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Modelling biological systems
open-in-new
<h2 id="standards">Standards</h2> <p>By far the most widely accepted standard format for storing and exchanging models in the field is the <a href="/facts/Systems_Biology_Markup_Language/f5X3n8Vv"> Systems Biology Markup Language (SBML)</a>.<a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a> The <a href="http://sbml.org/">SBML.org</a> 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 <a href="/facts/BioModels/9QrLDm62">BioModels</a>. Other markup languages with different emphases include <a href="/facts/BioPAX/9yNQC8RL">BioPAX</a> and <a href="/facts/CellML/rql491Tk">CellML</a>. </p> <h2 id="particular-tasks">Particular tasks</h2> <h3>Cellular model</h3> <p class="note">Main article: <a href="/facts/Cellular_model/Ww1X47JA">Cellular model</a></p> <p>Creating a cellular model has been a particularly challenging task of <a href="/facts/Systems_biology/HTH1z1RA">systems biology</a> and <a href="/facts/Mathematical_biology/pV9KCBSD">mathematical biology</a>. It involves the use of <a href="/facts/Computer_simulation/Re6o8vzN">computer simulations</a> of the many <a href="/facts/Cell_(biology)/bLM9UQvy">cellular</a> subsystems such as the <a href="/facts/Metabolic_network/BYx7DDMH">networks of metabolites</a>, <a href="/facts/Enzyme/DSI1Fh7y">enzymes</a> which comprise <a href="/facts/Metabolism/WxDrm8k5">metabolism</a> and <a href="/facts/Transcription_(biology)/bDgp9HDN">transcription</a>, <a href="/facts/Translation_(biology)/JcPC1rth">translation</a>, regulation and induction of gene regulatory networks.<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a> </p><p>The complex network of biochemical reaction/transport processes and their spatial organization make the development of a <a href="/facts/Predictive_modelling/VI5ThSKa">predictive model</a> of a living cell a grand challenge for the 21st century, listed as such by the <a href="/facts/National_Science_Foundation/FlDT8TbW">National Science Foundation</a> (NSF) in 2006.<a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a> </p><p>A whole cell computational model for the bacterium <i><a href="/facts/Mycoplasma_genitalium/DkncfS9T">Mycoplasma genitalium</a></i>, 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.<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a> </p><p>A dynamic computer model of intracellular signaling was the basis for Merrimack Pharmaceuticals to discover the target for their cancer medicine MM-111.<a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a> </p><p><a href="/facts/Membrane_computing/Bcl7Gq1v">Membrane computing</a> is the task of modelling specifically a <a href="/facts/Cell_membrane/vAXSD0DT">cell membrane</a>. </p> <h3>Multi-cellular organism simulation</h3> <p>An open source simulation of C. elegans at the cellular level is being pursued by the <a href="/facts/OpenWorm/AfPxuHN0">OpenWorm</a> community. So far the physics engine <a href="https://github.com/openworm/OpenWorm/wiki/Geppetto--Overview">Gepetto</a> has been built and models of the neural connectome and a muscle cell have been created in the NeuroML format.<a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a> </p> <h3>Protein folding</h3> <p class="note">Main article: <a href="/facts/Protein_folding_problem/sj6AbLjI">Protein folding problem</a></p> <p>Protein structure prediction is the prediction of the three-dimensional structure of a <a href="/facts/Protein/Jxmagu3E">protein</a> from its <a href="/facts/Amino_acid/WDxYwBny">amino acid</a> sequence—that is, the prediction of a protein's <a href="/facts/Tertiary_structure/VaN6MEYh">tertiary structure</a> from its <a href="/facts/Primary_structure/VaN6MEYh">primary structure</a>. It is one of the most important goals pursued by <a href="/facts/Bioinformatics/D5x2L8ee">bioinformatics</a> and <a href="/facts/Theoretical_chemistry/uqcyS5KH">theoretical chemistry</a>. <a href="/facts/Protein_structure_prediction/sj6AbLjI">Protein structure prediction</a> is of high importance in <a href="/facts/Medicine/2fnp90Je">medicine</a> (for example, in <a href="/facts/Drug_design/XAU0Ke3d">drug design</a>) and <a href="/facts/Biotechnology/8eFnI6FB">biotechnology</a> (for example, in the design of novel <a