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A review of simulation and modeling approaches in microbiology

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Russian Journal of Genetics: Applied Research

Abstract

Bacterial communities are closely interrelated systems consisting of numerous species making it challenging to analyze their structure and relations. At present, there are several experimental techniques providing heterogeneous data, concerning various aspects of this research object. The recent avalanche of available metagenomic data challenges not only biostatisticians but also biomodelers, since these data are essential for improving the modeling quality, while simulation methods are useful for understanding 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 focused on the description of microbial community aspects such as trophic structure, metabolic and population dynamics, genetic diversity, as well as spatial heterogeneity and expansion dynamics, are considered. We also propose a classification of the existing software designed for the simulation of microbial communities. It has been shown that, in spite of the prevailing trend for using multiscale/hybrid models, 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 for modeling microbial communities is based on the necessity of taking into account the heterogeneous data obtained from various sources by applying high-throughput genome investigation methods.

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References

  • Adler, J., Chemotaxis in bacteria, J. Supramol. Struct., 1976, vol. 4, pp. 305–317. doi 10.1146/annurev.bi.44.070175.002013

    Article  CAS  PubMed  Google Scholar 

  • Beardmore, R.E., Gudelj, I., Lipson, D.A., and Hurst, L.D., Metabolic trade-offs and the maintenance of the fittest and the flattest, Nature, 2011, vol. 472, pp. 342–346. doi 10.1038/nature09905

    Article  CAS  PubMed  Google Scholar 

  • Beslon, G., Parsons, D.P., Sanchez-Dehesa, Y., and Knibbe, C., Scaling laws in bacterial genomes: A side-effect of selection of mutational robustness?, Biosystems, 2010, vol. 102, pp. 32–40. doi 10.1016/j.biosystems.2010.07.009

    Article  CAS  PubMed  Google Scholar 

  • Chernavskii, D.S. and Ierusalimskii, N.D., On the question of the defining link in the system of enzymatic reactions, Izv. Akad. Nauk SSSR, Ser. Biol., 1965, vol. 5, pp. 665–672.

    Google Scholar 

  • Chewapreecha, C., Your gut microbiota are what you eat, Nat. Rev. Microbiol., 2013, vol. 12, p. 8. doi 10.1038/nrmicro3186

    Article  Google Scholar 

  • Comolli, L.R., Intra-and inter-species interactions in microbial communities, Front. Microbiol., 2014, vol. 5, pp. 1–3. doi 10.3389/fmicb.2014.00629

    Google Scholar 

  • Covert, M.W., Schilling, C.H., Famili, I., Edwards, J.S., Goryanin, I.I., Selkov, E., and Palsson, B.O., Metabolic modeling of microbial strains in silico, Trends Biochem. Sci., 2001, vol. 26, pp. 179–186. doi 10.1016/S0968-0004(00)01754-0

    Article  CAS  PubMed  Google Scholar 

  • De Jong, H., Modeling and simulation of genetic regulatory systems: A literature review, J. Comput. Biol., 2002, vol. 9, pp. 67–103. doi 10.1089/10665270252833208

    Article  PubMed  Google Scholar 

  • De Roy, K., Marzorati, M., Van den Abbeele, P., Van de Wiele T., and Boon, N., Synthetic microbial ecosystems: An exciting tool to understand and apply microbial communities, Environ. Microbiol., 2013, vol. 16, pp. 1472–1481. doi 10.1111/1462-2920.12343

    Article  PubMed  Google Scholar 

  • DeAngelis, D.L. and Mooij, W.M., Individual-based modeling of ecological and evolutionary processes 1, Annu. Rev. Ecol. Evol. Syst., 2005, vol. 36, pp. 147–168. doi 10.1146/annurev.ecolsys.36.102003.152644

    Article  Google Scholar 

  • Durot, M., Bourguignon, P.-Y., and Schachter, V., Genome-scale models of bacterial metabolism: Reconstruction and applications, FEMS Microbiol. Rev., 2009, vol. 33, pp. 164–190. doi 10.1111/j.1574-6976.2008.00146.x

    Article  CAS  PubMed  Google Scholar 

  • Emonet, T., Macal, C.M., North, M.J., Wickersham, C.E., and Cluzel, P., Agent-Cell: A digital single-cell assay for bacterial chemotaxis, Bioinformatics, 2005, vol. 21, pp. 2714–2721. doi 10.1093/bioinformatics/bti391

