Abstract
Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits.
Highlights
Synthetic biology is a nascent field with transformative potential to a variety of disciplines, ranging from development of therapeutics [1] to biofuel production [2]
In order to evaluate the scalability of the framework, we constructed synthetic libraries that consisted of synthetic promoter parts with parameter values within the experimentally measured range, with a sampling distribution that varied from uniform to gamma
In this paper we introduced a global mixed-integer non-linear programming framework for the automatic construction of synthetic gene circuits with either steady-state or temporal objectives
Summary
Synthetic biology is a nascent field with transformative potential to a variety of disciplines, ranging from development of therapeutics [1] to biofuel production [2]. Automation is one of the conceptual pillars of synthetic biology, designs still rely on a trial-and-error and tinkering approaches. When it comes to automated biological circuit design, computer-aided design (CAD) tools have still low penetrance to biological circuit design despite notable developments in the field. Recent advances include efforts to adapt electrical engineering concepts, such as Boolean optimization and Carnaugh maps, to biological circuit design of digital functions [3], and approaches that build formal high-level languages to translate from user-defined specifications to genetic circuits that adhere to digital logic [4], [5], [6]. The capabilities of that method are limited, as it targets only steady-state problems and it cannot guarantee optimality in non-convex problems, which usually is the case in biological systems
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