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

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.

Investigating and Modeling the Factors That Affect Genetic Circuit Performance

“…49 The fits were performed by minimizing the sum of the square of the relative error between each measured data point and the same point in the corresponding model simulation. As with the Hill function characterization algorithm, a random initial parameter value search was implemented following the GAMES workflow, 58 while simultaneously looking for the smallest chi-squared values for each fitting iteration. These scripts are listed in the Supporting Information documentation.…”

Investigating and Modeling the Factors that Affect Genetic Circuit Performance

“…Hill-function characterization algorithm: For the Hill-function parametrization method, a normalized least-squares method using the non-linear Least-Squares Minimization and Curve-Fitting (lmfit) Python package [32] was used, and random initial parameter estimations following the GAMES workflow [38].…”

“…The fits were performed by minimizing the sum of the square of the relative error between each measured data point and the same point in a corresponding model simulation. As with the Hill-function characterization algorithm a random initial parameter value search was implemented following the GAMES workflow [38], while simultaneously looking for the smallest chi-squared values for each fitting iteration. These scripts are in the supplementary information documentation.…”

“…Using the estimated values of , shown in Table 4, and using both Equations 3 and 4, the first fitting iteration was used to obtain , and x ss parameter estimate values using the ON-to-OFF characterization experiment results. Using the parameter estimation method proposed in [38], and the fixed values of obtained previously, the model was fitted to the experimental results using a minimizing function. The parameter values estimated with this method are shown in Table 4.…”

BMSS2: A Unified Database-Driven Modeling Tool for Systematic Biomodel Selection

“…The growing interest in model-guided design has led to the development of useful modeling tools − and workflows to aid the development of models. Tools for simulation and parameter estimation range from software packages with a graphical user interface (e.g., iBioSim, COPASI, JWS Online) to programmable python-based packages that offer higher flexibility, such as SloppyCell, which supports the Bayesian analysis of parameter uncertainty, and Tellurium, which supports simulation and standard files generation.…”