title: An integrated pathway system modeling of Saccharomyces cerevisiae HOG pathway: a Petri net based approach.
abstract: Biochemical networks comprise many diverse components and interactions between them. It has intracellular signaling, metabolic and gene regulatory pathways which are highly integrated and whose responses are elicited by extracellular actions. Previous modeling techniques mostly consider each pathway independently without focusing on the interrelation of these which actually functions as a single system. In this paper, we propose an approach of modeling an integrated pathway using an event-driven modeling tool, i.e., Petri nets (PNs). PNs have the ability to simulate the dynamics of the system with high levels of accuracy. The integrated set of signaling, regulatory and metabolic reactions involved in Saccharomyces cerevisiae's HOG pathway has been collected from the literature. The kinetic parameter values have been used for transition firings. The dynamics of the system has been simulated and the concentrations of major biological species over time have been observed. The phenotypic characteristics of the integrated system have been investigated under two conditions, viz., under the absence and presence of osmotic pressure. The results have been validated favorably with the existing experimental results. We have also compared our study with the study of idFBA (Lee et al., PLoS Comput Biol  4:e1000-e1086, 2008) and pointed out the differences between both studies. We have simulated and monitored concentrations of multiple biological entities over time and also incorporated feedback inhibition by Ptp2 which has not been included in the idFBA study. We have concluded that our study is the first to the best of our knowledge to model signaling, metabolic and regulatory events in an integrated form through PN model framework. This study is useful in computational simulation of system dynamics for integrated pathways as there are growing evidences that the malfunctioning of the interplay among these pathways is associated with disease.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700108, India. namrata_t@isical.ac.in
authors.forename: Namrata
authors.initials: N
authors.lastname: Tomar
authors.forename: Olivia
authors.initials: O
authors.lastname: Choudhury
authors.forename: Ankush
authors.initials: A
authors.lastname: Chakrabarty
authors.forename: Rajat K
authors.initials: RK
authors.lastname: De
mesh_terms: D003198:Computer Simulation; D025461:Feedback, Physiological; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D053858:Metabolic Networks and Pathways; D020928:Mitogen-Activated Protein Kinases / Q000502:physiology*; D008954:Models, Biological; D009997:Osmotic Pressure; D010744:Phosphoric Monoester Hydrolases / Q000502:physiology; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000502:physiology*; D015398:Signal Transduction; D013312:Stress, Physiological; D014882:Water-Electrolyte Balance
chemical_list: D029701:Saccharomyces cerevisiae Proteins; C088589:HOG1 protein, S cerevisiae; D020928:Mitogen-Activated Protein Kinases; D010744:Phosphoric Monoester Hydrolases