c62ade19efe91ffd85147eda1a14178b metabolites_a2013v3n3p838.pdf f246ab5b00287d06eaa52ba24d983150371dc4ff metabolites_a2013v3n3p838.pdf 80bebdeb0efda597c63258e46853f538d9be7fdb9cb4119d1d516e0d2b3e5390 metabolites_a2013v3n3p838.pdf Title: A Novel Methodology to Estimate Metabolic Flux Distributions in Constraint-Based Models Subject: Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production and consumption fluxes for each chemical species in the system. In some cases, the complexity of the solution space can be reduced by performing an additional optimization, while in other cases, knowing the range of variability of fluxes over the polytope provides a sufficient characterization of the allowed configurations. There are cases, however, in which the thorough information encoded in the individual distributions of viable fluxes over the polytope is required. Obtaining such distributions is known to be a highly challenging computational task when the dimensionality of the polytope is sufficiently large, and the problem of developing cost-effective ad hoc algorithms has recently seen a major surge of interest. Here, we propose a method that allows us to perform the required computation heuristically in a time scaling linearly with the number of reactions in the network, overcoming some limitations of similar techniques employed in recent years. As a case study, we apply it to the analysis of the human red blood cell metabolic network, whose solution space can be sampled by different exact techniques, like Hit-and-Run Monte Carlo (scaling roughly like the third power of the system size). Remarkably accurate estimates for the true distributions of viable reaction fluxes are obtained, suggesting that, although further improvements are desirable, our method enhances our ability to analyze the space of allowed configurations for large biochemical reaction networks. Keywords: "metabolic networks; flux balance analysis; belief propagation algorithm" Author: Francesco Alessandro Massucci, Francesc Font-Clos, Andrea De Martino, Isaac P´erez Cas Creator: LaTeX with hyperref package Producer: pdfTeX-1.40.12 CreationDate: Sun Sep 22 09:20:34 2013 ModDate: Sun Sep 22 09:22:16 2013 Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 15 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 444746 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- CTPMXW+NimbusRomNo9L-ReguItal Type 1 Custom yes yes no 36 0 YXUGVA+NimbusRomNo9L-Medi Type 1 Custom yes yes no 37 0 UTNTBS+NimbusRomNo9L-Regu Type 1 Custom yes yes no 38 0 MPQKUP+NimbusRomNo9L-MediItal Type 1 Custom yes yes no 39 0 PECVGR+CMBX8 Type 1 Builtin yes yes no 40 0 RTKTVU+CMMIB8 Type 1 Builtin yes yes no 41 0 RRTTYK+CMR8 Type 1 Builtin yes yes no 42 0 FKSCSL+CMMI12 Type 1 Builtin yes yes no 54 0 IQVOZQ+CMMIB10 Type 1 Builtin yes yes no 55 0 JKULLH+CMR12 Type 1 Builtin yes yes no 56 0 NQBRNM+CMSY10 Type 1 Builtin yes yes no 57 0 PPXGHU+CMMI8 Type 1 Builtin yes yes no 58 0 FSJFPM+CMEX10 Type 1 Builtin yes yes no 60 0 QDTWCG+MSBM10 Type 1 Builtin yes yes no 83 0 MZYSQS+CMSY6 Type 1 Builtin yes yes no 98 0 AXSDOU+CMMI6 Type 1 Builtin yes yes no 99 0 OJBARD+CMSY8 Type 1 Builtin yes yes no 100 0 QUXZYF+Helvetica Type 1C WinAnsi yes yes no 187 0 KEHQXZ+CMBX12 Type 1 Builtin yes yes no 210 0 Helvetica Type 1 WinAnsi no no no 255 0 LRYMTL+Helvetica Type 1C WinAnsi yes yes no 271 0 Jhove (Rel. 1.6, 2011-01-04) Date: 2016-12-13 02:43:34 CET RepresentationInformation: metabolites_a2013v3n3p838.pdf ReportingModule: BYTESTREAM, Rel. 1.3 (2007-04-10) LastModified: 2016-12-12 11:08:06 CET Size: 444746 Format: bytestream Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/octet-stream Checksum: 2ed3bccf Type: CRC32 Checksum: c62ade19efe91ffd85147eda1a14178b Type: MD5 Checksum: f246ab5b00287d06eaa52ba24d983150371dc4ff Type: SHA-1