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 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.

Developing, characterizing and modeling CRISPR-based point-of-use pathogen diagnostics

“…Future work could include testing targeted interventions to mitigate these limitations or predicting interventions that could improve certain performance metrics (considerations are listed in Supplementary Note 4). The model development process used in this study is an extension of the GAMES workflow 28 and the first instance in which the workflow was used to describe experimental observations. We anticipate that this approach may be extensible to other molecular diagnostic tests.…”

“…We performed iterative model formulation and parameter estimation based on a previously described workflow for dynamic model development 28 . To initialize this process and set criteria for success, we defined a set of qualitative modeling objectives (Table 1), chose a subset of the data to use as training data for each of the three high-throughput screening experiments (Data Sets 1-3, in order of collection date) (Supplementary Data 3, Supplementary Fig.…”

“…We constructed an ordinary differential equation (ODE) mechanistic model describing the core reaction scheme processes, as well as potential off target reactions that could occur in a one-pot formulation of the system. The use of a high-throughput acoustic liquid handling instrument enabled the generation of a large training dataset that was used with the Generation and Analysis of Models for Exploring Synthetic Systems (GAMES) 28 framework to develop and train the model. We found that the variability of the high throughput generated data created challenges to model building.…”