bioRxivBiophysicsPreprint
Computational Synthetic Inner Membrane Reveals Cardiolipin-Leak Control of ATP Output
Abstract
The inner mitochondrial membrane (IMM) is a densely packed bioenergetic surface where electron transfer, proton pumping, and ATP synthesis are tightly coupled, and where performance is shaped by membrane leak, lipid composition, and higher-order organization. Although experimental reconstruction of oxidative phosphorylation in proteoliposomes has advanced, systematic exploration of design tradeoffs remains challenging because key parameters covary across preparations. Here we present a fully reproducible computational synthetic IMM (syn-IMM) framework that models coupled membrane energization and ATP production, then uses structured perturbations, parameter sweeps, and sensitivity analyses to identify dominant control variables. In a {Delta}{psi}-proxy simulator, we show that ATP output is maximized within a narrow cardiolipin performance window (peak at cardiolipin fraction 0.18 in our benchmarked parameterization), while increasing leak globally suppresses performance, producing a cardiolipin x leak landscape in which coupling integrity is a primary gate. Perturbation experiments separate mechanistic regimes, reduced ATP synthase capacity yields an "energized but unproductive" state with preserved {Delta}{psi} but depressed ATP flux, whereas increased leak reduces usable output despite relatively preserved energization. Monte Carlo sensitivity analysis ranks {Delta}{psi} and respiratory capacity as strongest correlates of ATP output, with ATP synthase capacity contributing positively and leak contributing negatively. A multi-state extension introduces explicit{Delta}{psi} -{Delta}pH partitioning, finite CoQ redox pool dynamics, and cardiolipin-dependent supercomplex fraction S(t), enabling diagnosis of organization kinetics and driving-force partition effects. Together, this syn-IMM platform provides an interpretable bridge between component-level reconstruction and system-level design, offering quantitative acceptance tests and design rules for programmable bioenergetic membranes.
- research focus: cell signaling & trafficking
- research focus: chromatin & epigenetic regulation
- approach: deep learning
- approach: statistical model
- Citations
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