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Recent approaches in computational modelling for controlling pathogen threats

John A Lees1, Timothy W Russell2, Liam P Shaw3,4

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.

Life Science Alliance
|June 21, 2024
PubMed

Insights

This review explores computational modeling for pathogens, focusing on antimicrobial resistance (AMR), vaccine strain selection, and antibody dynamics. Advances in these areas are crucial for public health and disease control.

Area of Science:

  • Computational biology
  • Infectious disease dynamics
  • Immunology

Background:

  • Antimicrobial resistance (AMR) mechanisms are poorly understood, hindering prediction and control.
  • Selecting optimal bacterial strains for vaccines requires understanding strain dynamics post-vaccination.
  • The SARS-CoV-2 pandemic accelerated research in within-host antibody dynamics modeling.

Purpose of the Study:

  • To review the current state of computational modeling for pathogens.
  • To highlight interlinked research areas with diverse spatial and temporal scopes.
  • To identify persistent challenges in modeling complex biological systems.

Main Methods:

  • Review of existing literature on computational modeling in three key areas.
  • Analysis of spatial and temporal scopes of different modeling approaches.
  • Synthesis of findings related to AMR, vaccine design, and antibody dynamics.

Main Results:

  • Computational models are essential for understanding AMR, predicting its spread, and designing interventions.
  • Modeling aids in selecting optimal bacterial strains for vaccine development by analyzing strain competition.
  • Significant progress has been made in within-host antibody modeling, driven by pandemic data.

Conclusions:

  • Computational modeling is vital for addressing critical challenges in infectious disease research.
  • Further research is needed to refine models for AMR, vaccine efficacy, and immune responses.
  • Integrated modeling approaches are key to advancing our understanding of complex pathogen systems.

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