The importance of studying angiogenesis, the formation of new blood vessels from pre-existing vessels, is underscored by its involvement in both normal physiology, such as embryonic growth and wound healing, and pathologies, such as diabetes and cancer. Treatments targeting the molecular drive of angiogenesis have been developed, but many of the molecular mechanisms that mediate vascularization, as well as how these mechanisms can be targeted in therapy, remain poorly understood. The limited capacity to quantify angiogenesis properly curtails our molecular understanding and development of new drugs and therapies. Although there are a number of assays for angiogenesis, many of them strip away its important components and/or limit control of the variables that direct this highly cooperative and complex process. Here we review assays commonly used in endothelial cell biology and describe the progress toward development of a physiologically realistic platform that will enable a better understanding of the molecular and physical mechanisms that govern angiogenesis.

Mathematical models of biomolecular networks are commonly used to study mechanisms of cellular processes, but their usefulness is often questioned due to parameter uncertainty. Here, we employ Bayesian parameter inference and dynamic network analysis to study dominant reaction fluxes in models of extrinsic apoptosis. Although a simplified model yields thousands of parameter vectors with equally good fits to data, execution modes based on reaction fluxes clusters to three dominant execution modes. A larger model with increased parameter uncertainty shows that signal flow is constrained to eleven execution modes that use 53 out of 2067 possible signal subnetworks. Each execution mode exhibits different behaviors to in silico perturbations, due to different signal execution mechanisms. Machine learning identifies informative parameters to guide experimental validation. Our work introduces a probability-based paradigm of signaling mechanisms, highlights systems-level interactions that modulate signal flow, and provides a methodology to understand mechanistic model predictions with uncertain parameters.

Our work seeks to transform how new and emergent variants of pandemic causing viruses, specially SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences, and then finetuning a SARS-CoV-2 specific model on 1.5 million genomes, we show that GenSLM can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLM represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate the scaling of GenSLMs on both GPU-based supercomputers and AI-hardware accelerators, achieving over 1.54 zettaflops in training runs. We present initial scientific insights gleaned from examining GenSLMs in tracking the evolutionary dynamics of SARS-CoV-2, noting that its full potential on large biological data is yet to be realized.

X-ray Photon Correlation Spectroscopy (XPCS) probes the spontaneous fluctuation in spatial configurations of mixed phases, which can significantly impact the material properties of complex fluid. Tailored material design, however, requires navigation through massive multi-dimensional parameter space which is beyond the bandwidth of current XPCS beamline infrastructure. Using 3.7 μs-resolved XPCS, we demonstrate that Brownian dynamics of silica colloidal nanoparticles in a shielded pendant drop suspended from an electronic pipette is consistent with a sealed capillary, validating the use of pendant drop for automated XPCS measurement. Furthermore, the electronic pipette can be mounted on a robotic arm to access different stock solutions and create samples with highly-repeatable and precisely-controlled composition profiles (Video S2). This closed-loop, AI-executable experimental protocol is the first step towards autonomous material discovery and is applicable for not only XPCS, but also other high-throughput light and x-ray scattering techniques for multimodal, collaborative material characterization.

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. For instance, two orders of magnitude more ordinal (e.g., immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g., fluorescence) data for calibration of an apoptosis execution model. Notably, ordinal and nominal (e.g., cell fate observations) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.