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Modeling signaling pathways in cancer research

Grant number: 08/10011-1
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): May 01, 2009
Effective date (End): April 30, 2010
Field of knowledge:Interdisciplinary Subjects
Principal researcher:Eduardo Jordao Neves
Grantee:Michel Mozinho dos Santos
Home Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:06/03227-2 - Gene expression profile of tumors from the upper digestive track: from tumor biology to new diagnostic tools, AP.TEM


Rarely a biological function can be attributed to only one molecule. Instead, biomolecular entities interact in time and space, on scales ranging, respectively, from microseconds to hours and from cellular compartment to an organism as a whole. The understanding of network dynamics has been cogitated as a key challenge for biology in this century. Mathematical model analysis of biological networks, through dynamical systems tools, has contributed to understand the basics principles that drive evolutionarily the organization of biological circuits. However, the currently predominant approach to modeling is often subject to arbitrary choices and with very little experimental support in vivo. In this sense, it is proposed the use of an alternative modeling approach, recently developed by our group, the density-profile and the confrontation between generated models and experimental data. For example, time-series microarrays can capture the dynamic behavior of thousands of genes at the same time and the generated data can be compared with predictions of biological processes models. Microarrays have been employed in cancer research, particularly by the research group to which this project is integrated. In cancer, takes place the dysfunctions of various signaling pathways that can lead to multiple scales problems: intracellular, microenvironment and tissue. In this project, we want to collaborate in understand of cancer related biological process. (AU)

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