Michael P. H. Stumpf

Probing bacterial fitness in virtual chemostats

The paper develops computational algorithms to accurately estimate bacterial population growth rates using single-cell models and applies them to study antibiotic response heterogeneity and chemotaxis effects.

A spin glass representation of hierarchical gene regulation and the partial resolution of frustration

This paper develops a multicellular Ising model to study frustration in gene regulatory networks and shows how intercellular communication affects frustration resolution.

Molecular noise modulates transitions in the cell-fate differentiation landscape

The paper investigates how molecular noise affects transition paths between cell-fate valleys in non-equilibrium systems, showing that stochastic dynamics produce paths distinct from deterministic models.

Intercellular signaling drives robust cell fate and patterning in multicellularsystems

The paper develops a mathematical framework showing intercellular signaling stabilizes cell fate and tissue patterning by suppressing spontaneous switching and inducing coordinated behaviors.

Learning cell-specific networks from dynamics and geometry of single cells

The paper introduces locaTE, a method to infer cell-specific gene regulatory networks using single-cell dynamics and geometry, showing improved performance over existing methods.

The Topological Properties of the Protein Universe

The paper uses topology to analyze the protein universe, identifying structural and functional features and their implications for mutations and evolution.

A three-node Turing gene circuit forms periodic spatial patterns in bacteria

The paper engineers a synthetic three-node Turing gene circuit in E. coli that generates periodic spatial patterns, validated by a differential equation model.

Approximate Bayesian Computation for Inferring Waddington Landscapes from Single Cell Data

This study uses approximate Bayesian computation to infer Waddington landscapes from single cell data by evaluating distance measures and parameter sensitivity.

The timing of cellular events: a stochastic vs deterministic perspective

The paper compares deterministic and stochastic models of cellular event timing, showing significant differences in predicted trigger times under certain conditions.

Repeated Decision Stumping Distils Simple Rules from Single Cell Data

This paper introduces repeated decision stumping to identify key gene products driving cell fate transitions from single-cell data, finding robust and predictive models without prior knowledge.

BondGraphs.jl: Composable energy-based modelling in systems biology

BondGraphs.jl provides a Julia framework for energy-based modelling in systems biology, enforcing thermodynamic constraints through bond graphs.

The chemical Langevin equation for biochemical systems in dynamic environments

This paper introduces an extrinsic chemical Langevin equation to model biochemical systems with both intrinsic and extrinsic noise, showing improved computational efficiency while maintaining accuracy.

Gaining confidence in inferred networks

The paper introduces a method to assess network inference reliability by measuring functional assortativity in biological networks.

Noise Distorts the Epigenetic Landscape and Shapes Cell Fate Decisions

The paper examines how stochastic fluctuations affect the reconstruction of the Waddington epigenetic landscape and the identifiability of regulatory dynamics from single-cell transcriptomic data.

The Design Principles of Discrete Turing Patterning Systems

This study compares continuous and discrete models of Turing patterns, finding discrete models more robust and suggesting deeper principles in Turing mechanisms.

Uncertainty in cell fate decision making: Lessons from potential landscapes of bifurcation systems

The paper analyzes how noise affects the shape of potential landscapes in bifurcation systems, showing its impact on cell fate decision dynamics.

Decision tree models and cell fate choice

The paper develops decision tree models to interpret gene regulatory programs in cell fate decisions using single-cell transcriptomic data.

Multi-Model and Network Inference Based on Ensemble Estimates: Avoiding the Madness of Crowds

The paper evaluates when ensembles of models improve predictions in systems biology and network inference, emphasizing the importance of careful ensemble construction.

Exactly solvable models of stochastic gene expression

This paper introduces solvable stochastic models of gene expression that account for multiple states and factors affecting transcript abundance, enabling analytical and computational solutions for gene regulation.

Extrinsic noise and heavy-tailed laws in gene expression

This study derives analytical solutions for gene expression models to show that heavy-tailed RNA copy number distributions arise from extrinsic noise, not intrinsic stochasticity.

Parameter inference in dynamical systems with co-dimension 1 bifurcations

The paper analyzes how bifurcations affect parameter inference in dynamical systems and argues that identifying bifurcation diagrams should precede kinetic parameter estimation.

Parametric and Non-parametric Gradient Matching for Network Inference

This paper compares parametric and non-parametric methods for gene regulatory network inference using gradient matching and finds parametric methods often provide better results.

Transition State Characteristics During Cell Differentiation

The paper analyzes dynamical systems models of stem-cell differentiation to explore transition states and their role in cell fate changes.

Signalling pathways drive heterogeneity of ground state pluripotency

This study shows that 2i conditions increase transcriptional heterogeneity in pluripotent stem cells, driven by signaling pathways and negative feedback loss.

Turing patterns are common but not robust

The paper explores Turing pattern design space to determine how common and robust they are, finding that they are common but sensitive to parameter changes.

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

The paper introduces a method combining empirical Bayes and information theory to reconstruct gene regulatory networks from single-cell data, improving accuracy with prior information.

Gene regulatory network inference from single-cell data using multivariate information measures

This paper introduces PIDC, a method using multivariate information theory to infer gene regulatory networks from single-cell data, showing improved performance over pairwise methods in simulated data.

Stem cell differentiation is a stochastic process with memory

This study shows that stem cell differentiation involves stochastic transitions through hidden microstates, revealing epigenetic memory and a statistical mechanics framework for cellular identity.

Single Cell Phenotyping Reveals Heterogeneity among Haematopoietic Stem Cells Following Infection

The paper uses single-cell tracking to show that HSCs from infected mice exhibit heterogeneous movement patterns compared to controls, with increased persistence possibly aiding survival in altered niche environments.

Control mechanisms for stochastic biochemical systems via computation of reachable sets

The paper develops a reachability analysis framework to study control mechanisms in stochastic biochemical systems, providing bounds on population statistics despite parameter uncertainty and non-linearity.