Mechanistic-enriched models: integrating transcription factor networks and metabolic deregulation in cancer.
BACKGROUND
In the present paper we will examine methodological frameworks to study complex genetic diseases (e.g. cancer) from the stand point of theoretical-computational biology combining both data-driven and hypothesis driven approaches. Our work focuses in the apparent counterpoint between two formal approaches to research in natural science: data- and hypothesis-driven inquiries. For a long time philosophers have recognized the mechanistic character of molecular biology explanations. On these grounds we suggest that hypothesis and data-driven approaches are not opposed to each other but that they may be integrated by the development of what we call enriched mechanistic models.


METHODS
We will elaborate around a case study from our laboratory that analyzed the relationship between transcriptional de-regulation of sets of genes that present both transcription factor and metabolic activity while at the same time have been associated with the presence of cancer. The way we do this is by analyzing structural, mechanistic and functional approaches to molecular level research in cancer biology. Emphasis will be given to data integration strategies to construct new explanations.


RESULTS
Such analysis has led us to present a mechanistic-enriched model of the phenomenon. Such model pointed out to the way in which regulatory and thermodynamical behavior of gene regulation networks may be analyzed by means of gene expression data obtained from genome-wide analysis experiments in RNA from biopsy-captured tissue. The foundations of the model are given by the laws of thermodynamics and chemical physics and the approach is an enriched version of a mechanistic explanation.


CONCLUSIONS
After analyzing the way we studied the coupling of metabolic and transcriptional deregulation in breast cancer, we have concluded that one plausible strategy to integrate data driven and hypothesis driven approaches is by means of resorting to fundamental and well established laws of physics and chemistry since these provide a solid ground for assessment.
Computational Genomics, National Institute of Genomic Medicine, Periférico Sur 4809, México City, 14610, México. ehernandez@inmegen.gob.mx.|Center for Complexity Sciences, National Autonomous University of México, Ciudad Universitaria, México City, 04510, México. ehernandez@inmegen.gob.mx.
Enrique
E
Hernández-Lemus
Laboratorio de Redes, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, México City, 04510, México. jmario.siqueiros@iimas.unam.mx.
J Mario
JM
Siqueiros-García
D019295:Computational Biology; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D008958:Models, Molecular; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors