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Abstract Enzymes that oxidize aromatic substrates have shown utility in a range of cell-based technologies including live cell proximity labeling (PL) and electron microscopy (EM), but are associated with drawbacks such as the need for toxic H₂O₂. Here, we explore laccases as a novel enzyme class for PL and EM in mammalian cells. LaccID, generated via 11 rounds of directed evolution from an ancestral fungal laccase, catalyzes the one-electron oxidation of diverse aromatic substrates using O₂instead of toxic H₂O₂, and exhibits activity selective to the surface plasma membrane of both living and fixed cells. We show that LaccID can be used with mass spectrometry-based proteomics to map the changing surface composition of T cells that engage with tumor cells via antigen-specific T cell receptors. In addition, we use LaccID as a genetically-encodable tag for EM visualization of cell surface features in mammalian cell culture and in the fly brain. Our study paves the way for future cell-based applications of LaccID. 

We study the problem of analyzing the effects of inconsistencies in perception, intent prediction, and decision making among interacting agents. When accounting for these effects, planning is akin to synthesizing policies in uncertain and potentially partially-observable environments. We consider the case where each agent, in an effort to avoid a difficult planning problem, does not consider the inconsistencies with other agents when computing its policy. In particular, each agent assumes that other agents compute their policies in the same way as it does, i.e., with the same objective and based on the same system model. While finding policies on the composed system model, which accounts for the agent interactions, scales exponentially, we efficiently provide quantifiable performance metrics in the form of deltas in the probability of satisfying a given specification. We showcase our approach using two realistic autonomous vehicle case-studies and implement it in an autonomous vehicle simulator. 

This paper introduces a strategy for satisfying basic control objectives for systems whose dynamics are almost entirely unknown. This setting is motivated by a scenario where a system undergoes a critical failure, thus significantly changing its dynamics. In such a case, retaining the ability to satisfy basic control objectives such as reach-avoid is imperative. To deal with significant restrictions on our knowledge of system dynamics, we develop a theory of myopic control. The primary goal of myopic control is to, at any given time, optimize the current direction of the system trajectory, given solely the limited information obtained about the system until that time. Building upon this notion, we propose a control algorithm which simultaneously uses small perturbations in the control effort to learn local system dynamics while moving in the direction which seems to be optimal based on previously obtained knowledge. We show that the algorithm results in a trajectory that is nearly optimal in the myopic sense, i.e., it is moving in a direction that seems to be nearly the best at the given time, and provide formal bounds for suboptimality. We demonstrate the usefulness of the proposed algorithm on a high-fidelity simulation of a damaged Boeing 747 seeking to remain in level flight. 

Abstract Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood^1–3. Here, the Molecular Transducers of Physical Activity Consortium⁴profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and femaleRattus norvegicusover eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository (https://motrpac-data.org/).