A growing number of reconstructed metabolic reaction networks have appeared in recent years. Such reconstruction can be converted into a mathematical format that in turn can be used to analyze the properties of the networks. Genome-scale analysis of network properties leads to understanding of their normal physiological functions and malfunction leading to pathophysiological states. The first part of this R01 program was focused on the analysis of possible steady state flux distributions of metabolic networks using extreme pathways. This analysis has proven useful for a number of purposes but is limited in scope. Fortunately, we have found random sampling of the solution space to be a viable and useful alternative to extreme pathways. During the second part of this program we propose to 1) develop uniform randomized sampling of the feasible steady state solution spaces, 2) to apply these algorithms to a series of biological and medical examples, and 3) develop time-scale decomposition of large- scale kinetic models. If the proposed program is successfully executed it will advance the state of the art of building genome-scale models to account for concentrations and to develop first-pass network-scale dynamic models. The availability of such genome-scale models would expand their scope of predictions and thus their applications to various biological and health care issues.

Public Health Relevance

Project Narrative Public investment in DNA sequencing has led to the sequencing of entire genomes of a growing number of organisms. Scientists have determined how to assemble the information found in genomic sequences, along with data from the scientific literature, into the biochemical reaction networks that underlie cellular functions. This process is particularly advanced for metabolism;this proposal is focused on the mathematical description and analysis of metabolic functions in health and disease.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Jones, Warren
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Diego
Engineering (All Types)
Schools of Arts and Sciences
La Jolla
United States
Zip Code
Zielinski, Daniel C; Jamshidi, Neema; Corbett, Austin J et al. (2017) Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism. Sci Rep 7:41241
Du, Bin; Zielinski, Daniel C; Kavvas, Erol S et al. (2016) Evaluation of rate law approximations in bottom-up kinetic models of metabolism. BMC Syst Biol 10:40
Zielinski, Daniel C; Filipp, Fabian V; Bordbar, Aarash et al. (2015) Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis. Nat Commun 6:7101
Bordbar, Aarash; Nagarajan, Harish; Lewis, Nathan E et al. (2014) Minimal metabolic pathway structure is consistent with associated biomolecular interactions. Mol Syst Biol 10:737
Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash et al. (2014) Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance. Sci Rep 4:3925
Bordbar, Aarash; Monk, Jonathan M; King, Zachary A et al. (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107-20
Chang, Roger L; Xie, Lei; Bourne, Philip E et al. (2013) Antibacterial mechanisms identified through structural systems pharmacology. BMC Syst Biol 7:102
Bordbar, Aarash; Mo, Monica L; Nakayasu, Ernesto S et al. (2012) Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol Syst Biol 8:558
Bordbar, A; Palsson, B O (2012) Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med 271:131-41
Schellenberger, Jan; Zielinski, Daniel C; Choi, Wing et al. (2012) Predicting outcomes of steady-state ýýýýC isotope tracing experiments using Monte Carlo sampling. BMC Syst Biol 6:9

Showing the most recent 10 out of 27 publications