Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. The expression of many genes may, for instance, be determined by measuring mRNA levels with multiple techniques. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in various phenomena. Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. All of these techniques produce large amounts of data. Data Analysis involves developing statistical tools to analyze these high-throughput data.

	Data Analysis The amount of data produced in our labs can be daunting. To ease the process of giving sense tthis amount of data we do Data Analysis using statistics and computer scinece in general. As an example, we statistically analyze RNAseq and mass spectromety data serviced by partner labs. Our confocal microscopy produces several thousands of images every months that need Image Analysis to support, for instance, drug screenings. Our CyTOF2 produces huge volumes of data that we analyze through algorithms and statistics. We also work to offer to wet scientists easy tools to process their data in general.
Our group works in close contact with experimental biologists. A great deal of our work is related to data analysis in general, from expression levels to statistical analysis in general. We work side by side with biologists to create models. Modeling involves calculus and analysis at various stages, as illustrated in the figure on the right. A-priori data collection and analysis, model creation, parameter estimation and model diagnostics are just examples of the analyses we do.
Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. The expression of many genes may, for instance, be determined by measuring mRNA levels with multiple techniques. Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in various phenomena. Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. All of these techniques produce large amounts of data. Data Analysis involves developing statistical tools to analyze these high-throughput data.