Mathematical modeling is invaluable
for advancing understanding
and design of synthetic biological systems. However, the model development
process is complicated and often unintuitive, requiring iteration
on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction
and critical analysis of the development process itself, reducing
the potential impact and inhibiting further model development and
collaboration. To help practitioners manage these challenges, we introduce
the Generation and Analysis of Models for Exploring Synthetic Systems
(GAMES) workflow, which includes both automated and human-in-the-loop
processes. We systematically consider the process of developing dynamic
models, including model formulation, parameter estimation, parameter
identifiability, experimental design, model reduction, model refinement,
and model selection. We demonstrate the workflow with a case study
on a chemically responsive transcription factor. The generalizable
workflow presented in this tutorial can enable biologists to more
readily build and analyze models for various applications.
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce GAMES: a workflow for Generation and Analysis of Models for Exploring Synthetic systems that includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications.
A fter publication, we identified two minor mistakes in the code for the GAMES workflow. Each subtly affected our analysis of the example case study but had no effect on the GAMES workflow. Here we describe those mistakes and discuss how the resulting corrections modify the case study analysis.
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