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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">vavilov</journal-id><journal-title-group><journal-title xml:lang="ru">Вавиловский журнал генетики и селекции</journal-title><trans-title-group xml:lang="en"><trans-title>Vavilov Journal of Genetics and Breeding</trans-title></trans-title-group></journal-title-group><issn pub-type="epub">2500-3259</issn><publisher><publisher-name>Institute of Cytology and Genetics of Siberian Branch of the RAS</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.18699/VJ15.095</article-id><article-id custom-type="elpub" pub-id-type="custom">vavilov-493</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>Компьютерное моделирование</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>Computer Simulation</subject></subj-group></article-categories><title-group><article-title>Современные подходы к математическому и компьютерному моделированию в микробиологии</article-title><trans-title-group xml:lang="en"><trans-title>A review of simulation and modeling approaches in microbiology</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Клименко</surname><given-names>А. И.</given-names></name><name name-style="western" xml:lang="en"><surname>Klimenko</surname><given-names>A. I.</given-names></name></name-alternatives><email xlink:type="simple">klimenko@bionet.nsc.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мустафин</surname><given-names>З. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Mustafin</surname><given-names>Z. S.</given-names></name></name-alternatives><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Чеканцев</surname><given-names>А. Д.</given-names></name><name name-style="western" xml:lang="en"><surname>Chekantsev</surname><given-names>A. D.</given-names></name></name-alternatives><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Зудин</surname><given-names>Р. К.</given-names></name><name name-style="western" xml:lang="en"><surname>Zudin</surname><given-names>R. K.</given-names></name></name-alternatives><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Матушкин</surname><given-names>Ю. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Matushkin</surname><given-names>Yu. G.</given-names></name></name-alternatives><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Лашин</surname><given-names>С. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Lashin</surname><given-names>S. A.</given-names></name></name-alternatives><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Федеральное государственное бюджетное научное учреждение «Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук», Новосибирск, Россия&#13;
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Федеральное государственное автономное образовательное учреждение высшего образования «Новосибирский национальный исследовательский государственный университет», Новосибирск, Россия<country>Россия</country></aff><aff xml:lang="en">Institute of Cytology and Genetics SB RA S, Novosibirsk, Russia&#13;
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Novosibirsk State University, Novosibirsk, Russia<country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru">Федеральное государственное бюджетное научное учреждение «Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук», Новосибирск, Россия<country>Россия</country></aff><aff xml:lang="en">Institute of Cytology and Genetics SB RA S, Novosibirsk, Russia<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2015</year></pub-date><pub-date pub-type="epub"><day>05</day><month>01</month><year>2016</year></pub-date><volume>19</volume><issue>6</issue><fpage>745</fpage><lpage>752</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Клименко А.И., Мустафин З.С., Чеканцев А.Д., Зудин Р.К., Матушкин Ю.Г., Лашин С.А., 2016</copyright-statement><copyright-year>2016</copyright-year><copyright-holder xml:lang="ru">Клименко А.И., Мустафин З.С., Чеканцев А.Д., Зудин Р.К., Матушкин Ю.Г., Лашин С.А.</copyright-holder><copyright-holder xml:lang="en">Klimenko A.I., Mustafin Z.S., Chekantsev A.D., Zudin R.K., Matushkin Y.G., Lashin S.A.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vavilov.elpub.ru/jour/article/view/493">https://vavilov.elpub.ru/jour/article/view/493</self-uri><abstract><p>Бактериальные сообщества являются тесно взаимосвязанными системами, состоящими из большого числа видов, что значительно осложняет анализ их структуры и взаимоотношений. В настоящий момент существует ряд экспериментальных методов, предоставляющих гетерогенные данные, касающиеся различных аспектов этого объекта исследования. Произошедшее за последнее время резкое увеличение объема доступных метагеномных данных представляет интерес не только для биостатистиков, но и для специалистов в области моделирования биосистем, поскольку эти данные позволяют повысить качество моделей. В то же время методы математического и компьютерного моделирования оказываются полезны для понимания эволюции микробных сообществ и их функции в экосистеме. В статье представлен обзор существующих методов и средств математического и компьютерного моделирования, использующихся в области экологии микробных сообществ и опирающихся на различные типы экспериментальных данных. Рассмотрены подходы, фокусирующиеся на описании таких аспектов микробного сообщества, как его трофическая структура, метаболическая и популяционная динамика, генетическое разнообразие, а также пространственная гетерогенность и динамика распространения. В работе также приведена классификация существующих программных средств моделирования микробных сообществ. Показано, что несмотря на преобладание тенденции к использованию гибридных подходов к моделированию, остаются актуальными проблемы интеграции между моделями, описывающими различные уровни биологической организации сообществ. Многоаспектность интеграционных подходов, используемых для моделирования микробных сообществ, основана на необходимости учитывать гетерогенные данные, полученные из различных источников с помощью высокопроизводительных экспериментальных методов исследования генома.