Kinetic Monte Carlo Simulation in Biophysics and Systems Biology

Raychaudhuri, Subhadip

doi:10.5772/53709

Cited by 5 publications

(21 citation statements)

References 80 publications

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“…Monte Carlo approach has been shown to capture some of the complexities of signaling reactions such as the effect of spatial heterogeneity (Raychaudhuri 2013). Each run of our Monte Carlo simulation corresponds to apoptotic activation in a single cell, thus, Monte Carlo can capture cell-to-cell stochastic variability including inherent variability.…”

Section: Monte Carlo Simulation Of Cell Death Signalingmentioning

confidence: 99%

“…(1) a probabilistic rate constant based (implicit free energy) kinetic Monte Carlo simulation for various reaction moves such as diffusion, binding/unbinding and catalytic cleavage; (2) an explicit free-energy based model that captures clustering of ligand-bound death receptors utilizing energy-function based diffusion moves. Both of the above approaches have been utilized in previous works from this lab (Raychaudhuri 2013). At each Monte Carlo (MC) step molecules are randomly sampled N number of times, where N is the total number of molecules (either free or complexed) present in the system.…”

Section: Monte Carlo Simulation Of Cell Death Signalingmentioning

confidence: 99%

“…In the kinetic Monte Carlo part of the simulation, once a diffusion/reaction move is randomly sampled it is accepted only if a randomly generated number in [0,1] is less than the pre-defined probability constant for that particular move (otherwise the move is rejected). The detailed balance condition is satisfied through the ratio P on /P off , the probability constants for the binding and unbinding reactions, respectively (Raychaudhuri 2013). In the explicit free energy based part of the simulation, diffusion moves are accepted based on Metropolis criterion (P accept = min[1,exp(-ΔE/K B T)], where ΔE is the free energy difference between the new and the previous configuration (Newman and G.T.…”

Section: Monte Carlo Simulation Of Cell Death Signalingmentioning

confidence: 99%

“…P off (or P catalysis ) simulation parameters are obtained by multiplying k off (or k catalysis ) values by 10 -4 s (1 MC time-step). Probabilistic parameters for association reactions are determined using the following relation: P on = 10 2 × k on nM -1 s -1 (Raychaudhuri 2013). A typical simulation is run for 2 × 10 8 MC steps.…”

Section: Estimation Of Parameter Values (Used In Our Monte Carlo Simumentioning

confidence: 99%

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Death ligand concentration and the membrane proximal signaling module regulate the type 1/type 2 choice in apoptotic death signaling

Raychaudhuri

2013

Syst Synth Biol

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Apoptotic death pathways are frequently activated by death ligand induction and subsequent activation of the membrane proximal signaling module. Death receptors cluster upon binding to death ligands, leading to formation of a membrane proximal death-inducing-signaling-complex (DISC). In this membrane proximal signalosome, initiator caspases (caspase 8) are processed resulting in activation of both type 1 and type 2 pathways of apoptosis signaling. How the type 1/type 2 choice is made is an important question in the systems biology of apoptosis signaling. In this study, we utilize a Monte Carlo based in silico approach to elucidate the role of membrane proximal signaling module in the type 1/type 2 choice of apoptosis signaling. Our results provide crucial mechanistic insights into the formation of DISC signalosome and caspase 8 activation. Increased concentration of death ligands was shown to correlate with increased type 1 activation. We also study the caspase 6 mediated system level feedback activation of apoptosis signaling and its role in the type 1/type 2 choice. Our results clarify the basis of cell-to-cell stochastic variability in apoptosis activation and ramifications of this issue is further discussed in the context of therapies for cancer and neurodegenerative disorders.

