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


METHODS
https://github.com/opencobra/cobratoolbox .


BACKGROUND
ronan.mt.fleming@gmail.com or vempala@cc.gatech.edu.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
Hulda S
HS
Haraldsdóttir
School of Computer Science, Algorithms and Randomness Center, Georgia Institute of Technology, Atlanta, GA, USA.
Ben
B
Cousins
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
Ines
I
Thiele
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
Ronan M T
RMT
Fleming
School of Computer Science, Algorithms and Randomness Center, Georgia Institute of Technology, Atlanta, GA, USA.
Santosh
S
Vempala
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D012984:Software