Type of Document Dissertation Author Pokrzywa, Revonda Maria Author's Email Address email@example.com URN etd-10142009-122741 Title Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information Degree PhD Department Genetics, Bioinformatics, and Computational Biology Advisory Committee
Advisor Name Title Mendes, Pedro J. P. Committee Chair Hoeschele, Ina Committee Member Laubenbacher, Reinhard C. Committee Member Murali, T. M. Committee Member Shulaev, Vladimir Committee Member Keywords
- Systems Biology
- Biological Networks
Date of Defense 2009-10-08 Availability restricted AbstractOne of the primary goals of systems biology is to understand the complex underlying
network of biochemical interactions which allow an organism to respond to
environmental stimuli. Models of these biological interactions serve as a tool to both
codify current understanding of these interactions as well as a starting point for scientific
discovery. Due to the massive amount of information which is required for this modeling
process, systems biology studies must often attempt to construct models which reflect the
whole of the system while having access to only partial information. In some cases, the
missing information will not have a confounding effect on the accuracy of the model. In
other cases, there is the danger that this missing information will make the model useless.
The focus of this thesis is to study the effect which missing information has on systems
level studies within several different contexts. Specifically, we study two contexts : when
the missing information takes the role of incomplete molecular interaction network
knowledge and when it takes the role of unknown kinetic rate laws. These studies yield
interesting results. We show that when metabolism is isolated from gene expression, the
effects are not limited to those reactions under strong control by gene expression. Thus,
incomplete understanding of molecular interaction networks may have unexpected effects
on the resulting analysis. We also reveal that under the conditions of the current study,
mass action was shown to be the superior substitute when the true rate equations for a
biological system are unknown.
In addition to studying the effect of missing information in the aforementioned contexts,
we propose a method for limiting the parameter search space of biochemical systems.
Even in ideal scenarios where both the molecular interaction network and the relevant
kinetic rate equations are known, obtaining appropriate estimates for the unknown system
parameters can be challenging. By employing a method which limits the parameter
search space, we are able to acquire estimates for parameter values which are much
closer to the true values than those which could be obtained otherwise.
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