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Section: Monte Carlo Simulation Of Cell Death Signalingmentioning

confidence: 99%

Section: Monte Carlo Simulation Of Cell Death Signalingmentioning

confidence: 99%

Section: Monte Carlo Simulation Of Cell Death Signalingmentioning

confidence: 99%

Section: Estimation Of Parameter Values (Used In Our Monte Carlo Simumentioning

confidence: 99%

See 2 more Smart Citations

Death ligand concentration and the membrane proximal signaling module regulate the type 1/type 2 choice in apoptotic death signaling

Raychaudhuri

2013

Syst Synth Biol

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“…Both of the above approaches have been utilized in previous works from this lab [38]. In our previous studies of apoptotic death signaling, a kinetic Monte Carlo based approach was taken which allowed straightforward estimation of probabilistic rate constants from the known diffusion and kinetic rate constants.…”

Section: Methodsmentioning

confidence: 99%

Monte Carlo Study Elucidates the Type 1/Type 2 Choice in Apoptotic Death Signaling in Healthy and Cancer Cells

Raychaudhuri

2013

Cells

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Apoptotic cell death is coordinated through two distinct (type 1 and type 2) intracellular signaling pathways. How the type 1/type 2 choice is made remains a central problem in the biology of apoptosis and has implications for apoptosis related diseases and therapy. We study the problem of type 1/type 2 choice in silico utilizing a kinetic Monte Carlo model of cell death signaling. Our results show that the type 1/type 2 choice is linked to deterministic versus stochastic cell death activation, elucidating a unique regulatory control of the apoptotic pathways. Consistent with previous findings, our results indicate that caspase 8 activation level is a key regulator of the choice between deterministic type 1 and stochastic type 2 pathways, irrespective of cell types. Expression levels of signaling molecules downstream also regulate the type 1/type 2 choice. A simplified model of DISC clustering elucidates the mechanism of increased active caspase 8 generation and type 1 activation in cancer cells having increased sensitivity to death receptor activation. We demonstrate that rapid deterministic activation of the type 1 pathway can selectively target such cancer cells, especially if XIAP is also inhibited; while inherent cell-to-cell variability would allow normal cells stay protected.

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Effective Removal of Methylene Blue From Aqueous Solution Using Metal‐Organic Framework; Modelling Analysis, Statistical Physics Treatment and DFT Calculations

et al. 2021

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Methylene blue adsorption on zeolitic imidazolate‐8 metal‐organic framework (ZIF‐8) according to basic phenomena Different Multiphysics modeling methodologies were combined to reveal understand the adsorption processes. Rather than their functional groupings, sorbents′ porosity is important, affects methylene blue adsorption, according to a statistical physics method. Quantum chemistry calculations imply the ZIF‐8 has dispersive interactions and pore properties. The examined reactive sites reveal that the for both electrophilic and nucleophilic assaults, ZIF‐8 has the same favored sites., that explains this adsorbent‘s highest performance. Finally, Monte Carlo simulations reveal Methylene blue adsorption on ZIF‐8 in the most stable energy configuration. Using XRD, IR, SEM, EDX, and nitrogen adsorption‐desorption, the prepared ZIF‐8 was characterized. The effectiveness of ZIF‐8 as an adsorbent which studied by researching MB removal from aqueous solutions as organic pollutants. The isothermal adsorption method suits the Langmuir model, and qmax=522.95 mg/g was the maximum adsorption power. The adsorption kinetics knowledge matched the pseudo‐second‐order model well. Accordingly, it is possible to use ZIF‐8 with high performance and superior stability to remediate wastewater from organic contaminants.

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Kinetic Monte Carlo Simulation in Biophysics and Systems Biology

Cited by 5 publications

References 80 publications

Death ligand concentration and the membrane proximal signaling module regulate the type 1/type 2 choice in apoptotic death signaling

Death ligand concentration and the membrane proximal signaling module regulate the type 1/type 2 choice in apoptotic death signaling

Monte Carlo Study Elucidates the Type 1/Type 2 Choice in Apoptotic Death Signaling in Healthy and Cancer Cells

Effective Removal of Methylene Blue From Aqueous Solution Using Metal‐Organic Framework; Modelling Analysis, Statistical Physics Treatment and DFT Calculations

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