</p></abstract><trans-abstract xml:lang="en"><p>Bacterial communities are tightly interconnected systems consisting of numerous species making it challenging to analyze their structure and relations. There are several experimental techniques providing heterogeneous data concerning various aspects of this object. A recent avalanche of metagenomic data challenges not only biostatisticians but also biomodelers, since these data are essential to improve the modeling quality while simulation methods are useful to understand the evolution of microbial communities and their function in the ecosystem. An outlook on the existing modeling and simulation approaches based on different types of experimental data in the field of microbial ecology and environmental microbiology is presented. A number of approaches focusing on a description of such microbial community aspects as its trophic structure, metabolic and population dynamics, genetic diversity as well as spatial heterogeneity and expansion dynamics is considered. We also propose a classification of the existing software designed for simulation of microbial communities. It is shown that although the trend for using multiscale/hybrid models prevails, the integration between models concerning different levels of biological organization of communities still remains a problem to be solved. The multiaspect nature of integration approaches used to model microbial communities is based on the need to take into account heterogeneous data obtained from various sources by applying high-throughput genome investigation methods.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>микробные сообщества</kwd><kwd>экологическое моделирование</kwd><kwd>эволюционное моделирование</kwd><kwd>прокариоты</kwd></kwd-group><kwd-group xml:lang="en"><kwd>microbial communities</kwd><kwd>ecological simulation</kwd><kwd>evolutionary modeling</kwd><kwd>prokaryotes</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Гимельфарб А.А., Гинзбург Л.Р., Полуэктов Р.А., Пых Ю.А., Ратнер В.А. Динамическая теория биологических популяций. Наука, 1974.</mixed-citation><mixed-citation xml:lang="en">Adler J. Chemotaxis in bacteria. J. Supramol. Struct. 1976;4:305-317. DOI 10.1146/annurev.bi.44.070175.002013</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Колмакова О.В. Современные методы определения видоспецифичных биогеохимических функций бактериопланктона. Журнал сибирского федерального ун-та. Сер. биол. 2013;6(1): 73-95.</mixed-citation><mixed-citation xml:lang="en">Beardmore R.E., Gudelj I., Lipson D.A., Hurst L.D. Metabolic tradeoffs and the  maintenance of the ttest and the flattest. Nature. 2011;472:342-346. DOI 10.1038/nature09905</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Лихошвай В.А., Хлебодарова Т.М., Ратушный А.В., Лашин С.А., Турнаев И.И., Подколодная О.А., Ананько Е.А., Смирнова О.Г., Ибрагимова С.С., Колчанов Н.А. Компьютерный генетический конструктор: математическое моделирование генетических и метаболических подсистем E. сoli. Роль микроорганизмов в функционировании живых систем: фундаментальные проблемы и биоинженерные приложения. Ред. В.В. Власов, А.Г. Дегерменджи, Н.А. Колчанов, В.Н. Пармон, Е.А. Репин. Новосибирск: Изд-во СО РАН, 2010.</mixed-citation><mixed-citation xml:lang="en">Beslon G., Parsons D.P., Sanchez-Dehesa ., Peсa J.-M., Knibbe C. Scaling laws in  bacterial genomes: a side-effect of selection of mutational robustness? Biosystems.  2010;102:32-40. DOI 10.1016/j.biosystems.2010.07.009</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Логофет Д.О., Белова И.Н. Неотрицательные матрицы как инструмент моделирования динамики популяций: классические модели и современные обобщения. Фундаментальная и прикладная математика. 2007;13:145-164.</mixed-citation><mixed-citation xml:lang="en">Chernavskiy D.S., Ierusalimskiy N.D. On the determinant link in the system of enzyme  reactions. Izvestiya AN SSSR, seriya biologiya = Proceedings of USSR Academy of Sciences. Biology. 1965;5:665-672.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Нетрусов А.И., Котова И.Б. Микробиология. М.: Академия, 2007.</mixed-citation><mixed-citation xml:lang="en">Chewapreecha C. Your gut microbiota are what you eat. Nat. Rev. Microbiol. 2013;12:8. DOI 10.1038/nrmicro3186</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Ризниченко Г.Ю. Математические модели в биофизике и экологии. М.; Ижевск: Институт компьютерных исследований, 2003.</mixed-citation><mixed-citation xml:lang="en">Comolli L.R. Intra- and inter-species interactions in microbial communities. Front. Microbiol. 2014;5:1-3. DOI 10.3389/fmicb.2014.00629</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Ризниченко Г.Ю., Рубин А.Б. Математические модели биологических продукционных процессов. М.: Изд-во МГУ, 1993.</mixed-citation><mixed-citation xml:lang="en">Covert M.W., Schilling C.H., Famili I., Edwards J.S., Goryanin I.I., Selkov E.,  Palsson B.O. Metabolic modeling of microbial strains in silico. Trends Biochem. Sci.  2001;26:179-186. DOI 10.1016/S0968-0004(00)01754-0</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Чернавский Д.