Modeling complex gene interacting systems as Boolean networks lead to
a significant simplification of computational investigation. This can be
achieved by discretization of the expression level to ON or OFF states and
classifying the interactions to inhibitory and activating. In this respect,
Boolean functions are responsible for the evolution of the binary elements of
the Boolean networks. In this thesis, we investigate the mostly used Boolean
functions in modeling gene regulatory networks. Moreover, we introduce
a new type of function with strong inhibitory namely the veto function.
Our computational and analytic studies on the verity of the networks capable
of constructing the same State Transition Graph lead to define a new
concept namely the “degeneracy” of Boolean functions. We further derive
analytically the sensitivity of the Boolean functions to perturbations. It
turns out that the veto function forms the most robust dynamics. Furthermore,
we verify the applicability of veto function to model the yeast cell
cycle networks. In particular, we show that in an intracellular signal transduction
network [Helikar et al, PNAS (2008)], the functions with veto are
over-represented by a factor exceeding the over-representation of threshold
functions and canalyzing functions in the same system. The statistics of
the connections of the functional networks are studied in detail. Finally,
we look at a different scale of biological phenomena using a binary model.
We propose a simple correlation-based model to describe the pattern formation
of Fly eye. Specifically, we model two different procedures of Fly eye
formation, and provide a generic approach for Fly eye simulation.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:15-qucosa-205944 |
| Date | 05 July 2016 |
| Creators | Ebadi, Haleh |
| Contributors | Universität Leipzig, Fakultät für Mathematik und Informatik, Prof Pedro Miramontes |
| Publisher | Universitätsbibliothek Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf |
Page generated in 0.0018 seconds