title: Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.
abstract: Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve them.
authors.affiliation: Laboratory of Genetic Engineering and Molecular Biology, School of Biotechnology, Jawaharlal Nehru University, New Delhi -110067, India. rakeshbhatnagar@jnu.ac.in.
authors.forename: Neha
authors.initials: N
authors.lastname: Chaudhary
authors.affiliation: Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Drøbakveien 31, 1432 Ås, Norway.
authors.forename: Kristin
authors.initials: K
authors.lastname: Tøndel
authors.affiliation: Laboratory of Genetic Engineering and Molecular Biology, School of Biotechnology, Jawaharlal Nehru University, New Delhi -110067, India. rakeshbhatnagar@jnu.ac.in.
authors.forename: Rakesh
authors.initials: R
authors.lastname: Bhatnagar
authors.affiliation: Chair of Systems and Synthetic Biology, Wageningen University Dreijenplein 10, Building number 316, 6703 HB Wageningen, The Netherlands and Synthetic and Systems Biology Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany and LifeGlimmer GmbH, Markerlstrasse 38, Berlin, Germany.
authors.forename: Vítor A P Martins
authors.initials: VA
authors.lastname: dos Santos
authors.affiliation: Synthetic and Systems Biology Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany.
authors.forename: Jacek
authors.initials: J
authors.lastname: Puchałka
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016002:Discriminant Analysis; D016678:Genome; D016680:Genome, Bacterial; D016681:Genome, Fungal; D053858:Metabolic Networks and Pathways; D025341:Principal Component Analysis; D011550:Pseudomonas aeruginosa / Q000235:genetics; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism