12 research outputs found

    Mads floral integrators : Insights into molecular mechanisms of MADS domain proteins in the floral transition

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    The main aim of this thesis is understanding the molecular regulation of flowering time in Arabidopsis thaliana. More specifically, we focus on of key regulatory genes of flowering that integrate several internal and external flowering signals and examine in detail how they are regulated at the transcriptional and post-transcriptional level. Many of the key regulatory genes encode transcription factors (TFs), which are often functioning in larger protein complexes and are part of complex gene regulatory network. This thesis focuses on two important regulators that are MADS-domain TFs, SHORT VEGETATIVE PHASE (SVP) and SUPPERESSOR OF OVEREXPRESSION of CONSTANCE 1 (SOC1) and we studied the protein-protein interactions, chromosomal interactions and TF-DNA interactions, all connections that are part of the gene regulatory networks involved in flowering time control.</p

    Identification of a novel mutation in the CLN6 gene (CLN6) in South Hampshire sheep affected with Neuronal Ceroid Lipofuscinosis

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    Neuronal ceroid lipofuscinoses (NCL/Batten disease) are a group of fatal inherited neurodegenerative diseases that occur in many species including humans, sheep, dogs and cattle. Typical NCL symptoms include progressive loss of vision, regression of mental and motor development, epileptic seizures and premature death. Currently there is no effective treatment or cure for NCL, with the underlying disease mechanisms still poorly understood. Advances in molecular genetics in recent years have allowed the characterisation of hundreds of causative mutations and polymorphisms in at least 17 disease-causing genes across all species. For some species, research colonies have been established for studies relevant to the corresponding human NCL variants. Best characterised of all animal models for NCL is the New Zealand South Hampshire (SH) sheep which is a model for the human variant late-infantile form of NCL (vLINCL). Past studies have revealed the ovine CLN6 gene (CLN6) as a strong candidate gene for this disease in South Hampshire sheep however no disease-causing mutation was identified. The main objective of the present thesis is the identification and characterisation of the mutation responsible for NCL in the South Hampshire sheep. It was proposed that the mutation lies in the non-coding regions within or flanking the gene and that this mutation affects gene regulation. Bioinformatic tools were initially used to identify conserved non-coding sequences (CNCS) which are deemed potential regions of interest for regulatory mutations. Due to the limited ovine genome resource available when the study was commenced in 2006, CLN6 orthologous sequences from other species were initially used for identification of highly conserved regions. Of the five identified CNCS (5’ UTR, 3’UTR and introns 1, 2 and 6) the region upstream of CLN6 and intron 1 were considered priorities for sequencing. Given that the Sanger sequencing method was laborious and time-consuming, and that there was rapid development of technology; the Sanger sequencing approach was abandoned and Next-generation sequencing (NGS) methods utilised for the following studies. The 454 Pyrosequencing NGS technology was used to sequence the complete ovine Bacterial artificial chromosome (BAC) to generate an ovine reference sequence for mutation screening approaches. The first mutation screening approach, sequence capture and targeted sequencing approach failed; however, the second approach involving sequencing of long-range PCR (LR-PCR) products successfully identified the disease-causing mutation. LR-PCR amplification of 14 regions within the ovine genome region spanning the CLN6 and flanking sequences followed by SOLID sequencing-by-ligation NGS method identified the disease-associated mutation as a 402bp deletion and 1bp insertion in ovine CLN6, namely g.-251_+150del and g.+150_151insC. The mutation is predicted to lead to the deletion of the whole of exon 1 and the ATG start codon as well as flanking non-coding sequence. Identifying the disease-causing mutation for NCL in SH sheep provides the long-awaited confirmatory evidence that ovine CLN6 is the causative gene for NCL in SH sheep. Future research in this large animal model will allow for more effective strategies for developing therapeutic approaches for NCL in humans and further strengthens the invaluable role of this animal model for NCL studies

    A gene regulatory network model for control

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    The activity of a biological cell is regulated by interactions between genes and proteins. In artificial intelligence, this has led to the creation of developmental gene regulatory network (GRN) models which aim to exploit these mechanisms to algorithmically build complex designs. The emerging field of GRNs for control aims to instead exploit these natural mechanisms and this ability to encode a large variety of behaviours within a single evolvable genetic program for the solution of control problems. This work aims to extend the application domain of GRN models to previously unsolved control problems; the focus will here be on reinforcement learning problems, in which the dynamics of the system controlled are kept from the controller and only sparse feedback is given to it. This category of problems closely matches the challenges faced by natural evolution in generating biological GRNs. Starting with an existing GRN model, the fractal GRN (FGRN) model, a successful application to a standard control problem will be presented, followed by multiple improvements to the FGRN model and its associated genetic algorithm, resulting in better performances in terms of both reliability and speed. Limitations will be identified in the FGRN model, leading to the introduction of the Input-Merge- Regulate-Output (IMRO) architecture for GRN models, an implementation of which will show both quantitative and qualitative improvements over the FGRN model, solving harder control problems. The resulting model also displays useful features which should facilitate further extension and real-world use of the system

