2007 Winter School in Mathematical _ Computational Biology by hjkuiw354

VIEWS: 3 PAGES: 33

									         2007 Winter School
                  in
 Mathematical & Computational Biology
                                       25 - 29 June 2007
                                Queensland Bioscience Precinct
                                        Brisbane, QLD



                                        PROGRAM




                                            Hosted by:
                                  ARC Centre in Bioinformatics
                                               and
                               Institute for Molecular Bioscience

                                        Sponsors:
                                 The MathWorks Australia
                         Queensland Cyber Infrastructre Foundation
                                            SGI




ARC Centre in Bioinformatics
                        CONTENTS

Timetable ……………………………………………………………………………………………………… page 1

Abstracts ………………………………………………………………………………………………………. page 4
                                              TIMETABLE

                                  Bio-image analysis, quantification and
Monday 25 June 2007
                                  classification
08:30 a.m. – 09:30 a.m.   Registration and morning tea


09:30 a.m. – 09:40 a.m.   Opening and Welcome                                               Prof John Mattick


09:40 a.m. – 10:10 a.m.   An introduction to cellular imaging techniques                    Dr Rohan Teasdale

                          Automated interpretation of subcellular patterns in
10:10 a.m. – 11:40 a.m.   microscope images: bioimage informatics for systems               Dr Estelle Glory
                          biology

11:40 a.m. – 01:15 p.m    Lunch
.
                          On models and algorithms for analysis of biological
01:15 p.m. – 02:15 p.m.                                                                     A/Prof Tuan Pham
                          images


02:15 p.m. – 03:15 p.m.   High content cellular screening for drug discovery                Ms Leanne Bischof


03:15 p.m. – 03:45 p.m.   Afternoon tea


                                                                                            Oliver Cairncross
03:45 p.m. – 04:45 p.m.   Reconstructing the mammalian cell
                                                                                            Andrew Noske


05:30 p.m.                Welcome BBQ at rooftop, QBP (Sponsored by SGI)

                                  Modelling and simulation of cellular
Tuesday 26 June 2007
                                  processes
09:00 a.m. – 10:00 a.m.   The role of noise in cellular processes                           Prof Kevin Burrage


10:00 a.m. – 10:30 a.m.   Morning tea


10:30 a.m. – 11:30 p.m.   Virtual heart disease                                             Dr Edmund Crampin


12:00 p.m. – 01:30 p.m.   Lunch


                          The numerical solution of the master equations in
01:30 p.m. – 02:30 p.m.                                                                     Dr Markus Hegland
                          molecular biology – how, why and for what


02:30 p.m. – 03:00 p.m.   Afternoon tea




                                                          1
                             2007 Winter School in Mathematical and Computational Biology
                                                   25–29 June 2007
                                              TIMETABLE

                          Stochastic modeling and simulation of cellular                    Dr André Leier
03:00 p.m. – 03:45 p.m.
                          processes with delays


03:45 p.m. – 04:30 p.m.   Modelling and simulation in computational biology                 Mr Paul Taylor


04:30 p.m. – 05:00 p.m.   Discussion session

                                  Prediction and modelling of protein
Wednesday 27 June 2007
                                  structure and dynamics
09:00 a.m. – 09:45 a.m.   Stimulating biomolecular systems I                                Prof Alan Mark

                          Determination of protein complexes and multi-domain
09:45 a.m. – 10:30 a.m.   proteins using a combination of experiments and                   Dr Thomas Huber
                          calculations

10:30 a.m. – 11:00 a.m.   Morning tea


11:00 a.m. – 11:45 a.m.   Stimulating biomolecular systems II                               Prof Alan Mark


                          Computing and software resources for molecular
11:45 a.m. – 12:30 p.m.                                                                     Prof Bernard Pailthorpe
                          simulations


12:30 p.m. – 02:00 p.m.   Lunch


02:00 p.m. – 03:00 p.m.   Effective use of the RCSB Protein Data Bank                       Prof Phil Bourne


03:00 p.m. – 04:00 p.m.   Discussion session


Thursday 28 June 2007             Statistical analysis of gene expression

                          From gene expression to clinical diagnostic tool - are
09:00 a.m. – 10:00 a.m.                                                                     Prof Sue Wilson
                          we there yet?


10:00 a.m. – 10:30 a.m.   Morning tea


10:30 a.m. – 11:15 a.m.   Borrowing strength in microarray data analysis                    Dr Gordon Smyth


                          Detection of differential expression with microarray
11:15 a.m. – 12:00 p.m.                                                                     Prof Geoff McLachlan
                          data


12:00 p.m. – 1:30 p.m.    Lunch

                          A myogenin network-centric systems biology
01:30 p.m. – 02:15 p.m.   approach to the genetic dissection of complex traits in           Dr Antonio Reverter
                          beef cattle

                                                          2
                             2007 Winter School in Mathematical and Computational Biology
                                                   25–29 June 2007
                                              TIMETABLE

                          “Differential expression free” analysis of microarray
02:15 p.m. – 03:00 p.m.                                                                     Dr Harri Kiiveri
                          data


03:00 p.m. – 03:30 p.m.   Afternoon tea


03:30 p.m. – 04:15 p.m.   Assessing classifiers trained on gene expression data             Dr Ian Wood

                          Plenary talk – Ligand binding site searching and
05:00 p.m. – 06:30 p.m.   application to finding off-targets for major                      Prof Phil Bourne
                          pharmaceuticals

06:30 p.m. – 07:30 p.m.   Refreshments


Friday 29 June 2007               Computational neuroscience

                          Is your brain smarter than a computer? Introduction to
09:00 a.m. – 10:15 a.m.                                                                     A/Prof Geoff Goodhill
                          neuroscience and computational neuroscience


10:15 a.m. – 10:45 a.m.   Morning tea


                          Optimization principles of adaptive coding in the
10:45 a.m. – 11:45 a.m.                                                                     Dr Tatyana Sharpee
                          primary visual cortex


11:45 p.m. – 01:15 p.m.   Lunch


                          Smart computations in small brains: vision,
01:15 p.m. – 02:15 p.m.                                                                     Prof Mandyam Srinivasan
                          navigation, perception and cognition in honeybees

                          Towards a theory of learning and levels for
02:15 p.m. – 03:15 p.m.                                                                     Dr Anthony Bell
                          neurobiology


03:15 p.m. – 03:30 p.m.   Afternoon tea


                          Network structure of cerebral cortex shapes neuronal
03:30 p.m. – 04:30 p.m.                                                                     A/Prof Michael Breakspear
                          dynamics on multiple time scales



                                                   ~*~*~*~*~*~




                                                          3
                             2007 Winter School in Mathematical and Computational Biology
                                                   25–29 June 2007
                                           ABSTRACTS

Monday 25 June 2007
Bio-image analysis, quantification and classification

Session:    09:40 a.m. – 10:10 a.m.


Speaker:    Dr Rohan Teasdale
            ARC Centre in Bioinformatics, and Institute for Molecular Bioscience
            The University of Queensland


Bio:        Rohan Teasdale heads the "Computational Cell Biology" research group at Institute for
            Molecular Bioscience, University of Queensland. One of the innovations of his research group
            is a synergetic approach that combines experimental cell biology and microscopy with
            computational methods. His research group is focused on understanding how individual
            proteins are compartmentalised and defining the protein machinery responsible for their
            transport. He recently developed the LOCATE database (http://locate.imb.uq.edu.au/), a
            resource focused on all aspects relevant to understanding a proteins subcellular localisation.


Title:      An introduction to cellular imaging techniques


Abstract:   I will introduce the types of imaging techniques currently in use within the bio-medical field.
            Illustrative examples of research applications based on my group’s cellular imaging will be
            presented. Topics covered will include (1) basics of fluorescence; (2) microscopy types; (3)
            labelling techniques, and (4) selective applications.

            Recommended online resources include:

            http://www.olympusmicro.com/index.html
            http://www.microscopyu.com/
            http://probes.invitrogen.com/resources/education/




                                                       4
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Monday 25 June 2007
Bio-image analysis, quantification and classification

Session:    10:10 a.m. – 11:40 a.m.


Speaker:    Dr Estelle Glory
            Centre for Bioimage Informatics
            Carnegie Mellon University, Pittsburgh, PA, USA


Bio:        Estelle Glory is a postdoctoral fellow in the Center for Bioimage Informatics in Carnegie Mellon
            University. She joined the group of Robert F. Murphy after receiving her PhD in biological image
            analysis, working with J-C. Olivo-Marin (Institut Pasteur, Paris, France) and G. Stamon
            (University Paris 5). With a background in biochemistry and computer science, her work is
            focused on the extraction of quantitative information from microscopy images to analyze and
            interpret high throughput experiments. Her current research is related to the determination of
            subcellular location patterns using cell segmentation, feature extraction and machine learning
            methods.


Title:      Automated interpretation of subcellular patterns in microscope images: bioimage informatics for
            systems biology


Abstract:   In systems biology, protein structures, protein interaction, expression level have been largely
            analyzed for understanding and modeling cell pathways while protein location, which is critical
            information to build such spatiotemporal models has been less explored. The prediction of
            protein location from sequences is limited by the samples of the training set while the human
            annotations are restricted by the predefined vocabulary, for example the Gene Ontology. The
            Murphy lab has pioneered the application of machine learning methods to protein images. The
            automatic extraction of features from fluorescence microscope images has been developed to
            provide objective, accurate and reproducible description of protein patterns. The features have
            been designed to be robust enough to non informative characteristics, such as the rotation or
            the absolute position of protein, but sensitive enough to small variations characterizing slightly
            different patterns. These quantitative descriptors are used as input to different tools developed
            to automatically analyze high throughput experiments. Particularly useful tools include
            statistical analysis of the difference between two patterns, supervised classification to
            recognize subcellular compartments and the clustering of proteins into families that share the
            same protein pattern. The next step is the creation of models which capture the essence and
            variation of protein location patterns. This is achieved with a generative model. Beyond the
            compactness of the structured information, such a model is able to generate new cell images,
            combining the nuclear shape and texture, the cell boundary, and the relative location and
            density of proteins. The possibility to generate artificial cells containing thousands of protein
            patterns will doubtless be a major contribution in the field of systems biology. The goal of
            bioimage informatics is to provide a direct path for generating biological knowledge from images
            with a minimum of human intervention, and significant progress has been made towards this
            goal.




                                                       5
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                            ABSTRACTS

Monday 25 June 2007
Bio-image analysis, quantification and classification

Session:    01:15 p.m. – 02:15 p.m.


Speaker:    A/Prof Tuan Pham
            Director, Bioinformatics Applications Research Centre
            James Cook University


Bio:        Tuan D. Pham is an Associate Professor in the School of Mathematics, Physics, and
            Information Technology; and Director of the Bioinformatics Applications Research Centre at
            James Cook University. His research experience and interests are diverse which cover image
            processing, pattern recognition, signal processing, fuzzy logic, neural networks, genetic
            algorithms, and applied geostatistics with applications to bioinformatics and biomedical
            informatics. He has contributed pioneering research work on fuzzy finite element analysis of
            engineering problems; and applications of predictive coding and geostatistical models for
            analysis of bioimaging, microarray gene-expression, and mass-spectrometry data.


Title:      On models and algorithms for analysis of biological images


Abstract:   Recent advances in modern biotechnology offer interesting and challenging problems to
            computational scientists with respect to the handling and interpretation of complex biological
            data. Solutions to these problems are anticipated to revolutionize our way of living in the sense
            that human fatal diseases can be early detected and diagnosed for proper treatments, new
            therapeutic drugs be discovered and personalized medicine be developed. This talk will
            particularly address some recently developed computational models and algorithms for
            bio-imaging analysis and classification. It will highlight the importance of the incorporation of the
            skills and knowledge gained from biology, biomedicine, mathematics, engineering, computer
            and information sciences.




                                                        6
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                            ABSTRACTS

Monday 25 June 2007
Bio-image analysis, quantification and classification

Session:    02:15 p.m. – 03:15 p.m.


Speaker:    Ms Leanne Bischof
            Image Analyst, Biotech Imaging
            CSIRO Mathematical and Information Sciences


Bio:        Leanne Bischof is a senior member of the Biotech Imaging group at CSIRO Mathematical and
            Information Sciences. The group conducts image analysis research into segmentation and
            feature extraction techniques and develops commercial software for biotechnology
            applications, in particular for High Content Analysis (HCA). HCA refers to the high throughput
            analysis of fluorescence microscope images of cells to quantify cellular morphology and
            function. There are a handful of companies which supply HCA systems to the pharmaceutical
            industry to screen candidate drugs for efficacy and toxicity. The group's HCA software has been
            licensed to several of these companies.


Title:      High content cellular screening for drug discovery


Abstract:   In modern biology, important biological information is often captured in the form of images.
            Extracting the information from those images manually can often be tedious and time
            consuming. There is increasing demand for software to perform this analysis automatically.
            Modern image analysis techniques are making it possible to automate even the most
            challenging applications. We will illustrate what is possible by referring to our work in High
            Content Analysis (HCA). HCA refers to the automated analysis of mainly fluorescence
            microscope images of cells to quantify cellular morphology and function. High content analysis
            is used in the pharmaceutical industry to screen candidate drugs for efficacy and toxicity. It is
            increasingly being used in academia to expand the fundamental understanding of cellular
            biology. We will briefly mention some of the image segmentation and feature extraction
            techniques that we use and then show a series of biological assays which require a range of
            analysis techniques. These HCA assays will include neurite outgrowth analysis in 2D and 3D,
            analysis of mixed cell populations (such as neuron-astrocyte co-cultures or differentiating
            neural stem cells), analysis of protein translocation, co-localisation and sub-cellular localisation,
            and tracking of proteins over time (such as vesicles in TIRF microscope images). We will briefly
            canvas the software engineering challenges inherent in developing image analysis software for
            a range of environments - for direct use by biologists (such as our standalone HCA-Vision
            package), for integration into commercial HCA systems (where the host system provides the
            user interface) and for our in-house use to support one-off solutions for our research
            collaborators.




                                                        7
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                           ABSTRACTS

Monday 25 June 2007
Bio-image analysis, quantification and classification

Session:    03:45 p.m. – 04:45 p.m.


