Inferring Microbial Fitness Landscapes

Abstract

Microbes and viruses evolve. Their evolution is often more rapid and of greater practical importance than our own evolution. How can we understand, or even predict, the evolutionary trajectory of microbes as they adapt? For example, what determines how quickly, and by what specific mutations, avian influenza viruses will adapt to novel human hosts; or how readily infectious bacteria will escape antibiotics or the human immune system? In this research program we seek to combine mathematical models and statistical techniques to tackle this problem head-on: to infer from data the determinants of microbial evolution with sufficient resolution that we can quantify their evolutionary trajectories, and sometimes even predict the details of their evolution.

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Document Details

Document Type
Technical Report
Publication Date
Feb 25, 2016
Accession Number
AD1028419

Entities

People

  • Charles B Epstein
  • Joshua B. Plotkin

Organizations

  • University of Pennsylvania

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bacteria
  • Biology
  • Differential Equations
  • Engineering
  • Genetics
  • Immune System
  • Inequalities
  • Markov Processes
  • Mathematical Analysis
  • Mathematical Models
  • Microorganisms
  • Probability
  • Proteins
  • Standards
  • Stochastic Processes
  • Students
  • Viruses

Fields of Study

  • Biology

Readers

  • Computational Fluid Dynamics (CFD)
  • Microbial Pathology
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology