Trabajos y proyectos posdoctorales


Proyectos de Genomica Computacional


Trabajos Posdoctorales



Top Simulating regulatory networks in Escherichia coli by comparative modelling of transcription factors Posdoctoral fellow: Dr. Bruno Contreras Moreira.

This project is about integrating experimental data related with gene expression and genomic data through theoretical models in Escherichia coli, using microarray data, RegulonDB and the Protein Data Bank (PDB).

The goal is to simulate transcriptional regulation systems, in which transcription factors binding to regulatory regions of genes control the timing and the intensity of their expression. Our chosen system is Escherichia coli, a bacteria for which there is a wealth of experimental data organized in databases such as RegulonDB. This database contains a repertoire of transcription factors (105 confirmed experimentally and ~200 predicted), controlling the expression of ~750 open reading frames. We hope to expand the known regulatory network of E.coli using structural knowledge, prompted by recent progress by several groups suggesting that this knowledge can be used to predict what DNA sequences are preferably bound by regulatory proteins.



Top Identification of transcription factors binding motif in eukaryotic genomes

Posdoctoral fellow: Lucia Nikolaia López Bojorquez, Ph.D.

Identifying genomic location of transcription factor binding sites, mainly in higher eukaryotic genomes, has been an great challenge. Different experimental and computational approaches, has been used to identify these sites, methods involving computational comparisons of related genomes have been particularly successful. In this regard Bioinformatic approach to transcriptional regulation is a relatively new study area.

The current project is about the identification of the potential target genes of transcription factor TTF-1/NKX2.1 in metazoan genomes. This project has implications over Central Nervous System and Hypothalamic-Hypophysis-Thyroid axis development.


Top Mining the text biobibliome for experimental evidence of predicted gene regulation in E. coli

Posdoctoral fellow: Carlos Rodríguez Penagos, Ph.D.

The core of the project is to develop suitable software tools to mine the E. coli literature to extract information useful for the curatorial efforts of the Regulon database maintained at CCG. A promising line of research is employing Language Engineering techniques to locate experimental evidence in the biobibliome (The vast full-text repositories of biomedical research articles) for entities in the database that have been inferred or predicted by analytical and computational methods.

In general, we will evaluate how Natural Language Processing tools and techniques can have an impact in the curatorial and knowledge-discovery efforts focused on the regulatory mechanisms of the E. coli model organism.

Future Work

The explosive growth of available data for computational approaches to biological research has led in the last 7 years to the development of advanced methods for mining the vast literature known as the biobibliome in order to extract useful facts from free-form text. These Information Extraction and Retrieval methods go well beyond usual keyword searching and abstract scanning by doing semantic interpretation of full texts that can identify the bioentities involved, as well as the relationships between them that are being described. This project aims at developing Computational Linguistics tools to enrich and extend the curatorial efforts for the E. coli Regulon database, as well as exploring novel methodologies and algorithms for the task of locating relevant information from textual sources. The techniques involved range from rule-based approaches to statistical and Machine Learning methods that have proven to be accurate and robust in other domains of application. We will explore, in particular, the possibility of discovering in the literature repositories experimental evidence about bioentities (mainly genes and/or proteins) that have been put forward by analytical or computational methods.


Top Bioinformatic Systems Standardization.

Posdoctoral fellow: Dr. Juan Segura Salazar.

In bioinformatics is frequent to find that a laboratory produces programs and data bases that have been done or are simultaneously being elaborated in other places in the world. With base in this situation, some laboratories at international level are working in the elaboration of communication standards that facilitate one more a more rational integration of the effort of international investigation. The same problem happens to another scale: it is frequent that a student in a laboratory generates a great amount of programs within specific projects related to his studies, which are after finished the project in conditions difficult to be reused. It is therefore common that programs are rewritten by diverse members of a laboratory. To find a systematic way to modify this situation and generate a process that transforms possible and unstable program into stable and re-usable program will be very useful.


Top Genómica funcional en E. coli : Caracterización experimental y modelación de la red de regulación transcripcional

Posdoctoral fellow: Dr. Luis Gerardo Treviño Quintanilla. Nuestra línea de investigación se centra en caracterizar los sitios de unión, en las regiones promotoras de todo el genoma, de reguladores transcripcionales involucrados en la utilización de fuentes de carbono, fuentes de nitrógeno y crecimiento en aerobiosis/anaerobiosis en E. coli K-12 mediante la inmunoprecipitación de un complejo compuesto por fragmentos de DNA y el regulador transcripcional de interés marcado con un epítope y microarreglos. Además, estamos co labo rando con otros investigadores de nuestro grupo en la realización de proyectos experimentales-teóricos sobre la regulación transcripcional mediada por otros factores transcripcionales y/o factores sigma y su participación en la regulación jerárquica en E. coli . Por ultimo, participamos en la consolidación de un labo ratorio experimental en el departamento de Genómita Computacional del CCG dirigido por el Dr. Julio Collado Vides mediante la escritura de proyecto aprobado por la UNAM y el CONACYT para la realización y ampliación de la presente línea de investigación.