Speakers:   Mr Oliver Cairncross
            Mr Andrew Noske
            ARC Centre in Bioinformatics, and
            Institute for Molecular Bioscience
            The University of Queensland


Bio:        The Visible Cell™ project based at the Institute for Molecular Bioscience (IMB) and the ARC
            Centre in Bioinformatics (ACB) at the University of Queensland (UQ) represents a large-scale,
            cross-disciplinary, multi-institutional and international e-Science initiative that is changing the
            way we think about mammalian cells. The Visible Cell™ project aims to inform advanced in
            silico studies of cell and molecular organisation in 3D using the mammalian cell as a unitary
            example of an ordered complex system. This unique initiative is founded on the provision of
            complete sets of 3D spatio-temporal coordinates for whole mammalian cells at a range of
            resolutions and the integration of data on gene products, molecular interactions, pathways,
            networks and processes into the corresponding cellular coordinates. Investigators will interact
            with the cellular structures, molecules and processes (driven by user-supplied computational
            models) inside an integrated visualisation environment.


Title:      Reconstructing the mammalian cell


Abstract:   The Visible Cell™ project based at the Institute for Molecular Bioscience (IMB) and the ARC
            Centre in Bioinformatics (ACB) at the University of Queensland (UQ) represents a large-scale,
            cross-disciplinary, multi-institutional and international e-Science initiative that is changing the
            way we think about mammalian cells. The Visible Cell™ project aims to inform advanced in
            silico studies of cell and molecular organisation in 3D using the mammalian cell as a unitary
            example of an ordered complex system. This unique initiative is founded on the provision of
            complete sets of 3D spatio-temporal coordinates for whole mammalian cells at a range of
            resolutions and the integration of data on gene products, molecular interactions, pathways,
            networks and processes into the corresponding cellular coordinates. Investigators will interact
            with the cellular structures, molecules and processes (driven by user-supplied computational
            models) inside an integrated visualisation environment. In this talk the foundations of the Visible
            Cell™ will be outlined and a software demonstration given.




                                                       8
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                            ABSTRACTS

Tuesday 26 June 2007
Modelling and simulation of cellular processes

Session:    09:00 a.m. – 10:00 a.m.


Speaker:    Prof Kevin Burrage
            ARC Federation Fellow
            Advanced Computational Modelling Centre
            ARC Centre in Bioinformatics, and
            Institute for Molecular Bioscience
            The University of Queensland


Bio:        Kevin Burrage is a Federation Fellow of the Australian Research Council. He has joint positions
            within Mathematics and the IMB at the University of Queensland, is the Director of the
            Advanced Computational Modelling Centre and is one of the CIs in the ARC Centre of
            Excellence in Bioinformatics. His main research interests are in Computational and Systems
            Biology and Computational Science in general and has a specific interest in the role of noise in
            cellular dynamics. He has over 160 published scientific articles.


Title:      The role of noise in cellular processes


Abstract:   This talk will give a brief introduction to the sorts of noise processes that arise in Cell Biology.
            We will discuss the nature of these noise processes and how they can be modelled and
            simulated. We will focus on models from genetic regulation and cascading reactions in the
            cytosol in both discrete and continuous, and delayed and non-delayed setting. No prior
            knowledge of stochastic modelling is needed and the talk will focus on concepts rather than
            mathematical intricacies.




                                                        9
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                           ABSTRACTS

Tuesday 26 June 2007
Modelling and simulation of cellular processes

Session:    10:30 a.m. – 11:30 p.m.


Speaker:    Dr Edmund Crampin
            Bioengineering Institute and Department of Engineering
            University of Auckland, New Zealand


Bio:        Edmund Crampin is Senior Lecturer at the Auckland Bioengineering Institute where he leads
            the Systems Biology and Cell Modelling research group. His current research interests include
            mathematical modelling of metabolic, signalling and genetic networks, with a particular focus on
            computational modelling of cardiac myocytes. Edmund completed a DPhil in Applied
            Mathematics at Oxford. He was a Junior Research Fellow of Brasenose College, Oxford and
            was awarded a Research Fellowship from the Wellcome Trust to study mathematical models of
            heart disease. Edmund joined the University of Auckland as a Research Fellow and was
            subsequently appointed to a lectureship jointly between the Department of Engineering Science
            and the Auckland Bioengineering Institute.

Title:      Virtual heart disease


Abstract:   The heart is a relatively simple organ. It is composed predominantly of a single type of cell – the
            cardiac myocyte – and it performs a single function – to pump blood around the circulation. The
            mechanism by which electrical stimulation of a myocyte leads to generation of the action
            potential and, subsequently, to contraction of the muscle cell, is relatively well understood, and
            is well described in mathematical models. Reduction of the usual supply of oxygenated blood to
            a region of the heart muscle can have a devastating effect on the heart's ability to pump. The
            complex sequence of events in ischaemic heart disease, arising from the obstruction of a
            coronary artery and leading to life-threatening pump failure, are much less well known, despite
            the wealth of available experimental data.

            In this talk I will discuss our approach to mathematical modelling of the pathophysiology of
            cardiac myocytes during ischaemia, by focusing on key events including the acidification of the
            tissue (acidosis) and build-up of extracellular potassium (hyperkalaemia) that occur when the
            blood supply is reduced. I will show how these and other aspects of myocyte dysfunction in
            ischaemic heart disease can be understood through biophysically-based computational
            modelling of heart cells, and discuss how this may ultimately lead to a better understanding of
            the progression of ischaemic heart disease.




                                                       10
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                            ABSTRACTS

Tuesday 26 June 2007
Modelling and simulation of cellular processes

Session:    01:30 p.m. – 02:30 p.m.


Speaker:    Dr Markus Hegland
            Centre for Mathematics and its Applications
            Mathematical Sciences Institute
            The Australian National University


Bio:        Markus Hegland is a computational mathematician working at the ANU. His main interests
            relate to high-dimensional, in particular sparse grid approximation, with applications in
            computational biology and machine learning. He has also worked in the areas of ill-posed and
            inverse problems and high performance computing.


Title:      The numerical solution of the master equations in molecular biology – how, why and for what


Abstract:   It is now widely accepted that molecular processes in biology are a fundamentally noisy affair
            and are thus best modelled by stochastic processes. Here, we consider random fluctuations in
            protein and RNA levels. Interesting biological questions relate to how these levels change over
            time – in particular in response to external and internal stimuli -- and how these levels ultimately
            settle in on some stationary values. Both the state of the system at a fixed time and the time it
            takes to arrive at a stationary state are random variables and are characterised by an expected
            value or mean and a scale parameter like the variance or quantiles. These features of the
            stochastic process are traditionally obtained by simulation. It turns out that for complex systems
            the determination of these features is computationally demanding due to high numbers of
            replicated simulations of many interacting processes. Here we consider an alternative to
            simulation -- the numerical approximation of evolving probability distributions characterising the
            stochastic processes.

