Date of Award:

5-2015

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Computer Science

Committee Chair(s)

Nicholas Flann

Committee

Nicholas Flann

Committee

Gregory Podgorski

Committee

Xiaojun Qi

Committee

Minghui Jiang

Committee

Haitao Wang

Abstract

The incredible patterns of multicellular organisms emerge as a result of the operation of Gene Regulatory Networks (GRN) that work during development. Understanding how GRNs produce these complex multicellular patterns is a significant challenge in biology. The primary goal of this dissertation is to employ Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, to unravel the information complexity of GRNs and the resultant multicellular patterns. To obtain a better understanding of Kolmogorov complexity performance, first we study an application in cell image segmentation.

There are an estimated 20,000-25,000 protein-coding genes in the human genome. The sheer size of the human genome, as well as the huge number of protein and other gene product networks, requires systems biologists to use simplified computational models to gain insight into the behavior of the system. The approach taken in this work was to use a simplified model of a genetic regulatory network called a Boolean network, in which each gene is represented as a network node that takes binary values. Boolean networks represent a qualitative description of gene states and their interactions.

In this work, a model of embryonic cells in an epithelium field was simulated. Each cell holds a Boolean network and each Boolean network is designed to connect to the neighboring cells through cell-cell signaling. The state of each cellular network is initialized randomly by setting the state of each gene to 0 or 1. The state of the system during simulation is run synchronously until steady or cyclic state is reached for all individual cells. The steady or cyclic state, which is also referred to as attractor, is used to construct the multicellular body patterns by treating cells with the same attractor as the same cell types. The states of all the genes during the simulation of gene network dynamics along with multicellular patterns were encoded to strings and recoded for further analysis of information content. Kolmogorov complexity-based algorithms were applied to understand how the complexity of GRN configuration relates to the complexity of the spatial patterns that emerge as a consequence of network operation.

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