Deriving executable models of biochemical network dynamics from qualitative data

Simple item page
dc.contributor.advisorNakhleh, Luay K.en_US
dc.creatorRuths, Dereken_US
dc.date.accessioned2011-07-25T01:38:45Zen_US
dc.date.available2011-07-25T01:38:45Zen_US
dc.date.issued2009en_US
dc.description.abstractProgress in advancing our understanding of biological systems is limited by their sheer complexity, the cost of laboratory materials and equipment, and limitations of current laboratory technology. Computational and mathematical modeling provide ways to address these obstacles through hypothesis generation and testing without experimentation---allowing researchers to analyze system structure and dynamics in silico and, then, design lab experiments that yield desired information about phenomena of interest. These models, however, are only as accurate and complete as the data used to build them. Currently, most models are constructed from quantitative experimental data. However, since accurate quantitative measurements are hard to obtain and difficult to adapt from literature and online databases, new sources of data for building models need to be explored. In my work, I have designed methods for building and executing computational models of cellular network dynamics based on qualitative experimental data, which are more abundant, easier to obtain, and reliably reproducible. Such executable models allow for in silico perturbation, simulation, and exploration of biological systems. In this thesis, I present two general strategies for building and executing tokenized models of biochemical networks using only qualitative data. Both methods have been successfully used to model and predict the dynamics of signaling networks in normal and cancer cell lines, rivaling the accuracy of existing methods trained on quantitative data. I have implemented these methods in the software tools PathwayOracle and Monarch, making the new techniques I present here accessible to experimental biologists and other domain experts in cellular biology.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS COMP. SCI. 2009 RUTHSen_US
dc.identifier.citationRuths, Derek. "Deriving executable models of biochemical network dynamics from qualitative data." (2009) Diss., Rice University. <a href="https://hdl.handle.net/1911/61854">https://hdl.handle.net/1911/61854</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/61854en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectBiologyen_US
dc.subjectBioinformaticsen_US
dc.subjectComputer scienceen_US
dc.subjectApplied sciencesen_US
dc.titleDeriving executable models of biochemical network dynamics from qualitative dataen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
3362397.PDF
Size:
6.26 MB
Format:
Adobe Portable Document Format
Download
Collections
Rice University Theses and Dissertations