Transforming Data Across Environments Despite Structural Non-Identifiability
Date:
In this work, we developed a theoretical framework for batch correction in cell-free systems in the presence of parameter (structural) non-identifiability. After formally defining the problem, we develop a set of parameter consistency conditions which guarantee the accuracy of the batch correction methodology despite parameter uncertainty. We also demonstrated it on real genetic circuit behavior in two batches of cell free extracts.
The slides for the talk can be found here, and the conference paper can be found here
Proceedings:
V. Singhal and R. M. Murray, “Transforming Data Across Environments Despite Structural Non-Identifiability,” 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 5639-5646, doi: 10.23919/ACC.2019.8814953.