Paper List
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
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pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
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Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
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.