С., Иерусалимский Н.Д. К вопросу об определяющем звене в системе ферментативных реакций. Изв. АН СССР. Сер. биол. 1965;5:665-672.</mixed-citation><mixed-citation xml:lang="en">De Jong H. Modeling and simulation of genetic regulatory systems: a literature  review. J. Comput. Biol. 2002;9:67-103. DOI 10.1089/10665270252833208</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Adler J. Chemotaxis in bacteria. J. Supramol. Struct. 1976;4:305-317. DOI 10.1146/annurev.bi.44.070175.002013</mixed-citation><mixed-citation xml:lang="en">De Roy K., Marzorati M., Van den Abbeele P., Van de Wiele T., Boon N. Synthetic  microbial ecosystems: An exciting tool to understand and apply microbial  communities. Environ. Microbiol. 2013;16:1472- 1481. DOI 10.1111/1462-2920.12343</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Beardmore R.E., Gudelj I., Lipson D.A., Hurst L.D. Metabolic tradeoffs and the maintenance of the fittest and the flattest. Nature. 2011;472:342-346. DOI 10.1038/nature09905</mixed-citation><mixed-citation xml:lang="en">DeAngelis D.L., Mooij W.M. Individual-based modeling of ecological and evolutionary  processes 1. Annu. Rev. Ecol. Evol. Syst. 2005;36:147-168. DOI 10.1146/annurev.ecolsys.36.102003.152644</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Beslon G., Parsons D.P., Sanchez-Dehesa Y., Peсa J.-M., Knibbe C. Scaling laws in bacterial genomes: a side-effect of selection of mutational robustness? Biosystems. 2010;102:32-40. DOI 10.1016/j.biosystems.2010.07.009</mixed-citation><mixed-citation xml:lang="en">Durot M., Bourguignon P.-Y., Schachter V. Genome-scale models of bacterial  metabolism: reconstruction and applications. FEMS Microbiol. Rev. 2009;33:164-190. DOI 10.1111/j.1574-6976.2008.00146.x</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Chewapreecha C. Your gut microbiota are what you eat. Nat. Rev. Microbiol. 2013;12:8. DOI 10.1038/nrmicro3186</mixed-citation><mixed-citation xml:lang="en">Emonet T., Macal C.M., North M.J., Wickersham C.E., Cluzel P. Agent-Cell: a digital  single-cell assay for bacterial chemotaxis. Bioinformatics. 2005;21:2714-2721. DOI 10.1093/bioinformatics/bti391</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Comolli L.R. Intra- and inter-species interactions in microbial communities. Front. Microbiol. 2014;5:1-3. DOI 10.3389/fmicb.2014.00629</mixed-citation><mixed-citation xml:lang="en">Esteban P.G., Rodríguez-Patón A. Simulating a Rock-Scissors-Paper Bacterial Game  with a Discrete Cellular Automaton. New Challenges on Bioinspired Applications,  Lecture Notes in Computer Science. Eds J.M. Ferràndez, J.R. Álvarez Sànchez, F. de la Paz, F.J. Toledo. Springer Berlin Heidelberg, Berlin, Heidelberg, 2011. DOI 10.1007/978-3-642-21326-7</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Covert M.W., Schilling C.H., Famili I., Edwards J.S., Goryanin I.I., Selkov E., Palsson B.O. Metabolic modeling of microbial strains in silico. Trends Biochem. Sci. 2001;26:179-186. DOI 10.1016/S0968- 0004(00)01754-0</mixed-citation><mixed-citation xml:lang="en">Faust K., Raes J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 2012;10:538-550. DOI 10.1038/nrmicro2832</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">De Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 2002;9:67-103. DOI 10.1089/10665270252833208</mixed-citation><mixed-citation xml:lang="en">Frey E. Evolutionary game theory: Theoretical concepts and applications to microbial  communities. Phys. A Stat. Mech. its Appl. 2010; 389:4265-4298. DOI 10.1016/j.physa.2010.02.047</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">De Roy K., Marzorati M., Van den Abbeele P., Van de Wiele T., Boon N. Synthetic microbial ecosystems: An exciting tool to understand and apply microbial communities. Environ. Microbiol. 2013;16:1472- 1481. DOI 10.1111/1462-2920.12343</mixed-citation><mixed-citation xml:lang="en">Fuhrman J.A. Microbial community structure and its functional implications. Nature.  2009;459:193-199. DOI nature08058 [pii]\n10.1038/nature08058 [doi]</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">DeAngelis D.L., Mooij W.M. Individual-based modeling of ecological and evolutionary processes 1. Annu. Rev. Ecol. Evol. Syst. 2005;36:147-168. DOI 10.1146/annurev.ecolsys.36.102003.152644</mixed-citation><mixed-citation xml:lang="en">Gimel’farb A.A., Ginzburg L.R., Poluektov R.A., Pykh Yu.A., Ratner V.A.  Dinamicheskaya teoriya biologicheskikh populyatsiy [Dynamic theory of biological  populations]. Moscow, Nauka, 1974.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Durot M., Bourguignon P.-Y., Schachter V. Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol. Rev. 2009;33:164-190. DOI 10.1111/j.1574-6976.2008.00146.x</mixed-citation><mixed-citation xml:lang="en">Ginovart M., López D., Valls J. INDISIM, an individual-based discrete simulation  model to study bacterial cultures. J. Theor. Biol. 2002; 214:305-319. DOI 10.1006/jtbi.2001.2466</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Emonet T., Macal C.M., North M.J., Wickersham C.E., Cluzel P. Agent-Cell: a digital single-cell assay for bacterial chemotaxis. Bioinformatics. 2005;21:2714-2721. DOI 10.1093/bioinformatics/bti391</mixed-citation><mixed-citation xml:lang="en">Grimm V., Berger U., Bastiansen F., Eliassen S., Ginot V., Giske J., Goss-Custard  J., Grand T., Heinz S.K., Huse G., Huth A., Jepsen J. U., Jørgensen C., Mooij W.M.,  Müller B., Pe’er G., Piou C., Railsback S.F., Robbins A.M., Robbins M.M., Rossmanith  E., Rüger N., Strand E., Souissi S., Stillman R. a., Vabø R., Visser U., DeAngelis  D.L. A standard protocol for describing individual-based and agent-based models.  