Arun Kumar Tangirala

The paper introduces an improved framework for grey-box modeling of biological processes to enhance predictability and parameter estimation with fewer iterations.

This paper identifies design principles for perfect adaptation in biological networks using nonlinear systems theory, showing that only two network architectures can achieve local adaptation across the state space.

This paper introduces a Bayesian Optimal Experiment Design method for parameter estimation and model selection, demonstrating its effectiveness in systems biology models.

This paper defines new theoretical concepts of sloppiness, develops a method to assess model goodness, and applies it to HIV pharmacokinetic models to identify biologically relevant parameters.

This paper develops a systems-theoretic framework to identify robust adaptation mechanisms in biological networks under deterministic and stochastic conditions.

This paper develops a systems-theoretic method to design adaptive protein networks and shows that third-order networks with negative feedback or feed-forward motifs are necessary for adaptation.