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Kuksa, P. P.

Publications and source records attributed to Kuksa, P. P..

2 recordsLinked to original sources

Fast Parallel Algorithm for Large Fractal Kinetic Models with Diffusion

Chemical kinetic simulations are usually based on the law of mass action that applies to behavior of particles in solution. Molecular interactions in a crowded medium as in a cell, however, are not easily described by such conventional mathematical treatment. Fractal kinetics is emerging as a novel method for simulating kinetic reactions in such an environment. To date, there has not been a fast, efficient, and, more importantly, parallel algorithm for such computations. Here, we present an algorithm with several novel features for simulating large (with respect to size and time scale) fractal kinetic models. We applied the fractal kinetic technique and our algorithm to a canonical substrate-enzyme model with explicit phase-separation in the product, and achieved a speed-up of up to 8 times over previous results with reasonably tight bounds on the accuracy of the simulation. We anticipate that this technique and algorithm will have important applications to simulation of intra-cell biochemical reactions with complex dynamic behavior.

biophysics

INFERNO - INFERring the molecular mechanisms of NOncoding genetic variants

The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, where they affect regulatory elements including transcriptional enhancers. We propose INFERNO (INFERring the molecular mechanisms of NOncoding genetic variants), a novel method which integrates hundreds of diverse functional genomics data sources with GWAS summary statistics to identify putatively causal noncoding variants underlying association signals. INFERNO comprehensively infers the relevant tissue contexts, target genes, and downstream biological processes affected by causal variants. We apply INFERNO to schizophrenia GWAS data, recapitulating known schizophrenia-associated genes including CACNA1C and discovering novel signals related to transmembrane cellular processes.

bioinformatics