  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.
