Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints
This work addresses the problem of validating multiscale models in cancer biology for researchers, though it appears incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the challenge of evaluating a continuum model's descriptive capabilities in cancer biology by using deep learning to classify protein-specific lipid fingerprints from 1D statistics, achieving over 99.9% accuracy in simulations.
Capturing intricate biological phenomena often requires multiscale modeling where coarse and inexpensive models are developed using limited components of expensive and high-fidelity models. Here, we consider such a multiscale framework in the context of cancer biology and address the challenge of evaluating the descriptive capabilities of a continuum model developed using 1-dimensional statistics from a molecular dynamics model. Using deep learning, we develop a highly predictive classification model that identifies complex and emergent behavior from the continuum model. With over 99.9% accuracy demonstrated for two simulations, our approach confirms the existence of protein-specific "lipid fingerprints", i.e. spatial rearrangements of lipids in response to proteins of interest. Through this demonstration, our model also provides external validation of the continuum model, affirms the value of such multiscale modeling, and can foster new insights through further analysis of these fingerprints.