Application of gillespie algorithm for simulating evolution of fitness of microbial population

• Autorzy: Gil Jarosław , Polański Andrzej

Identyfikatory

DOI

10.35784/acs-2022-25

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

clonal evolution; mutation waves; yeast evolution; numerical modelling

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2022
• Tom: Vol. 18, no 4
• Strony: 5--15
• Opis fizyczny: Bibliogr. 20 poz., fig., tab.

Twórcy

autor

Gil Jarosław

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

• https://orcid.org/0000-0001-9228-9937

autor

Polański Andrzej

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

• https://orcid.org/0000-0002-1793-9546

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