Ecol. Modell. 2006;198:115-126. DOI 10.1016/j.ecolmodel.2006.04.023</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Esteban P.G., Rodríguez-Patón A. Simulating a Rock-Scissors-Paper Bacterial Game with a Discrete Cellular Automaton. New Challenges on Bioinspired Applications, Lecture Notes in Computer Science. Eds J.M. Ferràndez, J.R. Álvarez Sànchez, F. de la Paz, F.J. Toledo. Springer Berlin Heidelberg, Berlin, Heidelberg, 2011. DOI 10.1007/978-3-642-21326-7</mixed-citation><mixed-citation xml:lang="en">Halfen L.N., Castenholz R.W. Gliding motility in the blue-green alga oscillatoria princeps. 1971.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Faust K., Raes J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 2012;10:538-550. DOI 10.1038/nrmicro2832</mixed-citation><mixed-citation xml:lang="en">Hecker M., Lambeck S., Toepfer S., van Someren E., Guthke R. Gene regulatory network  inference: Data integration in dynamic models–A review. Biosystems. 2009;96:86-103.  DOI 10.1016/j.biosystems.2008.12.004</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Frey E. Evolutionary game theory: Theoretical concepts and applications to microbial communities. Phys. A Stat. Mech. its Appl. 2010; 389:4265-4298. DOI 10.1016/j.physa.2010.02.047</mixed-citation><mixed-citation xml:lang="en">Henrichsen J. Bacterial Surface Translocation: a Survey and a Classification. Bacteriol. Rev. 1972;36:478-503.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Fuhrman J.A. Microbial community structure and its functional implications. Nature. 2009;459:193-199. DOI nature08058 [pii]n10.1038/nature08058 [doi]</mixed-citation><mixed-citation xml:lang="en">Henson M.A., Hanly T.J. Dynamic flux balance analysis for synthetic microbial  communities. IET Syst. Biol. 2014;8:214-229. DOI 10.1049/iet-syb.2013.0021</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Ginovart M., López D., Valls J. INDISIM, an individual-based discrete simulation model to study bacterial cultures. J. Theor. Biol. 2002; 214:305-319. DOI 10.1006/jtbi.2001.2466</mixed-citation><mixed-citation xml:lang="en">Ishii N., Robert M., Nakayama Y., Kanai A., Tomita M. Toward largescale modeling of  the microbial cell for computer simulation. J. Biotechnol. 2004;113:281-294. DOI 10.1016/j.jbiotec.2004.04.038</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Grimm V., Berger U., Bastiansen F., Eliassen S., Ginot V., Giske J., Goss-Custard J., Grand T., Heinz S.K., Huse G., Huth A., Jepsen J. U., Jørgensen C., Mooij W.M., Müller B., Pe’er G., Piou C., Railsback S.F., Robbins A.M., Robbins M.M., Rossmanith E., Rüger N., Strand E., Souissi S., Stillman R. a., Vabø R., Visser U., DeAngelis D.L. A standard protocol for describing individual-based and agent-based models. Ecol. Modell. 2006;198:115-126. DOI 10.1016/j.ecolmodel.2006.04.023</mixed-citation><mixed-citation xml:lang="en">Karr J.R., Sanghvi J.C., MacKlin D.N., Gutschow M.V., Jacobs J.M., Bolival B.,  Assad-Garcia N., Glass J.I., Covert M.W. A wholecell computational model predicts  phenotype from genotype. Cell. 2012;150:389-401. DOI 10.1016/j.cell.2012.05.044</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Halfen L.N., Castenholz R.W. Gliding motility in the blue-green alga oscillatoria princeps. 1971.</mixed-citation><mixed-citation xml:lang="en">Karunakaran E., Mukherjee J., Ramalingam B., Biggs C.A. «Biofilmology »: a  multidisciplinary review of the study of microbial biofilms. Appl. Microbiol.  Biotechnol. 2011;90:1869-1881. DOI 10.1007/s00253-011-3293-4</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Hecker M., Lambeck S., Toepfer S., van Someren E., Guthke R. Gene regulatory network inference: Data integration in dynamic models–A review. Biosystems. 2009;96:86-103. DOI 10.1016/j.biosystems.2008.12.004</mixed-citation><mixed-citation xml:lang="en">Klimenko A.I., Matushkin Y.G., Kolchanov N.A., Lashin S.A. Modeling evolution of  spatially distributed bacterial communities: a simulation with the haploid  evolutionary constructor. BMC Evol. Biol. 2015;15:S3. DOI 10.1186/1471-2148-15-S1-S3</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Henrichsen J. Bacterial Surface Translocation: a Survey and a Classification. Bacteriol. Rev. 1972;36:478-503.</mixed-citation><mixed-citation xml:lang="en">Klitgord N., Segre D. Environments that induce synthetic microbial ecosystems. PLoS  Comput. Biol. 2010;101:1435-1439. DOI 10.1371/Citation</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Henson M.A., Hanly T.J. Dynamic flux balance analysis for synthetic microbial communities. IET Syst. Biol. 2014;8:214-229. DOI 10.1049/iet-syb.2013.0021</mixed-citation><mixed-citation xml:lang="en">Knibbe C., Fayard J.-M., Beslon G. The topology of the protein network influences  the dynamics of gene order: from systems biology to a systemic understanding of  evolution. Artif. Life. 2008;14:149- 156. DOI 10.1162/artl.2008.14.1.149</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Ishii N., Robert M., Nakayama Y., Kanai A., Tomita M. Toward largescale modeling of the microbial cell for computer simulation. J. Biotechnol. 2004;113:281-294. DOI 10.1016/j.jbiotec.2004.04.038</mixed-citation><mixed-citation xml:lang="en">Kolmakova O.V. Contemporary Methods for Identification of Bacterioplankton Species- Specific Biogeochemical Functions. Zhurnal sibirskogo federalnogo universiteta.  Biologiya = Journal of Siberian Federal University. Biology. 