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Metabolism is intertwined with various cellular processes, including controlling cell fate, influencing tumorigenesis, participating in stress responses and more. Metabolism is a complex, interdependent network, and local perturbations can have indirect effects that are pervasive across the metabolic network. Current analytical and technical limitations have long created a bottleneck in metabolic data interpretation. To address these shortcomings, we developed Metaboverse, a user-friendly tool to facilitate data exploration and hypothesis generation. Here we introduce algorithms that leverage the metabolic network to extract complex reaction patterns from data. To minimize the impact of missing measurements within the network, we introduce methods that enable pattern recognition across multiple reactions. Using Metaboverse, we identify a previously undescribed metabolite signature that correlated with survival outcomes in early stage lung adenocarcinoma patients. Using a yeast model, we identify metabolic responses suggesting an adaptive role of citrate homeostasis during mitochondrial dysfunction facilitated by the citrate transporter, Ctp1. We demonstrate that Metaboverse augments the user’s ability to extract meaningful patterns from multi-omics datasets to develop actionable hypotheses.

An outer-membrane complex exposes the cell-surface CglB adhesin at bacterial focal-adhesion sites to mediate gliding motility.

We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. These networks benefit from a significant reduction in the number of trainable parameters with respect to an equivalent fully connected network. These architectures are also versatile with respect to the input and output dimensionality. For example, encoder-decoder, decoder-encoder or decoder-encoder-decoder architectures are well suited to learn mappings between input and outputs of any dimensionality. We demonstrate the accuracy produced by such architectures when trained on a few high-fidelity and many low-fidelity data generated from models ranging from one-dimensional functions to Poisson equation solvers in two-dimensions. We finally discuss a number of implementation choices that improve the reliability of the uncertainty estimates generated by a dropblock regularizer, and compare uncertainty estimates among low-, high- and multi-fidelity approaches.

MXenes, a family of 2D transition‐metal carbides and nitrides, have excellent electrical conductivity and unique optical properties. However, MXenes oxidize in ambient conditions, which is accelerated upon heating. Intercalation of water also causes hydrolysis accelerating oxidation. Developing new tools to readily characterize MXenes’ thermal stability can enable deeper insights into their structure–property relationships. Here, in situ spectroscopic ellipsometry (SE) is employed to characterize the optical properties of three types of MXenes (Ti₃C₂T_x, Mo₂TiC₂T_x, and Ti₂CT_x) with varied composition and atomistic structures to investigate their thermal degradation upon heating under ambient environment. It is demonstrated that changes in MXene extinction and optical conductivity in the visible and near‐IR regions correlate well with the amount of intercalated water and hydroxyl termination groups and the degree of oxidation, measured using thermogravimetric analysis. Among the three MXenes, Ti₃C₂T_xand Ti₂CT_x, respectively, have the highest and lowest thermal stability, indicating the role of transition‐metal type, synthesis route, and the number of atomic layers in MXene flakes. These findings demonstrate the utility of SE as a powerful in situ technique for rapid structure–property relationship studies paving the way for the further design, fabrication, and property optimization of novel MXene materials.

A mass spectrometry and dialysis method detects metabolite-protein interactions that help to control physiology.