Computer Simulator for Biological Sequence Analysis

We will provide a computer software medium able to represent and analyze a number ofproblems in computational biology. The simulation tool will allow biologists to design models of proteinfamilies, compute multiple alignments, perform simulations of protein interactions, analyze DNA (or RNA)sequences, etc. The methodology consists in utilizing recent progress in machine learning algorithmsto extract pertinent information from biological data. The interest of this approach stems from therichness and availability of sequence data bases and from the lack of complete theories covering all theunderlying biological mechanisms. Specifically, we use learning systems such as Neural Networks orHidden Markov Models (HMMs) to parse DNA sequences and to construct models of protein familieswith clear medical interest. These families include immunoglobulins, kinases (involved in the regulationof basic cellular processes), G-coupled receptors (involved in the transduction of signals carried byhormones and neurotransmitters), growth factors, and several retroviral proteins (such as HIV membraneproteins). These models provide new solutions for several computational problems such as multiplesequence alignments, motif detections, data base searches, protein classifications and genome parsing.Direct commercial applications of such software tools are immediate in biological laboratories and biotechindustries.