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Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
Published on: August 18, 2023
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Genomic epidemiological models describe pathogen evolution across fitness valleys
Pablo Cárdenas1, Vladimir Corredor2, Mauricio Santos-Vega3
1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Science Advances
|July 20, 2022
Summary
Genomic epidemiology models help understand pathogen evolution. New tools like Opqua reveal how low transmission and host mobility facilitate pathogen adaptation, aiding infectious disease research.
Area of Science:
- Epidemiology
- Genomics
- Evolutionary Biology
Background:
- Genomics is transforming epidemiological research.
- Understanding pathogen evolution, including the emergence of drug resistance and increased transmissibility, requires advanced modeling tools.
- Existing models often lack the integration of epidemiological and genomic evolutionary processes.
Purpose of the Study:
- To introduce Opqua, a novel simulation framework linking pathogen epidemiology with genomic evolution and selection.
- To investigate the factors influencing pathogen evolution across fitness valleys using Opqua.
- To explore how environmental and host factors impact adaptive evolution in pathogens.
Main Methods:
- Development of Opqua, a flexible simulation framework.
- Utilizing Opqua to model the interplay between epidemiological dynamics and genomic sequence evolution.
- Simulating pathogen evolution under varying transmission rates, host mobility, and pathogen life cycle complexities.
Main Results:
- Competition can impede pathogen evolution in high-transmission settings.
- Low transmission rates, increased host mobility, and complex pathogen life cycles promote adaptation.
- Population bottlenecks and decoupled selective pressures facilitate the attainment of new adaptive peaks.
Conclusions:
- Opqua provides a powerful tool for genomic epidemiological modeling in infectious disease research.
- Environmental and host factors significantly shape pathogen evolutionary trajectories.
- This approach enhances our ability to predict and manage the evolution of infectious diseases.
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