Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information

Abstract

One 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.