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Prototype of integrated pseudo-dynamic crosstalk network for cancer molecular mechanism.

Wu SJ, Chen WY, Chou CH, Wu CT.

Math Biosci. 2013 May;243(1):81-98.

Department of Electrical Engineering, Dayeh University, Chang-Hwa, Taiwan, ROC. [email protected]

 

Abstract

In this study, we attempted to solve two important challenges in systems biology. First, although the Michaelis-Menten (MM) model provides local kinetic information, it is hard to generalize MM models to model a large system because increasingly large amounts of experimental data are necessary for the parameter identification. In addition, it is not possible to develop an MM model that provides information about the strength of the interactions in the system. Second, although the dynamic simulation of various signal transduction pathways is important in cancer research, it is impossible to theoretically derive a mathematical model to describe the cancer molecular mechanism. Predictive computational approaches can be used to analyze the dynamics of a system and to determine the dysfunction of a regulatory process. In this report, we first propose a pseudo-dynamic pathway to describe protein interactions in an MM system. We then discuss the dynamic behavior of two large-scale systems (antigrowth-signal-induced cell cycle and apoptotic-signal-transduction mechanism). These two systems were constructed through the in-series and organic integration, respectively, of MM modules with Petri net modules; moreover, more than 30% additional reactions were added during this integration step. We then described an extremely large multi-stream system (growth signal transduction); however, the analysis of this system to obtain dynamic predictions is critical but appears impossible. Thus, we introduced a fuzzy concept that can be used to develop a physically realizable model prototype. In the future, through step-by-step in vivo modifications, researchers will be able to develop a complete model of cancer metabolism to achieve accurate predictions.

Copyright © 2013 Elsevier Inc. All rights reserved.

 

 

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