Petr Šulc’s research focuses on application of computational modeling and statistical physics approaches to complex systems. In particular, his group uses computational models to study problems in biology, and bio-inspired nanotechnology systems. He is mainly interested in nucleic acids modeling (DNA and RNA) using coarse-grained models, which allow for simulations of longer time-scales and larger systems than if fully-atomistic representation is used. Such an approach is allows for efficient studies of nanotechnology system, as well as biologically relevant interactions between nucleic acids.
His research group is furthermore interested in applying computer simulations and statistical physics analysis to study properties of RNA molecules in vivo, in relation to RNAs expressed in tumor cells and viral genomes. The research is done in active collaboration with experimental groups.
He is an assistant professor at ASU. Prior to ASU, he was a fellow in physics and biology with The Rockefeller University (2014-2017) and served as a graduate research assistant with the Center for Nonlinear Studies at the Los Alamos National Laboratory, New Mexico (2009-2010). His prior research also involved mathematical modeling and optimization of smart-grid systems.
Petr Sulc's research focuses on application of computational modeling / statistical physics approaches to complex systems. In particular, his group uses computational models to study problems in biology, and bio-inspired nanotechnology systems. He is mainly interested in nucleic acids modeling (DNA and RNA) using coarse-grained models, which allow for simulations of longer time-scales and larger systems than if fully-atomistic representation is used. Such an approach is allows for efficient studies of nanotechnology system, as well as biologically relevant interactions between nucleic acids.
His research group is furthermore interested in applying computer simulations and statistical physics analysis to study properties of RNA molecules in vivo, in relation to RNAs expressed in tumor cells and viral genomes. The research is done in active collaboration with experimental groups.
His prior research also involved mathematical modeling and optimization of smart-grid systems.
Summer 2022 | |
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Course Number | Course Title |
BDE 792 | Research |
BDE 795 | Continuing Registration |
Spring 2022 | |
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Course Number | Course Title |
CHM 341 | Elementary Physical Chemistry |
BDE 792 | Research |
BDE 795 | Continuing Registration |
BDE 799 | Dissertation |
Fall 2021 | |
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Course Number | Course Title |
CHM 494 | Special Topics |
CHM 598 | Special Topics |
BDE 792 | Research |
BDE 799 | Dissertation |
Summer 2021 | |
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Course Number | Course Title |
BDE 792 | Research |
Spring 2021 | |
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Course Number | Course Title |
CHM 341 | Elementary Physical Chemistry |
BDE 792 | Research |
BDE 799 | Dissertation |
Fall 2020 | |
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Course Number | Course Title |
CHM 341 | Elementary Physical Chemistry |
Summer 2020 | |
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Course Number | Course Title |
BDE 792 | Research |
Spring 2020 | |
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Course Number | Course Title |
CHM 240 | Math Methods in Chemistry |
BDE 792 | Research |
BDE 799 | Dissertation |
Fall 2019 | |
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Course Number | Course Title |
CHM 501 | Current Topics in Chemistry |
CHM 598 | Special Topics |
Spring 2019 | |
---|---|
Course Number | Course Title |
CHM 240 | Math Methods in Chemistry |
BDE 792 | Research |
BDE 799 | Dissertation |
Spring 2018 | |
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Course Number | Course Title |
CHM 240 | Math Methods in Chemistry |