Stochastic Simulation of Cellular Metabolism

Clement, Emalie J.; Schulze, Thomas; Soliman, Ghada A.; Wysocki, Beata J.; Davis, Paul H.; Wysocki, Tadeusz A.

doi:10.1109/access.2020.2986833

Cited by 11 publications

(11 citation statements)

References 46 publications

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“…The purpose for the use of variation is to represent the concentration fluctuations in the cell. For this model to reflect the flow of metabolites in the cell as accurately as possible, the so-called “balancing flow” was used 1 , 2 . This feature allows for proper simulation of metabolite flow depending on the current needs of the cell (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Probability of substrate’s increment departure from each queue depends on the current concentration of the substrates and the kinetic constants of the reaction causing that departure. Every queue uses its individual Michaelis–Menten kinetic equation with kinetic constants normalized according to the method based on the formula described in 1 , to determine the likelihood that in this time step the reaction occurs. Since the reaction rates depend on the current concentration of molecules that change from step to step, the resulting inhomogeneous Poisson process implements the feedback loop, which results in a system with memory.…”

Section: Methodsmentioning

confidence: 99%

“…The acquired data can be used to create simulation models of biochemical reactions and entire metabolic pathways. Queueing theory can successfully model metabolic processes, as demonstrated by the example of the glycolysis pathway 1 and Krebs cycle 2 . The preparation of an accurate model simulating the course of PPP could potentially reduce the time needed for drug testing and reduce the number of laboratory animals on which new drugs are tested 3 .…”

Section: Introductionmentioning

confidence: 99%

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Queueing theory model of pentose phosphate pathway

Kloska

Pałczyński

Marciniak

et al. 2022

Sci Rep

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Due to its role in maintaining the proper functioning of the cell, the pentose phosphate pathway (PPP) is one of the most important metabolic pathways. It is responsible for regulating the concentration of simple sugars and provides precursors for the synthesis of amino acids and nucleotides. In addition, it plays a critical role in maintaining an adequate level of NADPH, which is necessary for the cell to fight oxidative stress. These reasons prompted the authors to develop a computational model, based on queueing theory, capable of simulating changes in PPP metabolites’ concentrations. The model has been validated with empirical data from tumor cells. The obtained results prove the stability and accuracy of the model. By applying queueing theory, this model can be further expanded to include successive metabolic pathways. The use of the model may accelerate research on new drugs, reduce drug costs, and reduce the reliance on laboratory animals necessary for this type of research on which new methods are tested.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Queueing theory model of pentose phosphate pathway

Kloska

Pałczyński

Marciniak

et al. 2022

Sci Rep

Self Cite

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“…Likewise, in a cell, signaling molecules are passed on, activating subsequent elements (proteins) of the cascade. To date, the queueing theory approach has been used to model simple metabolic networks [ 17 ], metabolic pathways such as glycolysis [ 18 ] and the Krebs cycle [ 19 ]. The presented model is an extension of the work [ 20 ] to include loops related to the regulation of cellular metabolism by mTOR complexes and mTORC1 regulation via GAPDH availability, or more precisely–‘occupancy’.…”

Section: Introductionmentioning

confidence: 99%

Queueing theory model of mTOR complexes’ impact on Akt-mediated adipocytes response to insulin

et al. 2022

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A queueing theory based model of mTOR complexes impact on Akt-mediated cell response to insulin is presented in this paper. The model includes several aspects including the effect of insulin on the transport of glucose from the blood into the adipocytes with the participation of GLUT4, and the role of the GAPDH enzyme as a regulator of mTORC1 activity. A genetic algorithm was used to optimize the model parameters. It can be observed that mTORC1 activity is related to the amount of GLUT4 involved in glucose transport. The results show the relationship between the amount of GAPDH in the cell and mTORC1 activity. Moreover, obtained results suggest that mTORC1 inhibitors may be an effective agent in the fight against type 2 diabetes. However, these results are based on theoretical knowledge and appropriate experimental tests should be performed before making firm conclusions.

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“…Queueing theory is the mathematical study of waiting lines [1][2][3][4]. Ranging from the all familiar supermarket and bank, to call centers [5,6], airplane boarding [7][8][9], telecommunication and computer systems [10][11][12][13][14][15], production lines and manufacturing [16][17][18][19], enzymatic and metabolic pathways [20][21][22][23][24][25][26][27], gene expression [28][29][30][31][32][33][34], and in transport phenomena [35][36][37][38][39][40][41][42][43][44][45][46], waiting lines and queues appear ubiquitously and play a central role in our lives. While the teller at the bank works at a (roughly) constant rate, other servers, e.g., computer systems [14], and molecular machines like enzym...…”

Section: Introductionmentioning

confidence: 99%

Mitigating long queues and waiting times with service resetting

Bonomo,

Pal,

Reuveni

2021

Preprint

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What determines the average length of a queue which stretches in front of a service station? The answer to this question clearly depends on the average rate at which jobs arrive to the queue and on the average rate of service. Somewhat less obvious is the fact that stochastic fluctuations in service and arrival times are also important, and that these are a major source of backlogs and delays. Strategies that could mitigate fluctuations induced delays are in high demand as queue structures appear in various natural and man-made systems. Here we demonstrate that a simple service resetting mechanism can reverse the deleterious effects of large fluctuations in service times, thus turning a marked drawback into a favourable advantage. This happens when stochastic fluctuations are intrinsic to the server, and we show that the added feature of service resetting can then dramatically cut down average queue lengths and waiting times. While the analysis presented herein is based on the M/G/1 queueing model where service is general but arrivals are assumed to be Markovian, Kingman's formula asserts that the benefits of service resetting will carry over to queues with general arrivals. We thus expect results coming from this work to find widespread application to queueing systems ranging from telecommunications, via computing, and all the way to molecular queues that emerge in enzymatic and metabolic cycles of living organisms.

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Stochastic Simulation of Cellular Metabolism

Cited by 11 publications

References 46 publications

Queueing theory model of pentose phosphate pathway

Queueing theory model of pentose phosphate pathway

Queueing theory model of mTOR complexes’ impact on Akt-mediated adipocytes response to insulin

Mitigating long queues and waiting times with service resetting

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