Saurabh Modi

This paper develops a computational method to infer intercellular gene regulatory networks between different cell types using fluorescence reporter data and validates it through in silico case studies.

This study analyzes proportional and derivative-based feedback controllers to suppress protein count fluctuations in gene expression, showing their effectiveness and trade-offs in noise suppression and sensitivity.

The paper develops a stochastic model to analyze optimal feedback mechanisms for cell number regulation, finding an optimal feedback strength that balances noise suppression and energy costs.

The paper analyzes noise mechanisms in cell size control, showing that volume partitioning errors are most impactful and that timer-adder models lead to power-law size distributions.