Bayesian design of synthetic biological systems

TL;DR

This paper introduces a Bayesian framework for designing synthetic biological systems, enabling efficient model selection and balancing complexity with predictive performance, demonstrated through various biological models.

Contribution

The paper presents a novel Bayesian design approach for synthetic biology that integrates model selection and in-silico prototyping, improving design efficiency and accuracy.

Findings

Bayesian model selection effectively ranks models based on their ability to produce desired data.

Approximate Bayesian computation enables model comparison without explicit likelihood calculations.

The approach successfully designs systems with adaptive and switch-like behaviors in biological models.

Abstract

Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, while in the latter, we seek to construct the system that produces the data that we would like to observe, i.e. the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. In order to deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models in order to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or in-silico prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.