Cells behave as complex systems with regulatory processes that make use of many elements
such as switches based on thresholds, memory, feedback, error-checking, and other
components commonly encountered in electrical engineering. It is therefore not surprising
that these complex systems are amenable to study by engineering methods. A great deal
of effort has been spent on observing how cells store, modify, and use information. Still,
an understanding of how one uses this knowledge to exert control over cells within a living
organism is unavailable. Our prime objective is "Personalized Cancer Therapy" which is
based on characterizing the treatment for every individual cancer patient. Knowing how
one can systematically alter the behavior of an abnormal cancerous cell will lead towards
personalized cancer therapy. Towards this objective, it is required to construct a model for
the regulation of the cell and utilize this model to devise effective treatment strategies. The
proposed treatments will have to be validated experimentally, but selecting good treatment
candidates is a monumental task by itself. It is also a process where an analytic approach
to systems biology can provide significant breakthrough. In this dissertation, theoretical
frameworks towards effective treatment strategies in the context of probabilistic Boolean
networks, a class of gene regulatory networks, are addressed. These proposed analytical
tools provide insight into the design of effective therapeutic interventions.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-08-2941 |
| Date | 2009 August 1900 |
| Creators | Vahedi, Golnaz |
| Contributors | Dougherty, Edward R. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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