Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
School of Mathematical Sciences, Queensland University of Technology | Centre for Data Science, Queensland University of Technology | ARC Centre of Excellence for Mathematical Analysis of Cellular Systems (MACSYS), Queensland University of Technology
The 30-Second View
IN SHORT: This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-dependent delays, which are crucial for modeling processes like gene transcription but are prohibitively expensive with existing methods.
Innovation (TL;DR)
- Methodology Generalizes the next reaction method and τ-leaping to support arbitrary inter-event time distributions for non-Markovian systems, maintaining computational scalability.
- Methodology Introduces a novel coupling scheme to generate positively correlated exact and approximate non-Markovian sample paths, a prerequisite for variance reduction techniques.
- Methodology Enables the application of multifidelity and multilevel Monte Carlo (MLMC) methods to non-Markovian systems for the first time, bridging a significant methodological gap.
Key conclusions
- The proposed non-Markovian simulation algorithms and coupling scheme successfully enable multifidelity inference, demonstrated on a gene regulation model with delayed auto-inhibition.
- The method achieves a computational speedup of two orders of magnitude (100x) in inference efficiency compared to standard approaches for the non-Markovian case study.
- The framework supports arbitrary delay distributions (state- and time-dependent), significantly extending the practical modeling scope beyond previous methods limited to simpler, time-only delays.
Abstract: Stochastic models of biochemical reaction networks are widely used to capture intrinsic noise in cellular systems. The typical formulation of these models are based on Markov processes for which there is extensive research on efficient simulation and inference. However, there are biological processes, such as gene transcription and translation, that introduce history dependent dynamics requiring non-Markovian processes to accurately capture the stochastic dynamics of the system. This greater realism comes with additional computational challenges for simulation and parameter inference. We develop efficient stochastic simulation algorithms for well-mixed non-Markovian stochastic biochemical reaction networks with delays that depend on system state and time. Our methods generalize the next reaction method and τ-leaping method to support arbitrary inter-event time distributions while preserving computational scalability. We also introduce a coupling scheme to generate exact non-Markovian sample paths that are positively correlated to an approximate non-Markovian τ-leaping sample path. This enables substantial computational gains for Bayesian inference of model parameters though multifidelity simulation-based inference schemes. We demonstrate the effectiveness of our approach on a gene regulation model with delayed auto-inhibition, showing substantial gains in both simulation accuracy and inference efficiency of two orders of magnitude. These results extend the practical applicability of non-Markovian models in systems biology and beyond.