Abstract
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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Manuscript text revised to improve clarity in multiple locations, Figure 4 minor revision, new Supplementary Figure 4 included to provide more detail on potential failure modes, Supplementary Information updated.