            The governing equations for the probability distributions are the chemical master equations.
            Often, the curse of dimensionality is cited as the main obstacle to the approximation of
            probability distributions and to the solution of the chemical master equations. Here, we will
            discuss methods to numerically approximate and solve these equations. In particular, we will
            introduce a method based on combining simple approximations which is capable to handle the
            curse of dimensionality to some extent. We will show that this approach is feasible for the
            solution of simple master equations involving 100 different substances. At the end of this
            presentation, the student should have some idea about biological questions which can be
            answered with this approach, the inherent computational challenges and the tools used to
            address these challenges.




                                                        11
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                           ABSTRACTS

Tuesday 26 June 2007
Modelling and simulation of cellular processes

Session:    03:00 p.m. – 03:45 p.m.


Speaker:    Dr André Leier
            Advanced Computational Modelling Centre
            The University of Queensland


Bio:        André Leier is a postdoctoral fellow in the Advanced Computational Modelling Centre at the
            University of Queensland, with research interests in Computational and Systems Biology, in
            particular stochastic, spatiotemporal, and multi-scale models of cell signalling and genetic
            regulation and the roles of delay in cellular processes. He studied Computer Science and
            Mathematics and received his PhD in Computer Science from the University of Dortmund,
            Germany.


Title:      Stochastic modeling and simulation of cellular processes with delays


Abstract:   Time delays associated with slow biochemical processes such as transcription, translation,
            nuclear and cytoplasmic translocations are known to affect the dynamics of genetic regulation.
            Temporal models of cellular signalling and genetic regulation have to take these delays into
            account in order to capture the dynamics more accurately and to allow for more reliable
            predictions.

            In this talk, we discuss stochastic delay modelling and simulation suitable to capture both
            stochasticity and delay in temporal models of cellular signalling and genetic regulation.




                                                       12
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Tuesday 26 June 2007
Modelling and simulation of cellular processes

Session:    03:45 p.m. – 04:30 p.m.


Speaker:    Mr Paul Taylor
            The MathWorks Australia Pty Ltd


Bio:        Paul Taylor is a senior applications engineer at MathWorks Australia. MathWorks is the leading
            global provider of software for technical computing and model-based design. Paul will present a
            workshop on MATLAB, a powerful interactive matrix-based environment for scientific and
            engineering modelling and computation. MATLAB has a large user base in Australia and
            overseas, and extensions are available for a number of application domains including
            bioinformatics.


Title:      Modeling and simulation in computational biology


Abstract:   Biological data has become so diversified and complex that flexible, non-niche data analysis
            and visualization tools are critical to the success of most biological research. The MathWorks’
            products for computational biology provide a user-friendly, flexible programming platform for
            analyzing complex biological data and systems. From a single environment a broad range of
            analysis, simulation, algorithm development can be undertaken to radically accelerate the
            research and discovery process.

            This presentation will highlight the benefits of SimBiology, a flexible environment for modelling,
            simulating and analysing biochemical pathways, and the MATLAB Distributed Computing
            Engine, enabling users to easily distribute large simulations across a computer cluster to
            dramatically reduce computation time.




                                                       13
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Wednesday 27June 2007
Prediction and modelling of protein structure and dynamics

Sessions:   09:00 a.m. – 09:45 a.m.
            11:00 a.m. – 11:45 a.m.

Speaker:    Prof Alan Mark
            ARC Federation Fellow
            Centre in Computational Molecular Sciences
            School of Molecular and Microbial Sciences and Institute for Molecular Bioscience
            The University of Queensland


Bio:        Prof. Alan E. Mark’s primary interest is in understanding how biological systems are regulated
            at an atomic level. He studied Chemistry and Biochemistry at the University of Sydney before
            undertaking a PhD in Physical Biochemistry at the ANU. After postdoc's at ANU, Groningen,
            The Netherlands and ETH, Zurich Switzerland he was appointed to a chair of Biomolecu;ar
            Simulation at the University of Groningen. In 2005 he moved to UQ on an ARC Federation
            Fellowship. The main focus of his current research is in understanding how biomolecular
            systems self-organize, in particular he uses the atomistic simulations to investigate processes
            such a protein and peptide folding, lipid aggregation, protein-ligand interactions and signal
            transduction processes within cells. Prof. Mark is closely associated with the GROMOS and
            GROMACS molecular simulation packages and the development of the GROMOS empirical
            force field.


Title:      Stimulating biomolecular systems


Abstract:   The lectures will provide a general introduction to the simulation of the dynamics of protein and
            lipid systems focusing in particular on how biomolecular systems are best represented in atomic
            or near atomic detail and the types of motion can be observed on currently accessible time
            scales. Calling on a wide range of example the lectures will illustrate what types of information
            can be obtained using atomistic simulation techniques and demonstrate how such simulations
            are not only enabling a more detailed interpretation of experimental data but in some cases also
            challenging our basic assumptions regarding how biomolecular systems work. Of equal
            importance the lectures will show examples that highlight dangers and pit falls of using
            simulations inappropriately.




                                                       14
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                            ABSTRACTS

Wednesday 27June 2007
Prediction and modelling of protein structure and dynamics

Session:    09:45 a.m. – 10:30 a.m.


Speaker:    Dr Thomas Huber
            Computational Biology and Bioinformatics Environment
            School of Molecular and Microbial Sciences
            The University of Queensland


Bio:        After his PhD studies, which were partly conducted at the Australian National University, Dr
            Huber returned to the ETH Zurich for a one year post-doctoral stint before in 1998 taking up a
            permanent position as computational chemist at the Australian National University’s
            Supercomputing Facility. In 2001 Dr Huber joint the University of Queensland, taking up a
            lectureship in computational biology / bioinformatics

            The main theme of his group’s work is centred in protein structure prediction. Protein structure
            predictions are still quite limited and additional (sparse) distance constraints generally greatly
            improves the quality of predictions. Currently they explore several sources of swiftly to obtain,
            additional information to aid predictions: Residue contact information from sequence evolution,
            distance information from chemical crosslinking, and structural restraints from paramagnetic
            NMR spectroscopy. In the framework of UQ’s Structural Genomics Programme, they work on
            identifying protein properties that affect high-throughput structure determination, and use these
            insights predictively to minimize protein loss due to insolubility and to maximize success in
            protein expression and crystallization. Recently, they also branched into the exciting new area
            of computational microbial ecology where we started to computationally analyze
            meta-genomics shotgun sequencing data from whole microbial communities.


Title:      Determination of protein complexes and multi-domain proteins using a combination of
            experiments and calculations


Abstract:   In biology the concept of bottom-up integration has inspired large scale programmes to make
            inventories of all molecular components in a cell, most publicized whole genome sequencing
            projects. More recently, numerous large scale initiatives have been launched to determine
            protein structures in high throughput, but while these initiatives have highly accelerated the
            structure determination process, such programmes are also heavily biased towards
            determining the structures of small, soluble, easy to crystallise and single chain proteins. To this
            end, very little insight is gained on how a protein interacts with other proteins, an information
            which in many cases is crucial to understand biological function. New biochemical experiments,
            most notable the two-hybrid methods and bait-tag purification approaches, have been
            developed to answer this fundamental question which proteins physically interact, and while
            they are very powerful to produce binary (yes/no) answers on large sets of proteins they fail to
            explain how proteins interact at an atomic level.

