Bayesian Optimization for Synthetic Gene Design

TL;DR

This paper introduces a Bayesian optimization framework for synthetic gene design, using Gaussian processes and biologically meaningful features to efficiently explore complex design spaces and optimize multiple gene characteristics simultaneously.

Contribution

It presents a novel three-step Bayesian optimization approach that models gene behavior, optimizes multiple objectives, and ranks candidate sequences for synthetic gene design.

Findings

Effective in real gene design experiments with mammalian cells

Allows multi-objective optimization of gene features

Provides a ranking method for candidate gene sequences

Abstract

We address the problem of synthetic gene design using Bayesian optimization. The main issue when designing a gene is that the design space is defined in terms of long strings of characters of different lengths, which renders the optimization intractable. We propose a three-step approach to deal with this issue. First, we use a Gaussian process model to emulate the behavior of the cell. As inputs of the model, we use a set of biologically meaningful gene features, which allows us to define optimal gene designs rules. Based on the model outputs we define a multi-task acquisition function to optimize simultaneously severals aspects of interest. Finally, we define an evaluation function, which allow us to rank sets of candidate gene sequences that are coherent with the optimal design strategy. We illustrate the performance of this approach in a real gene design experiment with mammalian cells.