Systems biology

An illustration of the systems approach to biology

Systems biology is the computational and mathematical modeling of complex biological systems. An emerging engineering approach applied to biological scientific research, systems biology is a biology-based inter-disciplinary 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. Particularly from year 2000 onwards, the concept has been used widely in the biosciences in a variety of contexts. For example, 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.[1] One of the outreaching 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 which fall under the remit of systems biology.[2] These typically involve metabolic networks or cell signaling networks.[3]


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

"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.).[7]
"Systems 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]

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.


Systems biology finds its roots in:

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.[12] 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.[13] 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.[14] Also in 1952, Alan Turing published The Chemical Basis of Morphogenesis, describing how non-uniformity could arise in an initially homogeneous biological system.[15]

In 1960, Denis Noble developed the first computer model of the heart pacemaker.[16]

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, entitled "Systems Theory and Biology".[17][18]

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 modelling of biological processes to become a somewhat minor field.[19]

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 [20][21][22][23][24] on systems medicine, systems genetics and systems biological engineering by B. J. Zeng were 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.[25]

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.

In 2002, 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.[26] In 2003, work at the Massachusetts Institute of Technology was began to CytoSolve, a method to model the whole cell by dynamically integrating multiple molecular pathway models.[27][28] 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.[29] As of summer 2006, due to a shortage of people in systems biology[30] 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.[31] 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

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:

Organismal variation in phenotype as it changes during its life span.
Organismal deoxyribonucleic acid (DNA) sequence, including intra-organisamal cell specific variation. (i.e., telomere length variation)
Organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e., DNA methylation, Histone acetylation and deacetylation, etc.).
Organismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression
Organismal, tissue, or cell-level transcript correcting factors (i.e., RNA interference)
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.
Organismal, tissue, or cell-level measurements of small molecules known as metabolites
Organismal, tissue, or cell-level measurements of carbohydrates
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. This includes:

Organismal, tissue, or cell level study of interactions between molecules. 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 such as those defined here.
Organismal, brain computing function as a dynamic system, underlying biophysical mechanisms and emerging computation by electrical interactions.
Organismal, tissue, or cell level measurements of molecular dynamic changes over time.
Systems analysis of the biome.
Analysis of the system of sign relations of an organism or other biosystem.
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, Next Generation Sequencing methods, siRNA-based gene knocking down screenings, computational modeling of the consequences of somatic mutations and genome instability).[33] 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.[34]

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. 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. A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists, engineers, 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.[35][36][37] For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics 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).[37]

Bioinformatics and data analysis

Other aspects of computer science, informatics, statistics are also used in systems biology. These include:

See also


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  2. Longo, Giuseppe; Montévil, Maël. Perspectives on Organisms - Springer. doi:10.1007/978-3-642-35938-5.
  3. Bu Z, Callaway DJ (2011). "Proteins MOVE! Protein dynamics and long-range allostery in cell signaling". Advances in Protein Chemistry and Structural Biology. Advances in Protein Chemistry and Structural Biology. 83: 163–221. doi:10.1016/B978-0-12-381262-9.00005-7. ISBN 978-0-123-81262-9. PMID 21570668.
  4. Snoep, Jacky L; Westerhoff, Hans V (2005). Alberghina, Lilia; Westerhoff, Hans V, eds. "Systems Biology: Definitions and Perspectives". Topics in Current Genetics. Berlin: Springer-Verlag. 13: 13–30. doi:10.1007/b106456. ISBN 978-3-540-22968-1. |chapter= ignored (help)
  5. "Systems Biology: the 21st Century Science". Institute for Systems Biology. Retrieved 15 June 2011.
  6. 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.
  7. 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.
  8. Kholodenko, Boris N; Sauro, Herbert M (2005). Alberghina, Lilia; Westerhoff, Hans V, eds. "Systems Biology: Definitions and Perspectives". Topics in Current Genetics. Berlin: Springer-Verlag. 13: 357–451. doi:10.1007/b136809. ISBN 978-3-540-22968-1. |chapter= ignored (help)
  9. Chiara Romualdi; Gerolamo Lanfranchi (2009). "Statistical Tools for Gene Expression Analysis and Systems Biology and Related Web Resources". In Stephen Krawetz. Bioinformatics for Systems Biology (2nd ed.). Humana Press. pp. 181–205. ISBN 978-1-59745-440-7.
  10. Voit, Eberhard (2012). A First Course in Systems Biology. Garland Science. ISBN 9780815344674.
  11. 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.
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  15. Turing, A. M. (1952). "The Chemical Basis of Morphogenesis" (PDF). Philosophical Transactions of the Royal Society B: Biological Sciences. 237 (641): 37–72. doi:10.1098/rstb.1952.0012. JSTOR 92463.
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  17. Mesarovic, Mihajlo D. (1968). Systems Theory and Biology. Berlin: Springer-Verlag.
  18. 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.
  19. 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 3343359Freely accessible.
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  21. Zeng (B.) J., On the concept of system biological engineering, Communication on Transgenic Animals, No. 6, June, 1994.
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  23. B. J. Zeng, "From positive to synthetic science", Communication on Transgenic Animals, No. 11, 1995 (on systems medicine).
  24. B. J. Zeng, "The structure theory of self-organization systems", Communication on Transgenic Animals, No.8-10, 1996. Etc.
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  26. American Association for the Advancement of Science, , Science
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  28. Massachusetts Institute of Technology
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  30. Kling, Jim (3 March 2006). "Working the Systems". Science. Retrieved 15 June 2011.
  31. Omenn, Gilbert S. (December 2006). "Grand Challenges and Great Opportunities in Science, Technology, and Public Policy". Science. 314 (5806): 1696–1704. doi:10.1126/science.1135003.
  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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 1 2 Tavassoly, Iman (2015). Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells. Springer International Publishing. ISBN 978-3-319-14961-5.

Further reading

External links

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