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| An epigenetic landscape (from Epigenetics Unraveled). |
Analyzing rare events in biology.
Changes in cellular phenotype can often be tightly correlated with changes in biochemistry. The macroscopic (phenotypical) and the microscopic (biochemical) descriptions of these phenotype switching events can be unified through the theoretical framework of epigenetic landscapes (EL). In approximate terms, the EL of a cellular system is a map that describes every possible system state (expressed in terms of a count of relevant DNA, proteins, etc.) and the probability of the system being in each of these states. An EL can be used to completely describe the dynamics of its associated system, allowing for a deep level of understanding and, potentially, control of the system. It is possible to take the list of chemical species and reactions involved in a cellular process and, using theory and modeling, generate the corresponding EL. Unfortunately, the computer time required to generate an EL using the standard stochastic simulation method, called Brute Force Sampling (BFS), makes it difficult to generate the EL of even a relatively simple multi-state system.
Changes in cellular phenotype can often be tightly correlated with changes in biochemistry. The macroscopic (phenotypical) and the microscopic (biochemical) descriptions of these phenotype switching events can be unified through the theoretical framework of epigenetic landscapes (EL). In approximate terms, the EL of a cellular system is a map that describes every possible system state (expressed in terms of a count of relevant DNA, proteins, etc.) and the probability of the system being in each of these states. An EL can be used to completely describe the dynamics of its associated system, allowing for a deep level of understanding and, potentially, control of the system. It is possible to take the list of chemical species and reactions involved in a cellular process and, using theory and modeling, generate the corresponding EL. Unfortunately, the computer time required to generate an EL using the standard stochastic simulation method, called Brute Force Sampling (BFS), makes it difficult to generate the EL of even a relatively simple multi-state system.
The Forward Flux Sampling (FFS) method, which belongs to a larger family of Enhanced Sampling methods, was previously developed by ten Wolde and coworkers to address this computational limitation. Although FFS can speed up EL simulations by multiple orders of magnitude, there is a commensurate increase in the complexity of simulation setup. Specifically, there are a number of novel free parameters that the simulation user must specify. Each of these parameters has a significant and indirect influence on the precision of an FFS simulation’s final results. It is, therefore, desirable to develop a version of the FFS algorithm in which the relationship of parameter choice to error is made explicit.
Max Klein and Dr. Elijah Roberts from Johns Hopkins Biophysics have designed and implemented a supercomputer-compatible version of the Forward Flux Enhanced Sampling method for stochastic simulation of biochemical networks. Their version greatly simplifies parameter choice and simulation setup relative to the base Forward Flux algorithm. A user of the program needs only to specify the desired level of precision error. The program will determine a set of parameters to use that will achieve this target error level while optimizing the simulation run time. The Forward Flux implementation has been tested and verified in terms of its ability to calculate switching rate constants and epigenetic landscapes. A preview build of the code is currently available here.
Related Articles:
[1] Dickson, Alex, and Aaron R. Dinner. "Enhanced sampling of nonequilibrium steady states." Annual review of physical chemistry 61 (2010): 441-459.
[2] Allen, Rosalind J., Daan Frenkel, and Pieter Rein ten Wolde. "Forward flux sampling-type schemes for simulating rare events: Efficiency analysis." The Journal of chemical physics 124.19 (2006): 194111.
[3] Becker, Nils B., and Pieter Rein ten Wolde. "Rare switching events in non-stationary systems." The Journal of chemical physics 136.17 (2012): 174119.
[4] Gardner, Timothy S., Charles R. Cantor, and James J. Collins. "Construction of a genetic toggle switch in Escherichia coli." Nature 403.6767 (2000): 339-342.
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Max C. Klein received his B.A. in Physics, from Reed College, Oregon in 2013. After graduation, he joined the Program in Molecular Biophysics at Johns Hopkins University. Currently, with Dr. Elijah Roberts from the Biophysics Department, Max is developing new methods to simulate decision making in cells using a hybrid multi-CPU/multi-GPU computational architecture.
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