. Subsequent refinements, like the iJO1366 model, incorporated thermodynamic constraints and transport reactions, enhancing predictive power for metabolic engineering. The bottom-up approach provides high mechanistic detail, allowing hypothesis testing through perturbations and revealing design principles like robustness in metabolic pathways. However, it faces challenges in parameter estimation, as comprehensive kinetic (e.g., Vmax⁡V_{\max} and KmK_m values) are often incomplete or context-dependent, necessitating approximations or sensitivity analyses to address uncertainties. Validation typically involves comparing simulations to experimental 'omics , such as measurements from tracer studies.

Integrative Approaches

Integrative approaches in systems biology merge experimental data from high-throughput techniques with computational reconstruction methods to develop comprehensive models that capture the complexity of biological systems. This synthesis addresses limitations of isolated strategies by combining empirical observations with mechanistic simulations, enabling the prediction of emergent properties in cellular processes. For instance, datasets such as transcriptomics and are integrated into computational frameworks to refine model accuracy and uncover hidden interactions. Key techniques in integrative approaches include constraint-based modeling, which incorporates multi-omics data into genome-scale metabolic models (GEMs) to constrain reaction fluxes and simulate metabolic phenotypes. In this method, steady-state assumptions and constraints are applied to GEMs, allowing the integration of condition-specific data like to predict cellular responses under varying environments. further enhances these models by performing gap-filling, where algorithms identify and propose missing reactions based on patterns in data, improving model completeness without manual curation. Tools like use to resolve gaps at both reaction and phenotypic levels, leveraging large datasets to infer plausible biochemical pathways. A representative example of hybrid modeling involves signaling pathways, where Bayesian networks fuse top-down perturbation data—such as experiments—with bottom-up kinetic parameters to infer causal relationships and dynamic behaviors. These networks probabilistically model dependencies among pathway components, enabling the reconstruction of regulatory structures from noisy experimental data and simulating how perturbations propagate through the system. Such integrations have been applied to signaling, revealing key regulators of cell fate decisions. Recent developments post-2020 emphasize multi-scale simulations that link cellular-level processes to tissue dynamics, particularly in cancer modeling. These approaches integrate GEMs with agent-based models to simulate tumor growth, , and immune interactions across scales, providing insights into therapeutic responses. For example, hybrid multi-scale frameworks have been used to model micrometastases, intracellular metabolic states with extracellular immune to predict patterns. This progression reflects a trend toward by incorporating patient-specific into scalable simulations.

Systems Biology and Bioinformatics

Bioinformatics serves as a foundational pillar for systems biology by providing essential computational tools for handling and interpreting large-scale , particularly in , annotation, and database querying, which are critical for reconstructing biological networks. These tools enable the integration of genomic, transcriptomic, and proteomic data to uncover patterns that inform systems-level models of cellular es. For instance, algorithms facilitate the identification of homologous genes and proteins, allowing researchers to infer functional relationships across and build comprehensive interaction maps. This overlap is evident in the use of bioinformatics pipelines to process data, transforming raw sequences into structured knowledge that supports holistic analyses of dynamic biological systems. Key contributions from bioinformatics include algorithms such as the Basic Local Alignment Search Tool (BLAST), which performs rapid sequence comparisons to detect homology and evolutionary relationships, aiding in the of and proteins within systems biology frameworks. BLAST's heuristic approach efficiently scans databases to identify similar sequences, enabling the reconstruction of gene regulatory and metabolic networks by highlighting conserved functional elements. Complementing this, the database integrates diverse data sources to predict protein-protein interactions, combining experimental evidence, computational predictions, and to generate association networks with confidence scores. These resources are indispensable for mapping interaction landscapes that underpin systems biology investigations into cellular signaling and pathway dynamics. In systems biology applications, bioinformatics pipelines are pivotal for genome-scale metabolic modeling (GEM), where tools for sequence annotation and database integration reconstruct constraint-based models that simulate metabolic fluxes and identify essential genes. For example, in the parasite Plasmodium falciparum, GEMs derived from bioinformatics-processed genomic data have predicted 48 essential genes with 95% accuracy through flux balance analysis, highlighting potential drug targets in metabolic pathways. Such models rely on automated annotation from databases like KEGG and UniProt to assemble reaction networks, allowing simulations of gene knockouts to reveal dependencies critical for organism viability. This approach exemplifies how bioinformatics data handling directly supports systems-level predictions of biological robustness and perturbation responses. While bioinformatics is predominantly data-centric, emphasizing the analysis and organization of sequence and structural information, systems biology extends this foundation by focusing on the dynamic interactions and emergent properties of biological networks. Bioinformatics provides the static building blocks—such as aligned sequences and interaction predictions—whereas systems biology employs these to model temporal behaviors, feedback loops, and system-wide responses, often through differential equations or stochastic simulations. This distinction underscores bioinformatics as an enabling discipline that supplies the empirical data layer for systems biology's integrative, predictive modeling.

