On a Queueing Theory Method to Simulate In-Silico Metabolic Networks

Clement, Emalie J.; Wysocki, Beata J.; Davis, Paul H.; Wysocki, Tadeusz A.

doi:10.2174/2213235x05666161221160658

Cited by 4 publications

(3 citation statements)

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“…Conversely, during inflow the scaling is inversely proportional. As previously mentioned, we have used queues to describe additional biological pathways and have provided detailed explanations of the proposed queueing theory methods [41], [42]. Additionally, the pseudocode of the queueing theory application is provided as a supplementary file.…”

Section: Methodsmentioning

confidence: 99%

Stochastic Simulation of Cellular Metabolism

et al. 2020

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Increased technological methods have enabled the investigation of biology at nanoscale levels. Such systems require the use of computational methods to comprehend the complex interactions that occur. The dynamics of metabolic systems have been traditionally described utilizing differential equations without fully capturing the heterogeneity of biological systems. Stochastic modeling approaches have recently emerged with the capacity to incorporate the statistical properties of such systems. However, the processing of stochastic algorithms is a computationally intensive task with intrinsic limitations. Alternatively, the queueing theory approach, historically used in the evaluation of telecommunication networks, can significantly reduce the computational power required to generate simulated results while simultaneously reducing the expansion of errors. We present here the application of queueing theory to simulate stochastic metabolic networks with high efficiency. With the use of glycolysis as a well understood biological model, we demonstrate the power of the proposed modeling methods discussed herein. Furthermore, we describe the simulation and pharmacological inhibition of glycolysis to provide an example of modeling capabilities.

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Section: Methodsmentioning

confidence: 99%

Stochastic Simulation of Cellular Metabolism

et al. 2020

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“…For example, queuing networks were able to accurately model nonviral gene delivery incorporating mitosis in cells (Wysocki et al, 2014). A more comprehensive assessment of the disadvantages of ODEs compared to the advantages of queuing theory has been reviewed previously (Clement et al, 2018). To summarize, alteration of initial conditions and their dependency on time and random factors makes achieving a solution to ODEs computationally intensive.…”

Section: Methodsmentioning

confidence: 99%

Modeling of an immune response: Queuing network analysis of the impact of zinc and cadmium on macrophage activation

Nitz

Smith

Wysocki

et al. 2020

Biotech & Bioengineering

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Chronic obstructive pulmonary disease is characterized by progressive, irreversible airflow obstruction resulting from an abnormal inflammatory response to noxious gases and particles. Alveolar macrophages rely on the transcription factors, nuclear factor κB and mitogen-activated protein kinase, among others, to facilitate the production of inflammatory mediators designed to help rid the lung of foreign pathogens and noxious stimuli. Building a kinetic model using queuing networks, provides a quantitative approach incorporating an initial number of individual molecules along with rates of the reactions in any given pathway. Accordingly, this model has been shown useful to model cell behavior including signal transduction, transcription, and metabolic pathways. The aim of this study was to determine whether a queuing theory model that involves lipopolysaccharide-mediated macrophage activation in tandem with changes in intracellular Cd and zinc (Zn) content or a lack thereof, would be useful to predict their impact on immune activation. We then validate our model with biologic cytokine output from human macrophages relative to the timing of innate immune activation. We believe that our results further prove the validity of the queuing theory approach to model intracellular molecular signaling and postulate that it can be useful to predict additional cell signaling pathways and the corresponding biological outcomes.

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“…Given the interdependency of the concentrations C 1 ( t 0 ), …, C N ( t 0 ), which is usually highly non-linear, and further dependency of other time varying and/or random factors, achieving the solution of such sets of equations is not only extremely computationally intensive, but also not guaranteed to produce a numerically stable result. The problem is further complicated by the fact that the concentrations C 1 ( t ), …, C N ( t ) are always non-negative, and as reported by Infante et al [30], and Erbe et al[31], this is a non-trivial task or such a solution may not even exist. One can force the numerical solver to produce the non-negative solution, for example by using MATLAB’s ‘NonNegative’ option is in the ‘odeset’ solver [32].…”

Section: Methodsmentioning

confidence: 99%

Dynamic Modeling and Stochastic Simulation of Metabolic Networks

Soliman

et al. 2018

Preprint

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17Increased technological methods have enabled the investigation of biology at nanoscale levels. 18 Nevertheless, such systems necessitate the use of computational methods to comprehend the complex 19 interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential 20 equations producing a deterministic result which neglects the intrinsic heterogeneity of biological systems. 21 More recently, stochastic modeling approaches have gained popularity with the capacity to provide more 22 realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. 23 Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, 24 reduces the computational power required to generate simulated results, while simultaneously reducing 25 expansion of errors inherent to classical deterministic approaches. Herein, we present the application of 26 queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize 27 glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and 28 pharmacological inhibition in glycolysis to further exemplify modeling capabilities. 30 Author Summary 31Computational biology is increasingly used to understand biological occurances and complex 32 dynamics. Biological modeling, in general, aims to represent a biological system with computational 33 approaches, as realistically and accurate as current methods allow. Metabolomics and metabolic systems 34 have emerged as an important aspect of cellular biology, allowing a more sentive view for understanding 35 the complex interactions occurring intracellularly as a result of normal or perturbed (or diseased) states. To 36 understand metabolic changes, many researchers have commonly used Ordianary Differential Equations to 37 produce in silico models of the in vitro system of interest. While these have been beneficial to date, 38 continuing to advance computational methods of analyzing such systems is of interest. Stochastic models 39 that include randomness have been known to produce more reaslistic results, yet the difficulty and intesive 40 time component urges additional methods and techniques to be developed. In the present research, we 3 41 propose using queueing networks as a technique to model complex metabolic systems, doing such with a 42 model of glycolysis, a core metabolic pathway. 43 44 58 are a representation of reality, aiming to accurately represent the system of study. Inclusion of all cellular 59 components indirectly or directly involved are considered far too complex to model. Consequently, 60 simplifications and assumptions must be made and often the perceived non-pivotal details, such as 61 stochasticity, omitted. Nevertheless, the accuracy and competence of the model is dependent on these 62 assumptions and simplifications. 63 Many approaches may be taken to model the dynamics of metabolic systems; importantly, the 64 categoriza...

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On a Queueing Theory Method to Simulate In-Silico Metabolic Networks

Cited by 4 publications

References 0 publications

Stochastic Simulation of Cellular Metabolism

Stochastic Simulation of Cellular Metabolism

Modeling of an immune response: Queuing network analysis of the impact of zinc and cadmium on macrophage activation

Dynamic Modeling and Stochastic Simulation of Metabolic Networks

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