Abstract: The emergence of high-throughput sequencing has brought substantial advances in human population and cancer genomics research. The current pace of advance of this technology made possible to assay with high depth of coverage the DNA sequence and the transcriptomes of hundreds of samples. The drop in the costs allowing the sequencing of increasingly larger cohorts creates an unprecendent explosion of data. Several large-scale projects are dedicated to the comprehensive sequencing, characterization and analysis of the genomic changes in different types of tumors, cell lines or tissues. In this talk some of the recent findings from projects that have sequenced the transcriptome from hundreds of individuals in a single or across multiple tissues will be described. Their relevance for population genomics and biomedical research as well as the applied methodology will be discussed.

Abstract: Metabolic Engineering (ME) deals with designing organisms with enhanced capabilities regarding the productivities of desired compounds. This field has received increasing attention within the last few years due to the extraordinary growth in the adoption of white or industrial biotechnological processes for the production of bulk chemicals, pharmaceuticals, food ingredients and enzymes, among other products.
Many different approaches have been used to aid in ME efforts that take available models of metabolism together with mathematical tools and/ or experimental data to identify metabolic bottlenecks or targets for genetic engineering. Our conceptual framework in the development of tools for in silico ME relies on three layers: accurate mathematical models (in our case constraint-based metabolic models), good simulation methods (e.g. steady state simulations with flux balance analysis) and robust strain optimization algorithms based on metaheuristics (e.g. Evolutionary Algorithms, Simulated Annealing).
This framework gave rise to the OptFlux platform, an open-source, user-friendly and modular software aimed at being the reference computational platform for ME applications.

Abstract: Computational Biology holds the promise to answer some of the most fundamental questions about life. Indeed, there are a deluxe of biological data and computational models available, but, despite that, the real value of those has been hardly attained. In this presentation the author will share his personal experience in developing computational models for biological problems and the struggles to make them usable in real world contexts.
We will focus on the use of time series data to reconstruct gene signaling networks. Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages. An improved method that integrates time series and static datasets to reconstruct dynamic models of host response to stimulus will be presented. It uses an Integer Programming formulation to select a subset of pathways that, together, explain the observed dynamic responses. Applying to study human response to HIV-1 led to accurate reconstruction of several known regulatory and signaling pathways and to novel mechanistic insights.