    Article  CAS  PubMed  Google Scholar 

  • Esteban, P.G. and Rodríguez-Patón, A., Simulating a rockscissors-paper bacterial game with a discrete cellular automaton, in New Challenges on Bioinspired Applications, Lecture Notes in Computer Science, Ferràndez, J.M., Àlvarez Sànchez, J.R., de la Paz, F., and Toledo, F.J., Eds., Berlin–Heidelberg: Springer Berlin Heidelberg, 2011. doi 10.1007/978-3-642-21326-7

  • Faust, K. and Raes, J., Microbial interactions: From networks to models, Nat. Rev. Microbiol., 2012, vol. 10, pp. 538–550. doi 10.1038/nrmicro2832

    Article  CAS  PubMed  Google Scholar 

  • Frey, E., Evolutionary game theory: Theoretical concepts and applications to microbial communities, Phys. A Stat. Mech. Its Appl., 2010, vol. 389, pp. 4265–4298. doi 10.1016/j.physa.2010.02.047

    Article  CAS  Google Scholar 

  • Fuhrman, J.A., Microbial community structure and its functional implications, Nature, 2009, vol. 459, pp. 193–199. doi 10.1038/nature08058

    Article  CAS  PubMed  Google Scholar 

  • Gimel’farb, A.A., Ginzburg, L.R., Poluektov, R.A., Pykh, Yu.A., and Ratner, V.A., Dinamicheskaya teoriya biologicheskikh populyatsii (Dynamic Theory of Biological Populations), Nauka, 1974.

  • Ginovart, M., Löpez, D., and Valls, J., INDISIM, an individual-based discrete simulation model to study bacterial cultures, J. Theor. Biol., 2002, vol. 214, pp. 305–319. doi 10.1006/jtbi.2001.2466

    Article  PubMed  Google Scholar 

  • 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., et al., A standard protocol for describing individual-based and agent-based models, Ecol. Modell., 2006, vol. 198, pp. 115–126. doi 10.1016/j.ecolmodel.2006.04.023

    Article  Google Scholar 

  • Halfen, L.N. and Castenholz, R.W., Gliding Motility in the Blue-Green Alga Oscillatoria Princeps, 1971.

    Google Scholar 

  • Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., and Guthke, R., Gene regulatory network inference: Data integration in dynamic models–A review, Biosystems, 2009, vol. 96, pp. 86–103. doi 10.1016/j.biosystems.2008.12.004

    Article  CAS  PubMed  Google Scholar 

  • Henrichsen, J., Bacterial surface translocation: A survey and a classification, Bacteriol. Rev., 1972, vol. 36, pp. 478–503.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Henson, M.A. and Hanly, T.J., Dynamic flux balance analysis for synthetic microbial communities, IET Syst. Biol., 2014, vol. 8, pp. 214–229. doi 10.1049/iet-syb.2013.0021

    Article  PubMed  Google Scholar 

  • Ishii, N., Robert, M., Nakayama, Y., Kanai, A., and Tomita, M., Toward large-scale modeling of the microbial cell for computer simulation, J. Biotechnol., 2004, vol. 113, pp. 281–294. doi 10.1016/j.jbiotec.2004.04.038

    Article  CAS  PubMed  Google Scholar 

  • Karr, J.R., Sanghvi, J.C., MacKlin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B., Assad-Garcia, N., Glass, J.I., and Covert, M.W., A whole-cell computational model predicts phenotype from genotype, Cell, 2012, vol. 150, pp. 389–401. doi 10.1016/j.cell.2012.05.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Karunakaran, E., Mukherjee, J., Ramalingam, B., and Biggs, C.A., “Biofilmology:” A multidisciplinary review of the study of microbial biofilms, Appl. Microbiol. Biotechnol., 2011, vol. 90, pp. 1869–1881. doi 10.1007/s00253-011-3293-4

    Article  CAS  PubMed  Google Scholar 

  • Klimenko, A.I., Matushkin, Y.G., Kolchanov, N.A., and Lashin, S.A., Modeling evolution of spatially distributed bacterial communities: A simulation with the haploid evolutionary constructor, BMC Evol. Biol., 2015, vol. 15, p. S3. doi 10.1186/1471-2148-15-S1-S3

