Edmund J. Crampin

This paper introduces a thermodynamic framework for reducing enzyme cycle models while preserving physical constraints, applicable to both hypothetical systems and real data like Na+/K+ ATPase.

The paper analyzes high-resolution RNA-seq time course data to show lncRNAs are expressed synchronously with adjacent protein-coding genes, suggesting they may be by-products rather than regulators.

This paper introduces BondGraphTools, a Python library for constructing and analysing energy-based models in biochemistry using bond graphs, ensuring consistency with physical laws.

The paper proposes using bond graphs to improve modularity and physical consistency in systems biology models, demonstrating their utility in modelling MAPK cascades and glycolysis.

The paper introduces a bond graph methodology to unify stoichiometry, thermodynamics, and kinetics in dynamic biomolecular modelling, demonstrating its application to E. coli.

The paper uses computational models to show that mitochondrial arrangement affects metabolite distribution and force dynamics in cardiac cells, with implications under hypoxia.

The paper shows that multiple microRNAs work together to control EMT by reducing noise and enhancing gene expression changes, more accurately reflecting endogenous regulation.

The paper uses cellular barcoding and DiSNE visualization to study hematopoietic fate in primates, identifying novel progenitor clusters with early T cell and later granulocyte production.

The paper assesses clonal kinetics in dendritic cell development and identifies multiple distinct trajectories with intrinsic programming.