Kinetic models are critical to predict the dynamic behaviour of metabolic networks.
Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting …

Highlights•Kinetic models are crucial to understand complex dynamic processes.•Not all
model parameters need to be known precisely due to overlap** regulatory …

We explore the relationship among experimental design, parameter estimation, and
systematic error in sloppy models. We show that the approximate nature of mathematical …

Experimental design attempts to maximise the information available for modelling tasks. An
optimal experiment allows the inferred models or parameters to be chosen with the highest …

Differential equations have proven to be a powerful mathematical tool in science and
engineering, leading to better understanding, prediction, and control of dynamic processes …

Plant Synthetic Biology: Quantifying the “Known Unknowns” and Discovering the “Unknown
Unknowns” | Plant Physiology | Oxford Academic Skip to Main Content Advertisement Oxford …

Modeling parts and circuits represents a significant roadblock to automating the Design-
Build-Test-Learn cycle in synthetic biology. Once models are developed, discriminating …

This book delivers a comprehensive and insightful account of applying mathematical
modelling approaches to very large biological systems and networks—a fundamental aspect …

Labels generated by human experts via comparisons exhibit smaller variance compared to
traditional sample labels. Collecting comparison labels is challenging over large datasets …

Stochastic reaction network models are often used to explain and predict the dynamics of
gene regulation in single cells. These models usually involve several parameters, such as …