Abstract
Rapid engineering of biological systems is currently hindered by limited integration of manufacturing constraints into the design process, ultimately reducing the yield of many synthetic biology workflows. Here we tackle DNA engineering as a multi-objective optimization problem aiming at finding the best tradeoff between design requirements and manufacturing constraints. We developed a new open-source algorithm for DNA engineering, called Multi-Objective Optimisation algorithm for DNA Design and Assembly, available as a Python and Anaconda package, as well as a Docker image. Experimental results show that our method provides near-optimal constructs and scales linearly with design complexity, effectively paving the way to rational engineering of DNA molecules from genes to genomes.
Highlights
Recent advances in synthetic biology and DNA synthesis technologies are enabling significant scientific and biotechnological breakthroughs, including the engineering of pathways for drug production [1], the construction of minimal bacterial cells [2] and the assembly of synthetic eukaryotic chromosomes [3].Pivotal to these achievements has been the adoption of an iterative engineering workflow, known as the Design-Built-Test-Learn cycle (DBTL)
We tackle DNA engineering as a multi-objective optimization problem aiming at finding the best tradeoff between design requirements and manufacturing constraints
We assume that our input sequences represent modular designs consisting of a set of transcription units (TUs) made of a promoter, a coding sequence (CDS) and a terminator [26]
Summary
Recent advances in synthetic biology and DNA synthesis technologies are enabling significant scientific and biotechnological breakthroughs, including the engineering of pathways for drug production [1], the construction of minimal bacterial cells [2] and the assembly of synthetic eukaryotic chromosomes [3] Pivotal to these achievements has been the adoption of an iterative engineering workflow, known as the Design-Built-Test-Learn cycle (DBTL). The inherent waterfall structure of the DBTL workflow introduces dependencies between steps, making engineering biological systems still a complex task This is especially true for the design and build steps; in particular, the design space is strongly constrained by the DNA synthesis process, since phosphoramidite synthesis poses limits on molecule length and content. Recoding the design to meet manufacturing constraints often leads to molecules with substantially different content and properties, effectively breaking the DBTL workflow
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