APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION
Jarosław GIL
jaroslaw.gil@polsl.plDepartment 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 modellingReferences
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
Authors
Jarosław GILjaroslaw.gil@polsl.pl
Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland
Authors
Andrzej POLAŃSKIDepartment of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland
Statistics
Abstract views: 208PDF downloads: 130
License
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Stanisław BŁAWUCKI, Kazimierz ZALESKI, CONSTRUCTION AND TECHNOLOGICAL ANALYSIS OF THE BROACH BLADE SHAPE USING THE FINITE ELEMENT METHOD , Applied Computer Science: Vol. 13 No. 1 (2017)
- Marcin Topczak, Małgorzata Śliwa, ASSESSMENT OF THE POSSIBILITY OF USING BAYESIAN NETS AND PETRI NETS IN THE PROCESS OF SELECTING ADDITIVE MANUFACTURING TECHNOLOGY IN A MANUFACTURING COMPANY , Applied Computer Science: Vol. 17 No. 1 (2021)
- Martin KRAJČOVIČ, Patrik GRZNÁR, UTILISATION OF EVOLUTION ALGORITHM IN PRODUCTION LAYOUT DESIGN , Applied Computer Science: Vol. 13 No. 3 (2017)
- Jarosław ZUBRZYCKI, Natalia SMIDOVA, Jakub LITAK, Andrei AUSIYEVICH, NUMERICAL ANALYSIS OF SPINAL LOADS IN SPONDYLOLISTHESIS TREATMENT USING PEDICLE SCREWS – PRELIMINARY RESEARCH , Applied Computer Science: Vol. 13 No. 3 (2017)
- Irena NOWOTYŃSKA, Stanisław KUT, COMPARATIVE ANALYSIS OF THE IMPACT OF DIE DESIGN ON ITS LOAD AND DISTRIBUTION OF STRESS DURING EXTRUSION , Applied Computer Science: Vol. 14 No. 4 (2018)
- Kuppan Chetty RAMANATHAN, Manju MOHAN, Joshuva AROCKIA DHANRAJ, BACKWARD MOTION PLANNING AND CONTROL OF MULTIPLE MOBILE ROBOTS MOVING IN TIGHTLY COUPLED FORMATIONS , Applied Computer Science: Vol. 17 No. 3 (2021)
You may also start an advanced similarity search for this article.