href="/facts/Enzymes/DSI1Fh7y">enzymes</a>). Every two years, the performance of current methods is assessed in the <a href="/facts/CASP/A7fWoph9">CASP</a> experiment. </p> <h3>Human biological systems</h3> <h4>Brain model</h4> <p>The <a href="/facts/Blue_Brain_Project/NfxJtHyf">Blue Brain Project</a> is an attempt to create a synthetic brain by <a href="/facts/Reverse_engineering/pmJCV7J7">reverse-engineering</a> the <a href="/facts/Mammalian_brain/EKla9NkJ">mammalian brain</a> down to the molecular level. The aim of this project, founded in May 2005 by the Brain and Mind Institute of the <i><a href="/facts/Ecole_Polytechnique_F%C3%A9d%C3%A9rale_de_Lausanne/IBPANwjM">École Polytechnique</a></i> in <a href="/facts/Lausanne/HwlVEYGN">Lausanne</a>, Switzerland, is to study the brain's architectural and functional principles. The project is headed by the Institute's director, Henry Markram. Using a <a href="/facts/Blue_Gene/y4EvbyS4">Blue Gene</a> <a href="/facts/Supercomputer/HT9NwGAN">supercomputer</a> running Michael Hines's <a href="/facts/Neuron_(software)/HfbwxM1c">NEURON software</a>, the simulation does not consist simply of an <a href="/facts/Artificial_neural_network/6V1jMlkx">artificial neural network</a>, but involves a partially biologically realistic model of <a href="/facts/Neuron/G7CyTVdH">neurons</a>.<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a><a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a> It is hoped by its proponents that it will eventually shed light on the nature of <a href="/facts/Consciousness/IAxmgt6c">consciousness</a>. There are a number of sub-projects, including the <a href="/facts/Cajal_Blue_Brain/NfxJtHyf">Cajal Blue Brain</a>, coordinated by the <a href="/facts/Supercomputing_and_Visualization_Center_of_Madrid/4oFHqYlX">Supercomputing and Visualization Center of Madrid</a> (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.<a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a><a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a> It is one of six pilot projects in the Future Emerging Technologies Research Program of the European Commission,<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a> competing for a billion euro funding. </p> <h4>Model of the immune system</h4> <p>The last decade has seen the emergence of a growing number of simulations of the immune system.<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a><a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a> </p> <h4>Virtual liver</h4> <p>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 <a href="/facts/Mathematical_physiology/9xH6YeqD">physiology</a>, morphology and function.<a class="footnote-ref" id="fnref:18" href="#fn:18"><sup>18</sup></a> </p> <h3>Tree model</h3> <p class="note">Main article: <a href="/facts/Simulated_growth_of_plants/gyCpMMgy">Simulated growth of plants</a></p> <p>Electronic trees (e-trees) usually use <a href="/facts/L-system/SGfQ416R">L-systems</a> to simulate growth. L-systems are very important in the field of <a href="/facts/Complexity_science/eGG3B1nn">complexity science</a> and <a href="/facts/A-life/YcA6tvfe">A-life</a>. A universally accepted system for describing changes in plant morphology at the cellular or modular level has yet to be devised.<a class="footnote-ref" id="fnref:19" href="#fn:19"><sup>19</sup></a> The most widely implemented tree generating algorithms are described in the papers <a href="http://portal.acm.org/citation.cfm?id=218427">"Creation and Rendering of Realistic Trees"</a> and <a href="https://doi.org/10.1007%2F978-3-540-25944-2_22">Real-Time Tree Rendering</a>. </p> <h3>Ecological models</h3> <p class="note">Main article: <a href="/facts/Ecosystem_model/QpT294xe">Ecosystem model</a></p> <p>Ecosystem models are <a href="/facts/Mathematics/pxTouaz4">mathematical</a> representations of <a href="/facts/Ecosystem/kVE8mSmR">ecosystems</a>. Typically they simplify complex <a href="/facts/Food_web/YaakLo08">foodwebs</a> down to their major components or <a href="/facts/Trophic_level/tHMpgkUC">trophic levels</a>, and quantify these as either numbers of <a href="/facts/Organism/j8JkL09u">organisms</a>, <a href="/facts/Biomass/4VHjaya3">biomass</a> or the <a href="/facts/Inventory/vSBu0d9r">inventory</a>/<a