Team/CINVESTAV-IPN-UNAM MX/Modelling.htm

Importance of Modeling and Simulation

 

Characterization of biological systems has reached an unparalleled level of detail. To organize this detail and arrive at a better fundamental understanding of life processes, it is essential   that powerful conceptual tools from mathematics and computer science be applied to the frontier problems in biology.

Modeling of biological systems is evolving into an important partner of experimental work. They attempt to predict and understand the behavior of complex biological systems before they are actually created. Also models can help to simplify the complexity of data and interactions involved into a more concise form with some measure of predictive ability. This can provide valuable insights into the working and general principles of organization of biological systems. Also it may suggest novel experiments for testing hypotheses, based on the modeling experiences.

Some of the tools that we used during the modeling process.

Construction of Model

Modeling the intercellular signaling process during regulation of PS genes expression in BioNetGEn.

Differential equations modeling the interactions between AppA and PpsR.
Modeling Steady State

Cellular automata approaches to biological modeling.

References used through this section.

1. Oh JI, Kaplan S. (2001) Generalized approach to the regulation and integration of gene expression. Mol Microbiol.

1. Zeilstra-Ryalls JH, Kaplan S. (1995) Aerobic and anaerobic regulation in Rhodobacter sphaeroides 2.4.1: the role of the fnrL gene. J Bacteriol.

 

1. Rakesh Pandey, Dietrich Flockerz. (2011) Modeling the Light- and Redox-Dependent Interaction of PpsR/AppA in Rhodobacter sphaeroides. Cell Press.

1. Shinji Masuda and Carl E. Bauer. (2002) AppA Is a Blue Light Photoreceptor that Antirepresses Photosynthesis Gene Expression in Rhodobacter sphaeroides. Cell Press.

1. Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics.