Marc Sturrock

Bayesian model discovery for reverse-engineering biochemical networks from data

This paper introduces a Bayesian sparse likelihood-free inference method to reverse-engineer biochemical networks from single-cell data, showing high accuracy and outperforming correlation-based methods.

Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC

This paper shows that Cauchy perturbation kernels outperform Normal kernels in ABC-SMC by addressing overinflation and overconcentration issues, improving performance in high dimensions.

An analytical framework for phenotypic selection of fitness-conferring genes

The paper develops an analytical framework to understand how phenotypic selection enriches fitness-conferring genes under stress, showing symmetry breaking and noise-driven enrichment mechanisms.

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

This paper identifies a five-protein stem cell signature using a deep neural network that predicts relapse in stage III colorectal cancer patients.

Efficient inference for agent-based models of real-world phenomena

This paper compares machine learning methods to improve inference efficiency for agent-based simulations of real-world phenomena like tumor growth and disease spread.

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

The paper benchmarks imputation methods for gene regulatory network inference using synthetic scRNA-seq data generated by a novel tool.

A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation

The paper develops a computational pipeline to evaluate inference accuracy across different model levels and parameter inference settings for negative feedback gene regulation.

A push-pull system of repressors matches levels of glucose transporters to extracellular glucose in budding yeast

The paper shows that budding yeast use a push-pull repression system to dynamically match glucose transporter levels to extracellular glucose concentrations.

Ohm's Law for increasing fitness gene expression with selection pressure

The paper shows antibiotic resistance genes in E. coli exhibit dose-responsive expression under increasing antibiotic concentration, analogous to Ohm's law, and identifies mechanisms involving mRNA/protein variation and cell memory.