Mapping the landscape of metabolic goals of a cell

Abstract

Genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. We introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories.

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Document Details

Document Type
Technical Report
Publication Date
May 23, 2016
Accession Number
AD1057625

Entities

People

  • Arion I. Stettner
  • Daniel Segrè
  • Ed Reznik
  • Ioannis Ch. Paschalidis
  • Qi Zhao

Organizations

  • Boston University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Biomedical Engineering
  • Computational Biology
  • Computational Complexity
  • Computer Programming
  • Engineering
  • Experimental Data
  • Gene Expression
  • Inverse Problems
  • Linear Programming
  • Metabolism
  • Metabolites
  • Operations Research
  • Optimization
  • Systems Biology
  • Systems Engineering
  • Two Dimensional

Readers

  • Microbial Pathology
  • Operations Research
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space