Data integration, systems approach and multilevel description of complex biosystems

4th National Meeting in Chaos, Complex System and Time Series

Hernández-Lemus, Enrique^1,2

Computational Genomics Department, National Institute of Genomic Medicine (INMEGEN), Periférico Sur 4809, Col. Arenal Tepepan, Tlalpan, 14610 México City, Mexico^1

Center for Complexity Sciences, National Autonomous University of México (UNAM), Torre de Ingeniería, Circuito Escolar s/n, 04510 México City, Mexico^2

关键词: Computational biology;    Computational learning;    Dimensionality reduction;    High throughput experiments;    Integration techniques;    Non-equilibrium physics;    Probabilistic graphical models;    Quantitative approach;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/475/1/012006/pdf
DOI  :  10.1088/1742-6596/475/1/012006

来源: IOP

【 摘 要 】

Recent years have witnessed the development of new quantitative approaches and theoretical tenets in the biological sciences. The advent of high throughput experiments in genomics, proteomics and electrophysiology (to cite just a few examples) have provided the researchers with unprecedented amounts of data to be analyzed. Large datasets, however can not provide the means to achieve a complete understanding of the underlying biological phenomena, unless they are supplied with a solid theoretical framework and with proper analytical tools. It is now widely accepted that by using and extending some of the paradigmatic principles of what has been called complex systems theory, some degree of advance in this direction can be attained. We will be presenting ways in which by using data integration techniques (linear, non-linear, combinatorial, graphical), multidimensional-multilevel descriptions (multifractal modeling, dimensionality reduction, computational learning), as well as an approach based in systems theory (interaction maps, probabilistic graphical models, non-equilibrium physics) have allowed us to better understand some problems in the interface of Statistical Physics and Computational Biology.