Systems biology

Last updated
An illustration of the systems approach to biology Genomics GTL Pictorial Program.jpg
An illustration of the systems approach to 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]

Contents

Particularly from year 2000 onwards, the concept has been used widely in biology in a variety of contexts. The Human Genome Project is an example of applied systems thinking in biology which has led to new, collaborative ways of working on problems in the biological field of genetics. [2] One of the aims of systems biology is to model and discover emergent properties, properties of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques of systems biology. [3] [1] These typically involve metabolic networks or cell signaling networks. [4] [1]

Overview

Systems biology can be considered from a number of different aspects.

As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway or the heart beats). [5] [6] [7]

As a paradigm, systems biology is usually defined in antithesis to the so-called reductionist paradigm (biological organisation), although it's fully 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.) [8] "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) [7]

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. [9] 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. [10]

As the application of dynamical systems theory to molecular biology. Indeed, the focus on the dynamics of the studied systems is the main conceptual difference between systems biology and bioinformatics. [11]

As a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel. [12]

This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However, the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide.

History

Systems biology finds its roots in[ citation needed ] the quantitative modeling of enzyme kinetics, a discipline that flourished between 1900 and 1970, the mathematical modeling of population dynamics, the simulations developed to study neurophysiology, control theory and cybernetics, and synergetics.

One of the theorists who can be seen as one of the precursors of systems biology is Ludwig von Bertalanffy with his general systems theory. [13] One of the first numerical simulations in cell biology was published in 1952 by the British neurophysiologists and Nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley, who constructed a mathematical model that explained the action potential propagating along the axon of a neuronal cell. [14] Their model described a cellular function emerging from the interaction between two different molecular components, a potassium and a sodium channel, and can therefore be seen as the beginning of computational systems biology. [15] Also in 1952, Alan Turing published The Chemical Basis of Morphogenesis, describing how non-uniformity could arise in an initially homogeneous biological system. [16]

In 1960, Denis Noble developed the first computer model of the heart pacemaker. [17]

The formal study of systems biology, as a distinct discipline, was launched by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland, Ohio, titled "Systems Theory and Biology". [18] [19]

The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the metabolic control analysis and the biochemical systems theory. The successes of molecular biology throughout the 1980s, coupled with a skepticism toward theoretical biology, that then promised more than it achieved, caused the quantitative modeling of biological processes to become a somewhat minor field. [20]

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 SystemsBiologyTrendsInMostCitedResearch.PNG
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

However, the birth of functional genomics in the 1990s meant that large quantities of high-quality data became available, while the computing power exploded, making more realistic models possible. In 1992, then 1994, serial articles [22] [23] [24] [25] [26] on systems medicine, systems genetics, and systems biological engineering by B. J. Zeng was published in China and was giving a lecture on biosystems theory and systems-approach research at the First International Conference on Transgenic Animals, Beijing, 1996. In 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a whole (hypothetical) cell. [27]

Around the year 2000, after Institutes of Systems Biology were established in Seattle and Tokyo, systems biology emerged as a movement in its own right, spurred on by the completion of various genome projects, the large increase in data from the omics (e.g., genomics and proteomics) and the accompanying advances in high-throughput experiments and bioinformatics. Shortly afterwards, the first departments wholly devoted to systems biology were founded (for example, the Department of Systems Biology at Harvard Medical School [28] ).

In 2003, work at the Massachusetts Institute of Technology was begun on CytoSolve, a method to model the whole cell by dynamically integrating multiple molecular pathway models. [29] Since then, various research institutes dedicated to systems biology have been developed. For example, the NIGMS of NIH established a project grant that is currently supporting over ten systems biology centers in the United States. [30] As of summer 2006, due to a shortage of people in systems biology [31] several doctoral training programs in systems biology have been established in many parts of the world. In that same year, the National Science Foundation (NSF) put forward a grand challenge for systems biology in the 21st century to build a mathematical model of the whole cell.[ citation needed ] In 2012 the first whole-cell model of Mycoplasma genitalium was achieved by the Karr Laboratory at the Mount Sinai School of Medicine in New York. The whole-cell model is able to predict viability of M. genitalium cells in response to genetic mutations. [32]

An important milestone in the development of systems biology has become the international project Physiome.

