LEARNING & INTEGRATING PETRI NET MODELS OF BIOLOGICAL SYSTEMS                   Ashwin Srinivasan 1, Michael Bain 2, Sande...
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
Sydney Bioinformatics Research Symposium 2012   posterbook
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Sydney Bioinformatics Research Symposium 2012 posterbook

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Just some of the great posters presented at the 2012 Sydney Bioinformatics Research Symposium on 9 November at The Garvan Institute:
1. Learning and Integrating Petri Net Models of Biological Systems (Dr Mike Bain, UNSW),
2. Gene regulatory networks in heart development (Dr Rom Bouveret, VCCRI),
3. Computational analysis of large rearranged immunoglobulin gene sequence sets (Dr Bruno Gaeta, UNSW),
4. Resources for Bioinformaticians & Computational Biologists (Dr Sean O'Donoghue, Garvan & CSIRO),
5. A Multi-dimensional Matrix for Systems Biology Research (Dr Ignatius Pang, UNSW),
6. Multi-dimensional Pathway Analysis - Identifying key insulin responsive pathways in plasma membrane trafficking of adipocytes. (Ellis Patrick, University of Sydney),
7. Co-option of ubiquitous transcription factors in the cardiac developmental gene regulatory network (Dr Mirana Ramialison, VCCRI),
8. Functional annotation of human chromosome 7 “missing” proteins - a bioinformatics approach (Prof Shoba Ranganathan, Macquarie University),
9. Cloudbusting: fast annotation done cheap (Mr Rupert Shuttleworth, VCCRI),
10. OCAP pipeline and a new hybrid protein identification method (Dr Penghao Wang, UNSW),
11. Integrative analysis of multiple –omics data for Pancreatic Cancer (Dr Jianmin Wu, Kinghorn Cancer Centre),
12. Re-Fraction: a machine learning R package for deterministic identification of protein homologues and slice variants in large-scale MS-based proteomics (Dr Pengyi Yang, Garvan Institute),

Symposium Organisers:
Nicola Armstrong (Garvan)
David Lovell (CSIRO/ABN)
Marc Wilkins (UNSW)
Jean Yang (USYD)
Cath Suter (VCCRI)

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Sydney Bioinformatics Research Symposium 2012 posterbook

  1. 1. LEARNING & INTEGRATING PETRI NET MODELS OF BIOLOGICAL SYSTEMS Ashwin Srinivasan 1, Michael Bain 2, Sandeep Kaur2,3 and Mark Temple4 1 Indrapastha Institute of Information Technology, New Delhi, India, 2 University of New South Wales, Sydney, Australia, 3Garvan Institute, Sydney, Australia and 4University of Western Sydney, Sydney, Australia 1. Introduction 2. A Petri net exampleQualitative modelling (QM) approaches for biologicalapplications have some limitations: spurious Initialbehaviours; lack of concurrency; not easily extended marking.to continuous or stochastic representations [1]. Petrinets (PN) are a QM approach that avoids theselimitations. PNs have a strong formal basis and havebeen widely used in modelling biological systems.However, relatively little work exists on learning suchmodels from biological data. We introduced a definiteclause representation for PNs called Guarded FinalTransition Systems and show that a known marking.combinatorial algorithm can be formulated as asearch through a lattice of clauses, enabling the useof ILP to learn PNs [2]. Advantages include: efficientsearch of ILP hypothesis space, compared toprevious algorithm; extending representation toidentify regulatory and metabolic models in the samemodelling framework; using existing networks in A Petri net representing construction of waterbackground knowledge to learn hierarchical models. (transition – bar) from reactants (places – circles). 3. Petri net reconstruction using ILP 4. Model integration: phenotype predictionA learned PN model for yeast pheromone response. PN model [3] of yeast response to H2O2 [4]Uses several generic components of signalling integrated deletant phenotypes, transcriptomics andpathways encoded as guarded transitions. proteomics data, highlighting potential pathways. 4. References [1] Srinivasan and King (2008) “Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming”. Journal of Machine Learning Research, 9:1475–1533. [2] Srinivasan and Bain (2012) "Knowledge-Guided Identification of Petri Net Models of Large Biological Systems", pp. 317-331, LNAI 7207, Springer. [3] Kaur (2012) "Phenotype Prediction with Models of Cellular Systems". Honours Thesis, School of Computer Science and Engineering, UNSW. [4] Temple, Perrone and Dawes (2005) "Complex cellular responses to reactive oxygen species". TRENDS in Cell Biology, 15(6):219-326.

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