Inference of gene expression networks using memetic gene expression programming

In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression networks are modelled by differential equations which represent temporal gene expression relations. Gene Expression Programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a memetic algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies

Comparison of evolutionary algorithms in gene regulatory network model inference

Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very di±cult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insu±cient. Results: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and o®er a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Conclusions: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identi¯ed and a platform for development of appropriate model formalisms is established

The Genetic Architecture of Complex Traits in Sheep

Many important traits in biology, medicine and agriculture are complex and quantitative in that they exhibit continuous variation and non-trivial patterns of genetic inheritance. They are largely polygenic and influenced by factors such as gene-gene and gene-environment interactions. Important reasons to study complex traits include trying to understand how the genetic components operate on their own and how they relate to each other, quantification of the contributions of these elements to trait variation, and elucidation of the underlying genetic architecture behind a trait. An understanding of the sources and consequences of variation in complex traits and identification of the genes involved provides us with a handle to manipulate biological systems, which can have direct applications in medicine and agricultural production. From an agricultural standpoint there are huge economic benefits to be achieved by a better understanding and exploitation of the genetic architecture of complex production traits such as milk yield in dairy animals and meat quality in e.g. sheep or cattle. This thesis is centred on making some inroads to better understand the genetic architecture of complex traits in sheep. The thesis progresses through a characterization of genetic structure and variability in Australian sheep populations, followed by a genome-wide association study for weight. Then a novel approach to improve estimates of genomic breeding values is discussed. Lastly, the inheritance and partitioning of gene expression variance is studied. A more detailed breakdown of the thesis follows

Gene regulatory network modelling with evolutionary algorithms -an integrative approach

Building models for gene regulation has been an important aim of Systems Biology over the past years, driven by the large amount of gene expression data that has become available. Models represent regulatory interactions between genes and transcription factors and can provide better understanding of biological processes, and means of simulating both natural and perturbed systems (e.g. those associated with disease). Gene regulatory network (GRN) quantitative modelling is still limited, however, due to data issues such as noise and restricted length of time series, typically used for GRN reverse engineering. These issues create an under-determination problem, with many models possibly fitting the data. However, large amounts of other types of biological data and knowledge are available, such as cross-platform measurements, knockout experiments, annotations, binding site affinities for transcription factors and so on. It has been postulated that integration of these can improve model quality obtained, by facilitating further filtering of possible models. However, integration is not straightforward, as the different types of data can provide contradictory information, and are intrinsically noisy, hence large scale integration has not been fully explored, to date. Here, we present an integrative parallel framework for GRN modelling, which employs evolutionary computation and different types of data to enhance model inference. Integration is performed at different levels. (i) An analysis of cross-platform integration of time series microarray data, discussing the effects on the resulting models and exploring crossplatform normalisation techniques, is presented. This shows that time-course data integration is possible, and results in models more robust to noise and parameter perturbation, as well as reduced noise over-fitting. (ii) Other types of measurements and knowledge, such as knock-out experiments, annotated transcription factors, binding site affinities and promoter sequences are integrated within the evolutionary framework to obtain more plausible GRN models. This is performed by customising initialisation, mutation and evaluation of candidate model solutions. The different data types are investigated and both qualitative and quantitative improvements are obtained. Results suggest that caution is needed in order to obtain improved models from combined data, and the case study presented here provides an example of how this can be achieved. Furthermore, (iii), RNA-seq data is studied in comparison to microarray experiments, to identify overlapping features and possibilities of integration within the framework. The extension of the framework to this data type is straightforward and qualitative improvements are obtained when combining predicted interactions from single-channel and RNA-seq datasets

Metabolic Network Model Identification-Parameter Estimation and Ensemble Modeling

Ph.DDOCTOR OF PHILOSOPH

Regulatory network discovery using heuristics

This thesis improves the GRN discovery process by integrating heuristic information via a co-regulation function, a post-processing procedure, and a Hub Network algorithm to build the backbone of the network.Doctor of Philosoph