A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data
Institution:
1. Center for Advanced Computing, Cornell University, 534 Rhodes Hall, Ithaca, NY, USA;2. Program of Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, USA;3. Tri-Institutional Training Program in Computational Biology and Medicine, 1300 York Avenue, Box 194, New York, NY, USA;4. School of Computer Science, The University of Manchester, Manchester, UK;5. Manchester Center for Integrative Systems Biology, The University of Manchester, Manchester, UK;6. Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY, USA;7. Institute of Biotechnology, Cornell University, Ithaca, NY, USA;8. Department of Computer Science, Boston University, 111 Cummington Street, Boston, MA, USA;9. Division of Nutritional Sciences, Cornell University, Savage Hall, Ithaca, NY, USA;1. Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China;2. Beijing National Laboratory for Condensed Matter Physics, and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;1. APHM, Hôpital de La Timone, Department of Vascular Surgery, Marseille, France;2. Aix-Marseille University, APHM, INSERM, IRD Biostatistics Department, SESSTIM, BIOSTIC, Marseille, France;3. APHM, Hôpital de La Timone, Department of Radiology, Marseille, France;4. APHM, Hôpital de La Timone, Department of Anesthesiology, Marseille, France;1. Department of Radiology, Indiana University School of Medicine, Indianapolis, Indiana;2. University of Amsterdam, The Netherlands;1. Georgetown University School of Medicine, Washington, DC;2. Department of Thoracic Surgery, MedStar Georgetown University Hospital, Washington, DC
Abstract:
A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with high-throughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability and improve our understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present an algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation, the ability to handle large enzyme complex rules that may incorporate multiple isoforms, and either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.
