Data-driven inference of digital twins for high-throughput phenotyping of motile and light-responsive microorganisms

Many microorganisms alter their movement in response to light. These responses can drive collective behaviours like photoaccumulation and photodispersion, which play a key role in broader biological functions like photosynthesis. Our understanding of these emergent phenomena is severely limited by difficulties in obtaining the data needed to establish accurate models that can serve as a basis for multi-scale analyses. Here, we address this issue by developing an integrated experimental and computational platform to collect large temporal imaging datasets that allow for the inference of 'digital twins'-mathematically precise computational models that accurately mirror the behaviour of individual microorganisms-and show that they can replicate the light response of diverse microorganisms in silico. We demonstrate that a generalized phenomenological model capable of simultaneously capturing dynamic speed variations and multiple light responses can be effectively parametrized from experimental data to capture key behavioural traits of two commonly studied photo-responsive microorganisms (Euglena gracilis and Volvox aureus). We also show our model's ability to accurately reproduce patterns of movement for individuals and populations in response to dynamic and spatially varying light patterns. This work takes steps towards the automated phenotyping of multi-scale behaviours in biology and unlocks new opportunities for the design of spatial control algorithms to guide collective microorganism behaviour.