Modelling biological systems

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Modelling biological systems is a significant task of systems biology and mathematical biology. [lower-alpha 1] Computational systems biology [lower-alpha 2] [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]

Contents

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

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

Cellular model

Part of the cell cycle Signal transduction pathways.svg
Part of the cell cycle
Summerhayes and Elton's 1923 food web of Bear Island (Arrows represent an organism being consumed by another organism). EltonFW.jpg
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. Lotka Volterra dynamics.svg
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

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

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

Brain model

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

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

Virtual liver

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

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

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

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

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

Notes

  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.

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Computational biology</span> Branch of biology

Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics. It differs from biological computing, a subfield of computer science and engineering which uses bioengineering to build computers.

Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

<span class="mw-page-title-main">Gene regulatory network</span> 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 which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).

<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

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 to biological research.

<span class="mw-page-title-main">Mathematical and theoretical biology</span> Branch of biology

Mathematical and theoretical biology, or biomathematics, 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.

<span class="mw-page-title-main">Metabolic engineering</span>

Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cell's production of a certain substance. These processes are chemical networks that use a series of biochemical reactions and enzymes that allow cells to convert raw materials into molecules necessary for the cell's survival. Metabolic engineering specifically seeks to mathematically model these networks, calculate a yield of useful products, and pin point parts of the network that constrain the production of these products. Genetic engineering techniques can then be used to modify the network in order to relieve these constraints. Once again this modified network can be modeled to calculate the new product yield.

<span class="mw-page-title-main">Metabolic network modelling</span> Form of biological modelling

Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, 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.

The Blue Brain Project is a Swiss brain research initiative that aims to create a digital reconstruction of the mouse brain. The project was founded in May 2005 by the Brain and Mind Institute of École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Its mission is to use biologically-detailed digital reconstructions and simulations of the mammalian brain to identify the fundamental principles of brain structure and function.

<span class="mw-page-title-main">Flux balance analysis</span>

Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks. In comparison to traditional methods of modeling, FBA is less intensive in terms of the input data required for constructing the model. Simulations performed using FBA are computationally inexpensive and can calculate steady-state metabolic fluxes for large models in a few seconds on modern personal computers. The related method of metabolic pathway analysis seeks to find and list all possible pathways between metabolites.

The Systems Biology Markup Language (SBML) is a representation format, based on XML, for communicating and storing computational models of biological processes. It is a free and open standard with widespread software support and a community of users and developers. SBML can represent many different classes of biological phenomena, including metabolic networks, cell signaling pathways, regulatory networks, infectious diseases, and many others. It has been proposed as a standard for representing computational models in systems biology today.

Fluxomics describes the various approaches that seek to determine the rates of metabolic reactions within a biological entity. While metabolomics can provide instantaneous information on the metabolites in a biological sample, metabolism is a dynamic process. The significance of fluxomics is that metabolic fluxes determine the cellular phenotype. It has the added advantage of being based on the metabolome which has fewer components than the genome or proteome.

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 and RNAi analysis. 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.

Bernhard Ørn Palsson is the Galletti Professor of Bioengineering and an adjunct professor of Medicine at the University of California, San Diego.

COPASI is an open-source software application for creating and solving mathematical models of biological processes such as metabolic networks, cell-signaling pathways, regulatory networks, infectious diseases, and many others.

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behaviour of biological molecules or complexes that can adopt a large number of possible functional states.

Nicolas Le Novère is a British and French biologist. His research focuses on modeling signaling pathways and developing tools to share mathematical models.

<span class="mw-page-title-main">Andreas Dräger</span> German bioinformatician (born 1980)

Andreas Dräger is a German bioinformatician who leads the research group for Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens at the University of Tübingen.

libRoadRunner is a C/C++ software library that supports simulation of SBML based models.. It uses LLVM to generate extremely high-performance code and is the fastest SBML-based simulator currently available. Its main purpose is for use as a reusable library that can be hosted by other applications, particularly on large compute clusters for doing parameter optimization where performance is critical. It also has a set of Python bindings that allow it to be easily used from Python.

References

  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

Further reading