Biosilico最新文献

The increasing amount of data produced by large-scale biological experiments has highlighted the inadequacies of traditional scientific data management methods such as laboratory notebooks. Databases designed to store biological information are becoming increasingly common, but there is little guidance in the literature about the best practices of biological database design. This paper suggests best practices, and provides examples for the implementation of these practices.

The rapid accumulation of complete genome sequences and the advance of high-throughput expression profiling technology have made a computational approach to the study of transcription regulation networks attractive and feasible. In this review, computational approaches to deciphering the transcriptional regulatory network, including promoter prediction, transcription factor binding site identification, combinatorial regulatory element predictions and transcription factor target gene identification are discussed. The role of comparative genomics in transcription regulatory region analysis is also reviewed.

Atul Butte is an Assistant in Endocrinology and Informatics and Attending Physician at Children's Hospital, Boston, USA (http://www.chip.org), and is an Instructor in Paediatrics at Harvard Medical School (http://www.harvard.edu). He received his undergraduate degree in Computer Science from Brown University in 1991, and worked in several stints as a software engineer at Apple Computer and Microsoft Corporation. He graduated from the Brown University School of Medicine in 1995, during which he worked as a research fellow at National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; http://www.niddk.nih.gov) through the Howard Hughes/NIH Research Scholars Program. He completed his residency in Paediatrics and Fellowship in Paediatric Endocrinology in 2001, both at Children's Hospital. During his research under Isaac Kohane (at Children's Hospital) he developed a novel methodology for analyzing large data sets of RNA expression, called Relevance Networks. His recent awards include the 2003 Emory University School of Medicine, Pathology Residents’ Choice Award, 2002 American Association for Clinical Chemistry Outstanding Speaker Award, 2002 Endocrine Society Travel Award based on presentation merit, 2001 American Association for Cancer Research Scholar-In-Training Award and the 2001 Lawson Wilkins Paediatric Endocrine Society Clinical Scholar Award.

In silico methods may benefit drug discovery and development significantly by saving an average of $130 million and 0.8 years per drug. Virtual high-throughput screening (vHTS) applies in silico approaches, such as docking and alignment, to large virtual molecular databases to enrich biologically active compounds in order to yield lead structures. In an industrial environment, the commonly used ligand-based and receptor-based methods outlined here need to be computationally faster to return the utmost benefit. Intelligent database searching using new fast feedback-driven screening methods appears to be particularly rewarding in terms of both cost and time benefits.

‘Ease-of-use…comes from conducting systematic usability engineering activities throughout the project lifecycle.’

John R. Wakeley is Thomas D. Cabot Associate Professor of Biology at the Department of Organismic and Evolutionary Biology, Harvard University (http://www.oeb.harvard.edu/). His research is theoretical population genetics and molecular evolution, with a focus on the analysis of DNA sequence data, with particular interest in models of population subdivision and the divergence of populations and species. Prof. Wakeley develops statistical models to study genetic and demographic components in the evolution of subpopulations within species. Born in Berkeley, CA, USA, Wakeley obtained a BS and MS in Biology from Stanford University in 1989; he then went on to do a PhD in Integrative Biology from the University of California, Berkeley (1994). Following this, he went to the National Institute of Genetics in Mishima, Japan (1994–1995), then on to do an NIH postdoc at Rutgers University (1995–1998) and moved to Harvard in 1998.