    Article  PubMed  PubMed Central  Google Scholar 

  • Klitgord, N. and Segre, D., Environments that induce synthetic microbial ecosystems, PLoS Comput. Biol., 2010, vol. 101, pp. 1435–1439. doi 10.1371/Citation

    Google Scholar 

  • Knibbe, C., Fayard, J.-M., and 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, vol. 14, pp. 149–156. doi 10.1162/artl.2008.14.1.149

    Article  PubMed  Google Scholar 

  • Kolmakova, O.V., Modern methods for determining speciesspecific biogeochemical functions of bacterioplankton, Zh. Sib. Fed. Univ., Ser. Biol., 2013, vol. 6, no. 1, pp. 73–95.

    Google Scholar 

  • Kutalik, Z., Razaz, M., and Baranyi, J., Connection between stochastic and deterministic modelling of microbial growth, J. Theor. Biol., 2005, vol. 232, pp. 285–299. doi 10.1016/j.jtbi.2004.08.013

    Article  PubMed  Google Scholar 

  • Larsen, P., Hamada, Y., and Gilbert, J., Modeling microbial communities: Current, developing, and future technologies for predicting microbial community interaction, J. Biotechnol., 2012, vol. 160, pp. 17–24. doi 10.1016/j.jbiotec. 2012.03.009

    Article  CAS  PubMed  Google Scholar 

  • Laspidou, C.S. and Rittmann, B.E., Evaluating trends in biofilm density using the UMCCA model, Water Res., 2004, vol. 38, pp. 3362–33672. doi 10.1016/j.watres.2004.04.051

    Article  CAS  PubMed  Google Scholar 

  • Lencstre 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., et al., Experimental methods and modeling techniques for description of cell population heterogeneity, Biotechnol. Adv., 2011, vol. 29, pp. 575–599. doi 10.1016/j.biotechadv.2011.03.007

    Article  PubMed  Google Scholar 

  • Leslie, P.H., On the use of matrices in certain population mathematics, Biometrika, 1945. doi 10.2307/2332297

    Google Scholar 

  • Likhoshvai, V.A. and Ratushny, A.V., Generalized Hill function method for modeling molecular processes, J. Bioinf. Comput. Biol., 2007, vol. 05, pp. 521–531. doi 10.1142/S0219720007002837

    Article  CAS  Google Scholar 

  • Likhoshvai, V.A., Khlebodarova, T.M., Ratushnyi, A.V., Lashin, S.A., Turnaev, I.I., Podkolodnaya, O.A., Anan’ko, E.A., Smirnova, O.G., Ibragimova, S.S., and Kolchanov, N.A., Computer genetic designer: Mathematical modeling of genetic and metabolic subsystems of E. coli, in The Role of Microorganisms in Functioning of Living Systems: Fundamental Problems and Bioengineering Applications, Vlasov, V.V., Degermendzhi, A.G., Kolchanov, N.A., Parmon, V.N., and Repin, E.A., Eds., Novosibirsk: Izd. SO RAN, 2010.

    Google Scholar 

  • Logofet, D.O. and Belova, I.N., Nonnegative matrices as a tool to model population dynamics: Classical models and contemporary expansions, Fundam. Prikl. Mat., 2007, vol. 13, pp. 145–164.

    Google Scholar 

  • Mahadevan, R. and Henson, M.A., Genome-based modeling and design of metabolic interactions in microbial communities, Comput. Struct. Biotechnol. J., 2012, vol. 3, pp. 1–7. doi 10.5936/csbj.201210008

    Article  Google Scholar 

  • Mburu, N., Rousseau, D.P.L., Stein, O.R., and Lens, P.N.L., Simulation of batch-operated experimental wetland mesocosms in AQUASIM biofilm reactor compartment, J. Environ. Manage., 2014, vol. 134, pp. 100–108. doi 10.1016/j.jenvman.2014.01.005

    Article  CAS  PubMed  Google Scholar 

  • Monod, J., La technique de culture continue. Theorie et applications, Ann. Inst. Pasteur, 1950, vol. 79, pp. 391–410.

    Google Scholar 

  • Netrusov, A.I. and Kotova, I.B., Mikrobiologiya (Microbiology), Moscow: Akademiya, 2007.

