Ayush Pandey

The paper develops a chemical reaction network model for PURE cell-free expression systems to predict protein synthesis kinetics using mass-action reactions.

This paper develops a multiscale model, GEAGS, to predict transient oscillations in synthetic gene circuits by coupling gene expression to growth phases, revealing amino acid recycling and growth transitions as key factors.

The paper builds and validates a chemical reaction network model for PURE protein synthesis using mass-action reactions, accurately predicting mRNA and protein production dynamics.

The paper presents a pipeline for modeling and parameter identification in biological systems, applied to characterize integrase and excisionase activity in cell-free systems.

The paper introduces bioscrape, a Python package for fast and flexible simulation and parameter estimation of chemical reaction networks in synthetic biology.

This paper introduces an automated model reduction algorithm for synthetic biological circuits, demonstrating its use in designing a consortium population control circuit.

BioCRNpyler compiles chemical reaction networks from modular biochemical parts, enabling rapid model development and exploration across diverse contexts.

The paper introduces an assume-guarantee contract framework for formalizing and implementing biological circuit designs using ordinary differential equations.

The paper develops a two-state model of gene expression that captures chemical reaction dynamics with better accuracy and robustness than traditional models.

The paper introduces a Python package to automatically generate phenomenological models of synthetic biological circuits by reducing complex chemical reaction models.

The paper demonstrates a synthetic two-member bacterial community that reaches density and composition steady states controlled by inducer inputs, but shows instability after perturbation.