Associated disciplines

Overview of signal transduction pathways Signal transduction pathways.svg
Overview of signal transduction pathways

According to the interpretation of Systems Biology as the ability to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools, some typical technology platforms are 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; metabolomics, measurements of small molecules known as metabolites in the system at the organismal, cell, or tissue level; [33] glycomics, organismal, tissue, or cell-level measurements of carbohydrates; lipidomics, organismal, tissue, or cell level measurements of lipids.

In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell.The interactions studied include organismal, tissue, cell, and molecular interactions within the cell (interactomics). [34] Currently, the authoritative molecular discipline in this field of study is protein-protein interactions (PPI), although the working definition does not preclude inclusion of other molecular disciplines. These molecular disciplines include; neuroelectrodynamics, an organismal network where the brain's computing function as a dynamic system includes underlying biophysical mechanisms and emerging computation by electrical interactions; [35] fluxomics, measurements of molecular dynamic changes over time in a system such as a cell, tissue, or organism; [33] biomics, systems analysis of the biome; and molecular biokinematics, the study of "biology in motion" focused on how cells transit between steady states such as in proteins molecular mechanism. [36]

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. [37]

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; [38] 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). [39] 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. [40]

The investigations are frequently combined with large-scale perturbation methods, including gene-based (RNAi, mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries.[ citation needed ] Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality.[ citation needed ] A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.

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. [41] [42] [43] [44] For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics [45] 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). [43] [45]

Bioinformatics and data analysis

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; [46] 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. [47] 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. [48]

Creating biological models

A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michealis Menten reaction. Toy Biological Model.jpg
A simple three protein negative feedback loop modeled with mass action kinetic differential equations. Each protein interaction is described by a Michealis Menten reaction.

Researchers begin by choosing a biological pathway and diagramming all of the protein interactions. After determining all of the interactions of the proteins, mass action kinetics is utilized to describe the speed of the reactions in the system. Mass action kinetics will provide differential equations to model the biological system as a mathematical model in which experiments can determine the parameter values to use in the differential equations. [50] These parameter values will be the reaction rates of each proteins interaction in the system. This model determines the behavior of certain proteins in biological systems and bring new insight to the specific activities of individual proteins. 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. [51] [49]

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. Toy Model Plot.jpg
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

Related Research Articles

Bioinformatics Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques.

Molecular biology Branch of biology dealing with biological activitys molecular basis

Molecular biology is the branch of biology that concerns the molecular basis of biological activity in and between cells, including molecular synthesis, modification, mechanisms and interactions.

Biophysics Study of biological systems using methods from the physical sciences

Biophysics is an interdisciplinary science that applies approaches and methods traditionally used in physics to study biological phenomena. Biophysics covers all scales of biological organization, from molecular to organismic and populations. Biophysical research shares significant overlap with biochemistry, molecular biology, physical chemistry, physiology, nanotechnology, bioengineering, computational biology, biomechanics, developmental biology and systems biology.

Proteomics Large-scale study of proteins

Proteomics is the large-scale study of proteins. Proteins are vital parts of living organisms, with many functions. The word proteome is a portmanteau of protein and genome, and was coined by Marc Wilkins in 1994 while he was a Ph.D. student at Macquarie University. Macquarie University also founded the first dedicated proteomics laboratory in 1995.

Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, ecological, behavioral, and social systems. The field is broadly defined and includes foundations in biology, applied mathematics, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, computer science and evolution.

Gene regulatory network collection of molecular regulators

A generegulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins. These play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).

Mathematical and theoretical biology

Mathematical and theoretical biology is a branch of biology which employs theoretical analysis, mathematical models and abstractions of the living organisms to investigate the principles that govern the structure, development and behavior of the systems, as opposed to experimental biology which deals with the conduction of experiments to prove and validate the scientific theories. The field is sometimes called mathematical biology or biomathematics to stress the mathematical side, or theoretical biology to stress the biological side. Theoretical biology focuses more on the development of theoretical principles for biology while mathematical biology focuses on the use of mathematical tools to study biological systems, even though the two terms are sometimes interchanged.