    Google Scholar 

  • Niu, B., Wang, H., Duan, Q., and Li, L., Biomimicry of quorum sensing using bacterial lifecycle model, BMC Bioinf., 2013, vol. 14, no. 8, p. S8. doi 10.1186/1471-2105-14-S8-S8

    Article  Google Scholar 

  • O’Donnell, A.G., Young, I.M., Rushton, S.P., Shirley, M.D., and Crawford, J.W., Visualization, modelling and prediction in soil microbiology, Nat. Rev. Microbiol., 2007, vol. 5, pp. 689–699. doi 10.1038/nrmicro1714

    Article  PubMed  Google Scholar 

  • Oberhardt, M.A. and Palsson, B.Ø, Papin, J.A., Applications of genome-scale metabolic reconstructions, Mol. Syst. Biol., 2009, vol. 5. doi 10.1038/msb.2009.77

  • Pfeiffer, T. and Schuster, S., Game-theoretical approaches to studying the evolution of biochemical systems, Trends Biochem. Sci., 2005, vol. 30, pp. 20–25. doi 10.1016/j.tibs.2004.11.006

    Article  CAS  PubMed  Google Scholar 

  • Price, N.D., Reed, J.L., and Palsson, B.Ö, Genome-scale models of microbial cells: Evaluating the consequences of constraints, Nat. Rev. Microbiol., 2004, vol. 2, pp. 886–897. doi 10.1038/nrmicro1023

    Article  CAS  PubMed  Google Scholar 

  • Ramkrishna, D., Population Balances: Theory and Applications to Particulate Systems in Engineering, Chemical Engineering, 2000.

    Google Scholar 

  • Riznichenko, G.Yu., Matematicheskie modeli v biofizike i ekologii (Mathematical Models in Biophysics and Ecology), Moscow, Izhevsk: Inst. Komp’yut. Issled., 2003.

    Google Scholar 

  • Riznichenko, G.Yu. and Rubin, A.B., Matematicheskie modeli biologicheskikh produktsionnykh protsessov (Mathematical Models of Biological Production Processes), Moscow: Izd. MGU, 1993.

    Google Scholar 

  • Rudge, T.J., Steiner, P.J., Phillips, A., and Haseloff, J., Computational modeling of synthetic microbial biofilms, ACS Synth. Biol., 2012, vol. 1, no. 8, pp. 345–352. doi 10.1021/sb300031n

    Article  CAS  PubMed  Google Scholar 

  • Salli, K.M. and Ouwehand, A.C., The use of in vitro model systems to study dental biofilms associated with caries: A short review, J. Oral Microbiol., 2015, vol. 7. doi 10.3402/jom.v7.26149

  • Sauer, U., Heinemann, M., and Zamboni, N., GENETICS: Getting closer to the whole picture, Science, 2007, vol. 316, pp. 550–551. doi 10.1126/science.1142502

    Article  CAS  PubMed  Google Scholar 

  • Scheffer, M., Baveco, J.M., DeAngelis, D.L., Rose, K.A., and van Nes, E.H., Super-individuals a simple solution for modelling large populations on an individual basis, Ecol. Modell., 1995, vol. 80, pp. 161–170. doi 10.1016/0304-3800(94)00055-M

    Article  Google Scholar 

  • Scheibe, T.D., Mahadevan, R., Fang, Y., Garg, S., Long, P.E., and Lovley, D.R., Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation, Microb. Biotechnol., 2009, vol. 2, pp. 274–286. doi 10.1111/j.1751-7915.2009.00087.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schuster, S., Fell, D.A., and Dandekar, T., A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks, Nat. Biotechnol., 2000, vol. 18, pp. 326–332. doi 10.1038/73786

    Article  CAS  PubMed  Google Scholar 

  • Segrè, D., Vitkup, D., and Church, G.M., Analysis of optimality in natural and perturbed metabolic networks, Proc. Natl. Acad. Sci. U.S.A., 2002, vol. 99, pp. 15112–15117. doi 10.1073/pnas.232349399

    Article  PubMed  PubMed Central  Google Scholar 

  • Shrout, J.D., A fantastic voyage for sliding bacteria, Trends Microbiol., 2015, vol. 23, pp. 244–246. doi 10.1016/j.tim.2015.03.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Song, H.-S., Cannon, W., Beliaev, A., and Konopka, A., Mathematical modeling of microbial community dynamics: a methodological review, Processes, 2014, vol. 2, pp. 711–752. doi 10.3390/pr2040711

