Bayesian approaches to building models for biological systems
Abstract/Contents
- Abstract
- Understanding the structure and dynamics of biological macromolecules is a central focus of biological research. To be able to study and gain insights into these systems, it is first necessary to have an accurate and informative model for the system of interest. However, such a model is often difficult to build. For example, during protein folding, many proteins collapse into transient kinetic intermediates on timescales too fast for high-resolution experimental techniques to detect, preventing structural characterization of these species. Alternatively, current algorithms for RNA design (i.e. predicting a sequence that folds into a desired target structure) cannot accurately model structure-sequence relationships and rely primarily on brute force stochastic search, leading to poor performance on complex targets. Here, we show that it is possible to improve the quality of models for biological systems by applying a common Bayesian approach to building them, i.e. incorporating prior information to impose informative constraints on the model parameters. Through this approach, it is possible to build high-resolution models of protein dynamics given limited experimental data, as well as a state-of-the-art computational RNA design agent that outperforms all currently existing algorithms.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Shi, Jiakun |
|---|---|
| Degree supervisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Degree supervisor | Pande, Vijay |
| Thesis advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Thesis advisor | Pande, Vijay |
| Thesis advisor | Fayer, Michael D |
| Degree committee member | Fayer, Michael D |
| Associated with | Stanford University, Department of Chemistry. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jade Shi. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Jiakun Shi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...