Supporting distributed multiplayer RoboTable games

Lincoln University is cooperating with Tufts University, USA, on the development of a RoboTable to facilitate interaction between groups learning about robotics and engineering problem solving. A RoboTable is a mixed reality tabletop learning environment and provides a distributed learning platform with groups of children at remotely located tables interacting and competing on robotic projects. This project investigates the development and support of distributed multiplayer games using the RoboTable environment. Currently, there are no general-purpose tools to support RoboTable game development or distributed game play. Hence a gap exists to develop a robust solution that will allow distributed multiplayer games to be created and played using RoboTable. To address these issues, we have implemented a set of toolkits to support distributed multiplayer RoboTable game development. The toolkits comprise a Network Toolkit, a Robot Tracking Toolkit, a Game Management Toolkit and a Communication Toolkit. In addition, we have developed a skeleton project to help the game developers. To evaluate the toolkits, we have used a number of approaches. The first approach was a case study of the game development process using the toolkits. The second approach was to establish baseline performance benchmarks for the system. The third approach was to carry out experiments to evaluate their real-world performance and the scalability of the toolkits using the game created in the case study. The results from the experiments have shown that the toolkits perform well within a distributed computer environment. The results from the case study have revealed that the development of a new RoboTable game is straightforward

Fundamental boolean network modelling for genetic regulatory pathways : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

A Boolean model is a switch-like behaviour model of which it is easy to ignore any effects at the intermediate levels. Boolean modelling has been applied in many areas, including mammalian cell cycle networks. However, little effort has been put into the consideration of activation, inhibition and protein decay networks to designate the direct roles of a gene or a synthesised protein, as an activator or inhibitor of a target gene. Hence, we proposed to split the conventional Boolean functions at the subfunction level into activation and inhibition domains, taking into account the effectiveness of protein decay. As a consequence, two novel data-driven Boolean models for genetic regulatory pathways, namely the fundamental Boolean model (FBM) and the temporal fundamental Boolean model (TFBM), have been proposed to draw insights into gene activation, inhibition, and protein decay. The novel Boolean models could reveal significant trajectories in genes and provide a new direction on Boolean modelling research. The proposed novel Boolean models are fine-grained. A novel network inference methodology named Orchard cube technology has been proposed to infer the related networks, namely fundamental Boolean networks (FBNs) and temporal fundamental Boolean networks (TFBNs) based on FBM and TFBM respectively. As a primary result of this study, an R package, called FBNNet, has been developed based on the proposed methodology and has been used to demonstrate the FBNs and TFBNs for mammalian cell cycle pathways and acute childhood leukaemia pathways respectively. Our experimental results show that the proposed FBM and TFBM could be used to explicitly reconstruct the internal networks of mammalian cell cycles and acute childhood leukaemia. Especially during the study, we produced the fundamental Boolean networks on the childhood acute lymphoblastic leukaemia gene expression data, which were produced in clinical settings. The pathways may be useful for pharmaceutical agents to identify any side effects when applying GC induced apoptosis on children

Fundamental Boolean network modelling for childhood acute lymphoblastic leukaemia pathways

Background: A novel data-driven Boolean model, namely, the fundamental Boolean model (FBM), has been proposed to draw genetic regulatory insights into gene activation, inhibition, and protein decay, published in 2018. This novel Boolean model facilitates the analysis of the activation and inhibition pathways. However, the novel model does not handle the situation well, where genetic regulation might require more time steps to complete. Methods: Here, we propose extending the fundamental Boolean modelling to address the issue that some gene regulations might require more time steps to complete than others. We denoted this extension model as the temporal fundamental Boolean model (TFBM) and related networks as the temporal fundamental Boolean networks (TFBNs). The leukaemia microarray datasets downloaded from the National Centre for Biotechnology Information have been adopted to demonstrate the utility of the proposed TFBM and TFBNs. Results: We developed the TFBNs that contain 285 components and 2775 Boolean rules based on TFBM on the leukaemia microarray datasets, which are in the form of short-time series. The data contain gene expression measurements for 13 GC-sensitive children under therapy for acute lymphoblastic leukaemia, and each sample has three time points: 0 hours (before GC treatment), 6/8 hours (after GC treatment) and 24 hours (after GC treatment). Conclusion: We conclude that the proposed TFBM unlocks their predecessor’s limitation, i.e., FBM, that could help pharmaceutical agents identify any side effects on clinic-related data. New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways

A Toolkit for bulk PCR-based marker design from next-generation sequence data: application for development of a framework linkage map in bulb onion (Allium cepa L.)

