GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems

Kate E. Dray, Joseph J. Muldoon, Niall M. Mangan, Neda Bagheri*, Joshua N. Leonard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 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.

Original languageEnglish (US)
Pages (from-to)1009-1029
Number of pages21
JournalACS synthetic biology
Volume11
Issue number2
DOIs
StatePublished - Feb 18 2022

Keywords

  • ODE model development
  • mathematical modeling
  • parameter estimation
  • parameter identifiability
  • synthetic biology

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

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