Systems Biology and Synthetic Biology

Systems biology provides foundational principles for by enabling the prediction and design of engineered biological circuits through quantitative modeling of their dynamic behaviors. In this intersection, systems-level approaches analyze how components such as promoters, repressors, and transcription factors interact to produce emergent properties in synthetic gene networks, much like in natural systems. For instance, the genetic toggle switch, a bistable synthetic circuit constructed in , demonstrates how mutual repression between two genes can maintain stable states, allowing the system to "remember" prior inputs; this was modeled using ordinary differential equations to predict switching thresholds and stability. Key examples of this synergy appear in initiatives like the (iGEM) competition, where teams employ systems biology models to design and optimize genetic circuits for applications such as biosensors and metabolic pathways. In iGEM projects, (ODE) modeling integrates experimental data to simulate circuit performance, ensuring robustness before fabrication. Similarly, CRISPR-based synthetic pathways leverage systems insights to engineer multi-gene constructs, such as those redirecting metabolic flux in microbes for production, by modeling activation and interference dynamics to achieve precise control. A central role of systems biology in involves repurposing natural regulatory networks to build novel circuits, often using mathematical frameworks to approximate regulatory interactions. For gene regulation, the Hill function commonly models activator or effects, capturing sigmoidal dose-response curves: f(x)=xnKn+xnf(x) = \frac{x^n}{K^n + x^n} where xx is the regulator concentration, nn is the Hill reflecting , and KK is the half-maximal constant; this formulation aids in predicting circuit responses when adapting motifs like from bacteria.00172-3) Despite these advances, challenges persist in synthetic systems, particularly unintended emergent behaviors arising from context-dependent interactions, such as resource competition or -induced variability that disrupt predictability. Systems biology tools help mitigate these by simulating whole-cell effects, but incomplete of host dynamics often leads to circuit failures .

Modeling Frameworks

Types of Biological Models

In systems biology, biological models are classified based on their representational approach, mathematical formalism, and suitability for capturing , such as the scale of interactions, temporal dynamics, or stochasticity. Structural models provide a static framework for visualizing interactions, while dynamic models incorporate ; stochastic variants address in low-molecule systems, and discrete or spatial models handle qualitative or heterogeneous behaviors. Selection criteria emphasize matching model granularity to the 's —for instance, deterministic models suffice for high-abundance processes, whereas ones are essential for . Structural models, often depicted as network diagrams, represent biological systems as graphs where nodes denote components like genes or proteins, and edges indicate interactions such as activation or inhibition. Directed graphs are particularly common for signaling pathways, capturing the flow of information from ligands to effectors in processes like cell response to stimuli. These models facilitate qualitative of connectivity and without requiring kinetic details, making them ideal for initial hypothesis generation in . For example, in mammalian signaling cascades, directed graphs model receptor-ligand bindings leading to downstream events. Dynamic models employ ordinary differential equations (ODEs) to simulate time-course behaviors, assuming continuous changes in concentrations over time and deterministic outcomes for well-mixed systems. These are suited to medium-complexity scenarios where rate laws describe fluxes, such as metabolic fluxes or oscillations. A seminal example is the Lotka-Volterra equations for predator-prey dynamics, adapted in systems biology to model ecological or cellular competition: dxdt=αx−βxy\frac{dx}{dt} = \alpha x - \beta x y dydt=δxy−γy\frac{dy}{dt} = \delta x y - \gamma y Here, xx and yy represent prey and predator populations, with parameters α,β,δ,γ\alpha, \beta, \delta, \gamma denoting growth, interaction, and decay rates; this framework reveals oscillatory equilibria in microbial consortia or tumor-immune interactions. ODEs are preferred for their analytical tractability in systems with abundant molecules, enabling predictions of steady states or bifurcations. Stochastic models extend dynamic approaches to account for inherent randomness, particularly in low-copy processes like transcription in single cells where molecule numbers are small (e.g., fewer than 10). The , a kinetic , simulates exact trajectories by sampling reaction propensities and waiting times, avoiding approximations in noisy environments. It is selected for high-complexity, discrete-event systems such as DNA damage repair or viral infections, where fluctuations drive phenotypic variability. For instance, Gillespie simulations quantify noise in gene circuits, revealing how stochasticity amplifies or buffers signals in bacterial populations. Other model types address specific complexities beyond continuous dynamics. Boolean networks model qualitative logic in regulatory systems, assigning binary states (on/off) to nodes and logical rules to edges, suitable for large-scale networks where thresholds dominate over kinetics. They enable attractor analysis in , such as segment polarity in , by simulating state transitions without quantitative parameters. Constraint-based models, such as (FBA), use steady-state assumptions and linear optimization to predict metabolic fluxes in genome-scale networks, ideal for large systems where kinetic data are unavailable; for example, FBA reconstructs organism-specific to identify essential genes or nutrient requirements. Agent-based models, in contrast, treat individuals (e.g., cells) as autonomous agents following rules in a spatial grid, ideal for heterogeneous, emergent behaviors in tissues. These capture migration and local interactions in or tumor microenvironments, prioritizing spatial complexity over global homogeneity. Validation of such models typically involves comparing simulated outputs to experimental or distributions.

