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Updated: Feb 8, 2026
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Published on: May 5, 2021
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Large-Scale Simulations of Bacterial Populations Over Complex Networks
Andreia Sofia Teixeira1, Pedro T Monteiro2, João A Carriço3
11 ATP Group, INESC-ID Lisboa and Instituto Superior Técnico , Universidade de Lisboa, Lisboa, Portugal .
Summary
Simulating bacterial evolution with limited sampling reveals population structure insights. This computational approach aids pathogen surveillance and epidemic outbreak studies by understanding genetic diversity factors.
Area of Science:
- Computational Biology
- Population Genetics
- Epidemiology
Background:
- Understanding bacterial population genetics and evolution is vital for epidemic outbreak studies and pathogen surveillance.
- Epidemiological studies often face limitations in sampling capacity due to bias or economic constraints, potentially hindering accurate knowledge of bacterial population structure.
- Mathematical models and large-scale simulations offer a framework to assess the impact of limited sampling on inferring true population structure.
Purpose of the Study:
- To develop and present an efficient computational approach for simulating bacterial genetic evolution.
- To investigate the influence of complex host contact networks on bacterial population structure.
- To analyze the relationship between computational power and simulation speedup.
Main Methods:
- Utilized the Wright-Fisher model for simulating genetic evolution of bacterial populations.
- Employed MapReduce on Apache Spark and the GraphX API for efficient large-scale simulations over complex host contact networks.
- Evaluated the impact of mutation rates, recombination rates, and network topology on bacterial population diversity.
Main Results:
- Demonstrated an efficient method for large-scale bacterial evolution simulations on complex networks.
- Quantified the relationship between cluster computing power and simulation speedup.
- Provided insights into how mutation, recombination, and network topology affect bacterial population diversity.
Conclusions:
- Large-scale simulations using computational frameworks like Apache Spark can effectively model bacterial evolution under complex host interactions.
- The study highlights the importance of considering network topology and evolutionary parameters (mutation, recombination) when assessing bacterial population structure from limited samples.
- This approach enhances the reliability of pathogen surveillance and epidemic outbreak analysis by providing a robust framework for understanding bacterial population dynamics.
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