Biosilico最新文献

Predictive biosimulation provides the unique ability to explore, in a human in vivo context, the potential impact of novel or known targets on clinical outcome. This approach also allows the identification and exploration of knowledge gaps, leading to a better understanding of disease pathophysiology, and can focus laboratory experimentation on the crucial pathways involved in the disease, making the process of target validation more efficient.

A solid definition and comprehensive graphical representation of biological networks is essential for efficient and accurate dissemination of information on biological models. Several proposals have already been made toward this aim. The most well known representation of this kind is a molecular interaction map, or ‘Kohn Map’. However, although the molecular interaction map is a well-defined and compact notation, there are several drawbacks, such as difficulties in intuitive understanding of temporal changes of reactions and additional complexities arising from particular graphical representations. This article proposes several improvements to the molecular interaction map, as well as the use of the ‘process diagram’ to help understand temporal sequences of reactions.

Colin Hill is the founder of Gene Network Sciences (GNS; http://www.gnsbiotech.com) and serves as President and Chief Executive Officer. He has extensive scientific experience in the areas of gene network modeling, pioneering the application of methods based in statistical physics and non-linear dynamics to the stochastic dynamics of gene expression. He is the co-founder of a multidisciplinary research effort at Cornell University dedicated to combining computational and experimental approaches to the study of signal transduction pathways. Hill is the co-creator of the Digital Cell™ software environment for the modeling of complex gene networks and biochemical pathways. He earned his BS degree in Physics from Virginia Polytechnic and State University and his MS degrees in Physics from McGill University and Cornell University.

Paper-based methods of clinical data collection, analysis and management have long been standard practice, but the development of online software solutions and the ubiquitous presence of the internet create opportunities to improve these processes. Adoption rates of the new technologies have been slow as result of in part resistance to change, but use of electronic solutions is on the upswing. The industry's overriding interest in accelerating clinical development of lead compounds, a newly supportive environment from the Food and Drug Administration, the presence of maturing technology and the emergence of stable vendors are the confluence of factors driving an inflection point.

Manifold examples from the current biological literature describe the application of integrative analysis methods. These examples demonstrate the need for bioinformatics methods that integrate data from diverse sources and data that have heterogeneous formats. The requirements of such methods can be demonstrated through examples of applications used to analyze high-throughput data (particularly gene expression data) in combination with biological knowledge (such as functional classification and pathways). On the basis of these examples, we suggest general requirements for integrative analyses and a scenario for the development of integrative bioinformatics systems.