Model Construction and Validation

Model construction in systems biology begins with formulating a mathematical representation based on established biological models, such as ordinary differential equations (ODEs), which capture dynamic interactions among components. Parameter estimation is a core step, where unknown parameters—such as reaction rates or binding affinities—are inferred from experimental data by minimizing the discrepancy between model predictions and observations. A widely used approach is fitting, which minimizes the sum of squared residuals between simulated outputs and measured data points, often assuming normally distributed errors; this method is implemented in tools like the Levenberg-Marquardt algorithm for local optimization or evolutionary strategies for global searches to avoid local minima. Following estimation, evaluates how variations in parameters affect model outputs, identifying influential parameters and assessing model robustness. Local examines small perturbations around nominal values, while global methods explore broader parameter ranges to reveal nonlinear effects and interactions, aiding in model reduction by prioritizing key variables. For instance, in fitting models to time-series data—such as profiles—software like COPASI facilitates parameter optimization using evolutionary algorithms and assesses goodness-of-fit via χ² statistics, which quantify the agreement between predicted and experimental trajectories while accounting for measurement noise. Validation ensures model reliability by testing predictions against independent data and analyzing dynamical properties. Cross-validation, particularly stratified random cross-validation, partitions data into training and testing sets to evaluate generalizability, reducing bias from specific partitioning schemes and providing stable assessments of model performance across scenarios like signaling pathway perturbations. Bifurcation analysis further verifies stability by identifying parameter thresholds where qualitative behaviors shift, such as from monostable to bistable states in regulatory networks, using methods to trace equilibrium points and their eigenvalues. Iterative refinement incorporates to improve model credibility, often through , which updates parameter distributions based on prior knowledge and likelihoods to propagate uncertainties in predictions. This approach, employing sampling, enables posterior estimation of parameter confidence intervals and via evidence computation, facilitating refinements in complex systems like metabolic pathways. Through these steps, models are iteratively calibrated and validated to yield reliable, predictive insights into biological processes.

Computational Tools and Resources

Software and Algorithms

Systems biology relies heavily on computational software and algorithms to model, simulate, and analyze complex biological networks, enabling researchers to integrate multi-scale data and predict system behaviors. These tools facilitate the transition from qualitative descriptions to quantitative predictions, often supporting standards like SBML (Systems Biology Markup Language) for . Open-source platforms dominate the field, promoting and community-driven development. Simulation software plays a pivotal role in exploring dynamic processes, such as biochemical reactions and cellular pathways. COPASI (COmplex PAthway SImulator) is a widely used tool for deterministic ordinary differential equation (ODE) modeling, stochastic simulations, and parameter estimation, handling tasks like metabolic flux analysis and bifurcation studies. It supports hybrid deterministic-stochastic approaches, making it suitable for systems with varying noise levels. CellDesigner, another key tool, focuses on visual pathway diagramming and simulation, allowing users to construct and edit SBML-compliant models through an intuitive graphical interface while integrating with simulation engines like COPASI. For network analysis, software emphasizes to uncover structural properties and interactions in biological systems. Cytoscape is a leading open-source platform for visualizing and analyzing molecular interaction networks, supporting plugins for tasks like centrality measures and clustering to identify functional modules. It enables integration of heterogeneous data types, such as and protein interactions, into interactive graphs. The igraph library, available in languages like and Python, provides efficient algorithms for community detection and , optimizing computations for large-scale biological graphs through methods like the Louvain algorithm. Constraint-based algorithms, particularly (FBA), are essential for genome-scale metabolic modeling under steady-state assumptions. Implemented in the (Constraint-Based Reconstruction and Analysis) Toolbox for and Python, FBA optimizes an objective function, such as production, subject to stoichiometric and capacity constraints. The core formulation is to maximize z=cTvz = c^T v subject to Sv=0S v = 0 and vmin⁡≤v≤vmax⁡v_{\min} \leq v \leq v_{\max}, where SS is the stoichiometric matrix, vv the flux vector, and cc the objective coefficients; this approach has been applied to predict microbial growth yields with high accuracy. In the 2020s, Jupyter-based platforms have gained traction for their interactive and reproducible workflows. PySB (Python Systems Biology) exemplifies this trend, enabling rule-based modeling of biomolecular interactions through declarative syntax, which simplifies the specification of complex reaction networks without exhaustive of . It integrates seamlessly with numerical solvers like for simulations, supporting and parameter inference in a notebook environment. These tools often interface briefly with external databases to incorporate experimental during model refinement.

Databases and Data Integration

Systems biology relies on comprehensive databases that curate and organize vast amounts of to enable holistic analyses of cellular processes and networks. These repositories provide structured information on molecular interactions, pathways, and functions, facilitating the reconstruction of biological systems from disparate experimental sources. Key examples include the Encyclopedia of Genes and Genomes (), which maps metabolic and signaling pathways across organisms to reveal functional modules in biological systems. The Biological General Repository for Interaction Datasets (BioGRID) compiles curated protein-protein, genetic, and chemical interactions from high-throughput experiments, supporting network-based studies in model organisms and humans. Similarly, serves as a central hub for protein sequence and functional annotations, integrating data from and to annotate over 199 million protein entries with details on structure, function, and interactions. Integrating data from these databases poses significant challenges, particularly with multi-omics datasets that combine , transcriptomics, , and , where heterogeneity in formats, scales, and noise levels complicates unified analyses. For instance, reconciling genomic variants with metabolomic profiles requires addressing discrepancies in data resolution and biological context, often leading to incomplete or biased system representations. Standards like the Systems Biology Markup Language (SBML) address these issues by providing an XML-based format for exchanging computational models and associated data, ensuring interoperability across tools and platforms in systems biology workflows. To overcome integration hurdles, techniques such as leverage controlled vocabularies and semantic frameworks to harmonize heterogeneous sources. Ontologies like the (GO) enable this by providing standardized terms for gene functions, processes, and components, allowing fusion of multi-omics data to infer emergent biological relationships. approaches further aid reconciliation by learning patterns across datasets, such as imputing missing values or aligning disparate interaction networks, thereby enhancing predictive accuracy in systems-level models. Adherence to principles like (Findable, Accessible, Interoperable, Reusable) ensures that biological data from these databases can be effectively shared and reused, promoting reproducibility and collaborative research in systems biology. Recent initiatives, such as , the European intergovernmental organization for life sciences infrastructure established in the , further support by federating bioinformatics resources across 21 member countries, including tools for systems biology modeling and multi-omics analysis.