href="/facts/Concentration/wqBmz5od">concentration</a> of some pertinent <a href="/facts/Chemical_element/0jducgWD">chemical element</a> (for instance, <a href="/facts/Carbon/ukrgQexo">carbon</a> or a <a href="/facts/Nutrient/34bn4skK">nutrient</a> <a href="/facts/Chemical_species/A8BxM3oW">species</a> such as <a href="/facts/Nitrogen/Nf1QpGDz">nitrogen</a> or <a href="/facts/Phosphorus/27xCeIqs">phosphorus</a>). </p> <h3>Models in ecotoxicology</h3> <p>The purpose of models in <a href="/facts/Ecotoxicology/B4R4EBIb">ecotoxicology</a> 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. <a href="http://www.ecotoxmodels.org/">Ecotoxicology and models</a> discusses some types of ecotoxicological models and provides links to many others. </p> <h3>Modelling of infectious disease</h3> <p class="note">Main articles: <a href="/facts/Mathematical_modelling_of_infectious_disease/cGrvYHsX">Mathematical modelling of infectious disease</a> and <a href="/facts/Epidemic_model/APq4DFha">Epidemic model</a></p> <p>It is possible to model the progress of most infectious diseases mathematically to discover the likely outcome of an <a href="/facts/Epidemic/N7fM3HYd">epidemic</a> or to help manage them by <a href="/facts/Vaccination/3SfpzPsd">vaccination</a>. This field tries to find <a href="/facts/Parameter/zFvVFMv9">parameters</a> for various <a href="/facts/Infectious_disease/182XrncP">infectious diseases</a> and to use those parameters to make useful calculations about the effects of a mass <a href="/facts/Vaccination/3SfpzPsd">vaccination</a> programme. </p> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Biological_data_visualization/XISBFDuf">Biological data visualization</a></li> <li><a href="/facts/Biosimulation/31foUSPN">Biosimulation</a></li> <li><a href="/facts/Gillespie_algorithm/IQfrhoIg">Gillespie algorithm</a></li> <li><a href="/facts/List_of_software_for_molecular_mechanics_modeling/RabeF7vR">Molecular modelling software</a></li> <li><a href="/facts/Stochastic_simulation/wsPs1hap">Stochastic simulation</a></li></ul> <h2 id="notes">Notes</h2> <h2 id="sources">Sources</h2> <ul><li>Antmann, S. S.; Marsden, J. E.; Sirovich, L., eds. (2009). <i>Mathematical Physiology</i> (2nd ed.). New York, New York: Springer. <a href="/facts/ISBN_(identifier)/15AdSPa9">ISBN</a> 978-0-387-75846-6.</li> <li>Barnes, D.J.; Chu, D. (2010), <a href="http://www.cs.kent.ac.uk/projects/imb/"><i>Introduction to Modelling for Biosciences</i></a>, Springer Verlag</li> <li><a href="http://anintroductiontoinfectiousdiseasemodelling.com/">An Introduction to Infectious Disease Modelling</a> by Emilia Vynnycky and Richard G White. An introductory book on infectious disease modelling and its applications.</li></ul> <h2 id="further-reading">Further reading</h2> <ul><li>Barab, A. -L.; Oltvai, Z. (2004). "Network biology* understanding the cell's functional organization". <i>Nature Reviews Genetics</i>. 5 (2): 101–113. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fnrg1272">10.1038/nrg1272</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/14735121">14735121</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:10950726">10950726</a>.</li> <li>Covert; Schilling, C.; Palsson, B. (2001). "Regulation of gene expression in flux balance models of metabolism". <i>Journal of Theoretical Biology</i>. 213 (1): 73–88. <a href="/facts/Bibcode_(identifier)/9HtdQSGB">Bibcode</a>:<a href="https://ui.adsabs.harvard.edu/abs/2001JThBi.213...73C">2001JThBi.213...73C</a>. <a href="/facts/CiteSeerX_(identifier)/SceDmd3c">CiteSeerX</a> <a href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.1647">10.1.1.110.1647</a>. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1006%2Fjtbi.2001.2405">10.1006/jtbi.2001.2405</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/11708855">11708855</a>.</li> <li>Covert, M. W.; Palsson, B. . (2002). <a href="https://doi.org/10.1074%2Fjbc.M201691200">"Transcriptional regulation in constraints-based metabolic models of Escherichia coli"</a>. <i>The Journal of Biological Chemistry</i>. 277 (31): 28058–28064. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1074%2Fjbc.M201691200">10.1074/jbc.M201691200</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/12006566">12006566</a>.</li> <li>Edwards; Palsson, B. (2000). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC25862">"The Escherichia coli MG1655 in silico metabolic genotype* its definition, characteristics, and capabilities"</a>. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 97 (10): 5528–5533. <a href="/facts/Bibcode_(identifier)/9HtdQSGB">Bibcode</a>:<a href="https://ui.adsabs.harvard.edu/abs/2000PNAS...97.5528E">2000PNAS...97.5528E</a>. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1073%2Fpnas.97.10.5528">10.1073/pnas.97.10.5528</a>. <a href="/facts/PMC_(identifier)/dX1zMt71">PMC</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC25862">25862</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/10805808">10805808</a>.</li> <li>Bonneau, R. (2008). "Learning biological networks* from modules to dynamics". <i>Nature Chemical Biology</i>. 4 (11): 658–664. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fnchembio.122">10.1038/nchembio.122</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/18936750">18936750</a>.</li> <li>Edwards, J. S.; Ibarra, R. U.; Palsson, B. O. (2001). "In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data". <i>Nature Biotechnology</i>. 19 (2): 125–130. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2F84379">10.1038/84379</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/11175725">11175725</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:1619105">1619105</a>.</li> <li>Fell, D. A. (1998). "Increasing the flux in metabolic pathways* A metabolic control analysis perspective". <i>Biotechnology and Bioengineering</i>. 58 (2–3): 121–124. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1002%2F%28SICI%291097-0290%2819980420%2958%3A2%2F3%3C121%3A%3AAID-BIT2%3E3.0.CO%3B2-N">10.1002/(SICI)1097-0290(19980420)58:2/3<121::AID-BIT2>3.0.CO;2-N</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/10191380">10191380</a>.</li> <li>Hartwell, L. H.; Hopfield, J. J.; Leibler, S.; Murray, A. W. (1999). <a href="https://doi.org/10.1038%2F35011540">"From molecular to modular cell biology"</a>. <i>Nature</i>. 402 (6761 Suppl): C47 – C52. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2F35011540">10.1038/35011540</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/10591225">10591225</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:34290973">34290973</a>.</li> <li>Ideker; Galitski, T.; Hood, L. (2001). "A new approach to decoding life* systems biology". <i>Annual Review of Genomics and Human Genetics</i>. 2 (1): 343–372. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1146%2Fannurev.genom.2.1.343">10.1146/annurev.genom.2.1.343</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/11701654">11701654</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:922378">922378</a>.</li> <li>Kitano, H. (2002). "Computational systems biology". <i>Nature</i>. 420 (6912): 206–210. <a href="/facts/Bibcode_(identifier)/9HtdQSGB">Bibcode</a>:<a href="https://ui.adsabs.harvard.edu/abs/2002Natur.420..206K">2002Natur.420..206K</a>. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fnature01254">10.1038/nature01254</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/12432404">12432404</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:4401115">4401115</a>.</li> <li>Kitano, H. (2002). "Systems biology* a brief overview". <i>Science</i>. 295 (5560): 1662–1664. <a href="/facts/Bibcode_(identifier)/9HtdQSGB">Bibcode</a>:<a href="https://ui.adsabs.harvard.edu/abs/2002Sci...295.1662K">2002Sci...295.1662K</a>. <a href="/facts/CiteSeerX_(identifier)/SceDmd3c">CiteSeerX</a> <a href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.473.8389">10.1.1.473.8389</a>. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1126%2Fscience.1069492">10.1126/science.1069492</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/11872829">11872829</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:2703843">2703843</a>.</li> <li>Kitano (2002). "Looking beyond the details* a rise in system-oriented approaches in genetics and molecular biology". <i>Current Genetics</i>. 41 (1): 1–10. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1007%2Fs00294-002-0285-z">10.1007/s00294-002-0285-z</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/12073094">12073094</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:18976498">18976498</a>.