Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method

Motivation: Time-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints. Results: In this work, an incremental and iterative parameter estimation method is proposed that combines and iterates between two estimation phases. One phase involves a decoupling method, in which a subset of model parameters that are associated with measured metabolites, are estimated using the minimization of slope errors. Another phase follows, in which the ODE model is solved one equation at a time and the remaining model parameters are obtained by minimizing concentration errors. The performance of this two-phase method was tested on a generic branched metabolic pathway and the glycolytic pathway of Lactococcus lactis. The results showed that the method is efficient in getting accurate parameter estimates, even when some information is missing. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Metformin Decreases Glucose Oxidation and Increases the Dependency of Prostate Cancer Cells on Reductive Glutamine Metabolism

Metformin inhibits cancer cell proliferation, and epidemiology studies suggest an association with increased survival in patients with cancer taking metformin; however, the mechanism by which metformin improves cancer outcomes remains controversial. To explore how metformin might directly affect cancer cells, we analyzed how metformin altered the metabolism of prostate cancer cells and tumors. We found that metformin decreased glucose oxidation and increased dependency on reductive glutamine metabolism in both cancer cell lines and in a mouse model of prostate cancer. Inhibition of glutamine anaplerosis in the presence of metformin further attenuated proliferation, whereas increasing glutamine metabolism rescued the proliferative defect induced by metformin. These data suggest that interfering with glutamine may synergize with metformin to improve outcomes in patients with prostate cancer.German Science Foundation (Grant FE1185)National Institutes of Health (U.S.)Glenn Foundation for Medical ResearchNational Institutes of Health (U.S.) (Grant 5-P50-090381-09)National Institutes of Health (U.S.) (Grant 5-P30-CA14051-39)Burroughs Wellcome FundSmith Family FoundationDamon Runyon Cancer Research FoundationNational Institutes of Health (U.S.) (Grant 1R01DK075850-01)National Institutes of Health (U.S.) (Grant 1R01CA160458-01A1

An Extension and Novel Solution to the (l,d)-Motif Challenge Problem

The (l,d )--motif challenge problem, as introduced by Pevzner and Sze [12], is a mathematical abstraction of the DNA functional site discovery task. Here we expand the (l,d )--motif problem to more accurately model this task and present a novel algorithm to solve this extended problem. This algorithm is guaranteed to find all (l,d )--motifs in a set of input sequences with unbounded support and length. We demonstrate the performance of the algorithm on publicly available datasets and show that the algorithm deterministically enumerates the optimal motifs

Parameter estimation of kinetic models from metabolic profiles: Two-phase dynamic decoupling method

ISSN:1367-4803ISSN:1460-205

High-Throughput Screen for Poly-3-Hydroxybutyrate in Escherichia coli and Synechocystis sp. Strain PCC6803

A novel, quantitative method for detecting poly-3-hydroxybutyrate (PHB) amounts in viable cells was developed to allow for high-throughput screening of mutant libraries. The staining technique was demonstrated and optimized for the cyanobacterium Synechocystis sp. strain PCC6803 and the eubacterium Escherichia coli to maximize the fluorescence difference between PHB-accumulating and control cells by flow cytometry. In Synechocystis, the level of nonspecific dye binding was reduced by using nonionic stain buffer that allowed quantitation of fluorescence levels. In E. coli, the use of a mild sucrose shock facilitated uptake of Nile red without significant loss of viability. The optimized staining protocols yielded a linear response for the mean fluorescence against (chemically measured) PHB. The staining protocols are novel methods useful in the high-throughput evaluation of combinatorial libraries of Synechocystis and E. coli using fluorescence-activated cell sorting to identify mutants with increased PHB-accumulating properties