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Reaction-Based Models of Biochemical Networks

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Pursuit of the Universal (CiE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9709))

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Abstract

Mathematical modeling and computational analyses of biological systems generally pose to modelers questions like: “Which modeling approach is suitable to describe the system we are interested in? Which computational tools do we need to simulate and analyze this system? What kind of predictions the model is expected to give?”. To answer these questions, some general tips are here suggested to choose the proper modeling approach according to the size of the system, the desired level of detail for the system description, the availability of experimental data and the computational costs of the analyses that the model will require. The attention is then focused on the numerous advantages of reaction-based modeling, such as its high level of detail and easy understandability, or the possibility to run both deterministic and stochastic simulations exploiting the same model. Some notes on the computational methods required to analyze reaction-based models, as well as their parallelization on Graphics Processing Units, are finally provided.

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References

  1. Bartocci, E., Lió, P.: Computational modeling, formal analysis, and tools for systems biology. PLoS Comput. Biol. 12(1), e1004591 (2016)

    Article  Google Scholar 

  2. Wellstead, P., Bullinger, E., Kalamatianos, D., Mason, O., Verwoerd, M.: The rôle of control and system theory in systems biology. Annu. Rev. Control 32(1), 33–47 (2008)

    Article  Google Scholar 

  3. Wolkenhauer, O.: Why model? Front. Physiol. 5(21), 1–5 (2014)

    Google Scholar 

  4. Akhtar, A., Fuchs, E., Mitchison, T., Shaw, R., St Johnston, D., Strasser, A., Taylor, S., Walczak, C., Zerial, M.: A decade of molecular cell biology: achievements and challenges. Nat. Rev. Mol. Cell Biol. 12(10), 669–674 (2011)

    Article  Google Scholar 

  5. Welch, C., Elliott, H., Danuser, G., Hahn, K.: Imaging the coordination of multiple signalling activities in living cells. Nat. Rev. Mol. Cell Biol. 12(11), 749–756 (2011)

    Article  Google Scholar 

  6. Cvijovic, M., Almquist, J., Hagmar, J., Hohmann, S., Kaltenbach, H.M., Klipp, E., Krantz, M., Mendes, P., Nelander, S., Nielsen, J., Pagnani, A., Przulj, N., Raue, A., Stelling, J., Stoma, S., Tobin, F., Wodke, J.A.H., Zecchina, R., Jirstrand, M.: Bridging the gaps in systems biology. Mol. Genet. Genomics 289(5), 727–734 (2014)

    Article  Google Scholar 

  7. Gonçalves, E., Bucher, J., Ryll, A., Niklas, J., Mauch, K., Klamt, S., Rocha, M., Saez-Rodriguez, J.: Bridging the layers: towards integration of signal transduction, regulation and metabolism into mathematical models. Mol. Biosyst. 9(7), 1576–1583 (2013)

    Article  Google Scholar 

  8. 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.: A whole-cell computational model predicts phenotype from genotype. Cell 150(2), 389–401 (2012)

    Article  Google Scholar 

  9. Besozzi, D.: Computational methods in systems biology: case studies and biological insights. In: Petre, I. (ed.) Proceedings of 4th International Workshop on Computational Models for Cell Processes. EPTCS, vol. 116, pp. 3–10 (2013)

    Google Scholar 

  10. Amara, F., Colombo, R., Cazzaniga, P., Pescini, D., Csikász-Nagy, A., Muzi Falconi, M., Besozzi, D., Plevani, P.: In vivo and in silico analysis of PCNA ubiquitylation in the activation of the Post Replication Repair pathway in S. cerevisiae. BMC Syst. Biol. 7(24) (2013)

    Google Scholar 

  11. Besozzi, D., Cazzaniga, P., Dugo, M., Pescini, D., Mauri, G.: A study on the combined interplay between stochastic fluctuations and the number of flagella in bacterial chemotaxis. EPTCS 6, 47–62 (2009)

    Article  Google Scholar 

  12. Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G., Colombo, S., Martegani, E.: The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. EURASIP J. Bioinform. Syst. Biol. 1, 10 (2012)

    Google Scholar 

  13. Cazzaniga, P., Nobile, M.S., Besozzi, D., Bellini, M., Mauri, G.: Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization. BioMed Res. Int. (2014). Article ID 863298

    Google Scholar 

  14. Intosalmi, J., Manninen, T., Ruohonen, K., Linne, M.L.: Computational study of noise in a large signal transduction network. BMC Bioinformatics 12(1), 1–12 (2011)

    Article  Google Scholar 

  15. Pescini, D., Cazzaniga, P., Besozzi, D., Mauri, G., Amigoni, L., Colombo, S., Martegani, E.: Simulation of the Ras/cAMP/PKA pathway in budding yeast highlights the establishment of stable oscillatory states. Biotechnol. Adv. 30, 99–107 (2012)

    Article  Google Scholar 

  16. Petre, I., Mizera, A., Hyder, C.L., Meinander, A., Mikhailov, A., Morimoto, R.I., Sistonen, L., Eriksson, J.E., Back, R.J.: A simple mass-action model for the eukaryotic heat shock response and its mathematical validation. Nat. Comput. 10(1), 595–612 (2011)

    Article  MathSciNet  Google Scholar 

  17. Barabási, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5(2), 101–113 (2004)

    Article  Google Scholar 

  18. Bordbar, A., Monk, J.M., King, Z.A., Palsson, B.Ø.: Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15(2), 107–120 (2014)

