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

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.

数学建模对于推动合成生物学系统的认知深化与设计优化而言，具有不可替代的重要价值。然而，模型开发流程复杂且往往缺乏直观性，需要对各类计算任务开展迭代，并与实验数据进行比对验证。特设式模型开发不仅会为模型复现与开发流程的批判性分析带来阻碍，还会削弱其潜在应用价值，进而抑制后续的模型开发与跨团队协作。
为帮助从业者应对上述挑战，我们提出了面向合成系统探索的模型生成与分析（Generation and Analysis of Models for Exploring Synthetic Systems, GAMES）流程，该流程同时涵盖自动化流程与人在回路式流程。
我们系统梳理了动态模型的开发全流程，涵盖模型构建、参数估计、参数可辨识性分析、实验设计、模型简化、模型细化与模型选择等关键环节。
我们以一项针对化学响应型转录因子的案例研究，对该流程进行了演示验证。
本教程中呈现的通用可复用流程，能够帮助生物学家更便捷地针对各类应用场景构建与分析相关模型。