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

Jarosław GIL; Andrzej POLAŃSKI

doi:10.35784/acs-2022-25

Open full text

pdf

Published: Oct 4, 2022

DOI: https://doi.org/10.35784/acs-2022-25

Issue Vol. 18 No. 4 (2022)

Articles

APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION
Jarosław GIL, Andrzej POLAŃSKI

5-15
HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS
Sheikh Amir FAYAZ, Majid ZAMAN, Muheet Ahmed BUTT, Sameer KAUL

16-27
DETERMINING THE DEGREE OF PLAYER ENGAGEMENT IN A COMPUTER GAME WITH ELEMENTS OF A SOCIAL CAMPAIGN USING COGNITIVE NEUROSCIENCE TECHNIQUES
Konrad BIERCEWICZ, Mariusz BORAWSKI, Anna BORAWSKA, Jarosław DUDA

28-52
ANALYSIS OF THE POSSIBILITY OF USING THE SINGULAR VALUE DECOMPOSITION IN IMAGE COMPRESSION
Edyta ŁUKASIK, Emilia ŁABUĆ

53-67
PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK
Monika KULISZ, Justyna KUJAWSKA, Zulfiya AUBAKIROVA, Gulnaz ZHAIRBAEVA, Tomasz WAROWNY

68-81
NUMERICAL AND EXPERIMENTAL ANALYSIS OF A CENTRIFUGAL PUMP WITH DIFFERENT ROTOR GEOMETRIES
Łukasz SEMKŁO, Łukasz GIERZ

82-95
A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS
Elmehdi BENMALEK, Jamal EL MHAMDI, Abdelilah JILBAB, Atman JBARI

96-115
IDENTIFICATION OF THE IMPACT OF THE AVAILABILITY FACTOR ON THE EFFICIENCY OF PRODUCTION PROCESSES USING THE AHP AND FUZZY AHP METHODS
Piotr WITTBRODT, Iwona ŁAPUŃKA, Gulzhan BAYTIKENOVA, Arkadiusz GOLA, Alfiya ZAKIMOVA

116-129

DOI

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

Andrzej POLAŃSKI

jaroslaw.gil@polsl.pl

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Issue Vol. 18 No. 4 (2022)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 18 No. 4 (2022)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License