TY - JOUR
T1 - ChemChains
T2 - A platform for simulation and analysis of biochemical networks aimed to laboratory scientists
AU - Helikar, Tomáš
AU - Rogers, Jim A.
N1 - Funding Information:
We thank Jeff Hamilton for creating the initial version of ChemChains, Philip Greiss for memory optimization, and Sean McClenathan for final code and performance optimization. This work was supported by a grant from the National Institutes of Health (GM067272, to J.A.R), and generous gifts from Patrick J. Kerrigan and Donald F. Dillon.
PY - 2009
Y1 - 2009
N2 - Background: New mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers. Results: We have developed a Boolean network-based simulation and analysis software tool, ChemChains, which combines the advantages of the parameter-free nature of logical models while providing the ability for users to interact with their models in a continuous manner, similar to the way laboratory biologists interact with laboratory data. ChemChains allows users to simulate models in an automatic fashion under tens of thousands of different external environments, as well as perform various mutational studies. Conclusion: ChemChains combines the advantages of logical and continuous modeling and provides a way for laboratory biologists to perform in silico experiments on mathematical models easily, a necessary component of laboratory research in the systems biology era.
AB - Background: New mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers. Results: We have developed a Boolean network-based simulation and analysis software tool, ChemChains, which combines the advantages of the parameter-free nature of logical models while providing the ability for users to interact with their models in a continuous manner, similar to the way laboratory biologists interact with laboratory data. ChemChains allows users to simulate models in an automatic fashion under tens of thousands of different external environments, as well as perform various mutational studies. Conclusion: ChemChains combines the advantages of logical and continuous modeling and provides a way for laboratory biologists to perform in silico experiments on mathematical models easily, a necessary component of laboratory research in the systems biology era.
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U2 - 10.1186/1752-0509-3-58
DO - 10.1186/1752-0509-3-58
M3 - Article
C2 - 19500393
AN - SCOPUS:67650351960
SN - 1752-0509
VL - 3
JO - BMC systems biology
JF - BMC systems biology
M1 - 58
ER -