Practical Applications

Medical and Pharmaceutical Uses

Systems biology has significantly advanced medical and pharmaceutical applications by integrating multi-omics data and computational modeling to understand complex disease mechanisms at a holistic level, enabling more targeted interventions in human health. This approach facilitates the analysis of interconnected biological networks, revealing emergent properties that traditional reductionist methods overlook, such as dynamic interactions between cellular components in disease states. In pharmaceuticals, it supports the prediction of drug responses and optimization of therapeutic strategies, reducing development timelines and costs. In cancer research, systems biology employs network models to dissect the (TME), capturing interactions among cancer cells, immune cells, and stromal elements that drive tumor progression and therapy resistance. For instance, signaling crosstalk within these networks, such as between EGFR and VEGF pathways, has been modeled to predict resistance to inhibitors in non-small cell , informing combination therapies that target multiple nodes simultaneously. These models integrate proteomic and transcriptomic data to simulate TME dynamics, highlighting how immune evasion mechanisms, like upregulation, contribute to failure and guiding patient stratification. Drug development benefits from systems biology through pharmacodynamic (PD) models that simulate drug effects on biological systems, enhancing and lead optimization. Quantitative systems pharmacology (QSP) frameworks combine pharmacokinetic (PK) data with network-based PD simulations to forecast and across patient populations. A key example is the use of the Emax model for dose-response relationships, expressed as E=Emax⁡CEC50+CE = \frac{E_{\max} C}{EC_{50} + C}, where EE is the effect, CC is drug concentration, Emax⁡E_{\max} is maximum effect, and EC50EC_{50} is the concentration for half-maximal effect; this equation integrates into larger network models to evaluate polypharmacology in complex diseases like . Such approaches have accelerated the identification of drug candidates by prioritizing those that modulate critical pathway hubs, as demonstrated in virtual screens for kinase inhibitors. Personalized medicine leverages systems biology by constructing patient-specific models from genomic and multi-omics data, tailoring treatments to individual variability in disease susceptibility and drug response. In , for example, genome-informed models predict risk by integrating genetic variants with electrophysiological simulations, such as those affecting channels in . These models use on personal genomes to forecast arrhythmia propensity, enabling preemptive interventions like genotype-guided beta-blocker dosing, which has improved outcomes in high-risk cohorts. By simulating whole-heart network dynamics, systems approaches identify subtle perturbations, such as those from mutations, that precipitate ventricular arrhythmias under stress. Case studies in systems illustrate its role in drug , particularly during the , where rapid network analyses repurposed existing drugs for infection. For instance, QSP models integrated host-pathogen interaction networks to identify , a JAK inhibitor, as a repurposed agent that mitigates storms by targeting inflammatory signaling hubs, leading to its authorization in 2020. Another example involved multi-scale models of viral entry and , which supported remdesivir's by predicting its interference with in host cells, validated through clinical trials that showed reduced recovery time in hospitalized patients. These efforts demonstrated how systems biology accelerates by prioritizing drugs that address emergent disease phenotypes, such as in complications.