2013;6(1):73-95.</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Karr J.R., Sanghvi J.C., MacKlin D.N., Gutschow M.V., Jacobs J.M., Bolival B., Assad-Garcia N., Glass J.I., Covert M.W. A wholecell computational model predicts phenotype from genotype. Cell. 2012;150:389-401. DOI 10.1016/j.cell.2012.05.044</mixed-citation><mixed-citation xml:lang="en">Kutalik Z., Razaz M., Baranyi J. Connection between stochastic and deterministic  modelling of microbial growth. J. Theor. Biol. 2005;232:285-299. DOI 10.1016/j.jtbi.2004.08.013</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Karunakaran E., Mukherjee J., Ramalingam B., Biggs C.A. «Biofilmology »: a multidisciplinary review of the study of microbial biofilms. Appl. Microbiol. Biotechnol. 2011;90:1869-1881. DOI 10.1007/s00253-011-3293-4</mixed-citation><mixed-citation xml:lang="en">Larsen P., Hamada Y., Gilbert J. Modeling microbial communities: Current,  developing, and future technologies for predicting microbial community interaction.  J. Biotechnol. 2012;160:17-24. DOI 10.1016/j.jbiotec.2012.03.009</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Klimenko A.I., Matushkin Y.G., Kolchanov N.A., Lashin S.A. Modeling evolution of spatially distributed bacterial communities: a simulation with the haploid evolutionary constructor. BMC Evol. Biol. 2015;15:S3. DOI 10.1186/1471-2148-15-S1-S3</mixed-citation><mixed-citation xml:lang="en">Laspidou C.S., Rittmann B.E. Evaluating trends in biofilm density using the UMCCA  model. Water Res. 2004;38:3362-33672. DOI 10.1016/j.watres.2004.04.051</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Klitgord N., Segre D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 2010;101:1435-1439. DOI 10.1371/Citation</mixed-citation><mixed-citation xml:lang="en">Lencastre Fernandes R., Nierychlo M., Lundin L., Pedersen A.E., Puentes Tellez P.E.,  Dutta A., Carlquist M., Bolic A., Schäpper D., Brunetti A.C., Helmark S., Heins  A.L., Jensen A.D., Nopens I., Rottwitt K., Szita N., van Elsas J.D., Nielsen P.H.,  Martinussen J., Sørensen S.J.,  Lantz A.E., Gernaey K.V. Experimental methods and  modeling techniques for description of cell population heterogeneity. Biotechnol. Adv. 2011;29:575-599. DOI 10.1016/j.biotechadv.2011.03.007</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Knibbe C., Fayard J.-M., Beslon G. The topology of the protein network influences the dynamics of gene order: from systems biology to a systemic understanding of evolution. Artif. Life. 2008;14:149-156. DOI 10.1162/artl.2008.14.1.149</mixed-citation><mixed-citation xml:lang="en">Leslie P.H. On the use of matrices in certain population mathematics. Biometrika. 1945. DOI 10.2307/2332297</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Kutalik Z., Razaz M., Baranyi J. Connection between stochastic and deterministic modelling of microbial growth. J. Theor. Biol. 2005;232:285-299. DOI 10.1016/j.jtbi.2004.08.013</mixed-citation><mixed-citation xml:lang="en">Likhoshvay V.A., Khlebodarova T.M., Ratushnyy A.V., Lashin S.A., Turnaev I.I.,  Podkolodnaya O.A., Anan’ko E.A., Smirnova O.G., Ibragimova S.S., Kolchanov N.A.  Kompyuternyy geneticheskiy konstruktor: matematicheskoe modelirovanie geneticheskikh  i metabolicheskikh podsistem E. soli [Computer-based genetic constructor: mathematical modelling of genetic and metabolic subsystems of E. coli]. Rol  mikroorganizmov v funktsionirovanii zhivykh sistem: fundamentalnye problemy i  bioinzhenernye prilozheniya [Role of Microorganisms in Living Systems Functioning:  Fundamental Problems and Bioengineering Applications]. Eds: Vlasov V.V.,  Degermendzhi A.G., Kolchanov N.A., Parmon V.N., Repin E.A. Novosibirsk, Izdatel’stvo  SO RAN, 2010:376-391.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">Larsen P., Hamada Y., Gilbert J. Modeling microbial communities: Current, developing, and future technologies for predicting microbial community interaction. J. Biotechnol. 2012;160:17-24. DOI 10.1016/j.jbiotec.2012.03.009</mixed-citation><mixed-citation xml:lang="en">Likhoshvai V.A., Ratushny A.V. Generalized Hill function method for modeling  molecular processes. J. Bioinform. Comput. Biol. 2007;05: 521-531. DOI 10.1142/S0219720007002837</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">Laspidou C.S., Rittmann B.E. Evaluating trends in biofilm density using the UMCCA model. Water Res. 2004;38:3362-33672. DOI 10.1016/j.watres.2004.04.051</mixed-citation><mixed-citation xml:lang="en">Logofet, D.O., Belova, I.N., 2007. Nonnegative matrices as a tool to model  population dynamics: Classical models and contemporary  expansions. Fundamentalnaya  i prikladnaya matematika = Fundamental and Applied Mathematics. 2007;13:145-164.  DOI:10.1007/s10958-008-9249-2</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">Lencastre Fernandes R., Nierychlo M., Lundin L., Pedersen A.E., Puentes Tellez P.E., Dutta A., Carlquist M., Bolic A., Schäpper D., Brunetti A.C., Helmark S., Heins A.L., Jensen A.D., Nopens I., Rottwitt K., Szita N., van Elsas J.D., Nielsen P.H., Martinussen J., Sørensen S.J., Lantz A.E., Gernaey K.V. Experimental methods and modeling techniques for description of cell population heterogeneity. Biotechnol. Adv. 2011;29:575-599. DOI 10.1016/j.biotechadv.2011.03.007</mixed-citation><mixed-citation xml:lang="en">Mahadevan R., Henson M.A. Genome-based modeling and design of metabolic interactions  in microbial communities. Comput. Struct. Biotechnol. J. 2012;3:1-7. DOI 10.5936/csbj.201210008</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">Leslie P.H. On the use of matrices in certain population mathematics. Biometrika. 