Abstract
With computational biology striving to provide more accurate theoretical accounts of biological systems, use of increasingly complex computational models seems inevitable. However, this trend engenders a challenge of optimal experimental design: due to the flexibility of complex models, it is difficult to intuitively design experiments that will efficiently expose differences between candidate models or allow accurate estimation of their parameters. This challenge is well exemplified in associative learning research. Associative learning theory has a rich tradition of computational modeling, resulting in a growing space of increasingly complex models, which in turn renders manual design of informative experiments difficult. Here we propose a novel method for computational optimization of associative learning experiments. We first formalize associative learning experiments using a low number of tunable design variables, to make optimization tractable. Next, we combine simulation-based Bayesian experimental design with Bayesian optimization to arrive at a flexible method of tuning design variables. Finally, we validate the proposed method through extensive simulations covering both the objectives of accurate parameter estimation and model selection. The validation results show that computationally optimized experimental designs have the potential to substantially improve upon manual designs drawn from the literature, even when prior information guiding the optimization is scarce. Computational optimization of experiments may help address recent concerns over reproducibility by increasing the expected utility of studies, and it may even incentivize practices such as study pre-registration, since optimization requires a pre-specified analysis plan. Moreover, design optimization has the potential not only to improve basic research in domains such as associative learning, but also to play an important role in translational research. For example, design of behavioral and physiological diagnostic tests in the nascent field of computational psychiatry could benefit from an optimization-based approach, similar to the one presented here.
Highlights
A major goal of computational biology is to find accurate theoretical accounts of biological systems
We focus on two model-based types of analyses that are common in the modeling literature on associative learning: parameter estimation within a single model, and model selection within a space of multiple models
We validated the proposed method in three scenarios, with one scenario targeting the goal of accurate parameter estimation, and the other two targeting the goal of accurate model selection
Summary
A major goal of computational biology is to find accurate theoretical accounts of biological systems. Given the complexity of biological systems, accurately describing them and predicting their behavior will likely require correspondingly complex computational models. The flexibility of complex models provides them with potential to account for a diverse set of phenomena, this flexibility engenders an accompanying challenge of designing informative experiments. Designing informative experiments entails formalizing them in terms of tunable design variables, and finding values for these variables that will allow accurate model selection and parameter estimation. This challenge is well exemplified—and yet unaddressed—in the field of associative learning research
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