Environmental and Agricultural Uses

Systems biology has been instrumental in analyzing microbiomes to model microbial networks involved in nutrient cycling, enhancing agricultural by optimizing processes like and transformations. In alpine meadows, long-term warming experiments revealed that bacterial communities, particularly keystone taxa such as Proteobacteria (32.88% relative abundance), Gemmatimonadetes (12.83%), and Actinobacteria (7.06%), drive multi-nutrient cycling through increased network complexity and negative interactions that stabilize ecosystem functions. These systems-level approaches, using high-throughput sequencing and phylogenetic molecular ecological networks, identify β-diversity as a key driver, informing strategies to bolster under climate stress. In crop improvement, systems biology employs genome-scale metabolic models to dissect plant stress responses, particularly drought tolerance through metabolic flux analysis. For instance, context-specific models of Arabidopsis thaliana under drought, reconstructed via transcriptome integration and flux balance analysis (FBA), highlight upregulated fluxes in photorespiration, plastidic glycolysis, and flavonoid biosynthesis (22–54 drought-specific reactions by days 10–13), with glutamate dehydrogenase emerging as critical for biomass maintenance. These insights guide metabolic engineering in staple crops like rice and maize, promoting osmoprotectant accumulation (e.g., flavonoids and sugars) to enhance yield stability. Metabolomics complements this by profiling dynamic changes, enabling targeted interventions for resilience without exhaustive genetic modifications. For , systems biology facilitates detection networks by integrating whole-genome sequencing (WGS) and to trace across s. The GenomeTrakr network, a distributed genomic database, has sequenced more than 1.6 million foodborne isolates as of 2025, enabling real-time outbreak investigations for agents like and through subtype-specific . detects pathogens without prior isolation, addressing unidentified sources in 80% of U.S. foodborne illnesses (38.4 million annually), while bioinformatics tools like BTyper characterize strains for precise control. This precision paradigm shifts from broad surveillance to microbiome-based interventions, reducing vulnerabilities. Synthetic microbial consortia exemplify systems biology applications in production, where engineered communities divide conversion tasks for efficiency. Co-cultures of and Pichia stipitis with hydrolytic enzymes convert food waste to , while cellulovorans and C. beijerinckii yield from corn cobs with over 30% productivity gains via pH-tolerant designs. Systems models predict dynamics in multi-strain setups using data, optimizing stability through spatial engineering like hydrogels. Advances in the 2020s have leveraged multi-omics and systems biology for climate-resilient , integrating , , and AI to engineer stress-tolerant crops. Multi-omics identifies resilience pathways, with advanced phenotyping (e.g., drone-based ) and accelerating breeding for and tolerance in vulnerable regions. engineering via bioinoculants further enhances and yield, supporting sustainable amid global challenges.

Challenges and Limitations

Technical and Computational Hurdles

High-throughput experiments in systems biology generate vast datasets, but these are plagued by and incompleteness, complicating accurate model construction. Biological originates from intrinsic processes, such as fluctuations in , and extrinsic technical variations in platforms like microarrays or , which can mask true biological signals and lead to erroneous inferences about network dynamics. For example, in single-cell RNA sequencing, arises from both intrinsic and extrinsic sources, necessitating advanced denoising algorithms to extract reliable features. Incompleteness arises from incomplete pathway coverage in or , where low-abundance molecules are often undetected, resulting in biased representations of cellular states. Scalability of omics datasets further intensifies these data issues, as the volume of multi-omics information—such as integrating with —can reach petabyte scales, overwhelming standard computational infrastructures and hindering holistic . Heterogeneity across data types exacerbates incompleteness, with discrepancies in resolution and temporal scales impeding integration efforts. Recent multi-omics studies highlight that datasets frequently suffer from missing values due to experimental limitations, requiring imputation methods that risk introducing additional artifacts. Computational demands pose another major hurdle, particularly for high-dimensional simulations of biological systems, which often require (HPC) clusters to manage the exponential complexity of interacting components. In genome-scale metabolic models, simulations involving thousands of reactions can be computationally intensive, with runtime scaling poorly beyond 100 dimensions due to and parallelism constraints. The curse of dimensionality amplifies this, as parameter spaces in network models grow combinatorially, leading to sparse data coverage and prohibitive search times for optimization— for instance, estimating parameters in a 50-variable system can require billions of evaluations. Surrogate modeling techniques, such as Gaussian processes, have been developed to approximate these simulations and mitigate intractability, but they still rely on HPC for training. Model accuracy remains elusive due to and challenges in ODE-based frameworks, which are staples for modeling dynamical biological processes like signaling cascades. Overfitting occurs when models capture experimental noise rather than core mechanisms, often in high-parameter regimes, leading to poor generalization; cross-validation strategies, such as hold-out testing, are essential but limited by data scarcity in biological contexts. issues stem from structural ambiguities in nonlinear ODEs, where multiple parameter sets produce indistinguishable outputs, as seen in viral dynamics models where reaction rates cannot be uniquely resolved without additional constraints like prior knowledge. Practical identifiability analysis, involving profile likelihood methods, reveals that parameters in typical systems biology ODEs are often non-identifiable under standard experimental designs, necessitating experimental redesigns for resolvability. As of 2025, integrating real-time data from wearables and sensors introduces fresh computational hurdles, as continuous streams of physiological metrics—such as or glucose levels—must be fused with static data for dynamic . Data quality varies due to motion artifacts and sensor drift, with a significant portion of readings requiring preprocessing, while real-time synchronization demands low-latency pipelines that current HPC setups struggle to scale for personalized models. Standardization gaps in formats like HL7 FHIR further complicate this, limiting the feasibility of closed-loop systems biology applications.