1945. DOI 10.2307/2332297</mixed-citation><mixed-citation xml:lang="en">Mburu N., Rousseau D.P.L., Stein O.R., Lens P.N.L. Simulation of batch-operated  experimental wetland mesocosms in AQUASIM biofilm reactor compartment. J. Environ.  Manage. 2014;134:100-108. DOI 10.1016/j.jenvman.2014.01.005</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">Likhoshvai V.A., Ratushny A.V. Generalized Hill function method for modeling molecular processes. J. Bioinform. Comput. Biol. 2007;05: 521-531. DOI 10.1142/S0219720007002837</mixed-citation><mixed-citation xml:lang="en">Monod J. La technique de culture continue. Theorie et applications. Ann. Inst. Pasteur. 1950;79:391-410.</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">Mahadevan R., Henson M.A. Genome-based modeling and design of metabolic interactions in microbial communities. Comput. Struct. Biotechnol. J. 2012;3:1-7. DOI 10.5936/csbj.201210008</mixed-citation><mixed-citation xml:lang="en">Netrusov A.I., Kotova I.B., 2007. Mikrobiologiya [Microbiology]. Moscow, Akademiya, 2007.</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">Mburu N., Rousseau D.P.L., Stein O.R., Lens P.N.L. Simulation of batch-operated experimental wetland mesocosms in AQUASIM biofilm reactor compartment. J. Environ. Manage. 2014;134:100-108. DOI 10.1016/j.jenvman.2014.01.005</mixed-citation><mixed-citation xml:lang="en">Niu B., Wang H., Duan Q., Li L. Biomimicry of quorum sensing using bacterial  lifecycle model. BMC Bioinformatics. 2013;14(Suppl. 8): S8. DOI 10.1186/1471-2105-14-S8-S8</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">Monod J. La technique de culture continue. Theorie et applications. Ann. Inst. Pasteur. 1950;79:391-410.</mixed-citation><mixed-citation xml:lang="en">O’Donnell A.G., Young I.M., Rushton S.P., Shirley M.D., Crawford J. W. Visualization, modelling and prediction in soil microbiology. Nat.  Rev. Microbiol.  2007;5:689-699. DOI 10.1038/nrmicro1714</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">Niu B., Wang H., Duan Q., Li L. Biomimicry of quorum sensing using bacterial lifecycle model. BMC Bioinformatics. 2013;14(Suppl. 8): S8. DOI 10.1186/1471-2105-14-S8-S8</mixed-citation><mixed-citation xml:lang="en">Oberhardt M.A., Palsson B.Ø., Papin J.A. Applications of genomescale metabolic  reconstructions. Mol. Syst. Biol. 2009;5. DOI 10.1038/msb.2009.77</mixed-citation></citation-alternatives></ref><ref id="cit46"><label>46</label><citation-alternatives><mixed-citation xml:lang="ru">O’Donnell A.G., Young I.M., Rushton S.P., Shirley M.D., Crawford J. W. Visualization, modelling and prediction in soil microbiology. Nat. Rev. Microbiol. 2007;5:689-699. DOI 10.1038/nrmicro1714</mixed-citation><mixed-citation xml:lang="en">Pfeiffer T., Schuster S. Game-theoretical approaches to studying the evolution of  biochemical systems. Trends Biochem. Sci. 2005;30: 20-25. DOI 10.1016/j.tibs.2004.11.006</mixed-citation></citation-alternatives></ref><ref id="cit47"><label>47</label><citation-alternatives><mixed-citation xml:lang="ru">Oberhardt M.A., Palsson B.Ø., Papin J.A. Applications of genomescale metabolic reconstructions. Mol. Syst. Biol. 2009;5. DOI 10.1038/msb.2009.77</mixed-citation><mixed-citation xml:lang="en">Price N.D., Reed J.L., Palsson B.Ø. Genome-scale models of microbial cells:  evaluating the consequences of constraints. Nat. Rev. Microbiol. 2004;2:886-897. DOI 10.1038/nrmicro1023</mixed-citation></citation-alternatives></ref><ref id="cit48"><label>48</label><citation-alternatives><mixed-citation xml:lang="ru">Pfeiffer T., Schuster S. Game-theoretical approaches to studying the evolution of biochemical systems. Trends Biochem. Sci. 2005;30: 20-25. DOI 10.1016/j.tibs.2004.11.006</mixed-citation><mixed-citation xml:lang="en">Ramkrishna D. Population Balances: Theory and Applications to Particulate Systems in Engineering, Chemical Engineering. 2000.</mixed-citation></citation-alternatives></ref><ref id="cit49"><label>49</label><citation-alternatives><mixed-citation xml:lang="ru">Price N.D., Reed J.L., Palsson B.Ø. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2004;2:886-897. DOI 10.1038/nrmicro1023</mixed-citation><mixed-citation xml:lang="en">Riznichenko G.Yu. Matematicheskie modeli v biofizike i ekologii [Mathematical models  in biophysics and ecology]. Moscow; Izhevsk, Institut kompyuternykh issledovaniy, 2003.</mixed-citation></citation-alternatives></ref><ref id="cit50"><label>50</label><citation-alternatives><mixed-citation xml:lang="ru">Ramkrishna D. Population Balances: Theory and Applications to Particulate Systems in Engineering, Chemical Engineering. 2000.</mixed-citation><mixed-citation xml:lang="en">Riznichenko G.Yu., Rubin A.B. Matematicheskie modeli biologicheskikh produktsionnykh  protsessov [Mathematical models of biological production processes]. Moscow, MGU Publ., 1993.</mixed-citation></citation-alternatives></ref><ref id="cit51"><label>51</label><citation-alternatives><mixed-citation xml:lang="ru">Rudge T.J., Steiner P.J., Phillips A., Haseloff J. Computational modeling of synthetic microbial biofilms. ACS Synthetic Biology 2012;1(8): 345-352. DOI 10.1021/sb300031n</mixed-citation><mixed-citation xml:lang="en">Rudge T.J., Steiner P.J., Phillips A., Haseloff J. Computational modeling of  synthetic microbial biofilms. ACS Synthetic Biology 2012;1(8): 345-352. DOI 10.1021/sb300031n</mixed-citation></citation-alternatives></ref><ref id="cit52"><label>52</label><citation-alternatives><mixed-citation xml:lang="ru">Salli K.M., Ouwehand A.C. The use of in vitro model systems to study dental biofilms associated with caries: a short review. J. Oral Microbiol. 