Verification of Dynamic Behaviour in Qualitative Molecular Networks Describing Gene Regulation, Signalling and Whole-cell Metabolism

• Autorzy: Grabowski M. , Bokota G. , Sroka J. , Kierzek A.

Identyfikatory

DOI

10.3233/FI-2018-1681

• Języki publikacji: EN

Abstrakty

EN

We present a tool for the verification of qualitative biological models. These models formalise observed behaviours and interrelations of molecular and cellular mechanisms. During its development a model is continuously verified. Predicted behaviours are compared with behaviours observed in experimental data. Moreover, the model must not exhibit behaviours which contradict existing knowledge about capabilities of the biological system under investigation. Model development is an iterative process involving many rounds of prediction, verification and refinement. Due to the complexity of biological systems this process is laborious and error prone, which motivates the development of “model debugging” tools. The qualitative models we investigate represent large-scale molecular interaction networks describing gene regulation, signalling and whole-cell metabolism. We integrate a steady state model of whole-cell metabolism with a dynamic model of gene regulation and signalling represented as a Petri net. This Quasi-Steady State Petri Net (QSSPN) representation allows the generation of dynamic sequences of molecular events satisfying substrate, activator, inhibitor and metabolic flux requirements at every state transition. The reachability graph of the dynamic part of the model is examined and for every transition in this graph the satisfaction of metabolic flux requirements is verified by well-established linear programming techniques. Our approach is based on network connectivity alone and does not require any kinetic parameters. We demonstrate the applicability of our method by analysing a large-scale model of a nuclear receptor network regulating bile acid homeostasis in human hepatocyte. To date, simulation and verification of QSSPN models have been performed exclusively by Monte Carlo simulation. Random walks through the state space were used to find examples of behaviour satisfying properties of interest. Here, we provide for the first time for QSSPN models an exhaustive analysis of the state space up to a finite depth, which is possible due to several effective optimisations. Contrary to the Monte Carlo approach, we can prove that certain behaviour cannot be realised by the model within a given number of steps. This allows rejection of models which are not capable to reproduce experimentally observed behaviours, as well as verification that biologically unrealistic behaviours cannot occur in the simulation. We show an example of how these features improve identification of problems in large scale network models.

Słowa kluczowe

EN

biological models; genetic regulation; cell metabolism; cell communication; biological systems

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2018
• Tom: Vol. 160, nr 1/2
• Strony: 199--219
• Opis fizyczny: Bibliogr. 30 poz., rys., wykr.

Twórcy

autor

Grabowski M.

• Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

autor

Bokota G.

• Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

autor

Sroka J.

• Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

autor

Kierzek A.

