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Related Experiment Videos

Evolution on distributive lattices.

Niko Beerenwinkel1, Nicholas Eriksson, Bernd Sturmfels

  • 1Department of Mathematics, University of California, Berkeley, CA 94720, USA. niko@math.berkeley.edu <niko@math.berkeley.edu>

Journal of Theoretical Biology
|May 3, 2006
PubMed
Summary
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This study introduces a new method to predict the risk of populations evolving resistance after interventions. We applied this to HIV drug resistance, quantifying the probability of escape mutants emerging.

Area of Science:

  • Evolutionary Biology
  • Mathematical Biology
  • Computational Biology

Background:

  • Interventions can drastically alter evolutionary fitness landscapes.
  • Populations may evolve resistance, leading to treatment failure or intervention escape.
  • Predicting the probability of such evolutionary escape is crucial.

Purpose of the Study:

  • To develop a computational framework for predicting the risk of evolutionary escape from interventions.
  • To model genotype spaces and fitness landscapes using algebraic combinatorics.
  • To apply the framework to understand drug resistance evolution in HIV.

Main Methods:

  • Genotype space modeled as a distributive lattice.
  • Fitness landscape represented as a real-valued function on the lattice.

Related Experiment Videos

  • Algebraic combinatorics used to compute the 'risk polynomial' encoding escape probability.

    • Main Results:

    • A method to compute the risk polynomial from the fitness landscape was established.
    • The risk of viral escape from protease inhibitors (ritonavir, indinavir) in HIV was analyzed.
    • Quantified the probability of escape mutant development before population extinction.

    Conclusions:

    • The risk polynomial provides a computable measure of evolutionary escape from interventions.
    • This framework offers insights into the dynamics of drug resistance in pathogens like HIV.
    • The approach facilitates prediction and mitigation of resistance evolution in various biological systems.