Ankit Gupta

This paper introduces the Filtered Finite State Projection (FFSP) method to solve high-dimensional stochastic filtering problems in biochemical reaction networks, demonstrating its effectiveness with numerical examples and real biological data.

This paper introduces and analyzes an antithetic dual-rein integral feedback controller for stochastic chemical reaction networks, showing it can control first moments and maintain tunable variance bounds.

The paper introduces cumulative dose response (cDR) and shows that incoherent feedforward loops always produce monotonic cDR despite being capable of non-monotonic dose responses.

This paper introduces cascaded antithetic integral feedback to improve dynamic performance and robust stability in synthetic biology systems.

This paper presents three biomolecular anti-windup topologies to prevent instability in integral controllers by linking control-theoretic strategies to chemical reaction networks.

This paper introduces a frequency domain method to estimate intrinsic eigenvalues of stochastic reaction networks using a small number of simulated trajectories, reducing computational burden.

The paper introduces a divide-and-conquer method to solve the Chemical Master Equation in high-dimensional gene expression systems, demonstrating its effectiveness in analyzing yeast transcription at single-cell resolution.

This paper identifies structural requirements for maximal robust perfect adaptation in biomolecular networks using linear-algebraic conditions and demonstrates their application through examples.

This paper develops a spectral theory and computational methods to analyze frequency spectra of noisy cellular networks, enabling topology discrimination and synthetic oscillator optimization.

This paper introduces a deep learning framework to solve high-dimensional chemical master equations using reinforcement learning and neural networks, enabling efficient approximation of solutions and their parameter sensitivities.

This paper proves that ACR systems contain an internal integral structure, enabling robustness to broader disturbances and facilitating modular synthetic biology design.

This paper designs and implements an integral feedback controller in E. coli to achieve robust and tunable protein regulation.

This paper proposes combining antithetic integral feedback with negative feedback to reduce variance in stochastic reaction networks, showing a trade-off between settling-time and variance.

This paper introduces an optogenetic platform for single-cell transcription control, achieving mRNA tunability and reduced variability in yeast cells.

This paper introduces a new regulatory motif called antithetic integral feedback that enables robust perfect adaptation in noisy biomolecular networks.