Semantic analysis of behavior in a DNA-functionalized molecular swarm
This work addresses the explainability of simulated experiments for designing and optimizing in-vitro molecular systems, representing an incremental improvement in feature extraction methods.
The paper tackled the problem of understanding and optimizing molecular swarm behaviors by applying semantic embedding to extract features from simulations of DNA-functionalized microtubule swarms, showing that the extracted semantic atoms match expected behaviors and accurately describe the impact of external controls.
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.