Published Papers and Citations
Information about development of OncoSolve™ has evinced keen interest among the research community world-wide. A number of papers has been written on the OncoSolve™ platform and its components. Researchers in the fields of computational biology and development of drug and treatment plans are using OncoSolve™ to build their thesis. You can view and read papers published on OncoSolve™ and papers published by other researchers that cite OncoSolve™ on this page.
PancreaSolveTM: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models
A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer.
A Distributed Computational Architecture for Integrating Multiple Biomolecular Pathways
Biomolecular pathways are building blocks of cellular biochemical function. Computational biology is in rapid transition from diagrammatic representation of pathways to quantitative and predictive mathematical models, which span time-scales, knowledge domains and spatial-scales. This transition is being accelerated by high-throughput experimentation which isolates reactions and their corresponding rate constants.
Integrating an Ensemble of Distributed Biochemical Network Models
A new system for integrating an ensemble of distributed biochemical network models is presented. Rapid growth in the number of biochemical network models, created in different formats, across different computing systems, with minimal input and output information, necessitates the need for such a system in order to build large scale models in a flexible and scalable manner.
Microdissection and the Study of Cancer Pathways
Microdissection involves the extraction of specific populations of cells under direct visualization. In this article, we will discuss the currently available techniques of microdissection, and briefly review how this material is being utilized in the study of cancer pathways.
Loss of Dpc4 Expression in Colonic Adenocarcinomas Correlates with the Presence of Metastatic Disease
DPC4 is a candidate tumor suppressor gene on chromosome 18q21, a region that shows high frequencies of allelic losses in pancreatic and colorectal adenocarcinomas. Biallelic inactivation of DPC4 has been reported in half of pancreatic cancers, but are relatively infrequent in other tumor types. Alterations in DPC4 are involved in the progression of a subset of colorectal carcinomas, especially those that present with advanced disease.
Nature Publishing Group – Combinatorial Drug Therapy for Cancer
Computational protocols, such as PancreaSolve, allow the combination of alternative models and generation of consensus hypotheses.
Setting a Research Agenda for Progressive MS: The International Collaborative on Progressive MS
Indeed, computational biology is shifting from diagrammatic representation of pathways to mathematical models. These techniques hold promise to provide the tools for interpreting genetic data across different knowledge domains.
Contribution of Genome-Wide Association Studies to Scientific Research: A Pragmatic Approach to Evaluate Their Impact
The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets.
The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models
The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.