Synthetic biology interdisciplinary branch of biology and engineering

Synthetic biology (SynBio) is a multidisciplinary area of research that seeks to create new biological parts, devices, and systems, or to redesign systems that are already found in nature.

Functional genomics field of molecular biology

Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional “gene-by-gene” approach.

Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems, to both analyze and visualize the complex connections of these cellular processes.

In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.

Metabolic network modelling

Metabolic network reconstruction and simulation allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology.

In biology, cell signaling is part of any communication process that governs basic activities of cells and coordinates multiple-cell actions. The ability of cells to perceive and correctly respond to their microenvironment is the basis of development, tissue repair, and immunity, as well as normal tissue homeostasis. Errors in signaling interactions and cellular information processing may cause diseases such as cancer, autoimmunity, and diabetes. By understanding cell signaling, clinicians may treat diseases more effectively and, theoretically, researchers may develop artificial tissues.

Biological network inference is the process of making inferences and predictions about biological networks.

Biological network

A biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks. The analysis of biological networks with respect to human diseases has led to the field of network medicine.

Cellular model

Creating a cellular model has been a particularly challenging task of systems biology and mathematical biology. It involves developing efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling.

Cancer systems biology encompasses the application of systems biology approaches to cancer research, in order to study the disease as a complex adaptive system with emerging properties at multiple biological scales. Cancer systems biology represents the application of systems biology approaches to the analysis of how the intracellular networks of normal cells are perturbed during carcinogenesis to develop effective predictive models that can assist scientists and clinicians in the validations of new therapies and drugs. Tumours are characterized by genomic and epigenetic instability that alters the functions of many different molecules and networks in a single cell as well as altering the interactions with the local environment. Cancer systems biology approaches, therefore, are based on the use of computational and mathematical methods to decipher the complexity in tumorigenesis as well as cancer heterogeneity.

Epistasis Genetic phenomenon in which a gene mutations effect depends on mutations in other genes

Epistasis is a phenomenon in genetics in which the effect of a gene mutation is dependent on the presence or absence of mutations in one or more other genes, respectively termed modifier genes. In other words, the effect of the mutation is dependent on the genetic background in which it appears. Epistatic mutations therefore have different effects on their own than when they occur together. Originally, the term epistasis specifically meant that the effect of a gene variant is masked by that of a different gene.

Machine learning, a subfield of computer science involving the development of algorithms that learn how to make predictions based on data, has a number of emerging applications in the field of bioinformatics. Bioinformatics deals with computational and mathematical approaches for understanding and processing biological data.

Hanah Margalit Principal Investigator in the Faculty of Medicine The Hebrew University of Jerusalem

Hanah Margalit is a Professor in the faculty of medicine at the Hebrew University of Jerusalem. Her research combines bioinformatics, computational biology and systems biology, specifically in the fields of gene regulation in bacteria and eukaryotes.

