DBTL Programming of Mitochondrial Inner-Membrane-Like ATP Modules

The mitochondrial inner membrane (IMM) achieves efficient ATP production by combining a low-proton-leak barrier, vectorial proton pumping, and FoF1-ATP synthase-mediated conversion of proton-motive force (pmf) into ATP. Although ATP-generating proteomembrane assemblies have been repeatedly demonstrated in vitro , a persistent engineering gap remains, namely, a decision grade workflow that links controllable manufacturing variables, lipid composition, detergents, assembly route, protein ratios, and orientation control, to functional outputs such as pmf, ATP rate and yield, stability, and tunability, in a way that supports rapid iteration and scale-up. Here we present a proof-of-concept Design-Build-Test-Learn (DBTL) framework for IMM-like energy modules that couples a physics-informed simulation of build records and quality control (QC) outcomes to interpretable machine-learning surrogates for prediction and experiment selection. Output magnitudes for ATP rate and yield are benchmarked to published reconstitution regimes, and stability behavior is represented through literature-consistent decay dynamics, providing a realistic scale for evaluating process decisions. Using the simulated manufacturing dataset, we implement explicit QC gates, train a random-forest surrogate model with an uncertainty proxy, quantify feature effects by permutation importance and partial dependence, construct robustness maps across leak and dispersity, and generate algorithmic next-build proposals by scoring candidate designs within manufacturable constraints. This work provides a transferable computational operating system for programming mitochondrial-like energy conversion with emphasis on manufacturability, auditability, and closed-loop iteration.