Various biological processes exhibit characteristics that vary dramatically in response to different input conditions or changes in the history of the process itself. One of the examples studied here, the Ras-PKC-mitogen-activated protein kinase (MAPK) bistable pathway, follows two distinct dynamics (modes) depending on duration and strength of EGF stimulus. Similar examples are found in the behavior of the cell cycle and the immune system. A classification methodology, based on time-frequency analysis, was developed and tested on these systems to understand global behavior of biological processes. Contrary to most traditionally used statistical and spectral methods, our approach captures complex functional relations between parts of the systems in a simple way. The resulting algorithms are capable of analyzing and classifying sets of time-series data obtained from in vivo or in vitro experiments, or in silico simulation of biological processes. The method was found to be considerably stable under stochastic noise perturbation and, therefore, suitable for the analysis of real experimental data. © 2005 by The National Academy of Sciences of the USA.

Barbano, P., Antoniotti, M., Feng, J., Spivak, M., & Mishra, B. (2005). A Coherent Framework for Multi-resolution Analysis of Biological Networks with Memory: RAS pathway, Cell Cycle and Immune System. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 102(18), 6245-6250 [10.1073/pnas.0500554102].

A Coherent Framework for Multi-resolution Analysis of Biological Networks with Memory: RAS pathway, Cell Cycle and Immune System

ANTONIOTTI, MARCO;
2005

Abstract

Various biological processes exhibit characteristics that vary dramatically in response to different input conditions or changes in the history of the process itself. One of the examples studied here, the Ras-PKC-mitogen-activated protein kinase (MAPK) bistable pathway, follows two distinct dynamics (modes) depending on duration and strength of EGF stimulus. Similar examples are found in the behavior of the cell cycle and the immune system. A classification methodology, based on time-frequency analysis, was developed and tested on these systems to understand global behavior of biological processes. Contrary to most traditionally used statistical and spectral methods, our approach captures complex functional relations between parts of the systems in a simple way. The resulting algorithms are capable of analyzing and classifying sets of time-series data obtained from in vivo or in vitro experiments, or in silico simulation of biological processes. The method was found to be considerably stable under stochastic noise perturbation and, therefore, suitable for the analysis of real experimental data. © 2005 by The National Academy of Sciences of the USA.
Articolo in rivista - Articolo scientifico
RAS pathway, Cell Cycle, Immune System, Modeling, Multi-resolution analysis
English
Barbano, P., Antoniotti, M., Feng, J., Spivak, M., & Mishra, B. (2005). A Coherent Framework for Multi-resolution Analysis of Biological Networks with Memory: RAS pathway, Cell Cycle and Immune System. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 102(18), 6245-6250 [10.1073/pnas.0500554102].
Barbano, P; Antoniotti, M; Feng, J; Spivak, M; Mishra, B
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/8623
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