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|King Abdullah University of Science and Technology (KAUST), Saudi Arabia|
|Posters & Accepted Abstracts: J Proteomics Bioinform|
|Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network and molecular levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.|
Xin Gao is an Associate Professor of Computer Science in the Computer, Electrical and Mathematical Sciences and Engineering Division at KAUST. He is also a PI in the Computational Bioscience Research Center at KAUST and an adjunct faculty member at David R. Cheriton School of Computer Science at University of Waterloo, Canada. Prior to joining KAUST, he was a Lane Fellow at Lane Center for Computational Biology in School of Computer Science at Carnegie Mellon University, USA. He earned his bachelor degree in Computer Science in 2004 from Computer Science and Technology Department at Tsinghua University, China, and his PhD degree in Computer Science in 2009 from David R. Cheriton School of Computer Science at University of Waterloo, Canada. His research interests are building computational models, developing machine learning techniques, and designing efficient and effective algorithms, with particular focus on applications to key open problems in structural biology, systems biology and synthetic biology. He has co-authored more than 100 research articles in the fields of computational biology and machine learning.
Email: [email protected]
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