Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors

Lee, Dongheon; Mohr, Alec; Kwon, Joseph; Wu, Hung‐Jen

doi:10.1016/j.compchemeng.2018.08.011

Cited by 21 publications

(27 citation statements)

References 65 publications

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“…In order to simulate the dynamics of a signaling pathway of interest, prior understandings of the system are formulated into a system of nonlinear ODEs as its first-principle model. Since the first-principle model incorporates underlying mechanisms of the system, the model can be used to predict the system dynamics under new conditions and infer unmeasured model states' dynamics once the model is properly calibrated by experiments [2][3][4]86]. However, the development of such a first-principle model is nontrivial, and one of the largest bottlenecks in the model development process is lack of fundamental knowledge that may lead to the inaccurate formulation of a first-principle model.…”

Section: Discussionmentioning

confidence: 99%

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling

2020

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Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

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Section: Discussionmentioning

confidence: 99%

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling

2020

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“…The existing literature shows that KMC simulations are powerful and versatile enough to tackle the above problem 12‐17 . This is possible because KMC describes spatio‐temporal evolution of chemical and biological systems at a reasonable computational cost 18‐22 . Specifically, KMC models have been used to study effect of temperature, 23 self‐diffusion, 24 passive layer formation, 25 and pulse charging strategy 26 on dendrite growth.…”

Section: Introductionmentioning

confidence: 99%

A computational approach to characterize formation of a passivation layer in lithium metal anodes

et al. 2020

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Li metal anode is the “Holy Grail” material of advanced Lithium‐ion‐batteries (LIBs). However, it is plagued by uncontrollable dendrite growth resulting in poor cycling efficiency and short‐circuiting of batteries. This has spurred a plethora of research to understand the underlying mechanism of dendrite formation. While experimental studies suggest that there are complex physical and chemical interactions between heterogeneous solid‐electrolyte interphase (SEI) and dendrite growth, most of the studies do not reveal the mechanisms triggering these interactions. To deal with this knowledge gap, we propose a multiscale modeling framework which couples kinetic Monte Carlo and Molecular Dynamics simulations. Specifically, the model has been developed to account for (a) heterogeneous SEI, (b) dendrite‐SEI interactions, and (c) effect of electrolyte on Li electrodeposition and potential dendrite formation. This allows the proposed computational model to be extended to various electrolytes and SEI species and generate results consistent with previous experimental studies.

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“…First, an appropriate modeling framework has to be selected. Previous studies have used stochastic kinetic models to capture the intrinsic source of the cellular heterogeneity, which results from stochastic fluctuations in reaction kinetics . Generally, it is computationally expensive to use a stochastic kinetic model to simulate an intracellular biochemical reaction dynamics of a cell population, which makes the stochastic kinetic modeling approach not suitable to simulate the dynamics of a population of cells, as the calibration procedure for a cell‐population model usually involves thousands of the model simulations .…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies 7,[9][10][11] have used stochastic kinetic models 12 to capture the intrinsic source of the cellular heterogeneity, which results from stochastic fluctuations in reaction kinetics. 4,6,13,14 Generally, it is computationally expensive to use a stochastic kinetic model to simulate an intracellular biochemical reaction dynamics of a cell population, 15,16 which makes the stochastic kinetic modeling approach not suitable to simulate the dynamics of a population of cells, as the calibration procedure for a cell-population model usually involves thousands of the model simulations. 17,18 Also, the stochastic kinetic modeling approach usually does not consider the extrinsic source of the cellular heterogeneity (i.e., the fundamental differences between cells that contribute to the cell-to-cell variability).…”

Section: Introductionmentioning

confidence: 99%

Identification of cell‐to‐cell heterogeneity through systems engineering approaches

Lee¹,

Jayaraman²,

Kwon³

2020

AIChE Journal

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Cells in a genetically homogeneous cell‐population exhibit a significant degree of heterogeneity in their responses to an external stimulus. To understand origins and importance of this heterogeneity, individual‐based population model (IBPM), where parameters follow probability density functions (PDFs) instead of being constants, has been previously developed. However, parameter identification for an IBPM is challenging as estimating PDFs is computationally expensive. Also, because of experimental limitations and nonlinearity of models, not all parameters' PDFs are identifiable. Motivated by the above considerations, a new methodology is proposed in this study. First, a subset of parameters whose PDFs is identifiable are determined through sensitivity analysis, and only these PDFs are estimated. Second, an artificial neural network model is developed to find an empirical relation between these parameter and output PDFs to reduce computational costs of the parameter identification. The proposed approach is validated by estimating PDFs of parameters of a tumor necrosis factor‐α signaling model.

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Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors

Cited by 21 publications

References 65 publications

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling

A computational approach to characterize formation of a passivation layer in lithium metal anodes

Identification of cell‐to‐cell heterogeneity through systems engineering approaches

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