Physiomics

Physiomics is a systematic study of physiome in biology. Physiomics employs bioinformatics to construct networks of physiological features that are associated with genes, proteins and their networks. A few of the methods for determining individual relationships between the DNA sequence and physiological function include metabolic pathway engineering[1] and RNAi analysis.[2] The relationships derived from methods such as these are organized and processed computationally to form distinct networks. Computer models use these experimentally determined networks to develop further predictions of gene function.[3][4]

History

Physiomics arose from the imbalance between the amount of data being generated by genome projects and the technological ability to analyze the data on a large scale.[3] As technologies such as high-throughput sequencing were being used to generate large amounts of genomic data, effective methods needed to be designed to experimentally interpret and computationally organize this data.[5] Science can be illustrated as a cycle linking knowledge to observations. In the post-genomic era, the ability of computational methods to aid in this observation became evident. This cycle, aided by computer models, is the basis for bioinformatics and, thus, physiomics.[6]

Physiome projects

In 1993, the International Union of Physiological Sciences (IUPS) in Australia presented a physiome project with the purpose of providing a quantitative description of physiological dynamics and functional behavior of the intact organism. The Physiome Project became a major focus of the IUPS in 2001.[7] The National Simulation Resource Physiome Project is a North American project at The University of Washington. The key elements of the NSR Project are the databasing of physiological, pharmacological, and pathological information on humans and other organisms and integration through computational modeling.[8] Other North American projects include the Biological Network Modeling Center at the California Institute of Technology, the National Center for Cell Analysis and Modeling at The University of Connecticut, and the NIH Center for Integrative Biomedical Computing at The University of Utah.

Research applications

There are many different possible applications of physiomics, each requiring different computational models or the combined use of several different models. Examples of such applications include a three dimensional model for tumor growth, the modelling of biological pattern formation, a mathematical model for the formation of stretch marks in humans, and predictive algorithms for the growth of viral infections within insect hosts.[9][10][11][12]

Modelling and simulation software

Collaborative physiomics research is promoted in part by the open availability of bioinformatics software such as simulation programs and modelling environments. There are many institutions and research groups that make their software available to the public. Examples of openly available software include:

  • JSim and Systems Biology Workbench – bioinformatics tools offered by The University of Washington.
  • BISEN – a simulation environment made available by The Medical College of Wisconsin.
  • SimTK – a collection of biological modelling resources made available by The National NIH Center for Biomedical Computing.
  • E-Cell System – a simulation and modelling environment for biological systems offered by Keio University in Tokyo, Japan.

Tools such as these are developed using markup languages specific to bioinformatics research. Many of these markup languages are freely available for use in software development, such as CellML, NeuroML, and SBML.

See also

References
  1. Bailey, J.E (1991). "Toward a science of metabolic engineering". Science. 252 (5013): 1668–1675. doi:10.1126/science.2047876. PMID 2047876.
  2. Kamath, Ravi S.; Fraser, Andrew G.; Dong, Yan; Poulin, Gino; Durbin, Richard; Gotta, Monica; Kanapin, Alexander (2003). "Systematic functional analysis of the Caenorhabditis elegans genome using RNAi". Nature. 421 (6920): 231–237. doi:10.1038/nature01278. hdl:10261/63159. PMID 12529635.
  3. Varner, J. D. (2000). "Large-scale prediction of phenotype: Concept". Biotechnol. Bioeng. 69 (6): 664–678. doi:10.1002/1097-0290(20000920)69:6<664::AID-BIT11>3.0.CO;2-H.
  4. Sanford, Karl; Soucaille, Phillipe; Whited, Gregg; Chotani, Gopal (2002). "Genomics to fluxomics and physiomics — pathway engineering". Current Opinion in Microbiology. 5 (3): 318–322. doi:10.1016/S1369-5274(02)00318-1.
  5. Welch, G. Rickey (2009). "Physiology, physiomics, and biophysics: A matter of words". Progress in Biophysics and Molecular Biology. 100 (1–3): 4–17. doi:10.1016/j.pbiomolbio.2009.08.001. PMID 19699228.
  6. Kell, D.B.; Oliver, S.G. (2004). "Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-drive science in the post-genomic era". BioEssays. 26 (1): 99–105. doi:10.1002/bies.10385. PMID 14696046.
  7. Hunter, P.; Borg, T. (2003). "Integration from proteins to organs: the Physiome Project". Nature Reviews Molecular Cell Biology. 4 (3): 237–243. doi:10.1038/nrm1054. PMID 12612642.
  8. Bassingthwaighte, JB (2000). "Strategies for the Physiome Project". Annals of Biomedical Engineering. 28 (8): 1043–1058. doi:10.1114/1.1313771. PMC 3425440. PMID 11144666.
  9. H. Perfahl, H.M. Byrne, T. Chen, V. Estrella, T. Alarcon, A. Lapin, R.A. Gatenby, R.J. Gillies, M.C. Lloyd, P.K. Maini, M. Reuss, M.R. Owen, 3D multiscale modelling of angiogenesis and vascular tumour growth, in, Micro and Nano Flow Systems Flow Systems for Bioanalysis, M.W. Collins and C.S. Konig (eds), Bioanalysis, 2,29-48(2013)https://people.maths.ox.ac.uk/maini/PKM%20publications/358.pdf
  10. A. Madzvamuse, R.D.K. Thomas, T. Sekimura, A.J. Wathen P.K. Maini, The moving grid finite element method applied to biological problems, In Morphogenesis and Pattern Formation in Biological Systems: Experiments and Models, Proceedings of Chubu 2002 Conference (T. Sekimura, S. Noji, N. Ueno and P.K. Maini, eds), Springer-Verlag Tokyo, 59-65 (2003) https://people.maths.ox.ac.uk/maini/PKM%20publications/158.pdf
  11. Gilmore, S.J.; Vaughan, Jr; Madzvamuse, A.; Maini, P.K. (2012). "A mechanochemical model of striae distensae" (PDF). Math. Biosci. 240 (2): 141–147. doi:10.1016/j.mbs.2012.06.007. PMID 22796062.
  12. White, S.M.; Burden, J.P.; Maini, P.K.; Hails, R.S. (2012). "Modelling the within-host growth of viral infections in insects" (PDF). J. Theor. Biol. 312: 34–43. doi:10.1016/j.jtbi.2012.07.022. PMID 22877574.
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