The goal of this project is to develop mathematical tools to model biochemical networks from experimental data. These tools will then be applied to transcriptomics, proteomics, and metabolomics data collected from a network of ten genes of the model organism Saccharomyces cerevisiae that are involved in oxidative stress response. ? ? The proposed approach is to develop a two-stage modeling process. Viewing a biochemical network as a continuous dynamical system, described by a collection of ordinary differential equations, we first approximate the system by a discrete-time system, using numerical methods. Several tools are then used to analyze this discrete system. One of these tools is a further reduction, achieved by categorizing the state values of the variables into a finite set of states. The resulting framework allows the use of well-developed methods from computational algebra to study the whole space of possible models for the discrete-time system on a finite state set. ? ? To demonstrate and test the mathematical methodologies, experiments will be carded out with the baker's yeast Saccharomyces cerevisiae. Cell cultures of wild type yeast will be perturbed by addition of cumene hydroperoxide, and oxidative stress, and samples will be measured in a time course. The experiment will be repeated ten times, each time with a different gene deletion mutant. Mutants will be selected according to the hypotheses generated by the mathematical models. In addition to supporting the mathematical developments, the experiments are expected to reveal details about the regulation of glutathione metabolism and related antioxidant molecules (such as Lascorbic acid). ? ? ?
| Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David et al. (2014) An algebra-based method for inferring gene regulatory networks. BMC Syst Biol 8:37 |
| Sha, Wei; Martins, Ana M; Laubenbacher, Reinhard et al. (2013) The genome-wide early temporal response of Saccharomyces cerevisiae to oxidative stress induced by cumene hydroperoxide. PLoS One 8:e74939 |
| Dimitrova, Elena S; Licona, M Paola Vera; McGee, John et al. (2010) Discretization of time series data. J Comput Biol 17:853-68 |
| Shulaev, Vladimir; Cortes, Diego; Miller, Gad et al. (2008) Metabolomics for plant stress response. Physiol Plant 132:199-208 |
| Bajad, Sunil; Shulaev, Vladimir (2007) Highly-parallel metabolomics approaches using LC-MS for pharmaceutical and environmental analysis. Trends Analyt Chem 26:625-636 |
| Jarrah, Abdul Salam; Raposa, Blessilda; Laubenbacher, Reinhard (2007) Nested Canalyzing, Unate Cascade, and Polynomial Functions. Physica D 233:167-174 |
| Stigler, B; Jarrah, A; Stillman, M et al. (2007) Reverse engineering of dynamic networks. Ann N Y Acad Sci 1115:168-77 |
| Camacho, Diogo; Vera Licona, Paola; Mendes, Pedro et al. (2007) Comparison of reverse-engineering methods using an in silico network. Ann N Y Acad Sci 1115:73-89 |
| Martins, Ana Margarida; Sha, Wei; Evans, Clive et al. (2007) Comparison of sampling techniques for parallel analysis of transcript and metabolite levels in Saccharomyces cerevisiae. Yeast 24:181-8 |
| Shulaev, Vladimir; Oliver, David J (2006) Metabolic and proteomic markers for oxidative stress. New tools for reactive oxygen species research. Plant Physiol 141:367-72 |
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