Wikipedia

Modelling biological systems

(Redirected from Computational biomodeling)

Modelling biological systems is a significant task of systems biology and mathematical biology.[a] Computational systems biology[b][1] 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 (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks), to both analyze and visualize the complex connections of these cellular processes.[2]

An unexpected emergent property of a complex system may be a result of the interplay of the cause-and-effect among simpler, integrated parts (see biological organisation). Biological systems manifest many important examples of emergent properties in the complex interplay of components. Traditional study of biological systems requires reductive methods in which quantities of data are gathered by category, such as concentration over time in response to a certain stimulus. Computers are critical to analysis and modelling of these data. The goal is to create accurate real-time models of a system's response to environmental and internal stimuli, such as a model of a cancer cell in order to find weaknesses in its signalling pathways, or modelling of ion channel mutations to see effects on cardiomyocytes and in turn, the function of a beating heart.

Standards

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By far the most widely accepted standard format for storing and exchanging models in the field is the Systems Biology Markup Language (SBML).[3] The SBML.org website includes a guide to many important software packages used in computational systems biology. A large number of models encoded in SBML can be retrieved from BioModels. Other markup languages with different emphases include BioPAX and CellML.

Particular tasks

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Cellular model

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Main article: Cellular model
 
Part of the cell cycle
 
Summerhayes and Elton's 1923 food web of Bear Island (Arrows represent an organism being consumed by another organism).
 
A sample time-series of the Lotka–Volterra model. Note that the two populations exhibit cyclic behaviour.

Creating a cellular model has been a particularly challenging task of systems biology and mathematical biology. It involves the use of computer simulations of the many cellular subsystems such as the networks of metabolites, enzymes which comprise metabolism and transcription, translation, regulation and induction of gene regulatory networks.[4]

The complex network of biochemical reaction/transport processes and their spatial organization make the development of a predictive model of a living cell a grand challenge for the 21st century, listed as such by the National Science Foundation (NSF) in 2006.[5]

A whole cell computational model for the bacterium Mycoplasma genitalium, including all its 525 genes, gene products, and their interactions, was built by scientists from Stanford University and the J. Craig Venter Institute and published on 20 July 2012 in Cell.[6]

A dynamic computer model of intracellular signaling was the basis for Merrimack Pharmaceuticals to discover the target for their cancer medicine MM-111.[7]

Membrane computing is the task of modelling specifically a cell membrane.

Multi-cellular organism simulation

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An open source simulation of C. elegans at the cellular level is being pursued by the OpenWorm community. So far the physics engine Gepetto has been built and models of the neural connectome and a muscle cell have been created in the NeuroML format.[8]

Protein folding

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Main article: Protein folding problem

Protein structure prediction is the prediction of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of a protein's tertiary structure from its primary structure. It is one of the most important goals pursued by bioinformatics and theoretical chemistry. Protein structure prediction is of high importance in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes). Every two years, the performance of current methods is assessed in the CASP experiment.

Human biological systems

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Brain model

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The Blue Brain Project is an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. The aim of this project, founded in May 2005 by the Brain and Mind Institute of the École Polytechnique in Lausanne, Switzerland, is to study the brain's architectural and functional principles. The project is headed by the Institute's director, Henry Markram. Using a Blue Gene supercomputer running Michael Hines's NEURON software, the simulation does not consist simply of an artificial neural network, but involves a partially biologically realistic model of neurons.[9][10] It is hoped by its proponents that it will eventually shed light on the nature of consciousness. There are a number of sub-projects, including the Cajal Blue Brain, coordinated by the Supercomputing and Visualization Center of Madrid (CeSViMa), and others run by universities and independent laboratories in the UK, U.S., and Israel. The Human Brain Project builds on the work of the Blue Brain Project.[11][12] It is one of six pilot projects in the Future Emerging Technologies Research Program of the European Commission,[13] competing for a billion euro funding.

