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This paper introduces a semantic embedding approach to analyze and optimize the behavior of DNA-functionalized molecular swarms composed of microtubules and kinesin motors. By extending a microtubule model with DNA interaction and applying semantic analysis to simulation data, the authors demonstrate that the extracted semantic atoms align with expected swarm behaviors and accurately reflect the impact of external controls. This method provides a pathway for explainable AI-driven design and optimization of in-vitro molecular systems.
Unlock explainable control of molecular swarms by mapping their behaviors to interpretable semantic embeddings.
In this paper, we propose applying semantic embedding to learn the range of behaviors exhibited by molecular swarms, thereby providing a richer set of features to optimize such systems. Specifically, we consider a standard molecular swarm where the individuals are cytoskeletal filaments (called microtubules) propelled by surface-adhered kinesin motors, with the addition of DNA functionalization for further control. We extend a microtubule model with that additional interaction and show that the extracted semantic atoms from simulation results match the expected behaviors. Moreover, the decomposition of each frame in the simulations accurately describes the expected impact of the external control values. Those results provide relevant leads towards the explainability of simulated experiments, making them more reliable for designing and optimizing in-vitro systems.