Inference of gene regulatory networks for embryological pattern formation

Abstract

Unlike molecular biology, systems biology study biological systems as a whole. It doesn``t agree with the intuition of breaking down systems into smaller parts, studying the parts one-by-one and hoping to be able to reassemble all the parts again at a later time. To be able to see "the bigger picture" of biological systems, which is full of mystery, redundancy and complexity, we need to include as much as possible, "necessary" information. But representations nowadays that describe relationships among molecules, organelles and cells up to physiological systems at the organism level often require excessive computations and resources. Oftentimes, it feels like we``re driving a sports car into the city streets where we can``t actually drive fast. In this thesis, we present an abstract way to modeling animal embryo development. As an easy reference, we use the drosophila embryo as a model system. By looking at the amount of proteins and nucleic acids expressed in cells, we can understand and infer how individual cells are organized, becomes diversified and how `architectural`` structure of organisms are constructed. A simple way of making two cells different from each other after cell division is to ensure that one cell activates genes different from those of the other. Thus, we refer to the genome which encodes genomic interactions as the genotype and the resulting pattern we see in terms of gene expression as the phenotype. The first part of our thesis introduces a methodology to describe how patterns in animals are formed in terms of gene expression levels. To do that, we describe gene regulatory networks with a method similar to the work of Bodnar et. Al. (Bodnar1996) where a set of Boolean rules represent inter-gene activity (inhibition or activation) that affect chromatin states of the genes involved. We refer to chromatin states as the protein contents of the cells and its neighboring cells prior to and after cell division. For instance; if a gene A activate...