    Article  Google Scholar 

  • Stauffer, D., Kunwar, A., and Chowdhury, D., Evolutionary ecology in silico: Evolving food webs, migrating population and speciation, Phys. A (Amsterdam, Neth.), 2005, vol. 352, pp. 202–215. doi 10.1016/j.physa.2004.12.036

    Article  Google Scholar 

  • Tang, Y. and Valocchi, A.J., An improved cellular automaton method to model multispecies biofilms, Water Res., 2013, vol. 47, pp. 5729–5742. doi 10.1016/j.watres.2013.06.055

    Article  CAS  PubMed  Google Scholar 

  • Tindall, M.J., Maini, P.K., Porter, S.L., and 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

    Google Scholar 

  • Tindall, M.J., Porter, S.L., Maini, P.K., Gaglia, G., and 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

    Google Scholar 

  • Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T., Matsuzaki, Y., Miyoshi, F., Saito, K., Tanida, S., Yugi, K., Venter, J., and Hutchison, C., E-CELL: Software environment for whole-cell simulation, Bioinf., 1999, vol. 15, pp. 72–84. doi 10.1093/bioinformatics/15.1.72

    Article  CAS  Google Scholar 

  • Tomita, M., Whole-cell simulation: A grand challenge of the 21st century, Trends Biotechnol., 2001, vol. 19, pp. 205–210. doi 10.1016/S0167-7799(01)01636-5

    Article  CAS  PubMed  Google Scholar 

  • Turing, A.M., The chemical theory of morphogenesis, Philos. Trans. R. Soc., 1952, vol. 13, p. 1.

    Google Scholar 

  • Wanner, O. and Morgenroth, E., Biofilm modeling with AQUASIM, Water Sci. Technol., 2004, vol. 49, pp. 137–144.

    CAS  PubMed  Google Scholar 

  • Wimpenny, J.W.T. and 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

    Google Scholar 

  • Wimpenny, J., Manz, W., and Szewzyk, U., Heterogeneity in biofilms, FEMS Microbiol. Rev., 2000. doi 10.1016/S0168-6445(00)00052-8

    Google Scholar 

  • Wolfe, B.E. and Dutton, R.J., Review fermented foods as experimentally tractable microbial ecosystems, Cell, 2015, vol. 161, pp. 49–55. doi 10.1016/j.cell.2015.02.034

    Article  CAS  PubMed  Google Scholar 

  • Wooley, J.C., Godzik, A., and Friedberg, I., A primer on metagenomics, PLoS Comput. Biol., 2010. doi 10.1371/journal.pcbi.1000667

    Google Scholar 

  • Zomorrodi, A.R. and Maranas, C.D., OptCom: A multilevel optimization framework for the metabolic modeling and analysis of microbial communities, PLoS Comput. Biol., 2012, vol. 8. doi 10.1371/journal.pcbi.1002363

  • Zomorrodi, A.R., Islam, M.M., and Maranas, C.D., D-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities, ACS Synth. Biol., 2014, vol. 3, pp. 247–257. doi 10.1021/sb4001307

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, pr. Akad. Lavrentyeva 10, Novosibirsk, 630090, Russia

    A. I. Klimenko, Z. S. Mustafin, A. D. Chekantsev, R. K. Zudin, Yu. G. Matushkin & S. A. Lashin

  2. Novosibirsk National Research State University, ul. Pirogova 2, Novosibirsk, 630090, Russia

    A. I. Klimenko, A. D. Chekantsev, R. K. Zudin & S. A. Lashin

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Original Russian Text © A.I. Klimenko, Z.S. Mustafin, A.D. Chekantsev, R.K. Zudin, Yu.G. Matushkin, S.A. Lashin, 2015, published in Vavilovskii Zhurnal Genetiki i Selektsii, 2015, Vol. 19, No. 6, pp. 745–752.

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Klimenko, A.I., Mustafin, Z.S., Chekantsev, A.D. et al. A review of simulation and modeling approaches in microbiology. Russ J Genet Appl Res 6, 845–853 (2016). https://doi.org/10.1134/S2079059716070066

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