References

  1. 1 2 3 Tavassoly, Iman; Goldfarb, Joseph; Iyengar, Ravi (2018-10-04). "Systems biology primer: the basic methods and approaches". Essays in Biochemistry. 62 (4): 487–500. doi:10.1042/EBC20180003. ISSN   0071-1365. PMID   30287586.
  2. Zewail, Ahmed (2008). Physical Biology: From Atoms to Medicine. Imperial College Press. p. 339.
  3. Longo, Giuseppe; Montévil, Maël (2014). Perspectives on Organisms - Springer. Lecture Notes in Morphogenesis. doi:10.1007/978-3-642-35938-5. ISBN   978-3-642-35937-8.
  4. Bu Z, Callaway DJ (2011). "Proteins MOVE! Protein dynamics and long-range allostery in cell signaling". Protein Structure and Diseases. Advances in Protein Chemistry and Structural Biology. 83. pp. 163–221. doi:10.1016/B978-0-12-381262-9.00005-7. ISBN   978-0-123-81262-9. PMID   21570668.
  5. Snoep, Jacky L; Westerhoff, Hans V (2005). "From isolation to integration, a systems biology approach for building the Silicon Cell". In Alberghina, Lilia; Westerhoff, Hans V (eds.). Systems Biology: Definitions and Perspectives. Topics in Current Genetics. 13. Berlin: Springer-Verlag. pp. 13–30. doi:10.1007/b106456. ISBN   978-3-540-22968-1.
  6. "Systems Biology: the 21st Century Science". Institute for Systems Biology. Retrieved 15 June 2011.
  7. 1 2 Noble, Denis (2006). The music of life: Biology beyond the genome. Oxford: Oxford University Press. p. 176. ISBN   978-0-19-929573-9.
  8. Sauer, Uwe; Heinemann, Matthias; Zamboni, Nicola (27 April 2007). "Genetics: Getting Closer to the Whole Picture". Science. 316 (5824): 550–551. doi:10.1126/science.1142502. PMID   17463274.
  9. Kholodenko, Boris N; Sauro, Herbert M (2005). "Mechanistic and modular approaches to modeling and inference of cellular regulatory networks". In Alberghina, Lilia; Westerhoff, Hans V (eds.). Systems Biology: Definitions and Perspectives. Topics in Current Genetics. 13. Berlin: Springer-Verlag. pp. 357–451. doi:10.1007/b136809. ISBN   978-3-540-22968-1.
  10. Chiara Romualdi; Gerolamo Lanfranchi (2009). "Statistical Tools for Gene Expression Analysis and Systems Biology and Related Web Resources". In Stephen Krawetz (ed.). Bioinformatics for Systems Biology (2nd ed.). Humana Press. pp. 181–205. doi:10.1007/978-1-59745-440-7_11. ISBN   978-1-59745-440-7.
  11. Voit, Eberhard (2012). A First Course in Systems Biology. Garland Science. ISBN   9780815344674.
  12. Baitaluk, M. (2009). "System Biology of Gene Regulation". Biomedical Informatics. Methods in Molecular Biology. 569. pp. 55–87. doi:10.1007/978-1-59745-524-4_4. ISBN   978-1-934115-63-3. PMID   19623486.
  13. von Bertalanffy, Ludwig (28 March 1976) [1968]. General System theory: Foundations, Development, Applications. George Braziller. p. 295. ISBN   978-0-8076-0453-3.
  14. Hodgkin, Alan L; Huxley, Andrew F (28 August 1952). "A quantitative description of membrane current and its application to conduction and excitation in nerve". Journal of Physiology. 117 (4): 500–544. doi:10.1113/jphysiol.1952.sp004764. PMC   1392413 . PMID   12991237.
  15. Le Novère, Nicolas (13 June 2007). "The long journey to a Systems Biology of neuronal function". BMC Systems Biology. 1: 28. doi:10.1186/1752-0509-1-28. PMC   1904462 . PMID   17567903.
  16. Turing, A. M. (1952). "The Chemical Basis of Morphogenesis" (PDF). Philosophical Transactions of the Royal Society B: Biological Sciences. 237 (641): 37–72. Bibcode:1952RSPTB.237...37T. doi:10.1098/rstb.1952.0012. JSTOR   92463.
  17. Noble, Denis (5 November 1960). "Cardiac action and pacemaker potentials based on the Hodgkin-Huxley equations". Nature. 188 (4749): 495–497. Bibcode:1960Natur.188..495N. doi:10.1038/188495b0. PMID   13729365.
  18. Mesarovic, Mihajlo D. (1968). Systems Theory and Biology. Berlin: Springer-Verlag.
