Anita T. Layton

A physiology-based mathematical model of the renin-angiotensin system, bone remodeling, and calcium homeostasis: Effects of estrogen and renin-angiotensin system inhibitors

The paper develops a mathematical model to study how estrogen decline and RAS inhibitors affect bone remodeling and calcium homeostasis in post-menopausal women.

Unraveling the Mechanistic Links Between Blood Pressure Regulation and Calcium-Magnesium Homeostasis: Insights into Hypertension, Hyperparathyroidism, and Mineral Disorders

This study develops a computational model of blood pressure regulation and calcium-magnesium homeostasis in rats, showing how disruptions in these systems affect each other and contribute to hypertension and hyperparathyroidism.

Predicted Effect of Circadian Regulation of Renal Transporters in a Hypertensive Rat

The paper simulates the diurnal effects of diuretics on renal transporters in normotensive and hypertensive kidneys, showing time-of-day differences in natriuresis, diuresis, and kaliuresis.

Modelling sex-specific whole-body metabolic responses to feeding and fasting

The paper develops a sex-specific whole-body metabolic model to explore how men and women differ in metabolic responses to feeding and fasting, identifying key organ-level differences in nutrient storage and oxidation.

A modeling analysis of whole-body potassium regulation on a high potassium diet: Proximal tubule and tubuloglomerular feedback effects

This study extends a mathematical model of whole-body potassium regulation to examine how reduced proximal tubule reabsorption and tubuloglomerular feedback affect K+ balance on a high K+ diet.

The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis

This paper introduces scVAEDer, a deep-learning model combining variational autoencoders and diffusion models to generate meaningful embeddings for single-cell transcriptomics data, enabling data generation, perturbation prediction, and regulator detection.

Adversarial training improves model interpretability in single-cell RNA-seq analysis

This study shows adversarial training improves robustness and interpretability in single-cell RNA-seq cell type classification models.

Fatecode: Cell fate regulator prediction using classification autoencoder perturbation

Fatecode predicts cell fate regulators using scRNA-seq data and deep learning, identifying known master regulators in blood and brain development datasets.

The Mixed Blessing of AMPK Signaling in Cancer Treatments

This study develops a computational model to explore the conflicting roles of AMPK in cancer by simulating metabolic signaling pathways and their effects on tumorigenesis.

Interactions among mTORC, AMPK, and SIRT: A Computational Model for Cell Energy Balance and Metabolism

The paper develops a computational model of ageing signalling pathways to study interactions among mTORC, AMPK, and SIRT, and their impacts on lifespan and health-span.

Modeling the circadian regulation of the immune system: sexually dimorphic effects of shift work

This study models the circadian regulation of the immune system in shift workers, showing sex-specific effects on immune response to inflammation.