Award Abstract #0540181 DDDAS-SMRP: Development of a closed-loop identification machine for bionetworks (CLIMB) and its application to nucleotide metabolism
Anita J. LaSalle
CNS Division of Computer and Network Systems
CSE Directorate for Computer & Information Science & Engineering
Start Date:
October 1, 2005
Expires:
September 30, 2008 (Estimated)
Awarded Amount to Date:
$589920
Investigator(s):
Herschel Rabitz hrabitz@princeton.edu (Principal Investigator)
Joshua Rabinowitz (Co-Principal Investigator)
Sponsor:
Princeton University
Off. of Research & Proj. Admin.
Princeton, NJ 08544 609/258-3090
NSF Program(s):
COMPUTER SYSTEMS
Field Application(s):
0000912 Computer Science
Program Reference Code(s):
HPCC, 9218, 7481
Program Element Code(s):
T546, T272, 7354
ABSTRACT
The chemical reaction network of cellular metabolism is one of the most highly conserved features of organisms. It is also a key target of disease treatment, with several important drugs inhibiting the biosynthesis of DNS and RNA precursors (nucleotide metabolism). Recent advances in systems biology, analytical chemistry, and computer science provide the opportunity to begin construction quantitative, dynamic models of the integrated behavior of bionetworks like nucleotide metabolism. A major barrier to obtaining accurate dynamic models, however, lies in the difficulty of extracting the quantitative models from noisy experimental data, given nonlinear network behavior and the limited number of laboratory measurements. The proposed research aims to develop a closed-loop identification machine for molecular bionetworks (CLIMB) to enable reliable and cost-effective model identification through iterative rounds of computation and computation-guided experimentation. CLIMB involves two core computational components, (1) a control module which designs optimized chemical fluxes for application to the target network for the purpose of parameter identification, and (2) an inversion module which calculates feasible parameter values based on the experimental results. The features that distinguish CLIMB from traditional adaptive identification techniques include the utilization of a non linear, global inversion algorithm to extract the distribution of model parameters consistent with the experimental measurements, and the utilization of this parameter distribution in a nonlinear, closed-loop learning control algorithm to guide further experiments. The CLIMB technique will be fully developed and applied to achieve a quantitative understanding of the nucleotide metabolism of E. coli and S. cerevisiae. CLIMB-designed chemical influxes will be introduced to the nutrient-limited, steady-state cultures of living cells. The dynamic response of the metabolic network will be monitored by analyzing intracellular nucleotide content during and after application of the CLIMB-designed chemical flux. The intellectual merit of the proposed research lies in applying iterative rounds of computation and experimentation to handle network identification challenges too difficult for classical approaches. It further lies in designing computational modules suitable for the real-world challenges of highly nonlinear networks and limited, noisy measurements. Finally, the effort will culminate in streamlined integration of computation with culture, collection, and metabolomic measurement of growing cells.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Brauer, M. J., Yuan, J., Bennett, B. D., Kimball, E., Botstein, D., Rabinowitz, J. D.. "Conservation of the metabolic response to starvation across two divergent microbes," Proc. Natl. Acad. Sci., v.103, 2006, p. 19302.
F. Liang. X.-J. Feng, M. Lowey, H. Rabitz. "Maximal use of minimal libraries through the adaptive substituent reordering
algorithm," J. Phys. Chem. B, v.109, 2005, p. 5842.
Hess, DC; Lu, WY; Rabinowitz, JD; Botstein, D. "Ammonium toxicity and potassium limitation in yeast," PLOS BIOLOGY, v.4, 2006, p. 2012-2023.
J. Rabinowitz, J. Hsiao, K. Gryncel, E. Kantrowitz, X. Feng, G. Li, H. Rabitz. "Dissecting enzyme regulation by multiple allosteric effectors: nucleotide regulation of aspartate transcarbamoylase," Biochemistry, v.47, 2008, p. 5881.
Kimball, E; Rabinowitz, JD. "Identifying decomposition products in extracts of cellular metabolites," ANALYTICAL BIOCHEMISTRY, v.358, 2006, p. 273-280.
L. Bieniasz, H. Rabitz. "High-dimensional model representation of cyclic voltammograms," Anal. Chem., v.78, 2006, p. 1807.
M. Hayes, B. Li, H. Rabitz. "Estimation of molecular properties by high-dimensional model representation," J. Phys. Chem. A, v.110, 2005, p. 264.
M. Piazza, X. Feng, J. Rabinowitz, H. Rabitz. "Diverse metabolic model parameters generate similar methionine cycle dynamics," journal theoretical biology, v.251, 2008, p. 628.
Rabinowitz, J. D.. "Cellular metabolomics of Escherichia coli.," Exp. Rev. Proteomics, v.4, 2007, p. 187.
W. Lu, E. Kimball, J. Rabinowitz. "A high-performance liquid chromatography-tandem mass spectrometry
method for quantitation of nitrogen-containing intracellular metabolites," J. Am. Soc. Mass. Spectrom., v.1, 2006, p. 37.
Yuan, J., Rabinowitz, J.D.. "Differentiating metabolites formed from de novo biosynthesis versus macromolecule decomposition," J. Am. Chem. Soc., v.129, 2007, p. 9294.
Yuan, J; Fowler, WU; Kimball, E; Lu, WY; Rabinowitz, JD. "Kinetic flux profiling of nitrogen assimilation in Escherichia coli," NATURE CHEMICAL BIOLOGY, v.2, 2006, p. 529-530.
