Simulation of tumor cord growth where conversion of the tumor to glycolytic (anaerobic) metabolism takes place under hypoxia. This video shows evolution of the region where the aerobic cells suffer from hypoxia (ATP deficit) as well as the limit where the glycolytic cells start suffering too. This video reflects work in progress and may be different from the final results.

This is a simulation of tumor cord growth, where cells suffer from hypoxia (energy deficit shown with color). The tumor grows along the blood vessel (coincides with x-axis). Red line shows the position of the tumor–host interface. This particular simulation was programmed in FreeFEM++ out of curiosity. The source code for simulation may be found at http://code.google.com/p/cord. This video reflects work in progress and may be different from the final results.

Projects to facilitate collaboration between biologists and computer scientists. Lecture from the Women in Bioinformatics series. Fran Lewitter, Ph.D. Director of the Bioinformatics and Research Computing Department, Whitehead Institute, MIT

The machinery of life depends on proteins--large organic molecules composed of tens, hundreds or even thousands of amino acids bound together and folded into specifically shaped structures. How they fold into these three-dimensional structures is known as the second genetic code and is one of great challenges in science today. Join UCSD biophysicist Jose Onuchic, as he explores how physics, chemistry, biology and mathematics are all being applied to crack the protein folding mystery. Series: Atoms to X-Rays [Science] [Show ID: 5553] UCTV: UC San Diego29 min 51 sec - Jul 25, 2001www.uctv.tv

This is a highly accurate visualization of the Bacteriophage T4 based on Cryo-EM datasets of the virus. The scope of the animation is to show the infection process of the T4 into an E. coli cell. All scientific data sets and motion based off of research from Michael Rossmann Laboratory (Purdue University). Courtesy of Seyet LLC.

An introduction to the Ruby language for the Computer-Aided Discovery Methods course taught at Baylor College of Medicine.

Dr. Laura Elnitski, Head of the Genomic Functional Analysis Section, Genome Technology Branch NHGRI/NIHDr. Elnitski uses experimental and Bioinformatic methods to discover non-coding functional elements in the human genome. On 7 March 2008, Dr. Elnitski came to MSU-Bozeman to participate in the Women In Bioinformatics Seminar Series.

Stage of muscular response, about 96 hrs. post fertilization.

The second cancer lecture for the Computer-Aided Discovery Methods course taught at Baylor College of Medicine. This lecture covers regulation of gene expression in cancer, growth factor signaling, growth inhibition signaling and apoptosis.

Prof. Ching Lau lectures on cancer biology: Cancer as a genetic disease; Mutations and repair; Environmental interactions and cancer. Part of the Computer Aided Discovery Methods course offered at Baylor College of Medicine

Java Digital Signal Processing (J-DSP) is an object-oriented visual programming tool that enables users to establish and run online signal processing simulations, and visualize Internet based interactive demos. It has been used in laboratories involving b

The goal of this exercise is to show how we can relate the results of two independent gene expression datasets to each other. In the case where one dataset provides "transcriptional signatures" of known oncogenic pathways, we can get clues as to which oncogenic pathways may be represented within the results obtained from another dataset. Lab for the Computer-Aided Discovery Methods course taught at Baylor College of Medicine.

Expression profiles of osteosarcoma that can predict response to chemotherapy. Lab for the Computer-Aided Discovery Methods course taught at Baylor College of Medicine.

Principles of image analysis. Segmentation, edge detection, and feature extraction. Imaging tissue section images obtained by H&E staining, immuno-histochemistry, and multi-color FISH. Part of the Computer-Aided Discovery Methods course taught at Baylor College of Medicine.

Originally posted by SDSC on SDSC's CI Channel at: www.cichannel.org Rommie Amaro, Computational and Theoretical Biophysical Chemist, discusses how she uses the resources at SDSC to conduct her drug discovery research on African Sleeping Sickness

If you’re shy or this is your first time trying out professional networking, then I have 3 useful tips that will help you network at conference, workshop or trade show.

Approaches to gene therapy and computational models. Lecture from the Women in Bioinformatics series. Tina M. Hernandez-Boussard, Ph.D. Scientific Curator, Computational Biologist, Department of Genetics, School of Medicine. PharmGKB at Stanford University.

This is an introduction to the Genome Variation Server at the University of Washington sponsored by GVS. You can get more freely available training materials on GVS including a longer introductory tutorial (40 minutes), slides, handouts and exercises at OpenHelix GVS Tutorial or visit the resource at GVS

Insights into cancer biology from the study of base pair-level changes in coding sequences. Genotyping vs. resequencing. Identifying cancer-related genes using association studies. Use of next-generation sequencing technologies to map chromosomal aberrations in cancer genomes. PCR-based resequencing vs. array-based enrichment. Using recurrence, pathway enrichment, and other signatures of positive selection to identify "driver" somatic mutations involved in cancer progression. Part of the Computer-Aided Discovery Methods course taught at Baylor College of Medicine