KAUST and EPFL Blue Brain Project researchers propose a new theory about a 'secret language' used by cells for internal communication regarding the external world. Using a computational model, they suggest that metabolic pathways can code details about neuromodulators that stimulate energy consumption. The model focuses on astrocytes and their cooperation with neurons in fueling the brain. Why it matters: This suggests a new avenue for understanding information processing in the brain and how cells contribute to the energy efficiency of brains compared to computers.
MBZUAI researchers have developed MorphDiff, a diffusion model that predicts cell morphology from gene expression data. MorphDiff uses the transcriptome to generate realistic post-perturbation images, either from scratch or by transforming a control image. The model combines a Morphology Variational Autoencoder (MVAE) with a Latent Diffusion Model, enabling both gene-to-image generation and image-to-image transformation. Why it matters: This could significantly accelerate drug discovery and biological research by allowing scientists to preview cellular changes before conducting experiments.
KAUST's Environmental Epigenetics Program (KEEP), led by Prof. Valerio Orlando, focuses on understanding how cells acquire and maintain memory, particularly in response to environmental factors. The research investigates the role of non-coding RNA and chromosomal components in regulating gene expression beyond the DNA sequence. Epigenetics explains how the same genome can be interpreted differently, allowing cells and organs to adapt to changing conditions. Why it matters: This research could provide insights into how environmental factors impact gene expression and cell function, potentially leading to advances in understanding and treating diseases.
KAUST researchers developed a new algorithm for detecting cause and effect in large datasets. The algorithm aims to find underlying models that generate data, helping uncover cause-and-effect dynamics. It could aid researchers across fields like cell biology and genetics by answering questions that typical machine learning cannot. Why it matters: This advancement could equip current machine learning methods with abilities to better deal with abstraction, inference, and concepts such as cause and effect.
MBZUAI researchers collaborated with Carnegie Mellon University and the Broad Institute of MIT and Harvard to develop a new statistical method for analyzing data used for gene regulatory network inference. The method addresses the challenge of distinguishing true zero expression values from dropouts in single-cell RNA sequencing data. This research will be presented at the Twelfth International Conference on Learning Representations (ICLR 2024). Why it matters: Improving gene regulatory network inference can lead to better understanding of disease mechanisms and inform the development of new medicines.