BACKGROUND: Although modern sequencing technologies permit the ready detection of numerous DNA sequence variants in any organisms, converting such information to PCR-based genetic markers is hampered by a lack of simple, scalable tools. Onion is an example of an under-researched crop with a complex, heterozygous genome where genome-based research has previously been hindered by limited sequence resources and genetic markers. RESULTS: We report the development of generic tools for large-scale web-based PCR-based marker design in the Galaxy bioinformatics framework, and their application for development of next-generation genetics resources in a wide cross of bulb onion (Allium cepa L.). Transcriptome sequence resources were developed for the homozygous doubled-haploid bulb onion line ‘CUDH2150’ and the genetically distant Indian landrace ‘Nasik Red’, using 454™ sequencing of normalised cDNA libraries of leaf and shoot. Read mapping of ‘Nasik Red’ reads onto ‘CUDH2150’ assemblies revealed 16836 indel and SNP polymorphisms that were mined for portable PCR-based marker development. Tools for detection of restriction polymorphisms and primer set design were developed in BioPython and adapted for use in the Galaxy workflow environment, enabling large-scale and targeted assay design. Using PCR-based markers designed with these tools, a framework genetic linkage map of over 800cM spanning all chromosomes was developed in a subset of 93 F(2) progeny from a very large F(2) family developed from the ‘Nasik Red’ x ‘CUDH2150’ inter-cross. The utility of tools and genetic resources developed was tested by designing markers to transcription factor-like polymorphic sequences. Bin mapping these markers using a subset of 10 progeny confirmed the ability to place markers within 10 cM bins, enabling increased efficiency in marker assignment and targeted map refinement. The major genetic loci conditioning red bulb colour (R) and fructan content (Frc) were located on this map by QTL analysis. CONCLUSIONS: The generic tools developed for the Galaxy environment enable rapid development of sets of PCR assays targeting sequence variants identified from Illumina and 454 sequence data. They enable non-specialist users to validate and exploit large volumes of next-generation sequence data using basic equipment

A novel data-driven Boolean model for genetic regulatory networks

A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug

Phosphate limitation increases coenzyme Q10 production in industrial Rhodobacter sphaeroides HY01

Coenzyme Q10 (CoQ10) is an important component of the respiratory chain in humans and some bacteria. As a high-value-added nutraceutical antioxidant, CoQ10 has excellent capacity to prevent cardiovascular disease. The content of CoQ10 in the industrial Rhodobacter sphaeroides HY01 is hundreds of folds higher than normal physiological levels. In this study, we found that overexpression or optimization of the synthetic pathway failed CoQ10 overproduction in the HY01 strain. Moreover, under phosphate- limited conditions (decreased phosphate or in the absence of inorganic phosphate addition), CoQ10 production increased significantly by 12% to220 mg/L, biomass decreased by 12%, and the CoQ10 productivity of unit cells increased by 27%. In subsequent fed-batch fermentation, CoQ10 production reached 272 mg/L in the shake-flask fermentation and 1.95 g/L in a 100-L bioreactor under phosphate limitation. Furthermore, to understand the mechanism associated with CoQ10 overproduction under phosphate- limited conditions, the comparatve transcriptome analysis was performed. These results indicated that phosphate limitation combined with glucose fed-batch fermentation represented an effective strategy for CoQ10 production in the HY01. Phosphate limitation induced a pleiotropic effect on cell metabolism, and that improved CoQ10 biosynthesis efficiency was possibly related to the disturbance of energy metabolism and redox potential. Keywords: R sphaeroides, CoQ10, Phosphate limitation, Overproduction, Scale-up, Transcriptom