            The focuses of this talk will be on approaches to combine limited experimental data with
            molecular modelling to determine structure of protein complexes and multi-domain proteins at
            atomic level. I will introduce and review these methods and illustrate results with calculations on
            real proteins. Finally, limitations are outlined by following the time honoured tradition of trying to
            understand things by breaking them.




                                                        15
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                           ABSTRACTS

Wednesday 27June 2007
Prediction and modelling of protein structure and dynamics

Session:    11:45 a.m. – 12:30 p.m.


Speaker:    Prof Bernard Pailthorpe
            CEO, Queensland Cyber Infrastructure Foundation
            Chair of Computational Science
            ARC Centre in Bioinformatics
            The University of Queensland


Bio:        Prof. Bernard Pailthorpe is a physicist who founded Sydney VisLab in 1992, to support
            computational and visualisation research across a broad spectrum of disciplines. During
            1999-2000 he directed the scientific visualisation program for NPACI and SDSC at UCSD
            (USA). The group efforts there were focused on scalable volume visualization, and participated
            in the opening show for the Hayden Planetarium in New York. He now holds the Foundation
            Chair of Computational Science at UQ and is CEO of QCIF Ltd, an Australian HPC Consortium
            that supports industry and research projects, and develops cyberinfrastructure. He has wide
            experience in physics education and developing new classes in Computational Physics. He has
            advised Governments at senior levels on HPC and e-Research, leading to a new funding
            program to establish the Australian Partnership for Advanced Computing in 2000.


Title:      Computing and software resources for molecular simulations


Abstract:   Molecular simulations in diverse field (bioscience, biomedicine, chemistry and others) require
            advanced computing, networking and data management, as well as community development
            and support. This presentation will provide an update on all these aspects of computing and
            software resources in Australia, particularly those being developed and organised under the
            National Collaborative Research Infrastructure Scheme.




                                                       16
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Wednesday 27June 2007
Prediction and modelling of protein structure and dynamics

Session:    02:00 p.m. – 03:00 p.m.


Speaker:    Prof Phil Bourne
            Co-Director, Protein Data Bank (PDB)
            Department of Pharmacology, and
            San Diego Supercomputer Center
            University of California, San Diego, USA


Bio:        Philip E. Bourne PhD is a Professor in the Department of Pharmacology at the University of
            California San Diego, Co-director of the Protein Data Bank and an Adjunct Professor at the
            Burnham Institute and the Keck Graduate Institute. He is a Past President of the International
            Society for Computational Biology. He is an elected fellow of the American Medical Informatics
            Association. He is the Founding Editor-in-Chief of the open access journal PLoS Computational
            Biology, on the Advisory Board of Biopolymers and on the Editorial Boards of Proteins:
            Structure Function and Bioinformatics, Biosilico and IEEE Trends in Computational Biology and
            Bioinformatics and a long standing member of the National Science Foundation and National
            Institutes of Health panels responsible for reviewing proposals relating to biological
            infrastructure and bioinformatics. He is a past member of the US National Committee for
            Crystallography, past chairman of the International Union of Crystallography Computing
            Commission IUCrCC and past chairman of the American Crystallography Association (ACA)
            Computing Committee.

            Recent awards include the Flinders University Convocation Medal for Outstanding
            Achievement 2004 and the Sun Microsystems Convergence Award 2002.

            Bourne's professional interests focus on bioinformatics and structural bioinformatics in
            particular. This implies algorithms, metalanguages, biological databases, biological query
            languages and visualization with special interest in evolution, cell signaling and apoptosis. He
            has published over 180 papers and 4 books, one of which sold over 120,000 copies. He has
            co-founded 3 companies: ViSoft Inc., Protein Vision Inc. and a company distributing
            independent films for free.


Title:      Effective use of the RCSB Protein Data Bank (PDB)


Abstract:   The RCSB PDB Web site at http://www.pdb.org provides access to all publically accessible
            data on the structure of biological macromolecules. To make these data most useful they have
            been integrated with at least 30 other sources of information [1]. Most recently the complete
            PDB dataset has been remediated to provide correspondence to the UniProt protein sequence,
            consistent features of ligands, standard nomenclature etc. I will provide a tour of these
            resources by way of addressing example questions at different levels of complexity.

            [1] N. Deshpande, et al. 2005 The RCSB Protein Data Bank: A Redesigned Query System and
            Relational Database Based on the mmCIF Schema Nucleic Acids Research. 33: D233-D237.




                                                       17
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Thursday 28 June 2007
Statistical analysis of gene expression

Session:    09:00 a.m. – 10:00 a.m.


Speaker:    Prof Sue Wilson
            Co-Director, Centre for Bioinformation Science
            The Australian National University


Bio:        Susan Wilson is Director of the Centre for Bioinformation Science, Mathematical Sciences
            Institute, The Australian National University (ANU). She obtained her B.Sc. from the University
            of Sydney, followed by her Ph.D. from the ANU in 1972, and then spent two years as a Lecturer
            in the Department of Probability and Statistics at Sheffield University. She returned to ANU
            towards the end of 1974 and has since held a variety of positions there, both in some of the
            Statistical Science groupings, as well as at the National Centre for Epidemiology and
            Population Health, and now heads the bioinformatics research facility at ANU.

            Sue has over 150 refereed publications in biometry and applied statistics, with a particular
            emphasis in statistical genetics/genomics. Many papers have arisen from her extensive
            consulting experience in the biological, social and medical sciences, leading to statistical
            modelling developments to answer substantive research questions in these disciplines.


Title:      From gene expression to clinical diagnostic tool - are we there yet?


Abstract:   The new 'omic' technologies have the potential to analyse your genome and cell processes so
            that decisions can be made as to whether to treat you, and if so how to choose the best course
            of treatment. The medical aim is to optimise your potential positive outcome/s while minimising
            any adverse effects. The resultant era in the offing is being called "personalised medicine". To
            underpin this medical progress, high quality statistical approaches need to be applied to the
            data on which these developments are based. The statistical evidence underpinning one of the
            first drugs licensed by the U.S. Food and Drug Administration (FDA) as a 'significant step
            towards personalised prescribing' has been judged as seriously flawed (Ellison in
            "Significance", 2006). More recently the FDA approved a cancer prognosis test based on gene
            expression microarray technology - a first. The claim is that the prognostic profile so produced
            potentially provides a powerful tool to tailor adjuvant systemic treatment that could greatly
            reduce the cost of (breast) cancer treatment, both in terms of adverse side effects and health
            care expenditure. So, what is the statistical evidence?

            Following a brief overview of microarray technology, including the fundamental importance of
            the need for careful quality control, the statistical evidence and challenges for development of
            predictive gene lists, often termed 'gene signatures' will be reviewed. Particular emphasis will
            be placed on how the signature produced depends on the selection of patients in the 'training
            set', and the lack of agreement when comparing the signature lists from different studies.




                                                       18
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Thursday 28 June 2007
Statistical analysis of gene expression

Session:    10:30 a.m. – 11:15 a.m.

Speaker:    Dr Gordon Smyth
            Senior Research Scientist, Bioinformatics Division
            Walter and Eliza Institute for Medical Research


Bio:        Gordon Smyth is a Senior Research Fellow and Lab Head at the Walter and Eliza Hall Institute
            of Medical Research. He develops statistical methods for microarray data analysis and
            functional genomics. He is the author of the popular limma software package for R for linear
            modelling of microarray data.