  19. Rosen, Robert (5 July 1968). "A Means Toward a New Holism". Science. 161 (3836): 34–35. Bibcode:1968Sci...161...34M. doi:10.1126/science.161.3836.34. JSTOR   1724368.
  20. Hunter, Philip (May 2012). "Back down to Earth: Even if it has not yet lived up to its promises, systems biology has now matured and is about to deliver its first results". EMBO Reports. 13 (5): 408–411. doi:10.1038/embor.2012.49. PMC   3343359 . PMID   22491028.
  21. Zou, Yawen; Laubichler, Manfred D. (2018-07-25). "From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013". PLOS One. 13 (7): e0200929. Bibcode:2018PLoSO..1300929Z. doi:10.1371/journal.pone.0200929. ISSN   1932-6203. PMC   6059489 . PMID   30044828.
  22. B. J. Zeng, "On the holographic model of human body", 1st National Conference of Comparative Studies Traditional Chinese Medicine and West Medicine, Medicine and Philosophy, April 1992 ("systems medicine and pharmacology" termed).
  23. Zeng (B.) J., On the concept of system biological engineering, Communication on Transgenic Animals, No. 6, June, 1994.
  24. B. J. Zeng, "Transgenic animal expression system – transgenic egg plan (goldegg plan)", Communication on Transgenic Animal, Vol.1, No.11, 1994 (on the concept of system genetics and term coined).
  25. B. J. Zeng, "From positive to synthetic science", Communication on Transgenic Animals, No. 11, 1995 (on systems medicine).
  26. B. J. Zeng, "The structure theory of self-organization systems", Communication on Transgenic Animals, No.8-10, 1996. Etc.
  27. Tomita, Masaru; Hashimoto, Kenta; Takahashi, Kouichi; Shimizu, Thomas S; Matsuzaki, Yuri; Miyoshi, Fumihiko; Saito, Kanako; Tanida, Sakura; et al. (1997). "E-CELL: Software Environment for Whole Cell Simulation". Genome Inform Ser Workshop Genome Inform. 8: 147–155. PMID   11072314 . Retrieved 15 June 2011.
  28. "HMS launches new department to study systems biology". Harvard Gazette. September 23, 2003.
  29. Ayyadurai, VA; Dewey, CF (March 2011). "CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models". Cell Mol Bioeng. 4 (1): 28–45. doi:10.1007/s12195-010-0143-x. PMC   3032229 . PMID   21423324.
  30. "Systems Biology - National Institute of General Medical Sciences". Archived from the original on 19 October 2013. Retrieved 12 December 2012.
  31. Kling, Jim (3 March 2006). "Working the Systems". Science. Retrieved 15 June 2011.
  32. Karr, Jonathan R.; Sanghvi, Jayodita C.; Macklin, Derek N.; Gutschow, Miriam V.; Jacobs, Jared M.; Bolival, Benjamin; Assad-Garcia, Nacyra; Glass, John I.; Covert, Markus W. (July 2012). "A Whole-Cell Computational Model Predicts Phenotype from Genotype". Cell. 150 (2): 389–401. doi:10.1016/j.cell.2012.05.044. PMC   3413483 . PMID   22817898.
  33. 1 2 Cascante, Marta; Marin, Silvia (2008-09-30). "Metabolomics and fluxomics approaches". Essays in Biochemistry. 45: 67–82. doi:10.1042/bse0450067. ISSN   0071-1365. PMID   18793124.
  34. Cusick, Michael E.; Klitgord, Niels; Vidal, Marc; Hill, David E. (2005-10-15). "Interactome: gateway into systems biology". Human Molecular Genetics. 14 (suppl_2): R171–R181. doi:10.1093/hmg/ddi335. ISSN   0964-6906. PMID   16162640.
  35. Aur, Dorian (2012). "From Neuroelectrodynamics to Thinking Machines". Cognitive Computation. 4 (1): 4–12. doi:10.1007/s12559-011-9106-3. ISSN   1866-9956.
  36. Diez, Mikel; Petuya, Víctor; Martínez-Cruz, Luis Alfonso; Hernández, Alfonso (2011-12-01). "A biokinematic approach for the computational simulation of proteins molecular mechanism". Mechanism and Machine Theory. 46 (12): 1854–1868. doi:10.1016/j.mechmachtheory.2011.07.013. ISSN   0094-114X.
  37. Loor, Khuram Shahzad and Juan J. (2012-07-31). "Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism". Current Genomics. 13 (5): 379–394. doi:10.2174/138920212801619269. PMC   3401895 . PMID   23372424.