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Integration strategies and data analysis methods for plant systems biology

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    Understanding how function relates to multiple layers of inactions between biological entities is one of the key goals of bioinformatics research, in particular in such areas as systems biology. However, the realisation of this objective is hampered by the sheer volume and multi-level heterogeneity of potentially relevant information. This work addressed this issue by developing a set of integration pipelines and analysis methods as part of an Ondex data integration framework. The integration process incorporated both relevant data from a set of publically available databases and information derived from predicted approaches, which were also implemented as part of this work. These methods were used to assemble integrated datasets that were of relevance to the study of the model plant species Arabidopsis thaliana and applicable for the network-driven analysis. A particular attention was paid to the evaluation and comparison of the different sources of these data. Approaches were implemented for the identification and characterisation of functional modules in integrated networks and used to study and compare networks constructed from different types of data. The benefits of data integration were also demonstrated in three different bioinformatics research scenarios. The analysis of the constructed datasets has also resulted in a better understanding of the functional role of genes identified in a study of a nitrogen uptake mutant and allowed to select candidate genes for further exploration

    Comparative genomics of early animal evolution

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    The explosion of genomics permits investigations into the origin and early evolution of the Metazoa at the molecular level. In this thesis, I am particularly interested in investigating the molecular foundation of the animal senses (i.e. how animals perceive their world). To understand the directionality of evolutionary innovation a well-developed phylogenetic framework is necessary. On one hand, the combination of molecular and morphological data sets has revolutionized our views of metazoan relationships over the past decades, but on the other hand, a number of nodes on the metazoan tree remain uncertain. Uncertainty is particularly high with reference to the taxa generally named “early branching metazoans”. Unfortunately, understanding the relationships among these taxa is key to understanding the evolution of sensory perception (Nielsen 2008). In this thesis I will investigate both animal phylogenetics (to attempt to resolve the phylogeny among the early branching Metazoa) and the evolution of the metazoan sensory receptors. The G-protein coupled receptor superfamily (GPCR) superfamily is the main family of metazoan surface receptors. In this thesis, after an initial introduction (Chapter 1), I address and substantially clarify the relationship among the early branching animals (Chapter 2) using novel genomic data and publicly available expressed sequence tags (ESTs). I then move forward (Chapter 3) to use network-based methods to study the early evolution of the GPCR superfamily in Eukaryotes and animals. Finally (Chapter 4), I focus on the study of a specific subset of GPCRs (the a-group, Rhodopsin-like receptors). This GPCR group is particularly interesting as it includes the best studied and, arguably, one of the most interesting among the GPCR families: the Opsin family. Opsins are key proteins used in the process of light detection, and the origin and early evolution of this family are still substantially unknown. Chapter 4 addresses both these problems. The thesis is then concluded by a general discussion (Chapter 5) and a future directions (Chapter 6) section. Overall, this thesis provides new insights into the origin and early evolution of the Metazoa and their senses

    Communication and complexity in a GRN-based multicellular system for graph colouring

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    Original article can be found at http://www.sciencedirect.com Copyright Elsevier [Full text of this article is not available in the UHRA]Artificial Genetic Regulatory Networks (GRNs) are interesting control models through their simplicity and versatility. They can be easily implemented, evolved and modified, and their similarity to their biological counterparts makes them interesting for simulations of life-like systems as well. These aspects suggest they may be perfect control systems for distributed computing in diverse situations, but to be usable for such applications the computational power and evolvability of GRNs need to be studied. In this research we propose a simple distributed system implementing GRNs to solve the well known NP-complete graph colouring problem. Every node (cell) of the graph to be coloured is controlled by an instance of the same GRN. All the cells communicate directly with their immediate neighbours in the graph so as to set up a good colouring. The quality of this colouring directs the evolution of the GRNs using a genetic algorithm. We then observe the quality of the colouring for two different graphs according to different communication protocols and the number of different proteins in the cell (a measure for the possible complexity of a GRN). Those two points, being the main scalability issues that any computational paradigm raises, will then be discussed.Peer reviewe
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