AI-Driven Synthetic Biology: Integrating Machine Learning with Genomic Engineering for Advanced Biomedical Applications

Dynamic, safe, and predictable synthetic transformation of one type and one microbe of a low-cost translational gene circuit was sought to implement a multispecies biocomputation system. As design criteria, the ability to provide a target environmental signal, the supply of a metabolic gene, modularity, and a numerical control mechanism were specified. An extended parametric design space was employed to allow ad hoc aspects such as a mechanical amplifier and saturation thresholds to be specified post hoc. Metabolic background genes targeting lemon scent and 3-hydroxypropionic acid production were autoannotated. Bayesian inference was employed to select a design load that would be slow and competitive for natural selection, while construction, embedding, and testing were guided by a balance of rules and experience. Experiments validated culture and micromillifluid formats, and a model-guided dosing strategy ensured quicker and richer responses, guiding further steps towards self-dosable living therapeutics and low-cost devices. The specification, exploration, and inference of load requirements significantly extend the capabilities of previous biocomputational designs and obviate the need for target-specific knowledge. All approaches should be emergently applicable to a multitude of natural and synthetic biocomputations. Potential ecological consequences should be manageable as the system should be functional only in specific analysis and health contexts. To address biological systems, synthesis, stability assurance, spatiotemporal addressability, and measurement were specified. Recursive sequential construction was sought to ensure modularity from the outset and enable the quantification of constituent interconnectivity probabilities. To parse the available solutions, the K-frame formalism was extended to active parts, and trusted parts were distinguished from potential ones. A level structure was adopted for the coherent generation of large, complex grammar-guided designs. Stochastic and kinetic threshold models were applied to encode containment and growth rate mismatch, respectively. To construct an interlocking multilayer graph, once trusted templates were prepared, their spatiotemporal specification was treated at the legal level. Briefs generated a readability-oriented pen screen to ensure clarity. Candidate selection was guided by electrostatics-derived scoring to efficiently focus mutagenesis on the most unreliable components.