Title:      Borrowing strength in microarray data analysis


Abstract:   At a molecular biology research institute, most microarray experiments are differential
            expression studies. Such studies can range from simple in design, perhaps comparing just two
            groups, to more complex designs involving multiple levels of several treatments. Whether
            simple or complex, these experiments invariably involve only a small number of biological
            replicates. This means that creative ways to borrow strength between genes and between
            samples are essential to the statistical analysis. This talk will describe an approach which has
            proved popular and effective, using linear models to borrow strength between samples, and
            empirical Bayes methods to borrow strength between genes. This approach leads to an
            elegant generalization of t-tests and F-tests. The talk will go on to consider information
            borrowing ideas for experiments with multiple error strata, and gene set tests which conduct
            hypothesis tests for ensembles of genes.




                                                       19
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Thursday 28 June 2007
Statistical analysis of gene expression

Session:    11:15 a.m. – 12:00 p.m.


Speaker:    Prof Geoff McLachlan
            ARC Centre in Bioinformatics, and
            Department of Mathematics
            The University of Queensland


Bio:        Geoff McLachlan is Professor of Statistics in the Department of Mathematics and a Professorial
            Research Fellow in the Institute for Molecular Bioscience. He is also a chief investigator in the
            ARC Centre of Excellence in Bioinformatics. His research has been recognized with various
            awards, including a DSc in 1994 and an ARC Professorial Fellowship in 2006. He has written
            over 165 articles, including 5 monographs, the last four as volumes in the prestigious Wiley
            series in Statistics. His research in statistics has been concentrated on the related fields of
            classification and machine learning, and in the field of statistical inference. The focus in the
            latter field has been on the theory and applications of finite mixture models and on estimation
            via the EM algorithm. More recently, he has become actively involved in the field of
            bioinformatics with the focus on the statistical analysis of microarray gene expression data in
            which he has coauthored a Wiley monograph.


Title:      Large-scale simultaneous inference for the detection of differential expression with microarray
            data


Abstract:   Microarrays allow the measurement of gene expressions for a biological sample (tissue) on a
            genome-wide scale, and form part of the high-throughput -omics methodology which is
            changing the face of biological research (genomics, proteomics and metabonomics). They are
            now standard tools in biology, with an ultimate goal for their use in clinical medicine for
            diagnosis and prognosis, in particular in cancer towards guiding therapeutic management. In
            this talk we consider an important problem in microarray experiments concerning the detection
            of genes that are differentially expressed in a given number of classes. It requires large-scale
            hypothesis testing problems, with hundreds or thousands of test statistics to consider at once.
            We consider the use of normal mixture models that provide a framework for the correct choice
            of a null distribution for simultaneous significance testing, and its effect on inference. This
            methodology is demonstrated on some real microarray data sets published in the literature.




                                                       20
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                            ABSTRACTS

Thursday 28 June 2007
Statistical analysis of gene expression

Session:    01:30 p.m. – 02:15 p.m.


Speaker:    Dr Antonio Reverter
            Principal Research Scientist, Bioinformatics Group
            CSIRO Livestock Industries


Bio:        Dr Antonio (Toni) Reverter is a principal research scientist with the Bioinformatics Group of
            CSIRO Livestock Industries. Toni's background is in statistical genetics, more specifically in
            methods for large-scale genetic evaluation and parameter estimation. His work in CSIRO
            involves the development and application of mathematical, computational and statistical
            methods for the analysis of gene-expression and mapping data including whole-genome SNP
            genotypes for complex traits in livestock species. Toni was the recipient of the inaugural 2005
            Eureka Prize for Bioinformatics.


Title:      A myogenin network-centric systems biology approach to the genetic dissection of complex
            traits in beef cattle


Abstract:   Despite the advances that have rendered new genetic technologies attractive to many animal
            geneticists it is still unclear how to best analyse the resulting data productively. The futility of
            simple statistical abstraction of genetic effects and sole reliance of genetic association on
            statistical significance alone are now apparent. This disappointing outcome is particularly
            obvious when dealing with complex traits governed by many interacting genetic effects. In order
            to shift genetic improvement of livestock species to improved frameworks, it is imperative to
            incorporate sound knowledge of the biology at the molecular genetics level of the traits of
            economic importance. Motivated by the added value of genetical genomics studies that merge
            expression profiling with marker-based genotyping, we propose a systems biology approach
            anchored to a gene network for Myogenin (MYOG), a muscle-specific transcription factor
            essential for the development of skeletal muscle. Using bovine gene expression and high
            density marker data, our objective is to evaluate the strength of the relationship between the
            association of a single nucleotide polymorphism (SNP) to a phenotype of interestwith the
            transcriptional activity of genes in the network.




                                                        21
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                           ABSTRACTS

Thursday 28 June 2007
Statistical analysis of gene expression

Session:    02:15 p.m. – 03:00 p.m.


Speaker:    Dr Harri Kiiveri
            Statistical Bioinformatics - Health
            CSIRO Mathematical and Information Sciences, Floreat, Western Australia


Bio:        Dr Harri Kiiveri is a research statistician who develops statistical methods for analysing very
            high dimensional multivariate data. He currently works in the area of bioinformatics with CSIRO
            Mathematical & Information Sciences (CMIS), specifically with the analysis of microarray data
            and SNP data. He has developed a methodology for fitting a large class of statistical models to
            data sets with many more variables than observations.

            He has also developed methods for the construction of local and global gene networks which
            enable the integration of different data sources such as microarrays, protein - protein
            interactions and transcription factor binding site information.


Title:      “Differential expression free” analysis of microarray data


Abstract:   In this talk we'll consider a way of analysing microarray data which does not focus on differential
            expression.

            Guided by a response of interest, we'll look at fitting models to data sets with many more
            variables than observations, constructing local gene networks, and simulations from such
            networks as a means of understanding the data and generating new hypotheses.




                                                       22
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Thursday 28 June 2007
Statistical analysis of gene expression

Session:    03:30 p.m. – 04:15 p.m.


Speaker:    Dr Ian Wood
            Research Fellow in Mathematical Sciences
            Queensland University of Technology


Bio:        Ian Wood is a researcher based in the ARC Center in Bioinformatics at the University of
            Queensland. He completed a PhD in 2004 at the University of Queensland on Boltzmann
            machines, optimisation and Markov chain Monte Carlo methods. He then completed a
            three-year postdoctoral fellowship in statistical genetics at Queensland University of
            Technology, which included collaboration with Genetic Solutions Pty Ltd and QIMR. His
            research interests include analysis of gene expression data, comparative genomics,
            classification, Monte Carlo methods, mixture models and machine learning.


Title:      Assessing classifiers trained on gene expression data


Abstract:   Levels of gene expression are typically estimated through the proxy of mRNA levels as
            measured after hybridization in microarray experiments. When measured on a set of subjects,
            they produce datasets which usually contain few observations (n<100) and thousands of
            possible predictors (p>>n). Studies have used microarray data for problems such as diagnosis
            and prognosis of disease and classification of tumours into subtypes.

