The whole-cell behavior arises from the interplay among signaling, metabolic, and regulatory processes, which differ not only in their mechanisms, but also in the time scale of their execution. Proper modeling of the overall function of the cell requires development of a new modeling approach that accurately integrates these three types of processes, using the representation that best captures each one of them, and the interconnections between them. Traditionally, signaling networks have been modeled with ordinary differential equations (ODEs), regulation with Boolean networks, and metabolic pathways with Petri nets – these approaches are widely accepted and extensively used. Nonetheless, each of these methods, while being effective, have had limitations pointed out to them. Particularly, ODEs generally require very thorough parameterization, which is difficult to acquire, Boolean networks have been argued to be not capable of capturing complex systems dynamics, and the effectiveness of Petri nets when comparing to other, steady-state methods, have been debated.
The main goal of this dissertation is to devise an integrated model that capture the whole-cell behavior and accurately combines these three components in the interplay between them. I provide a systematic study on using particle swarm optimization (PSO) as an effective approach for parameterizing ODEs. I survey different inference method for Boolean networks on the sets of complex dynamic data and demonstrate that they are, in fact, capable of capturing a variety of different systems. I review the existing use of Petri nets in modeling of biochemical system to show their effectiveness and, particularly, the ease for their integration with other methods. Finally, I propose an integrated hybrid model (IHM) that uses Petri nets to represent metabolic and signaling components, and Boolean networks to model regulation. The interconnections between these models allow to overcome the time scale differences of the processes by adding appropriate delay mechanisms. I validate IHM on two data sets. The significant advantage of IHM over other models is that it is able to capture the dynamics of all three components and can potentially identify novel and important cross-talk within the cell.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/71922 |
| Date | 16 September 2013 |
| Creators | Berestovsky, Natalie |
| Contributors | Nakhleh, Luay K. |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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