  38. Spill, Fabian; Bakal, Chris; Mak, Michael (2018). "Mechanical and Systems Biology of Cancer". Computational and Structural Biotechnology Journal. 16: 237–245. arXiv: 1807.08990 . Bibcode:2018arXiv180708990S. doi:10.1016/j.csbj.2018.07.002. PMC   6077126 . PMID   30105089.
  39. Barillot, Emmanuel; Calzone, Laurence; Hupe, Philippe; Vert, Jean-Philippe; Zinovyev, Andrei (2012). Computational Systems Biology of Cancer. Chapman & Hall/CRCMathematical & Computational Biology. p. 461. ISBN   978-1439831441.
  40. Byrne, Helen M. (2010). "Dissecting cancer through mathematics: from the cell to the animal model". Nature Reviews Cancer. 10 (3): 221–230. doi:10.1038/nrc2808. PMID   20179714.
  41. Gardner, Timothy .S; di Bernardo, Diego; Lorenz, David; Collins, James J. (4 July 2003). "Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling". Science. 301 (5629): 102–105. Bibcode:2003Sci...301..102G. doi:10.1126/science.1081900. PMID   12843395.
  42. di Bernardo, Diego; Thompson, Michael J.; Gardner, Timothy S.; Chobot, Sarah E.; Eastwood, Erin L.; Wojtovich, Andrew P.; Elliott, Sean J.; Schaus, Scott E.; Collins, James J. (March 2005). "Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks". Nature Biotechnology. 23 (3): 377–383. doi:10.1038/nbt1075. PMID   15765094.
  43. 1 2 Tavassoly, Iman (2015). Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells. Springer Theses. Springer International Publishing. doi:10.1007/978-3-319-14962-2. ISBN   978-3-319-14961-5.
  44. Korkut, A; Wang, W; Demir, E; Aksoy, BA; Jing, X; Molinelli, EJ; Babur, Ö; Bemis, DL; Onur Sumer, S; Solit, DB; Pratilas, CA; Sander, C (18 August 2015). "Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells". eLife. 4. doi:10.7554/eLife.04640. PMC   4539601 . PMID   26284497.
  45. 1 2 Gupta, Ankur; Rawlings, James B. (April 2014). "Comparison of Parameter Estimation Methods in Stochastic Chemical Kinetic Models: Examples in Systems Biology". AIChE Journal. 60 (4): 1253–1268. doi:10.1002/aic.14409. ISSN   0001-1541. PMC   4946376 . PMID   27429455.
  46. Ananadou, Sophia; Kell, Douglas; Tsujii, Jun-ichi (December 2006). "Text mining and its potential applications in systems biology". Trends in Biotechnology. 24 (12): 571–579. doi:10.1016/j.tibtech.2006.10.002. PMID   17045684.
  47. Glaab, Enrico; Schneider, Reinhard (2012). "PathVar: analysis of gene and protein expression variance in cellular pathways using microarray data". Bioinformatics. 28 (3): 446–447. doi:10.1093/bioinformatics/btr656. PMC   3268235 . PMID   22123829.
  48. Bardini, R.; Politano, G.; Benso, A.; Di Carlo, S. (2017-01-01). "Multi-level and hybrid modelling approaches for systems biology". Computational and Structural Biotechnology Journal. 15: 396–402. doi:10.1016/j.csbj.2017.07.005. ISSN   2001-0370. PMC   5565741 . PMID   28855977.
  49. 1 2 Transtrum, Mark K.; Qiu, Peng (2016-05-17). "Bridging Mechanistic and Phenomenological Models of Complex Biological Systems". PLOS Computational Biology. 12 (5): e1004915. arXiv: 1509.06278 . Bibcode:2016PLSCB..12E4915T. doi:10.1371/journal.pcbi.1004915. ISSN   1553-7358. PMC   4871498 . PMID   27187545.
  50. Chellaboina, V.; Bhat, S. P.; Haddad, W. M.; Bernstein, D. S. (August 2009). "Modeling and analysis of mass-action kinetics". IEEE Control Systems Magazine. 29 (4): 60–78. doi:10.1109/MCS.2009.932926. ISSN   1941-000X.
  51. Brown, Kevin S.; Sethna, James P. (2003-08-12). "Statistical mechanical approaches to models with many poorly known parameters". Physical Review E. 68 (2): 021904. Bibcode:2003PhRvE..68b1904B. doi:10.1103/physreve.68.021904. ISSN   1063-651X. PMID   14525003.

Further reading