@GalaganLab

The official account of the James Galagan Lab at Boston University

 Boston, MA
galaganlab.org/
Joined March 2020
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🎓Congratulations to Dr. Roman Addokhi on completing his PhD dissertation titled “Development and Validation of Wearable Devices for Real-Time Sweat Biochemical Sensing.” #PhDDefense #WearableTech #Biosensors #SweatSensing #BostonUniversity
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Roman’s work demonstrated cutting edge wearable sweat monitoring with a novel biosensor in a fully powered human study, paved the way for multiplexed novel analyte sensing, and introduced methods to utilize EIS as a systems identification tool for biosensors. Congrats Roman!!
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Our results provide insight into several global features of TF binding behavior, including clustering of binding sites, the importance of poorly conserved accessory bases, the physiological relevance of weak binding sites, and the background affinity of the genome.
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We compared BoltzNet to existing state of the art methods, and BoltzNet outperformed - demonstrating the ability of a biophysically designed neural network for interpretable machine learning and state-of-the-art accuracy. We’re proud to share this resource with the community.
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Thanks to all who contributed! @RegulonDB @wade_lab @PatDotPy @VhTierrafria
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🚨 Our BoltzNet paper is out @NatureComms! nature.com/articles/s41467-0… We are excited to provide a website with access to all models, code and data boltznet.bu.edu

Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA...

Nature Communications - The authors describe BoltzNet, a neural network that learns the energy of transcription factor (TF)-DNA binding from genomic data and can be used to design new binding...

nature.com
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We generated binding profiles for 139 TFs—nearly half of E. coli’s repertoire. All data is public: 📂 Raw → GEO 📈 Processed → @RegulonDB 🧠 Models/tools → boltznet.bu.edu Includes model viewer, browser, and access to our evolving codebase.
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We describe BoltzNet, a biophysical neural network that predicts transcription factor (TF)–DNA binding from ChIP-Seq data by directly mirroring a quantitative model of molecular interactions.
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Pleased to report our paper on #BoltzNet, a novel biophysically-designed neural network to quantitatively predict TF binding affinity to any DNA sequence. Many thanks to our collaborators @wade_lab and @RegulonDB biorxiv.org/content/10.1101/…

Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA...

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global...

biorxiv.org
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Applied to over 125 E. coli TFs, we quantitatively designed novel binding sites, and gained insight into the roles of clustered binding, bases outside of the core motif, and the affinity of the genome. We hope this tool and resource will be valuable to the scientific community.
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Hello World! We’ve got several exciting updates to catch up on since our last posting. Time for a quick 🧵
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And most recently, in collaboration with @Grinstaff lab led by @ukuzma2 and @ksankar93 we published a tutorial review discussing strategies for building and optimizing biosensors! pubs.acs.org/doi/abs/10.1021…

Strategy, Design, and Fabrication of Electrochemical Biosensors: A Tutorial

Abstract. Advanced healthcare requires novel technologies capable of real-time sensing to monitor acute and long-term health. The challenge relies on conve

pubs.acs.org
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We look forward to continue sharing the exciting work our lab is doing, and thank all of our wonderful researchers and collaborators for their hard work pushing the boundaries of science across our areas of study!
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