APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION

Jarosław GIL

jaroslaw.gil@polsl.pl
Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, (Poland)

Andrzej POLAŃSKI


Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, (Poland)

Abstract

In this study we present simulation system based on Gillespie algorithm for generating evolutionary events in the evolution scenario of microbial population. We present Gillespie simulation system adjusted to reproducing experimental data obtained in barcoding studies – experimental techniques in microbiology allowing tracing microbial populations with very high resolution. Gillespie simulation engine is constructed by defining its state vector and rules for its modifications. In order to efficiently simulate barcoded experiment by using Gillespie algorithm we provide modification - binning cells by lineages. Different bins define components of state in the Gillespie algorithm. The elaborated simulation model captures events in microbial population growth including death, division and mutations of cells. The obtained simulation results reflect population behavior, mutation wave and mutation distribution along generations. The elaborated methodology is confronted against literature data of experimental evolution of yeast tracking clones sub-generations. Simulation model was fitted to measurements in experimental data leading to good agreement.


Keywords:

clonal evolution, mutation waves, yeast evolution, numerical modelling

Baar, M., Coquille, L., Mayer, H., Hölzel, M., Rogava, M., Tüting, T., & Bovier, A. (2016). A stochastic model for immunotherapy of cancer. Scientific Reports, 6(1), 24169. https://doi.org/10.1038/srep24169
  Google Scholar

Beckman, R. A., & Loeb, L. A. (2005). Negative Clonal Selection in Tumor Evolution. Genetics, 171(4), 2123–2131. https://doi.org/10.1534/genetics.105.040840
  Google Scholar

Blundell, J. R., Schwartz, K., Francois, D., Fisher, D. S., Sherlock, G., & Levy, S. F. (2019). The dynamics of adaptive genetic diversity during the early stages of clonal evolution. Nature Ecology & Evolution, 3(2), 293–301. https://doi.org/10.1038/s41559-018-0758-1
  Google Scholar

Bozic, I., Antal, T., Ohtsuki, H., Carter, H., Kim, D., Chen, S., Karchin, R., Kinzler, K. W., Vogelstein, B., & Nowak, M. A. (2010). Accumulation of driver and passenger mutations during tumor progression. Proceedings of the National Academy of Sciences, 107(43), 18545–18550. https://doi.org/10.1073/pnas.1010978107
  Google Scholar

Bush, S. J., Foster, D., Eyre, D. W., Clark, E. L., De Maio, N., Shaw, L. P., Stoesser, N., Peto, T. E. A., Crook, D. W., & Walker, A. S. (2020). Genomic diversity affects the accuracy of bacterial single-nucleotide polymorphism–calling pipelines. GigaScience, 9(2), giaa007. https://doi.org/10.1093/gigascience/giaa007
  Google Scholar

Cao, Y., Gillespie, D. T., & Petzold, L. R. (2006). Efficient step size selection for the tau-leaping simulation method. The Journal of Chemical Physics, 124(4), 044109. https://doi.org/10.1063/1.2159468
  Google Scholar

Castillo, F., & Virgilio, N. (2015). Stochastic Modeling of Cancer Tumors using Moran Models and an Application to Cancer Genetics [Thesis, Rice University]. https://scholarship.rice.edu/handle/1911/87795
  Google Scholar

Desai, M. M., & Fisher, D. S. (2007). Beneficial Mutation–Selection Balance and the Effect of Linkage on Positive Selection. Genetics, 176(3), 1759–1798. https://doi.org/10.1534/genetics.106.067678
  Google Scholar

Foo, J., Leder, K., & Michor, F. (2011). Stochastic dynamics of cancer initiation. Physical Biology, 8(1), 015002. https://doi.org/10.1088/1478-3975/8/1/015002
  Google Scholar

Gillespie, D. T. (2001). Approximate accelerated stochastic simulation of chemically reacting systems. The Journal of Chemical Physics, 115(4), 1716–1733. https://doi.org/10.1063/1.1378322
  Google Scholar

Kinnersley, M., Schwartz, K., Yang, D.-D., Sherlock, G., & Rosenzweig, F. (2021). Evolutionary dynamics and structural consequences of de novo beneficial mutations and mutant lineages arising in a constant environment. BMC Biology, 19(1), 20. https://doi.org/10.1186/s12915-021-00954-0
  Google Scholar

Kvitek, D. J., & Sherlock, G. (2013). Whole Genome, Whole Population Sequencing Reveals That Loss of Signaling Networks Is the Major Adaptive Strategy in a Constant Environment. PLOS Genetics, 9(11), e1003972. https://doi.org/10.1371/journal.pgen.1003972
  Google Scholar

Levy, S. F., Blundell, J. R., Venkataram, S., Petrov, D. A., Fisher, D. S., & Sherlock, G. (2015). Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature, 519(7542), 181–186. https://doi.org/10.1038/nature14279
  Google Scholar

Marchetti, L., Priami, C., & Thanh, V. H. (2017). Simulation Algorithms for Computational Systems Biology. Springer International Publishing. https://doi.org/10.1007/978-3-319-63113-4
  Google Scholar

McFarland, C. D., Mirny, L. A., & Korolev, K. S. (2014). Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proceedings of the National Academy of Sciences, 111(42), 15138–15143. https://doi.org/10.1073/pnas.1404341111
  Google Scholar

Neher, R. A. (2013). Genetic draft, selective interference, and population genetics of rapid adaptation. Annual Review of Ecology, Evolution, and Systematics, 44(1), 195–215. https://doi.org/10.1146/annurev-ecolsys110512-135920
  Google Scholar

Nguyen Ba, A. N., Cvijović, I., Rojas Echenique, J. I., Lawrence, K. R., Rego-Costa, A., Liu, X., Levy, S. F., & Desai, M. M. (2019). High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast. Nature, 575(7783), 494– 499. https://doi.org/10.1038/s41586-019-1749-3
  Google Scholar

Wang, C.-H., Matin, S., George, A. B., & Korolev, K. S. (2019). Pinned, locked, pushed, and pulled traveling waves in structured environments. Theoretical Population Biology, 127, 102–119. https://doi.org/10.1016/j.tpb.2019.04.003
  Google Scholar

Wild, G. (2011). Inclusive Fitness from Multitype Branching Processes. Bulletin of Mathematical Biology, 73(5), 1028–1051. https://doi.org/10.1007/s11538-010-9551-2
  Google Scholar

Yakovlev, A. Y., Stoimenova, V. K., & Yanev, N. M. (2008). Branching Processes as Models of Progenitor Cell Populations and Estimation of the Offspring Distributions. Journal of the American Statistical Association, 103(484), 1357–1366. https://doi.org/10.1198/016214508000000913
  Google Scholar

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Published
2022-10-04

Cited by

GIL, J., & POLAŃSKI, A. (2022). APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION. Applied Computer Science, 18(4), 5–15. https://doi.org/10.35784/acs-2022-25

Authors

Jarosław GIL 
jaroslaw.gil@polsl.pl
Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland

Authors

Andrzej POLAŃSKI 

Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland

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