Abstract: Disclosed herein are systems and methods for determining metabolic pathway dynamics using time series multiomics data. In one example, after receiving time series multiomics data comprising time-series metabolomics data associated a metabolic pathway and time-series proteomics data associated with the metabolic pathway, derivatives of the time series multiomics data can be determined. A machine learning model, representing a metabolic pathway dynamics model, can be trained using the time series multiomics data and the derivatives of the time series multiomics data, wherein the metabolic pathway dynamics model relates the time-series metabolomics data and time-series proteomics data to the derivatives of the time series multiomics data. The method can include simulating a virtual strain of the organism using the metabolic pathway dynamics model.

Abstract: Disclosed herein include systems, devices, and methods for kinetic learning, which can include, for example, training and/or using a machine learning model, such as training a machine learning model and using the machine learning model to simulate a virtual strain of an organism or to determine possible modifications of an organism.

Abstract: Disclosed herein is Arrowland, a web-based software tool for inputting, managing and viewing multiomics data, such as transcriptomics, proteomics, metabolomics and fluxomics data in an interactive, intuitive and multiscale system.

Abstract: Disclosed herein include systems, devices, and methods for training and using a probabilistic predictive ensemble model for recommending experiment designs for a biology (e.g., synthetic biology) experiment. Also disclosed herein include methods for performing a biology (e.g., synthetic biology) experiment using a probabilistic predictive ensemble model for recommending experiment designs for biology.

Abstract: A method of unsupervised protein sequence generation includes determining a dataset of known protein sequences, wherein the dataset comprises unlabeled or sparsely labeled data. The method further includes training, by a processing device, a generative model on the dataset. The method further includes generating, using the generative model, a semantically-valid protein sequence example based on the dataset.

Abstract: Disclosed herein is Arrowland, a web-based software tool for inputting, managing and viewing multiomics data, such as transcriptomics, proteomics, metabolomics and fluxomics data in an interactive, intuitive and multiscale system.

Abstract: Disclosed herein are systems and methods for determining metabolic pathway dynamics using time series multiomics data. In one example, after receiving time series multiomics data comprising time-series metabolomics data associated a metabolic pathway and time-series proteomics data associated with the metabolic pathway, derivatives of the time series multiomics data can be determined. A machine learning model, representing a metabolic pathway dynamics model, can be trained using the time series multiomics data and the derivatives of the time series multiomics data, wherein the metabolic pathway dynamics model relates the time-series metabolomics data and time-series proteomics data to the derivatives of the time series multiomics data. The method can include simulating a virtual strain of the organism using the metabolic pathway dynamics model.