Ethical and Societal Issues

Systems biology, by integrating vast datasets from , , and other layers, enables but amplifies privacy risks associated with genomic data handling. In this context, anonymized datasets can often be re-identified through auxiliary information, exposing individuals to genetic discrimination in or . The European Union's (GDPR) addresses this by categorizing genomic data as "special category" information, mandating explicit consent, data minimization, and robust techniques to prevent re-identification, with violations carrying fines up to 4% of global annual turnover. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) safeguards , including genetic data, but its scope is narrower, applying primarily to covered entities and offering limited protections for de-identified data that may still enable re-identification via cross-referencing with public records. These regulatory frameworks underscore the ethical imperative for systems biology researchers to implement and methods to balance data utility with individual rights. Dual-use risks in systems biology emerge prominently at its overlap with , where tools for modeling and engineering biological networks could facilitate the creation of bioweapons. For example, post-2010s advancements in technologies—designed using systems-level simulations to propagate modifications through populations for disease control—have ignited debates over unintended ecological disruptions and potential weaponization, as these self-sustaining mechanisms could be adapted to enhance . 's ability to reconstruct organisms from digital blueprints, informed by systems biology predictive models, heightens threats, with scenarios modeling engineered pandemics potentially causing millions of casualties. Governance responses include international agreements like the and calls for enhanced laboratory oversight, such as screening dual-use research of concern (DURC) to prevent misuse while preserving scientific progress. Equity concerns in systems biology applications highlight disparities in access to derived therapies, particularly in low-resource settings where infrastructure gaps exacerbate global health divides. Therapies informed by systems biology, such as targeted gene edits for , remain concentrated in high-income countries due to prohibitive costs exceeding $2 million per treatment and limited clinical trial participation from low- and middle-income countries (LMICs). In LMICs like those in , where diseases amenable to systems-derived interventions are prevalent, barriers include inadequate regulatory frameworks and supply chain issues, resulting in a translational gap that perpetuates health inequities. Initiatives like the World Health Organization's equity-focused guidelines advocate for , subsidized pricing, and local to democratize access, ensuring that systems biology benefits extend beyond affluent populations. The societal impacts of systems-engineered crops, leveraging systems biology for optimized genetic designs, extend to public engagement on genetically modified organisms (GMOs) and broader implications for food systems. These crops, engineered for traits like pest resistance through holistic network modeling, have sparked polarized debates, with public skepticism rooted in fears of and corporate monopolization of seed markets. Systematic analyses reveal varied social outcomes, including improved farmer incomes in adopting regions but heightened community tensions in areas with low awareness, underscoring the need for inclusive dialogue. Effective public engagement strategies, such as participatory forums and transparent labeling, are essential to build trust and address perceptions, fostering acceptance of GMOs as tools for amid climate challenges.

Integration with Emerging Technologies

Artificial Intelligence Applications

Artificial intelligence, particularly and , plays a pivotal role in systems biology by processing vast, multidimensional datasets to uncover patterns, infer regulatory networks, and predict dynamic behaviors in biological systems. These techniques address the inherent complexity of integrating heterogeneous data sources, enabling more accurate simulations of cellular processes and organismal responses. For instance, AI models excel at handling noise and incompleteness in , outperforming traditional statistical methods in and precision. In network modeling, deep learning approaches like graph neural networks (GNNs) facilitate the inference of biological interactions by representing molecular entities as nodes and relationships as edges in graph structures. GNNs propagate information across these graphs to predict regulatory networks or protein-protein interactions, often achieving higher accuracy than classical inference algorithms, such as those based on correlation or . A key example is the use of graph convolutional networks guided by causal priors to reconstruct regulatory networks from expression data, demonstrating robust performance on benchmark datasets with improved edge prediction recall. This capability is crucial for systems biology, as it allows reconstruction of interaction maps that inform and functional annotations. Prominent applications include 's deep learning-based prediction of protein structures, which integrates seamlessly into systems biology workflows to elucidate molecular components of larger networks. Released in 2021, AlphaFold achieved median backbone RMSD errors below 1 Å for many targets in the CASP14 assessment, far surpassing prior methods and enabling the structural annotation of proteins within signaling and metabolic pathways. In systems contexts, these predictions support the modeling of protein complexes and their roles in disease-associated perturbations, as seen in applications to pipelines where structural insights prioritize network hubs. Similarly, enhances drug target identification by leveraging systems-level data to score potential targets based on network and phenotypic relevance. Advanced techniques such as optimize biological designs by framing sequence or circuit engineering as Markov decision processes, where agents learn policies to maximize objectives like binding affinity or expression levels. Model-based , for instance, has been applied to de novo protein design, generating sequences with fitness scores comparable to while requiring fewer evaluations. via AI further aids in identifying irregularities in datasets that signal disruptions in system , using autoencoders or isolation forests to isolate outliers with high specificity; the explainable E-ABIN module, introduced in 2025, combines regression and graph-based to pinpoint anomalous gene modules in expression profiles, providing interpretable scores for downstream validation. By 2025, generative AI has emerged as a tool for generation in pathway discovery, employing diffusion models or transformers to propose novel regulatory links from integrated knowledge graphs and data. These systems automate the ideation process, generating hypotheses that align with experimental evidence, as demonstrated in a study on bacterial transfer mechanisms where an AI-generated hypothesis matched experimental findings. Such methods often reference broader multi- integration to contextualize pathways without delving into resolution-specific analyses.

