Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

PhotoVoltaic DC-DC module integrated converter for novel cascaded and bypass grid connection topologies - design and optimisation

Grid connected PhotoVoltaic (PV) inverters fall into three broad categories — Central, String and Module Integrated Converers (MICs). MICs offer any avantaes in performance and flexibility, but are at a cost disadvantage. Two alternative novel approaches proposed by the author — cascaded dc-dc MICs and bypass dc-dc MICs — integrate a simple non-isolated intelligent dc-dc converter with each PV module to provide the advantages of dc-ac MICs at a lower cost. A suitable universal 150W 5A dc-dc converter design is presented based on two interleaved MOSFET half bridges. Testing shows Zero Voltage Switching (ZVS) keeps losses under 1W for bi-directional power flows up to 15W between two adjacent 12V PV modules for the bypass application, and efficiencies over 94% for most of the operational power range for the cascaded converter application. Based on the experimental results, potential optimizations to further reduce losses are discussed.

Test strategies on functionally partitioned module-based programmable architecture for base-band processing

A specialised reconfigurable architecture for telecommunication base-band processing is augmented with testing resources. The routing network is linked via virtual wire hardware modules to reduce the area occupied by connecting buses. The number of switches within the routing matrices is also minimised, which increases throughput without sacrificing flexibility. The testing algorithm was developed to systematically search for faults in the processing modules and the flexible high-speed routing network within the architecture. The testing algorithm starts by scanning the externally addressable memory space and testing the master controller. The controller then tests every switch in the route-through switch matrix by making loops from the shared memory to each of the switches. The local switch matrix is also tested in the same way. Next the local memory is scanned. Finally, pre-defined test vectors are loaded into local memory to check the processing modules. This algorithm scans all possible paths within the interconnection network exhaustively and reports all faults. Strategies can be inserted to bypass minor faults

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Efficient Bayesian inference for learning in the Ising linear perceptron and signal detection in CDMA

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in code division multiple access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also studied. © 2006 Elsevier B.V. All rights reserved.