• Certara Quantitative Systems Pharmacology, United Kingdom

Bibliografia

• [1] Fisher CP, Plant NJ, Moore JB, Kierzek AM. QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells. Bioinformatics, 2013. 29(24):3181-90.
• [2] Covert MW, Schilling CH, Palsson B. Regulation of Gene Expression in Flux Balance Models of Metabolism. Journal of Theoretical Biology, 2001. 213(1):73-88. doi:http://dx.doi.org/10.1006/jtbi.2001.2405. URL http://www.sciencedirect.com/science/article/pii/S0022519301924051.
• [3] Palsson B, Bhatia SN. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol., 2004. 11(2):886-897. doi:doi:10.1038/nrmicro1023.
• [4] Orth JD, Thiele I, Palsson B. What is flux balance analysis? Nature biotechnology, 2010. 28(3):245-248.
• [5] Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology, 2012. 10(4):291-305.
• [6] Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW. A whole-cell computational model predicts phenotype from genotype. Cell, 2012. 150(2):389-401. doi:10.1016/j.cell.2012.05.044. URL http://dx.doi.org/10.1016/j.cell.2012.05.044.
• [7] Covert MW, Xiao N, Chen TJ, Karr JR. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics, 2008. 24(18):2044-2050. doi:10.1093/bioinformatics/btn352. URL http://dx.doi.org/10.1093/bioinformatics/btn352.
• [8] Lee JM, Gianchandani EP, Eddy JA, Papin JA. Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks. PLoS Computational Biology, 2008. 4(5). doi:10.1371/journal.pcbi.1000086. URL http://dx.doi.org/10.1371/journal.pcbi.1000086.
• [9] Shinfuku Y, Sorpitiporn N, Sono M, Furusawa C, Hirasawa T, Shimizu H. Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum. Microbial Cell Factories, 2009. 8(1):43. doi:10.1186/1475-2859-8-43. URL https://doi.org/10.1186/1475-2859-8-43.
• [10] Edwards J, Ibarra R, Palsson B. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol., 2001. 19(2):125-30.
• [11] Brim L, Češka M, Šafrànek D. Model Checking of Biological Systems, pp. 63-112. Springer Berlin Heidelberg, Berlin, Heidelberg. ISBN 978-3-642-38874-3, 2013. doi:10.1007/978-3-642-38874-3_3. URL https://doi.org/10.1007/978-3-642-38874-3_3.
• [12] Fisher J, Piterman N. Model Checking in Biology, pp. 255-279. Springer Verlag, 2014. URL http://research.microsoft.com/apps/pubs/default.aspx?id=171296.
• [13] Fisher J, Piterman N, Hajnal A, Henzinger TA. Predictive Modeling of Signaling Crosstalk during C. elegans Vulval Development. PLoS Comput Biol, 2007. 3(5):e92. doi:10.1371/journal.pcbi.0030092. URL http://dx.plos.org/10.1371%2Fjournal.pcbi.0030092.
• [14] Nusser-Stein S, Beyer A, Rimann I, Adamczyk M, Piterman N, Hajnal A, Fisher J. Cell-cycle regulation of NOTCH signaling during C. elegans vulval development. Molecular Systems Biology, 2012. 8(1). doi:10.1038/msb.2012.51. URL http://dx.doi.org/10.1038/msb.2012.51.
• [15] Hinkelmann F, Brandon M, Guang B, McNeill R, Blekherman G, Veliz-Cuba A, Laubenbacher R. ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra. BMC Bioinformatics, 2011. 12(1):1-11. doi:10.1186/1471-2105-12-295. URL http://dx.doi.org/10.1186/1471-2105-12-295.
• [16] Beste DJ, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek AM, McFadden J. GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biology, 2007. 8(5):1-18. doi:10.1186/gb-2007-8-5-r89. URL http://dx.doi.org/10.1186/gb-2007-8-5-r89.
• [17] Mendum T, Newcombe J, Mannan A, Kierzek A, McFadden J. Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera. Genome Biology, 2011. 12(12):R127+. doi:10.1186/gb-2011-12-12-r127. URL http://dx.doi.org/10.1186/gb-2011-12-12-r127.
• [18] Pagliarini R, Sangiovanni M, Peron A, di Bernardo D. Combining flux balance analysis and model checking for metabolic network validation and analysis. Natural Computing, 2015. 14(3):341-354. doi:10.1007/s11047-014-9419-8. URL https://doi.org/10.1007/s11047-014-9419-8.
• [19] Zaitsev D. Toward the Minimal Universal Petri Net. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014.
• [20] Apache Software Foundation. GNU Linear Programming Kit. http://www.gnu.org/software/glpk/glpk.html.
• [21] Esparza J, Nielsen M. Decidability Issues for Petri Nets - a survey. Bulletin of the EATCS, 1994. 52:244-262.
• [22] Winskel G. Categories of Models for Concurrency. In: Brookes SD, Roscoe AW, Winskel G (eds.), Seminar on Concurrency, volume 197 of Lecture Notes in Computer Science. Springer. ISBN 3-540-15670-4, 1984 pp. 246-267.
• [23] van der Aalst WMP. The Application of Petri Nets to Workflow Management. Journal of Circuits, Systems, and Computers, 1998. 8(1):21-66.
• [24] Benatallah B, Hamadi R, Susanto A. A TopDown Petri Net-Based Approach for Dynamic Workflow Modeling. In: International Conference on Business Process Management (BPM 2003. Springer, 2003 pp. 336-353.
• [25] van der Aalst WMP, van Hee KM, Houben GJ. Modelling and analysing workflow using a Petri-Net based approach. In: De Michelis G, Ellis C, Memmi G (eds.), Proceedings of the 2nd Workshop on Computer-Supported Cooperative Work, Petri nets and related formalisms. 1994 URL http://tmitwww.tm.tue.nl/staff/wvdaalst/Publications/p17.PDF.
• [26] Chaouiya C, Remy E, Thieffry D. Petri net modelling of biological regulatory networks. Journal of Discrete Algorithms, 2008. 6(2):165-177. doi:http://dx.doi.org/10.1016/j.jda.2007.06.003. Selected papers from CompBioNets 2004 Algorithms and Computational Methods for Biochemical and Evolutionary Networks, URL http://www.sciencedirect.com/science/article/pii/S1570866707000524.
• [27] Thiagarajan PS. Elementary Net Systems. In: Brauer W, Reisig W, Rozenberg G (eds.), Advances in Petri Nets, volume 254 of Lecture Notes in Computer Science. Springer. ISBN 3-540-17905-4, 1986 pp. 26-59.
• [28] Heiner M, Richter R, Schwarick M. Snoopy: a tool to design and animate/simulate graph-based formalisms. In: Molnár S, Heath JR, Dalle O, Wainer GA (eds.), SimuTools. ICST. ISBN 978-963-9799-20-2, 2008 p. 15.
• [29] Baier C, Katoen JP. Principles of model checking. MIT Press, 2008. ISBN 978-0-262-02649-9.
• [30] Bloom B, Istrail S, Meyer AR. Bisimulation Can’t Be Traced. In: Ferrante J, Mager P (eds.), POPL. ACM Press. ISBN 0-89791-252-7, 1988 pp. 229-239.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).