Gibson, Michael A. and Bruck, Jehoshua (1998) An Efficient Algorithm for Generating Trajectories of Stochastic Gene Regulation Reactions. California Institute of Technology . (Unpublished) https://resolver.caltech.edu/CaltechPARADISE:1998.ETR026

PDF (Adobe PDF (2.1MB))
See Usage Policy. 2056Kb  

Postscript
See Usage Policy. 848Kb 
Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechPARADISE:1998.ETR026
Abstract
Systems of weakly coupled chemical equations occur in gene regulation and other biological systems. For small numbers of molecules (as in a small cell), the usual differential equations approach to chemical kinetics must be replaced with a stochastic approach. To deal with this kind of system, one generates trajectories through stochastic phase space. By generating a large enough number of trajectories, one can understand the statistics of the behavior of the complex, nonlinear system. The algorithms for dealing with sparsely connected stochastic processes are not as advanced as those for sparse deterministic processes. In particular. the existing algorithm of choice for generating trajectories, which is not optimized in any way for sparseness, is O(rE), where r is the number of reactions and E is the number of reaction events in the trajectory. \Ye present two algorithms of O(r + Elogr), one of which is a simple extension of the existing algorithm, and the other of which is more subtle. The latter is more easily extended to include stochastic processes of different types. We apply our faster algorithm to a model of bacteriophage lambda and are able to run the same calculations on a cluster of desktop workstations that previously required a supercomputer. This allows us to run more complicated calculations than could be done previously. As an example of this, we analyse the sensitivity of the lambda model to the values of several of its parameters. We find that the model is relatively insensitive to changes in the translation rate, protein dimerization rates and protein degradation rates; is somewhat sensitive to the transcription rate. and is extremely sensitive to the average number of proteins per mRNA transcript.
Item Type:  Report or Paper (Technical Report)  

Related URLs: 
 
ORCID: 
 
Group:  Parallel and Distributed Systems Group  
Record Number:  CaltechPARADISE:1998.ETR026  
Persistent URL:  https://resolver.caltech.edu/CaltechPARADISE:1998.ETR026  
Usage Policy:  You are granted permission for individual, educational, research and noncommercial reproduction, distribution, display and performance of this work in any format.  
ID Code:  26047  
Collection:  CaltechPARADISE  
Deposited By:  Imported from CaltechPARADISE  
Deposited On:  03 Sep 2002  
Last Modified:  22 Nov 2019 09:58 
Repository Staff Only: item control page