Learning networks in biological systems
Abstract/Contents
- Abstract
- Learning networks from experimental evidence is an important problem to understand many biological systems. With the help of recent technological developments, such as high dimensional flow cytometry and gene expression arrays, a large amount of quality data are available for the task. Many different approaches exist, to model and learn biological networks from such data. Among those, we have developed methods for probabilistic models and differential equation models. The first part of this paper will cover our research on the Gaussian network model learning and its application to recover signal transduction networks. We develop a fast algorithm to learn Gaussian networks from data, based on a novel heuristic. We show that this algorithm can be extended to handle difficult situations such as non-Gaussian noise as well as limited number of simultaneous measurements. The performance of the algorithm against the standard alarm network is showed. Finally we apply the method to learn signal transduction networks from single cell level multi- channel flow cytometry data. We show that the method can recover networks with limited number of channels and non-Gaussian measurement noise. The second part will cover our research on the dynamical model of gene regulation. In this model, gene regulation is represented with differential equations in rational forms. We show that this type of model is able to reflect complex behaviors of gene regulation, compared to other existing models. Reconstucting the structure and parameters of such dynamical model is not triv- ial, given that limited measurability of gene expression. We develop a novel method to recover networks from measurements at so-called perturbed equilibrium. We show that this method can reconstruct gene regulatory networks with well-established ex- perimental designs. Model benchmark based on simulation data will be presented.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Choi, Bokyung |
|---|---|
| Associated with | Stanford University, Department of Applied Physics. |
| Primary advisor | Fisher, Daniel S |
| Primary advisor | Wong, Wing Hung |
| Thesis advisor | Fisher, Daniel S |
| Thesis advisor | Wong, Wing Hung |
| Thesis advisor | Sabatti, Chiara |
| Advisor | Sabatti, Chiara |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bokyung Choi. |
|---|---|
| Note | Submitted to the Department of Applied Physics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Bokyung Choi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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