Model of the immune system

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The last decade has seen the emergence of a growing number of simulations of the immune system.[14][15]

Virtual liver

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The Virtual Liver project is a 43 million euro research program funded by the German Government, made up of seventy research group distributed across Germany. The goal is to produce a virtual liver, a dynamic mathematical model that represents human liver physiology, morphology and function.[16]

Tree model

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Main article: Simulated growth of plants

Electronic trees (e-trees) usually use L-systems to simulate growth. L-systems are very important in the field of complexity science and A-life. A universally accepted system for describing changes in plant morphology at the cellular or modular level has yet to be devised.[17] The most widely implemented tree generating algorithms are described in the papers "Creation and Rendering of Realistic Trees" and Real-Time Tree Rendering.

Ecological models

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Main article: Ecosystem model

Ecosystem models are mathematical representations of ecosystems. Typically they simplify complex foodwebs down to their major components or trophic levels, and quantify these as either numbers of organisms, biomass or the inventory/concentration of some pertinent chemical element (for instance, carbon or a nutrient species such as nitrogen or phosphorus).

Models in ecotoxicology

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The purpose of models in ecotoxicology is the understanding, simulation and prediction of effects caused by toxicants in the environment. Most current models describe effects on one of many different levels of biological organization (e.g. organisms or populations). A challenge is the development of models that predict effects across biological scales. Ecotoxicology and models discusses some types of ecotoxicological models and provides links to many others.

Modelling of infectious disease

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Main articles: Mathematical modelling of infectious disease and Epidemic model

It is possible to model the progress of most infectious diseases mathematically to discover the likely outcome of an epidemic or to help manage them by vaccination. This field tries to find parameters for various infectious diseases and to use those parameters to make useful calculations about the effects of a mass vaccination programme.

See also

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Notes

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  1. ^ Sometimes called theoretical biology, dry biology, or even biomathematics.
  2. ^ Computational systems biology is a branch that strives to generate a system-level understanding by analyzing biological data using computational techniques.

References

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  1. ^ Andres Kriete, Roland Eils, Computational Systems Biology, Elsevier Academic Press, 2006.
  2. ^ 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. S2CID 52922135.
  3. ^ Klipp, Liebermeister, Helbig, Kowald and Schaber. (2007). "Systems biology standards—the community speaks" (2007), Nature Biotechnology 25(4):390–391.
  4. ^ Carbonell-Ballestero M, Duran-Nebreda S, Montañez R, Solé R, Macía J, Rodríguez-Caso C (December 2014). "A bottom-up characterization of transfer functions for synthetic biology designs: lessons from enzymology". Nucleic Acids Research. 42 (22): 14060–14069. doi:10.1093/nar/gku964. PMC 4267673. PMID 25404136.
  5. ^ American Association for the Advancement of Science
  6. ^ Karr, J. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype Cell
  7. ^ McDonagh, CF (2012) Antitumor Activity of a Novel Bispecific Antibody That Targets the ErbB2/ErbB3 Oncogenic Unit and Inhibits Heregulin-Induced Activation of ErbB3. Molecular Cancer Therapeutics
  8. ^ OpenWorm Downloads
  9. ^ Graham-Rowe, Duncan. "Mission to build a simulated brain begins", NewScientist, June 2005.
  10. ^ Palmer, Jason. Simulated brain closer to thought, BBC News.
  11. ^ The Human Brain Project. Archived July 5, 2012, at the Wayback Machine
  12. ^ Video of Henry Markram presenting The Human Brain Project on 22 June 2012.
  13. ^ FET Flagships Initiative homepage.
  14. ^ Balicki, Jerzy (2004). "Multi-criterion Evolutionary Algorithm with Model of the Immune System to Handle Constraints for Task Assignments". Artificial Intelligence and Soft Computing - ICAISC 2004. Lecture Notes in Computer Science. Vol. 3070. pp. 394–399. doi:10.1007/978-3-540-24844-6_57. ISBN 978-3-540-22123-4.
  15. ^ "Computer Simulation Captures Immune Response To Flu". Retrieved 2009-08-19.
  16. ^ "Virtual Liver Network". Archived from the original on 2012-09-30. Retrieved 2016-10-14.
  17. ^ "Simulating plant growth". Archived from the original on 2009-12-09. Retrieved 2009-10-18.

Sources

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Further reading

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