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Title: CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models

Abstract

In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. We apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.

Authors:
 [1];  [2];  [1];  [1];  [2]
  1. University of Luxembourg, Belvaux (Luxembourg). Luxembourg Centre for Systems Biomedicine
  2. Georgia Inst. of Technology, Atlanta, GA (United States). School of Computer Science, Algorithms and Randomness Center
Publication Date:
Research Org.:
University of Luxembourg, Belvaux (Luxembourg)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23). Biological Systems Science Division
OSTI Identifier:
1424908
Grant/Contract Number:
SC0010429
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Bioinformatics
Additional Journal Information:
Journal Volume: 33; Journal Issue: 11; Journal ID: ISSN 1367-4803
Publisher:
Oxford University Press
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Haraldsdóttir, Hulda S., Cousins, Ben, Thiele, Ines, Fleming, Ronan M. T., and Vempala, Santosh. CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. United States: N. p., 2017. Web. doi:10.1093/bioinformatics/btx052.
Haraldsdóttir, Hulda S., Cousins, Ben, Thiele, Ines, Fleming, Ronan M. T., & Vempala, Santosh. CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. United States. doi:10.1093/bioinformatics/btx052.
Haraldsdóttir, Hulda S., Cousins, Ben, Thiele, Ines, Fleming, Ronan M. T., and Vempala, Santosh. Tue . "CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models". United States. doi:10.1093/bioinformatics/btx052. https://www.osti.gov/servlets/purl/1424908.
@article{osti_1424908,
title = {CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models},
author = {Haraldsdóttir, Hulda S. and Cousins, Ben and Thiele, Ines and Fleming, Ronan M. T. and Vempala, Santosh},
abstractNote = {In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. We apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.},
doi = {10.1093/bioinformatics/btx052},
journal = {Bioinformatics},
number = 11,
volume = 33,
place = {United States},
year = {Tue Jan 31 00:00:00 EST 2017},
month = {Tue Jan 31 00:00:00 EST 2017}
}

Journal Article:
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