2015;7. DOI 10.3402/jom.v7.26149</mixed-citation><mixed-citation xml:lang="en">Salli K.M., Ouwehand A.C. The use of in vitro model systems to study dental biofilms  associated with caries: a short review. J. Oral Microbiol. 2015;7. DOI 10.3402/jom.v7.26149</mixed-citation></citation-alternatives></ref><ref id="cit53"><label>53</label><citation-alternatives><mixed-citation xml:lang="ru">Sauer U., Heinemann M., Zamboni N. GENETICS: getting closer to the whole picture. Science. 2007;316:550-551. DOI 10.1126/science.1142502</mixed-citation><mixed-citation xml:lang="en">Sauer U., Heinemann M., Zamboni N. GENETICS: getting closer to the whole picture.  Science. 2007;316:550-551. DOI 10.1126/science.1142502</mixed-citation></citation-alternatives></ref><ref id="cit54"><label>54</label><citation-alternatives><mixed-citation xml:lang="ru">Scheffer M., Baveco J.M., DeAngelis D.L., Rose K.A., van Nes E.H. Super-individuals a simple solution for modelling large populations on an individual basis. Ecol. Modell. 1995;80:161-170. DOI 10.1016/0304-3800(94)00055-M</mixed-citation><mixed-citation xml:lang="en">Scheffer M., Baveco J.M., DeAngelis D.L., Rose K.A., van Nes E.H. Super-individuals  a simple solution for modelling large populations on an individual basis. Ecol.  Modell. 1995;80:161-170. DOI 10.1016/0304-3800(94)00055-M</mixed-citation></citation-alternatives></ref><ref id="cit55"><label>55</label><citation-alternatives><mixed-citation xml:lang="ru">Scheibe T.D., Mahadevan R., Fang Y., Garg S., Long P.E., Lovley D.R. Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation. Microb. Biotechnol. 2009;2:274-286. DOI 10.1111/j.1751-7915.2009.00087.x</mixed-citation><mixed-citation xml:lang="en">Scheibe T.D., Mahadevan R., Fang Y., Garg S., Long P.E., Lovley D.R. Coupling a  genome-scale metabolic model with a reactive transport model to describe in situ  uranium bioremediation. Microb. Biotechnol. 2009;2:274-286. DOI 10.1111/j.1751-7915.2009.00087.x</mixed-citation></citation-alternatives></ref><ref id="cit56"><label>56</label><citation-alternatives><mixed-citation xml:lang="ru">Schuster S., Fell D.A., Dandekar T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat. Biotechnol. 2000;18:326-332. DOI 10.1038/73786</mixed-citation><mixed-citation xml:lang="en">Schuster S., Fell D.A., Dandekar T. A general definition of metabolic pathways  useful for systematic organization and analysis of complex metabolic networks. Nat.  Biotechnol. 2000;18:326-332. DOI 10.1038/73786</mixed-citation></citation-alternatives></ref><ref id="cit57"><label>57</label><citation-alternatives><mixed-citation xml:lang="ru">Segrè D., Vitkup D., Church G.M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl Acad. Sci. USA. 2002;99: 15112-15117. DOI 10.1073/pnas.232349399</mixed-citation><mixed-citation xml:lang="en">Segrè D., Vitkup D., Church G.M. Analysis of optimality in natural and perturbed  metabolic networks. Proc. Natl Acad. Sci. USA. 2002;99: 15112-15117. DOI 10.1073/pnas.232349399</mixed-citation></citation-alternatives></ref><ref id="cit58"><label>58</label><citation-alternatives><mixed-citation xml:lang="ru">Shrout J.D. A fantastic voyage for sliding bacteria. Trends Microbiol. 2015;23:244-246. DOI 10.1016/j.tim.2015.03.001</mixed-citation><mixed-citation xml:lang="en">Shrout J.D. A fantastic voyage for sliding bacteria. Trends Microbiol. 2015;23:244-246. DOI 10.1016/j.tim.2015.03.001</mixed-citation></citation-alternatives></ref><ref id="cit59"><label>59</label><citation-alternatives><mixed-citation xml:lang="ru">Song H.-S., Cannon W., Beliaev A., Konopka A. Mathematical modeling of microbial community dynamics: a methodological review. Processes. 2014;2:711-752. DOI 10.3390/pr2040711</mixed-citation><mixed-citation xml:lang="en">Song H.-S., Cannon W., Beliaev A., Konopka A. Mathematical modeling of microbial  community dynamics: a methodological review. Processes. 2014;2:711-752. DOI 10.3390/pr2040711</mixed-citation></citation-alternatives></ref><ref id="cit60"><label>60</label><citation-alternatives><mixed-citation xml:lang="ru">Stauffer D., Kunwar A., Chowdhury D. Evolutionary ecology in silico: Evolving food webs, migrating population and speciation. Physica A. 2005;352:202-215. DOI 10.1016/j.physa.2004.12.036</mixed-citation><mixed-citation xml:lang="en">Stauffer D., Kunwar A., Chowdhury D. Evolutionary ecology in silico: Evolving food  webs, migrating population and speciation. Physica A. 2005;352:202-215. DOI 10.1016/j.physa.2004.12.036</mixed-citation></citation-alternatives></ref><ref id="cit61"><label>61</label><citation-alternatives><mixed-citation xml:lang="ru">Tang Y., Valocchi A.J. An improved cellular automaton method to model multispecies biofilms. Water Res. 2013;47:5729-5742. DOI 10.1016/j.watres.2013.06.055</mixed-citation><mixed-citation xml:lang="en">Tang Y., Valocchi A.J. An improved cellular automaton method to model multispecies  biofilms. Water Res. 2013;47:5729-5742. DOI 10.1016/j.watres.2013.06.055</mixed-citation></citation-alternatives></ref><ref id="cit62"><label>62</label><citation-alternatives><mixed-citation xml:lang="ru">Tindall M.J., Maini P.K., Porter S.L., Armitage J.P. Overview of mathematical approaches used to model bacterial chemotaxis II: Bacterial populations. Bull. Math. Biol. 2008a. DOI 10.1007/s11538-008-9322-5</mixed-citation><mixed-citation xml:lang="en">Tindall M.J., Maini P.K., Porter S.L., Armitage J.P. Overview of mathematical approaches used to model bacterial chemotaxis II: Bacterial  populations. Bull.  