Study Of Gene Regulatory Networks Inference Methods From Gene Expression Data
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Study of Gene Regulatory Networks Inference Methods from Gene Expression Data
A cell is a the basic structural and functional unit of every living thing, it is protein-based an that regulates itself. The cell eats to stay alive, it grows and develops; reacting to the environment, while subjected to evolution. It also makes copies of itself. These processes are governed by chain of chemical reactions, creating a complex system. The scientific community has proposed to model the whole process with Gene Regulatory Networks (GRN). The understanding of these networks allows gaining a systems-level acknowledgment of biological organisms and also to genetically related diseases. This thesis focused on network inference from gene expression data, will contribute to this field of knowledge by studying different techniques that allows a better reconstruction of GRN. Gene expression datasets, are characterised by having thousands of noisy variables measured only with tens of samples. Moreover, these variables presents non-linear dependencies between them. Therefore, recovering a model that is capable of capturing the relationships contained in this data, constitutes a major challenge. The main contribution of this thesis is a set of fair and sound studies of different GRN inference methods and post-processing algorithms. First, we present a novel approach for inferring gene networks and we compare it with other methods. It is inspired by the concept of "variable importance" in feature selection. However, many algorithms can be proposed to infer GRNs, so there is a need to assess the quality of these algorithms. Secondly, and motivated by the fact that the previous comparison was not informative enough, we introduce a new framework for in silico performance assessment of GRN inference methods. This work has led to an open source R/Bioconductor package called NetBenchmark. Finally, and thanks to this tool we have corroborated that inferring gene regulatory networks from expression data is a tough problem. The different algorithms have some particular biases and strengths, and none of them is the best across all types of data and datasets. Therefore, we present a framework for evaluating and standardising network consensus methods to aggregate various network inferences.
Drosophila Eye Development
Author: Kevin Moses
language: en
Publisher: Springer Science & Business Media
Release Date: 2002-03-12
1 Kevin Moses It is now 25 years since the study of the development of the compound eye in Drosophila really began with a classic paper (Ready et al. 1976). In 1864, August Weismann published a monograph on the development of Diptera and included some beautiful drawings of the developing imaginal discs (Weismann 1864). One of these is the first description of the third instar eye disc in which Weismann drew a vertical line separating a posterior domain that included a regular pattern of clustered cells from an anterior domain without such a pattern. Weismann suggested that these clusters were the precursors of the adult ommatidia and that the line marks the anterior edge of the eye. In his first suggestion he was absolutely correct - in his second he was wrong. The vertical line shown was not the anterior edge of the eye, but the anterior edge of a moving wave of patterning and cell type specification that 112 years later (1976) Ready, Hansen and Benzer would name the "morphogenetic furrow". While it is too late to hear from August Weismann, it is a particular pleasure to be able to include a chapter in this Volume from the first author of that 1976 paper: Don Ready! These past 25 years have seen an astonishing explosion in the study of the fly eye (see Fig.
Probabilistic Boolean Networks
This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes." "Researchers in mathematics, computer science, and engineering are exposed to important applications in systems biology and presented with ample opportunities for developing new approaches and methods. The book is also appropriate for advanced undergraduates, graduate students, and scientists working in the fields of computational biology, genomic signal processing, control and systems theory, and computer science.