AI and Quantum for Biology and Biotechnology – Enabling Microbial Digital Twining Across Scale

Platforms that enable concurrent spatiotemporal and biochemical monitoring are generating multi-modal datasets for quantifying microbial dynamics and outcomes of molecular to cellular level communication. Using these data sets, we are developing in silico multicellular models to probe how bacteria respond to changing environments with the ultimate goal of using molecular communication networks to modulate community response. Hybrid multiscale methods that integrate physics-based or mechanism driven models such as systems biology informed agent-based models (ABM) with data-driven machine learning/artificial intelligence methods are invaluable approaches for capturing and predicting the dynamics of these microbe-microbe and microbe-environment interactions. We will discuss hybrid ensemble modeling approaches used in digitizing Eschericia coli K12 populations to investigate state dependent population response and stress adaptation. The need for various mathematical and computing modalities to describe molecular to cellular interactions and how the potential of quantum computing enabled simulations changes the feasible space for high resolution microbial digital twins will be highlighted.