Optimally Designed Model Selection for Synthetic Biology

Abstract

Modeling parts and circuits represents a significant roadblock to automating the Design-Build-Test-Learn cycle in synthetic biology. Once models are developed, discriminating among them requires informative data, computational resources, and skills that might not be readily available. The high cost entailed in model discrimination frequently leads to subjective choices on the selected structures and, in turn, to suboptimal models. Here, we outline frequentist and Bayesian approaches to model discrimination. We ranked three candidate models of a genetic toggle switch, which was adopted as a test case, according to the support from in vivo data. We show that, in each framework, efficient model discrimination can be achieved via optimally designed experiments. We offer a dynamical-systems interpretation of our optimization results and investigate their sensitivity to key parameters in the characterization of synthetic circuits. Our approach suggests that optimal experimental design is an effective strategy to discriminate between competing models of a gene regulatory network. Independent of the adopted framework, optimally designed perturbations exploit regions in the input space that maximally distinguish predictions from the competing models.

GAMES: A dynamic model development workflow for rigorous characterization of synthetic genetic systems

“…While there exists a deep body of knowledge that can guide the model development process, from model formulation 18,19 and parameter estimation [19][20][21][22][23][24][25] , to model selection 23,26,27 , parameter identifiability analysis 25,[28][29][30][31][32][33][34][35][36][37] , and experimental design 7,29,31,38,39 , navigating these tasks can be slow and cumbersome, posing a barrier to entry. This challenge is heightened by the fact that published studies often focus on a final optimized model rather than the process by which the model was generated, which is often through laborious iteration that is understandably difficult to fully capture in a report.…”

“…The Akaike information criterion (AIC) 26, 27, 53 is a metric that is used to quantitatively compare models (Equation 12) . The AIC is defined as: where d k is the number of free parameters in model k with calibrated parameter set θ m .…”

“…The PPL indicated that all free parameters were identifiable in Model D, and therefore no further rounds of model reduction or experimental design were necessary. The Akaike information criterion (AIC) 26,27,53 is a metric that is used to quantitatively compare models (Equation 12). The AIC is defined as: 12)…”

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

“…Recent advances in experimental techniques enable not only the precise characterization and subdivision of this burden [61,78,79], but also its accurate and multilevel control [80]. Complementing these high-throughput technologies, computational tools and quantitative models enable the design of complex biosystems [3,4,30,81].…”

On structural and practical identifiability

“…The process of determining the most informative targets and time points for the new measurements is known as optimal experimental design and is frequently applied in different modelling fields, e.g. metabolic models [61], animal science [62], linear perturbation networks [63] or synthetic biology [64]. The task is related to the search of an additional measurement that contains the maximal information about the system or parts of it.…”