Math. Biol. 2008a. DOI 10.1007/s11538-008-9322-5</mixed-citation></citation-alternatives></ref><ref id="cit63"><label>63</label><citation-alternatives><mixed-citation xml:lang="ru">Tindall M.J., Porter S.L., Maini P.K., Gaglia G., Armitage J.P. Overview of mathematical approaches used to model bacterial chemotaxis I: The single cell. Bull. Math. Biol. 2008b. DOI 10.1007/s11538-008-9321-6</mixed-citation><mixed-citation xml:lang="en">Tindall M.J., Porter S.L., Maini P.K., Gaglia G., Armitage J.P. Overview of  mathematical approaches used to model bacterial chemotaxis  I: The single cell.  Bull. Math. Biol. 2008b. DOI 10.1007/s11538-008-9321-6</mixed-citation></citation-alternatives></ref><ref id="cit64"><label>64</label><citation-alternatives><mixed-citation xml:lang="ru">Tomita M. Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol. 2001;19:205-210. DOI 10.1016/S0167-7799(01)01636-5</mixed-citation><mixed-citation xml:lang="en">Tomita M. Whole-cell simulation: a grand challenge of the 21st century. Trends  Biotechnol. 2001;19:205-210. DOI 10.1016/S0167-7799(01)01636-5</mixed-citation></citation-alternatives></ref><ref id="cit65"><label>65</label><citation-alternatives><mixed-citation xml:lang="ru">Tomita M., Hashimoto K., Takahashi K., Shimizu T., Matsuzaki Y., Miyoshi F., Saito K., Tanida S., Yugi K., Venter J., Hutchison C. E-CELL: software environment for whole-cell simulation. Bioinformatics. 1999;15:72-84. DOI 10.1093/bioinformatics/15.1.72</mixed-citation><mixed-citation xml:lang="en">Tomita M., Hashimoto K., Takahashi K., Shimizu T., Matsuzaki Y., Miyoshi F., Saito  K., Tanida S., Yugi K., Venter J., Hutchison C. E-CELL: software environment for  whole-cell simulation. Bioinformatics. 1999;15:72-84. DOI  10.1093/bioinformatics/15.1.72</mixed-citation></citation-alternatives></ref><ref id="cit66"><label>66</label><citation-alternatives><mixed-citation xml:lang="ru">Turing A.M. The chemical theory of morphogenesis. Phil. Trans. Roy.Soc. 1952;13:1.</mixed-citation><mixed-citation xml:lang="en">Turing A.M. The chemical theory of morphogenesis. Phil. Trans. Roy. Soc. 1952;13:1.</mixed-citation></citation-alternatives></ref><ref id="cit67"><label>67</label><citation-alternatives><mixed-citation xml:lang="ru">Wanner O., Morgenroth E. Biofilm modeling with AQUASIM. Water Sci. Technol. 2004;49:137-144.</mixed-citation><mixed-citation xml:lang="en">Wanner O., Morgenroth E. Biofilm modeling with AQUASIM. Water Sci. Technol. 2004;49:137-144.</mixed-citation></citation-alternatives></ref><ref id="cit68"><label>68</label><citation-alternatives><mixed-citation xml:lang="ru">Wimpenny J., Manz W., Szewzyk U. Heterogeneity in biofilms. FEMS Microbiol. Rev. 2000. DOI 10.1016/S0168-6445(00)00052-8</mixed-citation><mixed-citation xml:lang="en">Wimpenny J., Manz W., Szewzyk U. Heterogeneity in biofilms. FEMS Microbiol. Rev. 2000. DOI 10.1016/S0168-6445(00)00052-8</mixed-citation></citation-alternatives></ref><ref id="cit69"><label>69</label><citation-alternatives><mixed-citation xml:lang="ru">Wimpenny J.W.T., Colasanti R. A unifying hypothesis for the structure of microbial biofilms based on cellular automaton models. FEMS Microbiol. Ecol. 1997. DOI 10.1016/S0168-6496(96)00078-5</mixed-citation><mixed-citation xml:lang="en">Wimpenny J.W.T., Colasanti R. A unifying hypothesis for the structure of microbial  biofilms based on cellular automaton models. FEMS Microbiol. Ecol. 1997. DOI 10.1016/S0168-6496(96)00078-5</mixed-citation></citation-alternatives></ref><ref id="cit70"><label>70</label><citation-alternatives><mixed-citation xml:lang="ru">Wolfe B.E., Dutton R.J. Review fermented foods as experimentally tractable microbial ecosystems. Cell. 2015;161:49-55. DOI 10.1016/j.cell.2015.02.034</mixed-citation><mixed-citation xml:lang="en">Wolfe B.E., Dutton R.J. Review fermented foods as experimentally tractable microbial  ecosystems. Cell. 2015;161:49-55. DOI 10.1016/j.cell.2015.02.034</mixed-citation></citation-alternatives></ref><ref id="cit71"><label>71</label><citation-alternatives><mixed-citation xml:lang="ru">Wooley J.C., Godzik A., Friedberg I. A primer on metagenomics. PLoS Comput. Biol. 2010. DOI 10.1371/journal.pcbi.1000667</mixed-citation><mixed-citation xml:lang="en">Wooley J.C., Godzik A., Friedberg I. A primer on metagenomics. PLoS Comput. Biol. 2010. DOI 10.1371/journal.pcbi.1000667</mixed-citation></citation-alternatives></ref><ref id="cit72"><label>72</label><citation-alternatives><mixed-citation xml:lang="ru">Zomorrodi A.R., Islam M.M., Maranas C.D. D-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities. ACS Synth. Biol. 2014;3:247-257. DOI 10.1021/sb4001307</mixed-citation><mixed-citation xml:lang="en">Zomorrodi A.R., Islam M.M., Maranas C.D. D-OptCom: dynamic multi-level and multi- objective metabolic modeling of microbial communities. ACS Synth. Biol. 2014;3:247- 257. DOI 10.1021/sb4001307</mixed-citation></citation-alternatives></ref><ref id="cit73"><label>73</label><citation-alternatives><mixed-citation xml:lang="ru">Zomorrodi A.R., Maranas C.D. OptCom: A multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput. Biol. 2012;8. DOI 10.1371/journal.pcbi.1002363</mixed-citation><mixed-citation xml:lang="en">Zomorrodi A.R., Maranas C.D. OptCom: A multi-level optimization framework for the  metabolic modeling and analysis of microbial communities. PLoS Comput. Biol. 2012;8.  DOI 10.1371/journal.pcbi.1002363</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