            In constructing a classifier, the goal is to minimise and assess the error rate expected on new
            data. Methods of assessment such as cross-validation split the data into training and test sets.
            The test data should not be used in the choice of any aspect of the classifier being assessed.
            Failure to do this leads to selection biases of varying severity. Methods to detect and avoid
            these will be described.




                                                       23
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Thursday 28 June 2007
Statistical analysis of gene expression


Session:    05:00 p.m. – 06:30 p.m. – Plenary talk


Speaker:    Prof Phil Bourne
            Co-Director, Protein Data Bank (PDB)
            Department of Pharmacology, and
            San Diego Supercomputer Center
            University of California, San Diego, USA


Bio:        Refer to page 17.


Title:      Ligand binding site searching and application to finding off-targets for major pharmaceuticals


Abstract:   We have recently developed a new method we refer to as the geometric potential for defining
            ligand binding sites in cases where a ligand-receptor complex exists [1]. Using this descriptor
            and a sequence order independent profile-profile analysis (SOIPPA) approach we have been
            able to uncover new evolutionary relationships between families of proteins. [2]. Since a variety
            of major pharmaceuticals are found in the PDB bound to receptors, in addition we have an
            excellent approach for studying off-site binding of these ligands. When coupled with
            biochemical evidence and clinical evidence of side effects interesting stories emerge. I will
            describe the tamoxifen and other stories [3].

            [1] L. Xie and P.E.Bourne 2007 A robust and efficient algorithm for the shape description of
                protein structures and its application in predicting ligand binding sites. BMC Bioinformatics,
                8(Suppl 4):S9

            [2] L. Xie and P.E. Bourne 2007 Detecting Evolutionary Linkages Across Fold and Functional
                Space with Sequence Order Independent Profile-profile Alignments. Submitted.

            [3] L. Xie and PE. Bourne 2007 Proteome-wide Elucidation of the Molecular Mechanism
                Defining the Adverse Effect of Selective Estrogen Receptor Modulators. Submitted.




                                                       24
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Friday 29 June 2007
Computational neuroscience

Session:    09:00 a.m. – 10:15 a.m.


Speaker:    A/Prof Geoff Goodhill
            Queensland Brain Institute
            The University of Queensland


Bio:        Geoff Goodhill is an Associate Professor in both the Queensland Brain Institute and the School
            of Physical Sciences at the University of Queensland. His research focuses on understanding
            the computational mechanisms controlling the development of connections between neurons.
            He is particularly interested in how the tips of growing nerve fibres are guided by molecular
            gradients in the developing nervous system, and how the statistics of the visual input influence
            the structure of topographic maps in the visual cortex.


Title:      Is your brain smarter than a computer? Introduction to neuroscience and computational
            neuroscience.


Abstract:   Biological nervous systems are far more powerful, robust and efficient computing devices than
            any computers humans have yet designed. The goal of computational neuroscience is to
            understand the nature of the computations nervous systems have to perform, how they perform
            them, and how this can lead to better silicon-based computers. In my talk I will introduce some
            of the amazing computational abilities of biological brains, and some of the main themes in
            current computational neuroscience research. Illustrations of these issues will include recent
            results from my lab on the computational principles underlying the development of neuronal
            wiring.




                                                       25
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                            ABSTRACTS

Friday 29 June 2007
Computational neuroscience

Session:    10:45 a.m. – 11:45 a.m.


Speaker:    Dr Tatyana Sharpee
            Salk Institute for Biological Studies
            San Diego, CA, USA


Bio:        Tatyana Sharpee is an assistant professor in the Computational Neurobiology laboratory at the
            Salk Institute for Biological Studies. Her research focuses on statistical physics and information
            theory approaches to characterizing signal processing in the nervous system. In particular, she
            is interested in how sensory processing in the brain is matched to the statistics of real-world
            signals, why might the evolved hierarchy of neural representations be optimal, and how it can
            be best adapted to track rapid changes in the statistics of inputs. She applied
            information-theoretic methods to characterize neural feature selectivity with natural stimuli, and
            showed that certain aspects of neural filtering in visual cortex could adapt to even fairly complex
            statistical parameters in natural scenes.


Title:      Optimization principles of adaptive coding in the primary visual cortex


Abstract:   The idea that adaptation serves to adjust properties of neurons and their populations to
            optimally encode incoming stimuli is one of the central and oldest in sensory neuroscience,
            dating back to Adrian. However, most theoretical predictions for specific parameters of neural
            sensitivity, such as receptive fields, were made using a linear (or threshold-linear) model of
            neural response. While these predictions adequately describe sensitivity of sensory neurons at
            the periphery (e.g. retina and lateral geniculate nucleus), we have found that cortical neurons
            exhibit adaptive filtering with qualitative new features that do not fit with optimal encoding
            arguments for a linear system. Instead, we have assumed that neural response can be
            described by a generalized linear-nonlinear model, where the neural response is an arbitrary
            nonlinear function of the outputs of a set of linear filters applied to incoming stimuli. In this
            framework, a classic receptive field corresponds to the case where a single linear filter
            determines the spike probability. We ask whether it is possible to predict relative changes in the
            filters that would maintain optimal coding for a changing power spectrum. Optimality of
            encoding can be preserved by changing the linear filters so that the product of their amplitude
            spectra with those of the inputs remains unchanged. We tested this prediction by probing
            neurons in the cat primary visual cortex with white noise and natural inputs matched in
            luminance and contrast. To account for the fact that natural inputs are strongly non-Gaussian,
            which introduces differences between filters computed for the fully linear and the
            linear-nonlinear models, we computed filters as maximally informative dimensions. Optimal
            encoding arguments based on filtering in linear-nonlinear system quantitative described the
            changes in neural filtering at all spatial and temporal frequencies where tuning was changing.
            For example, at low temporal frequencies, low spatial frequencies are more common in the
            natural stimuli than in the white noise ensemble, and neurons become correspondingly less
            sensitive to those frequencies. Adaptation occurs over 40 seconds to many minutes, longer
            than most previously reported forms of adaptation.




                                                        26
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                            ABSTRACTS

Friday 29 June 2007
Computational neuroscience

Session:    01:15 p.m. – 02:15 p.m.


Speaker:    Prof Mandyam Srinivasan
            ARC Federation Fellow
            Queensland Brain Institute
            The University of Queensland


Bio:        Mandyam Srinivasan FRS is Professor of Visual Neuroscience at the Queensland Brain
            Institute of the University of Queensland. His research seeks to understand the principles of
            visual processing, perception and cognition in simple natural systems, and to apply these
            principles to machine vision and robotics. He is a Federation Fellow and in 2006 received the
            Prime Minister's Prize for Science. He has published over 180 papers, including several in
            Nature and Science.


Title:      Smart computations in small brains: Vision, navigation, perception and cognition in honeybees


Abstract:   Insects, in general, and honeybees, in particular, perform remarkably well at seeing and
            perceiving the world and navigating effectively in it, despite possessing a brain that weighs less
            than a milligram and carries fewer than 0.01% as many neurons as ours does.

