Karl Fraser

Filtering for Nonlinear Genetic Regulatory Networks With Stochastic Disturbances

In this paper, the filtering problem is investigated for nonlinear genetic regulatory networks with stochastic disturbances and time delays, where the nonlinear function describing the feedback regulation is assumed to satisfy the sector condition, the stochastic perturbation is in the form of a scalar Brownian motion, and the time delays exist in both the translation process and the feedback regulation process. The purpose of the addressed filtering problem is to estimate the true concentrations of the mRNA and protein. Specifically, we are interested in designing a linear filter such that, in the presence of time delays, stochastic disturbances as well as sector nonlinearities, the filtering dynamics of state estimation for the stochastic genetic regulatory network is exponentially mean square stable with a prescribed decay rate lower bound beta. By using the linear matrix inequality (LMI) technique, sufficient conditions are first derived for ensuring the desired filtering performance for the gene regulatory model, and the filter gain is then characterized in terms of the solution to an LMI, which can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.

Robust filtering for stochastic genetic regulatory networks with time-varying delay

This paper addresses the robust filtering problem for a class of linear genetic regulatory networks (GRNs) with stochastic disturbances, parameter uncertainties and time delays. The parameter uncertainties are assumed to reside in a polytopic region, the stochastic disturbance is state-dependent described by a scalar Brownian motion, and the time-varying delays enter into both the translation process and the feedback regulation process. We aim to estimate the true concentrations of mRNA and protein by designing a linear filter such that, for all admissible time delays, stochastic disturbances as well as polytopic uncertainties, the augmented state estimation dynamics is exponentially mean square stable with an expected decay rate. A delay-dependent linear matrix inequality (LMI) approach is first developed to derive sufficient conditions that guarantee the exponential stability of the augmented dynamics, and then the filter gains are parameterized in terms of the solution to a set of LMIs. Note that LMIs can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.

Robust filtering for gene expression time series data with variance constraints

In this paper, an uncertain discrete-time stochastic system is employed to represent a model for gene regulatory networks from time series data. A robust variance-constrained filtering problem is investigated for a gene expression model with stochastic disturbances and norm-bounded parameter uncertainties, where the stochastic perturbation is in the form of a scalar Gaussian white noise with constant variance and the parameter uncertainties enter both the system matrix and the output matrix. The purpose of the addressed robust filtering problem is to design a linear filter such that, for the admissible bounded uncertainties, the filtering error system is Schur stable and the individual error variance is less than a prespecified upper bound. By using the linear matrix inequality (LMI) technique, sufficient conditions are first derived for ensuring the desired filtering performance for the gene expression model. Then the filter gain is characterized in terms of the solution to a set of LMIs, which can easily be solved by using available software packages. A simulation example is exploited for a gene expression model in order to demonstrate the effectiveness of the proposed design procedures.

Microarray image analysis : an algorithmic approach

Introduction Overview Current state of art Experimental approach Key issues Contribution to knowledge Structure of the book Background Introduction Molecular biology Microarray technology Microarray analysis Copasetic microarray analysis framework overview Summary Data Services Introduction Image transformation engine Evaluation Summary Structure Extrapolation I Introduction Pyramidic contextual clustering Evaluation Summary Structure Extrapolation II Introduction Image layout-master blocks Image structure-meta-blocks Summary Feature Identification I Introduction Spatial binding Evaluation of feature identification Evaluation of copasetic microarray analysis framework Summary Feature Identification II Background Proposed approach-subgrid detection Experimental results Conclusions Chained Fourier Background Reconstruction Introduction Existing techniques A new technique Experiments and results Conclusions Graph-Cutting for Improving Microarray Gene Expression Reconstructions Introduction Existing techniques Proposed technique Experiments and results Conclusions Stochastic Dynamic Modeling of Short Gene Expression Time Series Data Introduction Stochastic dynamic model for gene expression data An EM algorithm for parameter identification Simulation results Discussions Conclusions and future work Conclusions Introduction Achievements Contributions to microarray biology domain Contributions to computer science domain Future research topics Appendix A: Microarray Variants Appendix B: Basic Transformations Appendix C: Clustering Appendix D: A Glance on Mining Gene Expression Data Appendix E: Autocorrelation and GHT References

Analysing Microarray Data using the Multi-functional Immune Ontologiser

Abstract Gene expression microarrays are a prominent experimental tool in functional genomics allowing researchers to gain a deeper understanding of biological processes. To date, no such tool has been developed to allow researchers with a specialised biological research interest to distinctively identify those genes and gene functionalities associated more strongly with the research area. Based on this functional analysis capability we present a specialised multi-functional Immune Ontologiser – a software, specialised for immunologists to annotate multiple genes from microarray datasets within two new ontologies: a newly structured Immune Ontology focussed at immunology and haematology and a uniquely curated ImmunoArray-PubOntology. The Immune Ontology functionally annotates genes identifying immunology related functions enriched with upregulated or downregulated genes of interest. The ImmunoArray-PubOntology compares and contrasts gene functionality of microarray datasets, comparing genes of interest with the differential gene expression matrices published amongst immunologyrelated microarray literature. This aspect facilitates literature mining by extracting publications containing gene sets of interest in a well-structured immunological context where the literature has been categorised according to disease types. The software consists of a query-optimised database of two parts – the ImmunoGene-database and a unique Database of Immunological Microarray Publications (DIMP) to provide the user with a more detailed insight into other studies involving their genes and research groups investigating similar research areas. Using our Immune Ontologiser software to analyse tolerance array data we identify 70 interesting up-regulated genes in terms of their functionality within tolerance. Furthermore, from these 70 genes we identify 15 genes to have immunology-related functions. More interestingly, the remaining 55 genes were not previously known to be directly involved within the immunology related condition and hence we have identified target genes for future investigation. Among the 70 genes, 21 have been identified by our software to be studied within various immunology-related diseases via microarray experiments performed by other laboratories. The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchareas/mi/publications/supplementary. A detailed microarray protocol is available at http://www.ebi.ac.uk/arrayexpress under the Accession Number: E-MEXP-283.