</li> <li>Gilman, A. G.; Simon, M. I.; Bourne, H. R.; Harris, B. A.; Long, R.; Ross, E. M.; Stull, J. T.; Taussig, R.; Bourne, H. R.; Arkin, A. P.; Cobb, M. H.; Cyster, J. G.; Devreotes, P. N.; Ferrell, J. E.; Fruman, D.; Gold, M.; Weiss, A.; Stull, J. T.; Berridge, M. J.; Cantley, L. C.; Catterall, W. A.; Coughlin, S. R.; Olson, E. N.; Smith, T. F.; Brugge, J. S.; Botstein, D.; Dixon, J. E.; Hunter, T.; Lefkowitz, R. J.; Pawson, A. J. (2002). <a href="https://deepblue.lib.umich.edu/bitstream/2027.42/62977/1/nature01304.pdf">"Overview of the Alliance for Cellular Signaling"</a> (PDF). <i>Nature</i>. 420 (6916): 703–706. <a href="/facts/Bibcode_(identifier)/9HtdQSGB">Bibcode</a>:<a href="https://ui.adsabs.harvard.edu/abs/2002Natur.420..703G">2002Natur.420..703G</a>. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fnature01304">10.1038/nature01304</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/12478301">12478301</a>. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:4367083">4367083</a>.</li> <li>Palsson, Bernhard (2006). <i>Systems biology* properties of reconstructed networks</i>. Cambridge: Cambridge University Press. <a href="/facts/ISBN_(identifier)/15AdSPa9">ISBN</a> 978-0-521-85903-5.</li> <li>Kauffman; Prakash, P.; Edwards, J. S. (2003). "Advances in flux balance analysis". <i>Current Opinion in Biotechnology</i>. 14 (5): 491–496. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1016%2Fj.copbio.2003.08.001">10.1016/j.copbio.2003.08.001</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/14580578">14580578</a>.</li> <li>Segrè, D.; Vitkup, D.; Church, G. M. 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PMID 25404136. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267673" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267673</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>American Association for the Advancement of Science <a href="https://www.science.org/doi/full/10.1126/science.1135003" target="_blank">https://www.science.org/doi/full/10.1126/science.1135003</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>Karr, J. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype Cell <a href="http://www.cell.com/abstract/S0092-8674%2812%2900776-3" target="_blank">http://www.cell.com/abstract/S0092-8674%2812%2900776-3</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>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 <a href="http://mct.aacrjournals.org/content/early/2012/02/17/1535-7163.MCT-11-0820.short" target="_blank">http://mct.aacrjournals.org/content/early/2012/02/17/1535-7163.MCT-11-0820.short</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>OpenWorm Downloads <a href="http://www.openworm.org/downloads.html" target="_blank">http://www.openworm.org/downloads.html</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>Graham-Rowe, Duncan. "Mission to build a simulated brain begins", NewScientist, June 2005. <a href="https://www.newscientist.com/article/dn7470--mission-to-build-a-simulated-brain-begins.html" target="_blank">https://www.newscientist.com/article/dn7470--mission-to-build-a-simulated-brain-begins.html</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>Palmer, Jason. Simulated brain closer to thought, BBC News. <a href="http://news.bbc.co.uk/2/hi/science/nature/8012496.stm" target="_blank">http://news.bbc.co.uk/2/hi/science/nature/8012496.stm</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>The Human Brain Project. Archived July 5, 2012, at the Wayback Machine <a href="http://www.humanbrainproject.eu/index.html" target="_blank">http://www.humanbrainproject.eu/index.html</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>Video of Henry Markram presenting The Human Brain Project on 22 June 2012. <a href="https://www.youtube.com/watch?v=n4a-Om-1MrQ" target="_blank">https://www.youtube.com/watch?v=n4a-Om-1MrQ</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> <li id="fn:15"><p>FET Flagships Initiative homepage. <a href="http://cordis.europa.eu/fp7/ict/programme/fet/flagship/home_en.html" target="_blank">http://cordis.europa.eu/fp7/ict/programme/fet/flagship/home_en.html</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></p></li> <li id="fn:16"><p>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. 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