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

CD8+ cytotoxic T-lymphocytes (CTLs) perform a critical role in the immune control of viral infections, including those caused by human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV). As a result, genetic variation at CTL epitopes is strongly influenced by host-specific selection for either escape from the immune response, or reversion due to the replicative costs of escape mutations in the absence of CTL recognition. Under strong CTL-mediated selection, codon positions within epitopes may immediately “toggle” in response to each host, such that genetic variation in the circulating virus population is shaped by rapid adaptation to immune variation in the host population. However, this hypothesis neglects the substantial genetic variation that accumulates in virus populations within hosts. Here, we evaluate this quantity for a large number of HIV-1– (n ≥ 3,000) and HCV-infected patients (n ≥ 2,600) by screening bulk RT-PCR sequences for sequencing “mixtures” (i.e., ambiguous nucleotides), which act as site-specific markers of genetic variation within each host. We find that nonsynonymous mixtures are abundant and significantly associated with codon positions under host-specific CTL selection, which should deplete within-host variation by driving the fixation of the favored variant. Using a simple model, we demonstrate that this apparently contradictory outcome can be explained by the transmission of unfavorable variants to new hosts before they are removed by selection, which occurs more frequently when selection and transmission occur on similar time scales. Consequently, the circulating virus population is shaped by the transmission rate and the disparity in selection intensities for escape or reversion as much as it is shaped by the immune diversity of the host population, with potentially serious implications for vaccine design.

Background
The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.

Results
Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper.

Conclusion
It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.

Acetylcholinesterase (AChE) rapidly hydrolyzes acetylcholine in the neuromuscular junctions and other cholinergic synapses to terminate the neuronal signal. In physiological conditions, AChE exists as tetramers associated with the proline-rich attachment domain (PRAD) of either collagen-like Q subunit (ColQ) or proline-rich membrane-anchoring protein. Crystallographic studies have revealed that different tetramer forms may be present, and it is not clear whether one or both are relevant under physiological conditions. Recently, the crystal structure of the tryptophan amphiphilic tetramerization (WAT) domain of AChE associated with PRAD ([WAT]4PRAD), which mimics the interface between ColQ and AChE tetramer, became available. In this study we built a complete tetrameric mouse [AChET]4–ColQ atomic structure model, based on the crystal structure of the [WAT]4PRAD complex. The structure was optimized using energy minimization. Block normal mode analysis was done to investigate the low-frequency motions of the complex and to correlate the structure model with the two known crystal structures of AChE tetramer. Significant low-frequency motions among the catalytic domains of the four AChE subunits were observed, while the [WAT]4PRAD part held the complex together. Normal mode involvement analysis revealed that the two lowest frequency modes were primarily involved in the conformational changes leading to the two crystal structures. The first 30 normal modes can account for more than 75% of the conformational changes in both cases. The evidence further supports the idea of a flexible tetramer model for AChE. This model can be used to study the implications of the association of AChE with ColQ.

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 video is courtesy of EU-IndiaGrid and shows the setup of a simple simulation using BEMuSE, software for faster folding of proteins using grid computing. (http://euindia.ictp.it/bemuse/video-tutorials/) This video shows:
* The structure of the restarts * The configuration of a simulation * The submission of the jobs

Background
The present study was designed to test the hypothesis that inactivation of virtually any component within the pathway containing the BRCA1 and BRCA2 proteins would increase the risks for lymphomas and leukemias. In people who do not have BRCA1 or BRCA2 gene mutations, the encoded proteins prevent breast/ovarian cancer. However BRCA1 and BRCA2 proteins have multiple functions including participating in a pathway that mediates repair of DNA double strand breaks by error-free methods. Inactivation of BRCA1, BRCA2 or any other critical protein within this "BRCA pathway" due to a gene mutation should inactivate this error-free repair process. DNA fragments produced by double strand breaks are then left to non-specific processes that rejoin them without regard for preserving normal gene regulation or function, so rearrangements of DNA segments are more likely. These kinds of rearrangements are typically associated with some lymphomas and leukemias.

Methods
Literature searches produced about 2500 epidemiology and basic science articles related to the BRCA pathway. These articles were reviewed and copied to a database to facilitate access. Meta-analyses of statistical information compared risks for hematologic cancers vs. mutations for the components in a model pathway containing BRCA1/2 gene products.

Results
Deleterious mutations of genes encoding proteins virtually anywhere within the BRCA pathway increased risks up to nearly 2000 fold for certain leukemias and lymphomas. Cancers with large increases in risk included mantle cell lymphoma, acute myeloid leukemia, acute lymphocytic leukemia, chronic lymphocytic leukemia, and prolymphocytic leukemia. Mantle cell lymphoma is defined by a characteristic rearrangement of DNA fragments interchanged between chromosomes 11 and 14. DNA translocations or rearrangements also occur in significant percentages of the other cancers.

Conclusion
An important function of the BRCA pathway is to prevent a subgroup of human leukemias and lymphomas that may involve non-random, characteristic gene rearrangements. Here, the genetic defect in BRCA pathway deficiencies is a chromosomal misrepair syndrome that may facilitate this subgroup of somatic cancers. Inactivation of a single gene within the pathway can increase risks for multiple cancers and inactivation of a different gene in the same pathway may have similar effects. The results presented here may have clinical implications for surveillance and therapy.

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.

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.

Introduction to the biology of pond Algae.

Introduction to the biology of bacteria and the environment.

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

A video accompanying a poster presentation by Erika Gracia from Chula Vista High School. Understanding color-blindness. From the RCSB Summer Course in Introductory Bioinformatics as part of the Howard Hughes Scholars Program, UCSD.

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.