Multi-Omics and Single-Cell Analysis

Multi-omics integration in systems biology involves the simultaneous analysis of multiple layers of biological data, such as transcriptomics, , and , to achieve a holistic understanding of cellular and organismal processes. This approach reveals interactions and regulatory mechanisms that are obscured in single-omics studies by correlating molecular changes across levels, for instance, linking patterns to protein abundance and metabolite profiles. Seminal methods like iCluster employ joint latent variable models to cluster samples from heterogeneous genomic datasets, enabling the identification of subtypes in diseases such as and by integrating copy number variations, , and data. More recent frameworks extend this by incorporating , as seen in community-guided integrations that emphasize standardized workflows for combining these to model pathway dynamics in metabolic disorders. Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq), provide high-resolution data to uncover cellular heterogeneity within tissues, a cornerstone for systems biology models that account for population diversity rather than bulk averages. scRNA-seq captures profiles from individual cells, allowing the dissection of rare subpopulations and transient states that drive biological functions. Tools like Seurat facilitate this by integrating scRNA-seq with spatial or multimodal data through , enabling clustering of cell states based on shared programs and visualization of developmental or disease-related transitions. In applications, multi-omics and single-cell analyses map developmental trajectories by inferring pseudotime orders that reconstruct lineage progression from static snapshots, as demonstrated in studies of hematopoiesis where scRNA-seq trajectories reveal branching fates regulated by transcription factors. For tumor heterogeneity, these methods model intratumoral diversity, such as in , where scRNA-seq identifies distinct transcriptional subtypes within the same tumor, informing personalized therapeutic strategies by highlighting resistant subpopulations. Advances in the 2020s have introduced , which preserves tissue architecture while profiling , linking positional context to systems-level functions like cell-cell interactions in organoids or tumor microenvironments. Technologies such as Visium and Slide-seq enable untargeted spatial mapping of thousands of genes across sections, integrated with scRNA-seq to deconvolve cell types and their spatial distributions, thus enhancing models of tissue organization in and . briefly aids in processing these complex datasets for , but the experimental foundations remain central.

