Advisor Information
Paul Davis, PhD
Location
UNO Criss Library, Room 231
Presentation Type
Oral Presentation
Start Date
2-3-2018 2:45 PM
End Date
2-3-2018 3:00 PM
Abstract
Throughout our current generation, scientific studies, with the help of increased technological methods, have enabled the investigation of biology at nanoscale levels. Nevertheless, such systems necessitate the use of computational methods to comprehend the complex interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential equations producing a deterministic result which negates the intrinsic heterogeneity of intracellular systems. More recently, stochastic modeling approaches have gained popularity with the capability of providing more realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, reduces the computational power required to generate simulated results, while simultaneously reducing expansion of errors inherent to classical deterministic approaches. Herein, we present the application of queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and pharmacological inhibition in glycolysis to further exemplify modeling capabilities.
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Included in
Dynamic Modeling and Stochastic Simulation of Metabolic Networks
UNO Criss Library, Room 231
Throughout our current generation, scientific studies, with the help of increased technological methods, have enabled the investigation of biology at nanoscale levels. Nevertheless, such systems necessitate the use of computational methods to comprehend the complex interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential equations producing a deterministic result which negates the intrinsic heterogeneity of intracellular systems. More recently, stochastic modeling approaches have gained popularity with the capability of providing more realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, reduces the computational power required to generate simulated results, while simultaneously reducing expansion of errors inherent to classical deterministic approaches. Herein, we present the application of queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and pharmacological inhibition in glycolysis to further exemplify modeling capabilities.