Information about development of CannabiSolve™ has evinced keen interest among the research community world-wide. A number of papers has been written on the CannabiSolve™ platform and its components. Researchers in the fields of computational biology and development of drug and treatment plans are using CannabiSolve™ to build their thesis.
You can view and read papers published on CannabiSolve™ and papers published by other researchers that cite CannabiSolve™ on this page.
The information coming from biomedical ontologies and computational pathway models is expanding
continuously: research communities keep this process up and their advances are generally shared by
means of dedicated resources published on the web.
CannabiSolveTM: 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.
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.
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.
Computational protocols, such as CannabiSolve, allow the combination of alternative models and generation of consensus hypotheses.
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.