            Although most insects lack stereo vision, they use a number of ingenious strategies for
            perceiving their world in three dimensions and navigating successfully in it. For example,
            distances to objects are gauged in terms of the apparent speeds of motion of the objects'
            images, rather than by using complex stereo mechanisms. Objects are distinguished from
            backgrounds by sensing the apparent relative motion at the boundary. Narrow gaps are
            negotiated by balancing the apparent speeds of the images in the two eyes. Flight speed is
            regulated by holding constant the average image velocity as seen by both eyes. Bees landing
            on a horizontal surface hold constant the image velocity of the surface as they approach it, thus
            automatically ensuring that flight speed is close to zero at touchdown. Foraging bees gauge
            distance flown by integrating optic flow: they possess a visually-driven "odometer" that is robust
            to variations in wind, body weight, energy expenditure, and the properties of the visual
            environment.

            Recent research on honeybee perception and cognition is beginning to reveal that these insects
            may not be the simple, reflexive creatures that they were once assumed to be. For example,
            bees can learn rather general features of flowers and landmarks, such as colour, orientation
            and symmetry, and apply them to distinguish between objects that they have never previously
            encountered. Bees exhibit "top-down" processing: that is, they are capable of using prior
            knowledge to detect poorly visible or camouflaged objects. They can navigate through
            labyrinths by learning path regularities, and by using symbolic signposts. They can learn to form
            complex associations and to acquire abstract concepts such as "sameness" and "difference.
            Bees are also capable of associative recall: that is, a familiar scent can trigger recall of an
            associated colour, or even of a navigational route to a food location. All of these observations
            suggest that there is no hard dichotomy between invertebrates and vertebrates in the context of
            perception, learning and "cognition"; and that brain size is not necessarily a reliable predictor of
            perceptual capacity.

            Finally, some of the above principles - especially those that relate vision and navigation - are
            offering novel, computationally elegant solutions to persistent problems in machine vision and
            robot navigation. Thus, we have been using some of the insect-based strategies described
            above to design, implement and test biologically-inspired algorithms for the guidance of
            autonomous terrestrial and aerial vehicles.

                                                        27
                           2007 Winter School in Mathematical and Computational Biology
                                                 25–29 June 2007
                                           ABSTRACTS

Friday 29 June 2007
Computational neuroscience

Session:    02:15 p.m. – 03:15 p.m.


Speaker:    Dr Anthony Bell
            Redwood Center for Theoretical Neuroscience
            The University of California, Berkeley, USA


Bio:        Tony Bell was awarded his M.A. in Computer Science and Philosophy at the University of St.
            Andrews in Scotland in 1986, and his Ph.D. in Artificial Intelligence from the Free University of
            Brussels in Belgium in 1993. He worked at Terry Sejnowski's Computational Neurobiology lab
            at the Salk Institute in San Diego in several periods from 1990 to 2001, during which time he
            also worked at Interval Research in Silicon Valley. Since 2002, he has been a research scientist
            at the Redwood Neuroscience Center, first in Silicon Valley, and now at the University of
            California at Berkeley.

            His current research interest is to connect machine learning ideas with processes known to
            occur in neurons and at synapses, specifically to show how molecular and spike
            timing-dependent processes may be implementing an `inter-level' unsupervised learning
            process. Earlier work was on `within-level' information maximisation at the spiking level (with
            Lucas Parra) and at the rate-coding level (with Terry Sejnowski). The latter algorithm yielded
            many `ICA' results: learning neural receptive fields, separating EEG signals and fMRI signals,
            and it has gone on to be used for analysing earthquakes, the internet, tumors and satellite
            images, amongst many other things.


Title:      Towards a theory of learning and levels for neurobiology


Abstract:   Learning, plasticity, adaptivity: these occur at the ecological, behavioural, neural and molecular
            levels amongst others. Yet each level is just a different description of the same processes.
            Defined structural relations exist between the levels (networks within networks), and these
            define causal relations in time. I will describe how these causal relations are essentially
            inter-level in nature, consisting of downward 'boundary conditions' and upward 'emergence'.
            Using them, information can travel from the top to the bottom of the reductionist hierarchy and
            vice-versa, a revolutionary idea for computational modelling. It opens the possibility of defining
            new kinds of learning algorithm which exploit inter-level mappings for representational
            purposes. This arises because mappings can be thought of as adaptive probabilistic models, as
            easily demonstrated by the Infomax/ICA algorithm (which I will use to illustrate this concept).
            The inter-level mapping of most interest to neuroscientists is the massively over complete
            neuron-to-synapse mapping. I will argue that Spike Timing-Dependent Synaptic Plasticity
            (STDP) is optimising this mapping, and that the informational readout is at the post-synaptic
            density, not the axon hillock. The talk is primarily conceptual and forward-looking, with a review
            of many empirical neuroscience findings, but technical machine learning ideas are explained,
            as the goal of this work is to connect biology and learning theory.




                                                       28
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                           ABSTRACTS

Friday 29 June 2007
Computational neuroscience

Session:    03:30 p.m. – 04:30 p.m.


Speaker:    A/Prof Michael Breakspear
            School of Psychiatry
            The Black Dog Institute
            University of New South Wales


Bio:        Michael Breakspear is Associate Professor of Psychiatry at the University of New South Wales
            and The Black Dog Institute, Sydney. He studies nonlinear neuronal dynamics in mathematical
            models of the brain and in experimental neuroscience data. He is interested in how
            disturbances of these dynamics may explain disorders such as epilepsy, schizophrenia and
            bipolar disorder.


Title:      Network structure of cerebral cortex shapes neuronal dynamics on multiple time scales


Abstract:   In the cerebral cortex, neuronal dynamics unfolding on structural connections give rise to
            complex patterns of neural activity, even in the absence of any external input. This presentation
            will overview graph-based analyses of large- scale brain architectures, and the complex
            structure-function relationships that emerge when nonlinear neuronal activity is simulated on
            them. Graph theory allows one to characterize both local and global properties of connected
            systems, including the cliqueshness, "path length", efficiency and robustness to damage. We
            start by reviewing the evidence for "small world" properties in mammalian cortex.
            Physiologically-based nonlinear dynamics are them simulated, with individual brain "nodes"
            inter connected according to the known anatomy of macaque neocortex. We find
            structure-function relations at multiple temporal scales. Functional networks recovered from
            long windows of neural activity (minutes) exhibit significant overlap with underlying structural
            networks. Hubs in these "slow" functional networks largely correspond to hubs in structural
            networks. However, the sequence of functional networks recovered from consecutive shorter
            time windows (seconds) exhibits significant fluctuations in overall functional topology. As the
            informational couplings between brain regions transiently shift, the centrality of nodes within the
            functional network is altered. Transient couplings between brain regions occur in a coordinated
            manner, producing two anticorrelated functional clusters, and the clusters in turn are linked by
            prefrontal and parietal regions that are hubs in the underlying structural network.




                                                       29
                          2007 Winter School in Mathematical and Computational Biology
                                                25–29 June 2007
                                         SPONSORS

2007 Winter School in Mathematical and Computational Biology is sponsored by the following
organisations:




ARC Centre in Bioinformatics



                                                     ARC Centre in Bioinformatics




Institute for Molecular Bioscience, The University of Queensland




The MathWorks Australia Pty Ltd




Queensland Cyber Infrastructure Foundation




SGI

								
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