Blackiston, D. | bioRxiv Science

bioRxiv2026

A Closed-Loop Robot Scientist for Autonomous Biological Discovery

Biological systems respond to a wide range of physical inputs and an overarching goal of biological research is understanding how these signaling events coordinate cellular, tissue, and organism-level outcomes. Yet the vast combinatorial space of physical and chemical interventions that influence these processes remains largely unexplored. Thus, a search process is required that efficiently learns how inputs affect a biological target via a series of automatically generated interventions, and a robot scientist that can conduct them. To this end we here introduce the Multimodal Organismal Modulation Robot (MOMbot), a robot scientist that integrates four physical intervention modalities - chemical delivery, electrical field application, mechanical vibration, and thermal modulation -within a single hardware platform. MOMbots multimodal hardware is paired with high-resolution imaging and an online active learning algorithm that autonomously generates, executes, and refines interventions based on accumulated data. We validate the system across biological materials spanning three orders of magnitude in scale, including Xenopus laevis embryos, motile mucociliary organoids, and disaggregated ectodermal stem cells, to show the utility of this approach across diverse biological disciplines. Using these biological models, we demonstrate precise thermal control of embryonic developmental rate, automated chemical perturbation with behavioral readouts, programmable electrical field stimulation, and vibrational assembly of functional mucociliary organoids from loose cells. We also describe the active learning algorithm that autonomously concentrates interventions near regions of maximal outcome uncertainty, yielding sample-efficient discovery of an electrical duration-response relationship for motile organoids. These results establish MOMbot as a scalable robot/AI scientist team for biological discovery, enabling autonomous, multimodal physical experimentation and AI-driven exploration of complex biological systems.

developmental biology↗

bioRxiv2024

Revealing non-trivial information structures in aneural biological tissues via functional connectivity

A central challenge in the progression of a variety of open questions in biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Information-theoretic approaches provide a quantitative framework for extracting patterns from data, but so far have been predominantly applied to neuronal systems at the tissue-level. Here, we demonstrate how time series of Ca2+ dynamics can be used to identify the structure and information dynamics of other biological tissues. To this end, we expressed the calcium reporter GCaMP6s in an organoid system of explanted amphibian epidermis derived from the African clawed frog Xenopus laevis, and imaged calcium activity pre- and post- a puncture injury, for six replicate organoids. We constructed functional connectivity networks by computing mutual information between cells from time series derived using medical imaging techniques to track intracellular Ca2+. We analyzed network properties including degree distribution, spatial embedding, and modular structure. We find organoid networks exhibit more connectivity than null models, with high degree hubs and mesoscale community structure with spatial clustering. Utilizing functional connectivity networks, we show the tissue retains non-random features after injury, displays long range correlations and structure, and non-trivial clustering that is not necessarily spatially dependent. Our results suggest increased integration after injury, possible cellular coordination in response to injury, and some type of generative structure of the anatomy. While we study Ca2+ in Xenopus epidermal cells, our computational approach and analyses highlight how methods developed to analyze functional connectivity in neuronal tissues can be generalized to any tissue and fluorescent signal type. Our framework therefore provides a bridge between neuroscience and more basal modes of information processing. Author summaryA central challenge in understanding several diverse processes in biology, including morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Significant progress in understanding information integration has occurred in neuroscience via the use of observable live calcium reporters throughout neural tissues. However, these same techniques have seen limited use in the non-neural tissues of multicellular organisms despite similarities in tissue communication. Here we utilize methods designed for neural tissues and modify them to work on any tissue type, demonstrating how non-neural tissues also contain non-random and potentially meaningful structures to be gleaned from information theoretic approaches. In the case of epidermal tissue derived from developing amphibians, we find non-trivial informational structure over greater spatial and temporal scales than those found in neural tissue. This hints at how more exploration into information structures within these tissue types could provide a deeper understanding into information processing within living systems beyond the nervous system.

systems biology↗