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Uncovering cellular metabolism with generative learning
Complex metabolic behaviour in cells can be captured with dynamic kinetic models. Such models are challenging to develop owing to the lack of knowledge about the characteristic kinetic parameter values that govern the cellular physiology of organisms. A new generative deep learning framework called REKINDLE has been developed by Subham Choudhury et al. to efficiently parameterize large-scale kinetic models, which helps to navigate the complex physiologies of various types of cellular organisms. Transfer learning in the low data regime allows REKINDLE to significantly expand the potential applications of kinetic modelling.
There is growing interest in using machine learning to mitigate climate change. But as avoiding catastrophic temperature rises becomes more urgent, action is also needed to understand the environmental impact of machine learning research.
Directed, active transport of cargo is essential for life on all length scales. A new system of artificial microtubules — consisting of a fibre with an embedded periodic array of magnetic inclusions — provides controlled active transport of microcargo by a rotating magnetic field, even under adverse flow conditions.
Deep learning models for sequential data can be trained to make accurate predictions from large biological datasets. New tools from computer vision and natural language processing can help us make these models interpretable to biologists.
Designing viable molecular candidates is pivotal to devising low-cost and sustainable storage systems. A reinforcement learning framework has been developed that can identify stable candidates for redox flow batteries in the large search space of organic radicals.
It has become rapidly clear in the past few years that the creation, use and maintenance of high-quality annotated datasets for robust and reliable AI applications requires careful attention. This Perspective discusses challenges, considerations and best practices for various stages in the data-to-AI pipeline, to encourage a more data-centric approach.
Targeted drug delivery is an exciting application of nanorobotics, but directing particles in the blood stream to the right location and in sufficient number is challenging. Gu and colleagues have developed a microtubule scaffold with embedded micromagnets that allows cargo, such as drug particles, to be transported in microvascular networks with precision and speed.
Federated learning and unsupervised anomaly detection are common techniques in machine learning. The authors combine them, using multicentred datasets and various diseases, to automate the segmentation of brain abnormalities without the need for annotations or sharing private local data.
Single-cell datasets continue to grow in size and complexity, calling for computational tools to process and analyse data. Yang et al. present a contrastive learning framework to learn cell representations from single-cell multiomics datasets.
Kinetic models of metabolism capture time-dependent behaviour of cellular states and provide valuable insights into cellular physiology, but, due to the lack of experimental data, traditional kinetic modelling can be unreliable and computationally inefficient. A generative framework based on deep learning called REKINDLE can efficiently parameterize large-scale kinetic models, enabling new opportunities to study cellular metabolic behaviour.
Finding stable radical compounds for redox flow batteries is a challenging molecular design task. Sowndarya et al. combine an AlphaZero-based framework with a surrogate objective function trained on quantum chemistry simulations to generate suitable radical candidates that are stable. The approach promises to contribute to the development of low-cost, reliable energy storage technologies.