CUDA-Powered Stochastic Simulation of Antisense Oligonucleotide Sequestration by Cellular mRNA with PhD Aleksandra Khatova

Abstract:

Antisense oligonucleotides (ASOs) are short synthetic nucleic acids designed to bind specific RNA targets and modulate their expression. In the cellular environment, however, ASOs can also bind to other mRNAs, potentially sequestering them away from their intended targets. Mechanistic modeling of these interactions leads to large reaction networks spanning multiple timescales and involving species with widely varying molecular abundances. These properties make exact stochastic simulation using the Gillespie Stochastic Simulation Algorithm (GSSA) computationally demanding. Approximate approaches, such as τ-leaping and hybrid methods, can reduce computational cost, but provided insufficient speedup for our models. To address this bottleneck, I developed a GPU-accelerated stochastic simulator implementing a parallelized version of the GSSA in CUDA. In this talk, I will present the design and implementation of the solver, discuss the challenges of mapping the GSSA to GPU architectures, and evaluate its performance on biochemical reaction networks of varying size and complexity. (Based on joint work with Alexander Schliep.)

 Bio:

Aleksandra (Sasha) Khatova is a PhD student in Medical Bioinformatics at Brandenburg University of Technology Cottbus-Senftenberg. Her research focuses on kinetic modeling of oligonucleotide action, with a particular interest in how intracellular RNA dynamics influence the efficacy of oligonucleotide therapeutics. Her work uses stochastic simulations to study these systems and their variability at the single-cell level. To enable efficient simulation of large and computationally demanding models, she also develops GPU-accelerated methods for stochastic simulation of biochemical reaction networks.