An attempted illustration of the systems approach to biology

Systems biology is a term used to describe a number of trends in bioscience research, and a movement which draws on those trends. Proponents describe systems biology as a biology-based inter-disciplinary study field that focuses on complex interactions in biological systems, claiming that it uses a new perspective (holism instead of reduction). Particularly from year 2000 onwards, the term is used widely in the biosciences, and in a variety of contexts. An often stated ambition of systems biology is the modeling and discovery of emergent properties, properties of a system whose theoretical description is only possible using techniques which fall under the remit of systems biology. These typically involve cell signaling networks, via long-range allostery^[1].

Overview[link]

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).^[2][3]

• As a paradigm, usually defined in antithesis to the so-called reductionist paradigm (biological organisation), although 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^[4]

"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^[5]

• 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.^[6] 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.

• 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.

• 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.

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[link]

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 growth,
• the simulations developed to study neurophysiology, and
• control theory and cybernetics.

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.^[7] 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.^[8] 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.^[9] In 1960, Denis Noble developed the first computer model of the heart pacemaker.^[10]

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".^[11][12]

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.^{[citation needed]}

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 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a whole (hypothetical) cell.^[13]

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. 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 [2] in the United States. As of summer 2006, due to a shortage of people in systems biology^[14] 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.^[15]

Associated disciplines[link]

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-organisamal cell specific variation. (i.e. Telomere length variation etc.).
• Epigenomics / Epigenetics: Organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e. DNA methylation, Histone Acetylation 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)
• Translatomics / 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: Organismal, tissue, or cell level measurements of all small-molecules known as metabolites.
• 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. This includes:^{[citation needed]}

• Interactomics: 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 pre-clude inclusion of other molecular disciplines such as those defined here.
• NeuroElectroDynamics: Organismal, brain computing function as a dynamic system, underlying biophysical mechanisms and emerging computation by electrical interactions.
• Fluxomics: Organismal, tissue, or cell level measurements of molecular dynamic changes over time.
• Biomics: systems analysis of the biome.

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, 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.^[16][17] 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.

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

• New forms of computational model, such as the use of process calculi to model biological processes (notable approaches include stochastic Failed to parse (Missing texvc executable; please see math/README to configure.): \pi

-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.
• 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.
• Development of syntactically and semantically sound ways of representing biological models.^{[citation needed]}

See also[link]

Systems science portal

• Bioinformatics
• Computational biology
• Network Biology
• Synthetic biology
• List of omics topics in biology
• List of systems biology research groups
• Category:Systems biologists
• Systems microbiology
• Systems biomedicine

References[link]

Further reading[link]

Wikimedia Commons has media related to: Systems biology

Look up systems biology in Wiktionary, the free dictionary.

• Kitano, Hiroaki (15 October 2001). Foundations of Systems Biology. MIT Press. pp. 320. ISBN 978-0-262-11266-6. 
• Werner, Eric (29 March 2007). "All systems go". Nature 446 (7135): 493–494. DOI:10.1038/446493a.  provides a comparative review of three books:
  • Alon, Uri (7 July 2006). An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall. pp. 301. ISBN 978-1-58488-642-6. 
  • Kaneko, Kunihiko (15 September 2006). Life: An Introduction to Complex Systems Biology. Springer-Verlag. pp. 371. ISBN 978-3-540-32666-3. 
  • Palsson, Bernhard O (16 January 2006). Systems Biology: Properties of Reconstructed Networks. Cambridge University Press. pp. 334. ISBN 978-0-521-85903-5. 