Noise filtering and microarray image reconstruction via chained fouriers

Microarrays allow biologists to determine the gene expressions for tens of thousands of genes simultaneously, however due to biological processes, the resulting microarray slides are permeated with noise. During quantification of the gene expressions, there is a need to remove a genes noise or background for purposes of precision. This paper presents a novel technique for such a background removal process. The technique uses a genes neighbour regions as representative background pixels and reconstructs the gene region itself such that the region resembles the local background. With use of this new background image, the gene expressions can be calculated more accurately. Experiments are carried out to test the technique against a mainstream and an alternative microarray analysis method. Our process is shown to reduce variability in the final expression results.

“Copasetic clustering: making sense of large-scale images

In an information rich world, the task of data analysis is becoming ever more complex. Even with the processing capability of modern technology, more often than not, important details become saturated and thus, lost amongst the volume of data. With analysis problems ranging from discovering credit card fraud to tracking terrorist activities the phrase “a needle in a haystack” has never been more apt. In order to deal with large data sets current approaches require that the data be sampled or summarised before true analysis can take place. In this paper we propose a novel pyramidic method, namely, copasetic clustering, which focuses on the problem of applying traditional clustering techniques to large-scale data sets while using limited resources. A further benefit of the technique is the transparency into intermediate clustering steps; when applied to spatial data sets this allows the capture of contextual information. The abilities of this technique are demonstrated using both synthetic and biological data.

CIDA: An integrated software for the design, characterisation and global comparison of microarrays

Abstract Microarray technology has had a significant impact in the field of systems biology involving the investigation into the biological systems that regulate human life. Identifying genes of significant interest within any given disease on an individual basis is no doubt time consuming and inefficient when considering the complexity of the human genome. Thus, the genetic profiling of the entire human genome in a single experiment has resulted in microarray technology becoming a widely used experimental tool. However, without the use of tools for several aspects of microarray data analysis the technology is limited. To date, no such tool has been developed that allows the integration of numerous microarray results from different research laboratories as well as the design of customised gene chips in a cost-effective manner. In light of this, we have designed the first integrated and automated software called Chip Integration, Design and Annotation (CIDA) for the cross comparison, design and functional annotation of microarray gene chips. The software provides molecular biologists with the control to cross compare the biological signatures generated from multiple microarray studies, design custom microarray gene chips based on their research requirements and lastly characterise microarray data in the context of immunogenomics. Through the relative comparison of related microarray experiments we have identified 258 genes with common gene expression profiles that are not only upregulated in anergic T cells, but also in cells over-expressing the transcription factor Egr2, that has been identified to play a role in T cell anergy. Using the gene chip design aspect of CIDA we have designed and subsequently fabricate immuno-tolerance gene chips consisting of 1758 genes for further research. The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk/CIDA/. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchgroups/mi/publications/supplementary/cida

Pyramidic Clustering of Large-Scale Microarray Images

With ongoing research and development of imaging techniques such as those involved in brain MRIs, cDNA microarrays and satellite reconnaissance, the need for tools that can intelligently parse larger images is ever increasing. One group of such techniques often used is that of segmentation, an example of which is that of clustering algorithms. In order to deal with large data sets, current approaches require the data to be sampled or summarized before true analysis can take place. In this paper we propose a novel image analysis technique using pyramidic type grouping, namely copasetic clustering, which focuses on the problem of applying traditional clustering techniques to these large-scale image data sets with limited resources. A further benefit of the technique is the transparency of its intermediate clustering steps; when applied to spatial data sets this allows the capture and incorporation of contextual information to improve result accuracy. The algorithm achieves an ∼1--3 dB w-to-noise ratio when compared with the conventional techniques described.

Copasetic analysis: automated analysis of biological gene expression images

In the past decade computational biology has come to the forefront of the publics perception with advancements in domain knowledge and a variety of analysis techniques. With the recent completion of projects like the human genome sequence, and the development of microarray chips it has become possible to simultaneously analyse expression levels for thousands of genes. Typically, a slide surface of less than 24 cm/sup 2/, receptors for 30,000 genes can be printed, but currently the analysis process is a time consuming semi-autonomous step requiring human guidance. The paper proposes a framework, which facilitates automated processing of these images. This is supported by real world examples, which demonstrate the techniques capabilities along with results, which show a marked improvement over existing implementations.