1 / 1

Computational Biology

In-vivo. In-Silico.

pamela-campbell
Download Presentation

Computational Biology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. In-vivo In-Silico Using the Potts Model, a statistical mechanics simulation technique based on cellular automata, scientists develop in-silico computational models based on in-vivo observations about developmental processes. The OSL researches high-performance software and visualization systems used for Potts Model simulations. // Acceptance based on probability doubleprob = acceptanceFunction->accept(temp, change); if (prob >= 1 || rand->getRatio() < prob) { // Accept the change energy += change; cellField->set(neighbor, cell); flips++; } The Potts Model effectively models many complex behaviours of biological systems, from cell sorting and chemotaxis to various lifecycle stages of Dictyostelium and chicken limb development. Computational Biology Dictyostelium Simulation Bioinformatics Vector processors in modern computers enable the direct comparison of large genomes. However, obtaining the results is only the first challenge. Presenting the results in a meaningful way to scientists is difficult. E-Coli …AGGATGACCAGATAGGAGTGACCGATTACCGGATAGC… Human …AGGATGACCAGATAGGAGTGACCGATTACCGGATAGC… Rat …AGGATGACCAGATAGGAGTGACCGATTACGGGATAGC… Salamander …AGGATGACCAGATAGGAGTGACCGATTA---GATAGC… Large genomes are compared using multiple vector processors void GenomeCompare(unsigned char *data, long len, unsigned char *result) { // “Diagnol” sum of all the values in data long i = 0; vector unsigned char score, score1, score2, vperm, newsum; newsum = vec_splat_u8(0); // create a constant for(i = 0; i < len - 16; i++) { // Load each vector if((i & 0x0000000f) == 0) { // aligned case score = vec_ld(0, &(data[i])); } newsum = vec_add(score, newsum); } vec_st(newsum, 0, result); // aligned store return; } • This research focuses on: • Studying the use of high-performance techniques on common bioinformatics algorithms • Using large format display walls and high-resolution (> 200 dpi) displays to present the results • Developing techniques for abstracting vector and cluster level parallelism to make these techniques accessible to scientists The results are rendered to high-resolution formats

More Related