Information–theoretical analysis of gene expression data to infer transcriptional interactions
K. Baca–López ª, b, E. Hernández–Lemus ª, c, and M. Mayorga b
ª Departamento de Genómica Computacional, Instituto Nacional de Medicina Genómica, Periférico Sur No. 4124, Torre Zafiro 2, Piso 6 Col. Ex Rancho de Anzaldo, Álvaro Obregón 01900, México, D.F., México.
b Facultad de Ciencias, Universidad Autónoma del Estado de México, Av. Instituto Literario 100 Ote. Centro 50000, Toluca, Estado de México, México.
c Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Torre de Ingeniería, Piso 6, Circuito Escolar s/n Ciudad Universitaria, Coyoacán, 04510, México, D.F., México.
]]> Recibido el 4 de agosto de 2009
Abstract
The majority of human diseases are related with the dynamic interaction of many genes and their products as well as environmental constraints. Cancer (and breast cancer in particular) is a paradigmatic example of such complex behavior. Since gene regulation is a non–equilibrium process, the inference and analysis of such phenomena could be done following the tenets of non–equilibrium physics. The traditional programme in statistical mechanics consists in inferring the joint probability distribution for either microscopic states (equilibrium) or mesoscopic–states (non–equilibrium), given a model for the particle interactions (e.g. the potentials). An inverse problem in statistical mechanics, in the other hand, is based on considering a realization of the probability distribution of micro– or meso–states and used it to infer the interaction potentials between particles. This is the approach taken in what follows. We analyzed 261 whole–genome gene expression experiments in breast cancer patients, and by means of an information–theoretical analysis, we deconvolute the associated set of transcriptional interactions, i.e. we discover a set of fundamental biochemical reactions related to this pathology. By doing this, we showed how to apply the tools of non–linear statistical physics to generate hypothesis to be tested on clinical and biochemical settings in relation to cancer phenomenology.
Keywords: Cancer genomics; information theory; molecular networks.
Resumen
La mayoría de las enfermedades humanas están relacionadas con la interacción de muchos genes, y con condicionantes ambientales, lo que las hace fenómenos complejos. El análisis de las interacciones bioquímicas relacionadas se basa frecuentemente en la consideración de las relaciones de regulación genética. Puesto que la regulación genética es un proceso fuera del equilibrio, la inferencia y el análisis de ésta puede hacerse siguiendo los principios de la termodinámica irreversible y la mecánica estadística fuera del equilibrio. El enfoque tradicional de la mecánica estadística es inferir la distribución de probabilidad conjunta para los estados del sistema en términos de un modelo para las interacciones. Un problema inverso en mecánica estadística consiste en considerar una realización de la distribución de probabilidad y emplearla para inferir las interacciones entre las partículas. Tomamos este enfoque para analizar 261 experimentos de expresión de mRNA de genoma completo, en pacientes con cáncer de mama y, a través de una medida basada en la teoría de la información descubrir el conjunto de interacciones transcripcionales asociadas. Mostramos como aplicar las herramientas de la física estadística no–lineal para generar hipótesis (es decir, el conjunto de interacciones inferidas) que pueden ser probadas en ensayos clínicos y bioquímicos con relación a la fenomenología del cáncer.
Descriptores: Genómica del cáncer; teoría de la información; redes moleculares.
]]>PACS: 87.10.Vg; 87.16.Yc; 87.18.Cf; 89.75.Hc; 89.70.Cf
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