Project Grant FA95502210379

This Project Grant award from the National Science Foundation's (NSF) Biological Sciences program (CFDA 47.074) provides $885,642 to Trustees of Boston University to conduct research aimed at defining the underlying principles behind the function of gene regulatory networks. The project uses an optogenetic strategy to test how dynamic input signals are propagated within regulatory networks with increasing levels of complexity, first in well-defined synthetic networks and then in the endogenous...

This National Science Foundation (NSF) Biological Sciences program Project Grant, with an award date of October 1, 2023 and total funding of $256,667.00, supports the development of advanced computational models to measure cellular traits in microscopy images. The goal is to create a universal deep-learning model that can readily quantify cell morphology in any microscopy image, with minimal training required. Specific objectives include developing methods for learning and extracting...

This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program provides $240,000 in funding to Yale University to develop a new class of computational frameworks that integrate large language models with neural operator learning techniques. The goal is to address key challenges in modeling spatiotemporal phenomena in biomedical research, such as gaining critical insights into cellular function, disease progression,...

This Project Grant award of $166,757 from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research to develop new mathematical models and machine learning approaches for analyzing spatial gene expression data. The key aims are to generalize connections between stochastic reaction-diffusion models and spatial point processes to better understand gene expression patterns at subcellular and multicellular scales. This comprehensive research...

This federal Project Grant award of $267,098.00 from the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859) supports research to develop statistical methods for learning gene regulatory networks (GRNs) from large-scale RNA-seq data, with a focus on addressing challenges posed by latent confounding and data dependence. The key objectives are to: 1) Formulate GRN inference as a causal discovery problem through...

This federal Project Grant award of $598,744.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research by Rensselaer Polytechnic Institute (RPI) to develop a hybrid AI model that integrates deep learning with probabilistic graphical models. The goal is to overcome the limitations of modern deep learning models, such as high data dependency, poor generalization, and low interpretability, by leveraging prior knowledge encoded in...

This $243,764 federal Project Grant award from the National Science Foundation's (NSF) Biological Sciences (CFDA 47.074) program will create a computational framework to help scientists understand the complex network of events inside living cells. The research will automate the construction of causal models of biological processes, allowing for large-scale analysis of massive biological datasets. The project will extend the CausalPath modeling framework to new domains, including the...

This $685,925 National Science Foundation Project Grant supports research at Georgia Tech Research Corporation to develop novel graph neural networks and machine learning strategies to understand, predict, and design allosteric transcription factors. The NSF Division of Chemistry and Divisions of Chemical, Bioengineering, Environmental and Transport Systems and Information and Intelligent Systems are jointly supporting this work under the Mathematical and Physical Sciences program (CFDA 47.049)....

This National Science Foundation (NSF) Project Grant award of $384,214 to Northeastern University will develop statistical inference methods that leverage advanced techniques like reinforcement learning, Bayesian statistics, and machine learning. The project aims to 1) incorporate expert knowledge into the modeling process without requiring expert oversight, and 2) systematize data collection for accurate inference of complex systems and processes. The proposed approaches will be applied in...

This National Science Foundation (NSF) Engineering (CFDA 47.041) Project Grant award to Carnegie Mellon University (CMU) provides $500,000 in funding from July 1, 2024 to June 30, 2027 to develop a new computational framework for predicting neuron growth and transport regulation in biological neural circuits (BNCs). The project aims to advance knowledge of the fundamental mechanisms of neural growth, material transport regulation, and circuit dynamics through three specific objectives: (1)...