This biographical article needs additional citations for verification. Please help by adding reliable sources. Contentious material about living persons that is unsourced or poorly sourced must be removed immediately, especially if potentially libelous or harmful. (October 2011)

Leroy Hood
Hood at a book signing in May 2011
Born	(1938-10-10) October 10, 1938 (age 73) Missoula, Montana
Citizenship	American
Fields	biotechnology, genomics
Institutions	Institute for Systems Biology
Alma mater	Johns Hopkins University, California Institute of Technology
Doctoral advisor	William J. Dreyer
Doctoral students	Trey Ideker
Notable awards	Albert Lasker Award (1987), Kyoto Prize (2002), Lemelson–MIT Prize (2003), Heinz Award (2006), Kistler Prize (2010) Russ Prize (2011)

Leroy Hood is an American biologist. He won the 2011 Fritz J. and Dolores H. Russ Prize “for automating DNA sequencing that revolutionized biomedicine and forensic science” and the 2003 Lemelson-MIT Prize for inventing "four instruments that have unlocked much of the mystery of human biology" by helping decode the genome.^[1] Hood also won the 2002 Kyoto Prize for Advanced Technology, and the 1987 Albert Lasker Award for Basic Medical Research. His inventions include the automated DNA sequencer and an automated tool for synthesizing DNA. Hood co-founded the Institute for Systems Biology.

Biography[link]

Dr. Leroy Hood was born October 10, 1938 in Missoula, Montana. He is recognized as one of the world's leading scientists in molecular biotechnology and genomics. He holds numerous patents and awards for his scientific breakthroughs and prides himself on his life-long commitment to making science accessible and understandable to the general public, especially children. One of this foremost goals is bringing hands-on, inquiry-based science to K-12 classrooms.

Hood was also a founding member of Amgen.

Dr. Hood earned an M.D. From Johns Hopkins University in 1964 and a Ph.D. in biochemistry from the California Institute of Technology in 1968, supervised by William J. Dreyer. Since then, his research has focused on the study of molecular immunology and biotechnology. Dr. Hood has published more than 600 peer-reviewed papers, received 14 patents, and co-authored textbooks in biochemistry, immunology, molecular biology, and genetics, and is a member of the National Academy of Sciences, the American Philosophical Society and the American Academy of Arts and Sciences, the National Academy of Engineering and the Institute of Medicine. Hood received a D.Sc. from Bates College in 1999.

His professional career began at Caltech where he and his colleagues pioneered four instruments--the automated DNA sequencer and synthesizer, and the protein synthesizer and sequencer--which comprise the technological foundation for contemporary molecular biology. In particular, the DNA sequencer has revolutionized genomics by allowing the rapid automated sequencing of DNA. Dr. Hood was also one of the first advocates of and is a key player in the Human Genome Project--the quest to decipher the sequence of the human DNA. He also played a pioneering role in deciphering the secrets of antibody diversity.

In 1992, Dr. Hood moved to the University of Washington to create the cross-disciplinary Department of Molecular Biotechnology. In his role as the Professor of Biomedical Science, Dr. Hood applied his laboratory expertise in DNA sequencing to the analysis of human and mouse immune receptors and initiated studies in prostate cancer, autoimmunity, and hematopoietic stem cell development.

In 2000, Dr. Hood co-founded the Institute for Systems Biology in Seattle, Washington to pioneer systems approaches to biology and medicine. He serves as President of the Institute and continues to pursue his interest in biology, medicine, technology, development, and computational biology.

Dr. Hood has played a role in founding numerous biotechnology companies, including Amgen, Applied Biosystems, Systemix, Darwin, Rosetta, and MacroGenics.

Dr. Hood was awarded the 1987 Lasker Prize for his studies on the mechanism of immune diversity; the Association of Biomolecular Resource Facilities Award for outstanding contributions to Biomolecular Technologies in 2000; the 2002 Kyoto Prize in Advanced Technology for technology development; the 2003 Lemelson-MIT Prize for Innovation and Invention for the development of the DNA sequencer; the 12th Annual Heinz Award in Technology, the Economy and Employment^[2] in 2006 for his extraordinary breakthroughs in biomedical science; and the 2006 Wharton Infosys Business Transformation Award for his society-transforming use of information technology. In 2007 he was inducted into the National Inventors Hall of Fame.

References[link]

External links[link]

• My Life and Adventures Integrating Biology and Technology Commemorative lecture given when awarded the 2002 Kyoto Prize in Advanced Technologies.
• Biography of Leroy Hood at the website of the Institute for Systems Biology.
• Interview with Leroy Hood on the website of the Royal Society of Chemistry.
• The Heinz Awards, Leroy Hood profile
• SystemsX.ch - The Swiss Initiative in Systems Biology