Team/CINVESTAV-IPN-UNAM MX/Tools.htm
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<li><a href="/Team/CINVESTAV-IPN-UNAM_MX/Labs.htm">Labs<span class="flecha">▼</span></a></li> | <li><a href="/Team/CINVESTAV-IPN-UNAM_MX/Labs.htm">Labs<span class="flecha">▼</span></a></li> | ||
<li><a href="/Team/CINVESTAV-IPN-UNAM_MX/Sponsor.htm">Sponsor<span class="flecha">▼</span></a></li> | <li><a href="/Team/CINVESTAV-IPN-UNAM_MX/Sponsor.htm">Sponsor<span class="flecha">▼</span></a></li> | ||
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<li><a href="/Team/CINVESTAV-IPN-UNAM_MX/Acknowledgments.htm">Acknowledgments<span class="flecha">▼</span></a></li> | <li><a href="/Team/CINVESTAV-IPN-UNAM_MX/Acknowledgments.htm">Acknowledgments<span class="flecha">▼</span></a></li> | ||
Revision as of 04:05, 27 September 2012
Wolfram Research Mathematica
Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about protein-protein interactions into a model. BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction.
http://demonstrations.wolfram.com/
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