    Article  Google Scholar 

  19. Cazzaniga, P., Damiani, C., Besozzi, D., Colombo, R., Nobile, M.S., Gaglio, D., Pescini, D., Molinari, S., Mauri, G., Alberghina, L., Vanoni, M.: Computational strategies for a system-level understanding of metabolism. Metabolites 4(4), 1034–1087 (2014)

    Article  Google Scholar 

  20. Novère, N.L.: Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16(3), 146–158 (2015)

    Article  Google Scholar 

  21. Morris, M.K., Saez-Rodriguez, J., Sorger, P.K., Lauffenburger, D.A.: Logic-based models for the analysis of cell signaling networks. Biochemistry 49(15), 3216–3224 (2010)

    Article  Google Scholar 

  22. Stelling, J.: Mathematical models in microbial systems biology. Curr. Opin. Microbiol. 7(5), 513–518 (2004)

    Article  Google Scholar 

  23. Wilkinson, D.: Stochastic modelling for quantitative description of heterogeneous biological systems. Nat. Rev. Genet. 10(2), 122–133 (2009)

    Article  MathSciNet  Google Scholar 

  24. Iancu, B., Czeizler, E., Czeizler, E., Petre, I.: Quantitative refinement of reaction models. Int. J. Unconv. Comput. 8(5/6), 529–550 (2012)

    MATH  Google Scholar 

  25. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Comput. Phys. 81, 2340–2361 (1977)

    Google Scholar 

  26. Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35–55 (2007)

    Article  Google Scholar 

  27. Butcher, J.C.: Numerical Methods for Ordinary Differential Equations. John Wiley & Sons, Ltd., Chichester (2003)

    Book  MATH  Google Scholar 

  28. Voit, E.O., Martens, H.A., Omholt, S.W.: 150 years of the mass action law. PLoS Comput. Biol. 11(1), e1004012 (2015)

    Article  Google Scholar 

  29. Wolkenhauer, O., Ullah, M., Kolch, W., Cho, K.H.: Modeling and simulation of intracellular dynamics: choosing an appropriate framework. IEEE Trans. Nanobiosci. 3(3), 200–207 (2004)

    Article  Google Scholar 

  30. Aldridge, B.B., Burke, J.M., Lauffenburger, D.A., Sorger, P.K.: Physicochemical modelling of cell signalling pathways. Nat. Cell Biol. 8, 1195–1203 (2006)

    Article  Google Scholar 

  31. Chou, I.C., Voit, E.O.: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 219(2), 57–83 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  32. Demattè, L., Prandi, D.: GPU computing for systems biology. Brief Bioinform. 11(3), 323–333 (2010)

    Article  Google Scholar 

  33. Harvey, M.J., De Fabritiis, G.: A survey of computational molecular science using graphics processing units. WIREs Comput. Mol. Sci. 2(5), 734–742 (2012)

    Article  Google Scholar 

  34. Nobile, M.S., Cazzaniga, P., Besozzi, D., Pescini, D., Mauri, G.: cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems. PLoS ONE 9(3), e91963 (2014)

    Article  Google Scholar 

  35. Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G.: GPU-accelerated simulations of mass-action kinetics models with cupSODA. J. Supercomput. 69(1), 17–24 (2014)

    Article  Google Scholar 

  36. Dräger, A., Kronfeld, M., Ziller, M.J., Supper, J., Planatscher, H., Magnus, J.B.: Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies. BMC Syst. Biol. 3(5) (2009)

    Google Scholar 

  37. Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13(11), 2467–2474 (2003)

    Article  Google Scholar 

  38. Nobile, M.S., Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G.: Reverse engineering of kinetic reaction networks by means of Cartesian genetic programming and particle swarm optimization. In: 2013 IEEE Congress on Evolutionary Computation, vol. 1, pp. 1594–1601. IEEE (2013)

    Google Scholar 

  39. Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: A GPU-based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds.) EvoBIO 2012. LNCS, vol. 7246, pp. 74–85. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  40. Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running on GPUs. In: Soule, T. (ed.) Proceedings of 14th International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2012, pp. 1421–1422. ACM (2012)

    Google Scholar 

  41. Nobile, M.S., Pasi, G., Cazzaniga, P., Besozzi, D., Colombo, R., Mauri, G.: Proactive particles in swarm optimization: a self-tuning algorithm based on fuzzy logic. In: Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2015)

    Google Scholar 

  42. Cazzaniga, P., Nobile, M.S., Besozzi, D.: The impact of particles initialization in PSO: parameter estimation as a case in point. In: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1–8 (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy

    Daniela Besozzi

  2. SYSBIO.IT, Centre of Systems Biology, Milano, Italy

    Daniela Besozzi

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  1. Daniela Besozzi
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Correspondence to Daniela Besozzi .

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Editors and Affiliations

  1. Swansea University, Swansea, United Kingdom

    Arnold Beckmann

  2. Université Paris-Diderot - Paris 7, Paris, France

    Laurent Bienvenu

  3. University of South Florida, Tampa, Florida, USA

    Nataša Jonoska

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Besozzi, D. (2016). Reaction-Based Models of Biochemical Networks. In: Beckmann, A., Bienvenu, L., Jonoska, N. (eds) Pursuit of the Universal. CiE 2016. Lecture Notes in Computer Science(), vol 9709. Springer, Cham. https://doi.org/10.1007/978-3-319-40189-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-40189-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40188-1

  • Online ISBN: 978-3-319-40189-8

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