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Systems biology

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Not to be confused with Systematic biology.

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]

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

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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

In 1968, the term “systems biology” was first introduced at a conference.[17] 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.[18] [19][20][21]An acceleration of systems understanding came with the publication of the first ground-breaking text compiling molecular, physiological, and anatomical individuality in animals[22], which has been described as a revolution.[23]

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 have been widely applied in several fields including agriculture and medicine.

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[24]

Approaches to systems biology

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Top-down approach

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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

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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

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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

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What is a model?

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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

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  • 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]
  • 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]
  • 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]
  • 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

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A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michaelis–Menten reaction.[68]

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.[69] 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.[70][68]

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).[71]

Tools and database

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Tools/database Description Citation
3Omics A web-based systems biology tool designed to visualize and integrate human transcriptomic, proteomic, and metabolomic data, combining five key analyses—correlation networks, coexpression, phenotyping, pathway enrichment, and Gene Ontology (GO) enrichment—for rapid and comprehensive data integration. [72]
Biocyc Microbial genome portal that integrates thousands of genomes with computational, imported, and curated information. Powered by Pathway Tools, it supports regulatory networks, omics analysis, and metabolic modeling. Recent updates include redesigned pages, new search and alignment tools, pathway visualization, and SmartTables for easy biological data analysis. [73]
BooleSim BooleSim (Boolean network simulator) is an open-source in-browser tool for simulation and manipulation of Boolean networks. It can be used specifically for the modeling of gene regulatory or signal transduction networks. [74]
Cell Illustrator It is a tool to draw, model, illustrate, simulate complex biological process and system [75]
COBRA toolbox Constraint based reconstruction and analysis uses available knowledge to define a set of feasible a set of states for biological network [76]
Cytoscape Tools to visualise biological interaction networks can then be integrated with annotations and gene expression profiles [77]
GIME3E Algorithm that enable development of condition specific models of cellular metabolism developed from metabolomics and expression data [78]
Biomodels This database comprises mathematical models of biological and biomedical systems. It contains literature based mechanistic models in standard formats. [79]
Inmex Integrative meta-analysis of expression data (INMEX) is a tool that enables the analysis of multiple gene expression data with metabolomics experiments. [80]
IMPaLa Integrated Molecular pathway Level Analysis is a tool to analyse pathways in transcriptomics, proteomics and metabolomics data. It performs over-representation or enrichment analysis with user-specified lists of metabolites and genes using over 3000 pre-annotated pathways from 11 databases. [81]
KAPPA-view KaPPA-View, a new metabolic pathway database, is able to overlay gene-to-gene and/or metabolite-to-metabolite relationships as curves on a metabolic pathway map, or on a combination of up to four maps. This representation would help to discover, for example, novel functions of a transcription factor that regulates genes on a metabolic pathway. [82]
Mapman4 A Novel Biological Context-Based Framework. The MapMan4 ontology represents a comprehensive set of common biological processes and incorporates genetic information from a wide variety of plant species. [83]
MetaboAnalyst MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. It is able to process a wide variety of metabolomic data types. [84]
Metabolights MetaboLights is a database for Metabolomics experiments and derived information. The database is cross-species, cross-technique and covers metabolite structures and their reference spectra as well as their biological roles, locations and concentrations, and experimental data from metabolic experiments. [85]
Metascape 2 This tool is used for visualization and analysis of metabolomic and gene expression data in the context of metabolic networks that can be used to map metabolic pathways. [86]
PaintOmics PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. [87]
Pathvisio 3 It is a tool that uses omics data for pathway visualization, analysis, integration and enrichment analysis. [88]
Recon 3D This tool contains several data and enables genome Scale metabolic network reconstruction, metabolic pathway analysis, System-wide analysis. [89]
VitisNet This tool enables gene and protein network analysis, pathway enrichment analysis, and integrates omics data [90]
xCellerator It is a mathematical package used for biological modeling This tool allows cellular analysis, metabolic network modeling and data integration [91]
XPPAUT It is a tool for solving differential equations.It is also  to solve for the steady-states and also perform some graphical analysis, such as phase portraits and time-series plots [92]

Applications in system biology

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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. [93] It aids in the comprehension of plant-pathogen interactions in disease resistance.[94] It is utilized in nutritional quality to enhance nutritional content through metabolic engineering.[95]

Cancer:

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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. [96]

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.[96]

Drug development:

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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.[97]

Cell systems biology represents a phenotypic drug discovery method that integrates the complexity of human disease biology with combinatorial design to develop assays.[98] BioMAP® systems, founded on the principles of cell systems biology, consist of assays based on primary human cells that are designed to replicate intricate human disease and tissue biology in a feasible in vitro environment. Primary human cell types and co-cultures are activated using combinations of pathway activators to create cell signaling networks that align more closely with human disease. These systems are analyzed by assessing the levels of both secreted proteins and cell surface mediators. The distinct variations in protein readouts resulting from drug effects are recorded in a database that enables users to search for functional similarities (or biological ‘read across’). In this method, inhibitors or activators targeting specific pathways are discovered to consistently affect the levels of multiple endpoints, often exhibiting a uniquely defined pattern, so that the resulting signatures can be linked to particular mechanisms of action.[97][99][100]

Plot of Concentrations vs time for the simple three protein negative feedback loop. All parameters are set to either 0 or 1 for initial conditions. The reaction is allowed to proceed until it hits equilibrium. This plot is of the change in each protein over time.

See also

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References

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Further reading

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