References

  1. https://isbscience.org/what-is-systems-biology/
  2. https://irp.nih.gov/catalyst/19/6/systems-biology-as-defined-by-nih
  3. https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2010.00009/full
  4. https://pmc.ncbi.nlm.nih.gov/articles/PMC1626627/
  5. https://www.pnas.org/doi/10.1073/pnas.0502024102
  6. https://pmc.ncbi.nlm.nih.gov/articles/PMC3933630/
  7. https://www.pnas.org/doi/10.1073/pnas.0610759104
  8. https://www.nature.com/articles/npjsba201632
  9. https://www.nature.com/articles/nrg1471
  10. https://www.science.org/doi/10.1126/science.1069492
  11. https://pubmed.ncbi.nlm.nih.gov/21544748/
  12. https://pmc.ncbi.nlm.nih.gov/articles/PMC3483864/
  13. https://plato.stanford.edu/entries/teleology-biology/
  14. https://direct.mit.edu/books/oa-monograph/4581/Cybernetics-or-Control-and-Communication-in-the
  15. http://www.scholarpedia.org/article/Noble_model
  16. https://www.nature.com/articles/238413a0
  17. https://laskerfoundation.org/leroy-hood-leading-the-systems-biology-revolution/
  18. https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2022.987135/full
  19. https://pmc.ncbi.nlm.nih.gov/articles/PMC3678833/
  20. https://www.sciencedirect.com/science/article/abs/pii/S0040162506001910
  21. https://pmc.ncbi.nlm.nih.gov/articles/PMC4066586/
  22. https://isbscience.org/our-history/
  23. https://nihrecord.nih.gov/sites/recordNIH/files/pdf/2007/NIH-Record-2007-09-07.pdf
  24. https://pmc.ncbi.nlm.nih.gov/articles/PMC7501203/
  25. https://pmc.ncbi.nlm.nih.gov/articles/PMC9289742/
  26. https://www.nature.com/articles/nature11247
  27. https://pmc.ncbi.nlm.nih.gov/articles/PMC6474416/
  28. https://extremephysiolmed.biomedcentral.com/articles/10.1186/2046-7648-2-8
  29. https://pmc.ncbi.nlm.nih.gov/articles/PMC3401895/
  30. https://pmc.ncbi.nlm.nih.gov/articles/PMC3564595/
  31. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2001-2-2-reviews1005
  32. https://bmcbiol.biomedcentral.com/articles/10.1186/1741-7007-8-84
  33. https://royalsocietypublishing.org/doi/10.1098/rsif.2016.0555
  34. https://pmc.ncbi.nlm.nih.gov/articles/PMC2888109/
  35. https://doi.org/10.1073/pnas.0610772104
  36. https://doi.org/10.1038/nprot.2009.203
  37. https://pmc.ncbi.nlm.nih.gov/articles/PMC4734377/
  38. https://www.sciencedirect.com/science/article/pii/S2665906921000052
  39. https://pmc.ncbi.nlm.nih.gov/articles/PMC10383060/
  40. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1730-3
  41. https://www.nature.com/articles/s41467-023-38110-7
  42. https://pmc.ncbi.nlm.nih.gov/articles/PMC1839891/
  43. https://academic.oup.com/bioinformatics/article/21/6/741/199074
  44. https://pmc.ncbi.nlm.nih.gov/articles/PMC8652070/
  45. https://www.nature.com/articles/s41540-024-00472-z
  46. https://pmc.ncbi.nlm.nih.gov/articles/PMC8932948/
  47. https://pmc.ncbi.nlm.nih.gov/articles/PMC6585387/
  48. https://academic.oup.com/nar/article/51/D1/D638/6825349
  49. https://pmc.ncbi.nlm.nih.gov/articles/PMC8778254/
  50. https://www.nature.com/articles/35002131
  51. https://pmc.ncbi.nlm.nih.gov/articles/PMC11474008/
  52. https://pmc.ncbi.nlm.nih.gov/articles/PMC9890466/
  53. https://www.mdpi.com/1099-4300/25/10/1380
  54. https://www.science.org/doi/10.1126/scisignal.2001965
  55. https://pmc.ncbi.nlm.nih.gov/articles/PMC9582792/
  56. https://biodatamining.biomedcentral.com/articles/10.1186/1756-0381-4-10
  57. https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2022.1021897/full
  58. https://pmc.ncbi.nlm.nih.gov/articles/PMC4262058/
  59. https://www.science.org/doi/10.1126/scisignal.2001973
  60. https://www.researchgate.net/publication/255385879_Stochastic_Models_of_Biological_Processes
  61. https://academic.oup.com/bib/article/10/4/435/297333
  62. https://scfbm.biomedcentral.com/articles/10.1186/1751-0473-3-16
  63. https://www.sciencedirect.com/science/article/pii/S2001037022002495
  64. https://pmc.ncbi.nlm.nih.gov/articles/PMC3640333/
  65. https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.968409/full
  66. https://febs.onlinelibrary.wiley.com/doi/10.1111/j.1742-4658.2008.06844.x
  67. https://academic.oup.com/bioinformatics/article/22/24/3067/208398
  68. https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-017-0484-3
  69. https://www.genome.jp/kegg/
  70. http://thebiogrid.org/
  71. https://www.uniprot.org/
  72. https://sbml.org/
  73. http://geneontology.org/
  74. https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-024-01350-3
  75. https://www.nature.com/articles/sdata201618
  76. https://elixir-europe.org/
  77. https://pmc.ncbi.nlm.nih.gov/articles/PMC2802670/
  78. https://www.annualreviews.org/content/journals/10.1146/annurev-bioeng-103122-030552
  79. https://www.sciencedirect.com/science/article/pii/S2001037016300435
  80. https://www.nature.com/articles/s41392-020-00280-x
  81. https://pmc.ncbi.nlm.nih.gov/articles/PMC8282606/
  82. https://ascpt.onlinelibrary.wiley.com/doi/10.1002/psp4.12669
  83. https://pmc.ncbi.nlm.nih.gov/articles/PMC4567444/
  84. https://www.frontiersin.org/journals/systems-biology/articles/10.3389/fsysb.2022.1063308/full
  85. https://pmc.ncbi.nlm.nih.gov/articles/PMC5606339/
  86. https://pmc.ncbi.nlm.nih.gov/articles/PMC3745651/
  87. https://www.ahajournals.org/doi/10.1161/ATVBAHA.125.319221
  88. https://pmc.ncbi.nlm.nih.gov/articles/PMC7996547/
  89. https://www.sciencedirect.com/science/article/pii/S2590124924000075
  90. https://www.ahajournals.org/doi/10.1161/CIRCRESAHA.122.321879
  91. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1136187/full
  92. https://pmc.ncbi.nlm.nih.gov/articles/PMC7241242/
  93. https://www.sciencedirect.com/science/article/pii/S2214514125000145
  94. https://www.fda.gov/food/whole-genome-sequencing-wgs-program/genometrakr-fast-facts
  95. https://journals.asm.org/doi/10.1128/jcm.00081-16
  96. https://pmc.ncbi.nlm.nih.gov/articles/PMC6584884/
  97. https://www.nature.com/articles/s41467-025-61957-x
  98. https://pubmed.ncbi.nlm.nih.gov/40941864/
  99. https://pmc.ncbi.nlm.nih.gov/articles/PMC7611518/
  100. https://www.sciencedirect.com/science/article/pii/S2452310025000174
  101. https://pmc.ncbi.nlm.nih.gov/articles/PMC9496467/
  102. https://spj.science.org/doi/10.34133/bdr.0059
  103. https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S2
  104. https://royalsocietypublishing.org/doi/10.1098/rsif.2015.0571
  105. https://arxiv.org/pdf/2504.11617
  106. https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf052/8152781
  107. https://www.sciencedirect.com/science/article/abs/pii/S0010465525003042
  108. https://pmc.ncbi.nlm.nih.gov/articles/PMC4493957/
  109. https://www.sciencedirect.com/science/article/pii/S2452310025000186
  110. https://pmc.ncbi.nlm.nih.gov/articles/PMC3140286/
  111. https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-025-00759-4
  112. https://www.researchgate.net/publication/392464248_Integration_of_Wearable_Device_Data_into_Predictive_Health_Analytics_Systems
  113. https://www.sciencedirect.com/science/article/abs/pii/S2211285525008407
  114. https://www.nature.com/articles/s41592-024-02391-7
  115. https://www.nature.com/articles/s41598-024-71864-8
  116. https://www.nature.com/articles/s41586-021-03819-2
  117. https://www.nature.com/articles/s41392-023-01381-z
  118. https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.3158
  119. https://research.google/pubs/model-based-reinforcement-learning-for-biological-sequence-design/
  120. https://academic.oup.com/nargab/article/7/4/lqaf135/8296753
  121. https://www.sciencedirect.com/science/article/pii/S0092867425009730
  122. https://academic.oup.com/bioinformatics/article/25/22/2906/180866
  123. https://pmc.ncbi.nlm.nih.gov/articles/PMC6523452/
  124. https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-023-04150-2
Add your contribution
Related Hubs
User Avatar
No comments yet.