Call for Participation

Joint Second FVG International Summer School on Bioinformatics
Seventh International Summer School on Biology, Computation and Information (BCI 2012)

September 10–14, 2012, Udine, Italy

CALL FOR PARTICIPATION

The School of Biology, Computation, and Information (BCI), reaching this year its seventh edition, as a joint event with the second FVG summer school on bioinformatics, aims at bringing  together Teachers and Students in Biology, Mathematics,  and Computer Science. The main goal of the School is to give an updated overview of interdisciplinary techniques and problems cross-bordering the three fields.

This year's edition will be dedicated to the study of interaction networks in biological systems, particularly on genetic regulatory networks and biochemical networks. The topics of the school will cover such issues as production of experimental data, construction of mathematical and computational models, and statistical validation and fitting of models against data.

The three distinguished speakers for this year's edition are Bijoern Usadel (Biology), Vincent Danos (Computer Science), and Guido Sanguinetti (Mathematics) and the school will take place during the second week of September (September 10–14, 2012).

COURSES

Main topic: biological interaction networks

Area: Mathematics
Lecturer: Dr. Guido Sanguinetti, University of Edinburgh, UK.
Abstract: Uncertainty is inherent in many aspects of biology, from the intrinsic noise of cellular reactions, to the extrinsic noise due to fluctuating environments, to inevitable experimental noise in the measurement process. Proper handling of uncertainty is essential in many steps of model development. In these lectures, I will review the mathematical foundations of stochastic modelling and introduce some more advanced tools for statistical inference in models of biological systems. I will introduce the basic concepts of probability theory and focus on Bayes' theorem as a tool for calibration and uncertainty quantification. I will explain some concepts of statistical inference such as Markov chain Monte Carlo and variational methods. I will then present some basic time-series models and their use in biology, and conclude discussing more advanced continuous time stochastic models.
Bio: Guido Sanguinetti received his degree in Physics from the University of Genova and his DPhil in Mathematics from the University of Oxford. He was a postdoc, then lecturer, in the Department of Computer Science at the University of Sheffield prior to joining the faculty at the School of Informatics, University of Edinburgh, in 2010. His interests focus on mathematical and statistical models of dynamic biological systems. 

Area: Computer Science
Lecturer: Prof. Vincent Danos, University of Edinburgh, UK.
Abstract: We will describe a new methodology to describe, simulate and investigate complex biomolecular networks. This method is called rule-based modelling and has the advantage that it can cope better with combinatorial molecular systems than usual reaction-based methods. The following aspects will be covered: knowledge representation, simulation, causal analysis, model reduction techniques.
Bio: Vincent Danos graduated in Engineering, obtained a PhD in maths. He has a 20 years academic career in logic and theoretical computer science, with an increasing concern for applications, mostly in formalising, modeling and analysing complex systems—e.g., biomolecular networks. 

Area: Biology
Lecturer: Prof. Bijoern Usadel, RWTH Aachen University, Gernamy
Abstract: The last decade has seen a massive explosion of omics data becoming available to the individual researcher. Initially, the focus was on individual experiments focusing on the limited study of a certain condition. However, given the massive growth of omics data in public data bases, these data can be holistically integrated and novel inferences made. In the beginning this was e.g. based on large scale approaches using simple correlation and a guilt by association approach, having lead to a massive knowledge gain for experimental biologists. Here we present several different streams of how to combine public (and own) dataset stemming from different disciplines in order to make new inferences about the plant as a whole. Firstly we present a novel normalization method for Affymetrix type microarrays beneficial for correlation analysis. We then show how this normalized transcript data from focused areas can be used to predict plant status which we validate using metabolite data. Based on these models we combined metabolite and transcript data sets to make informed decisions about gene knock-out experiments validating our predictions. We also show that guilt by association approaches can be improved by incorporating novel measures, if a large data set is properly mined using expert rules. These results also imply that the automatic incorporation of additional e.g. sequence information will aid in data interpretation. As a proof of concept we show that integration of sequence with simple microarray derived expression data leads to an improved predictor for plant protein chloroplast import. We finish by showing that next-generation sequencing data is not making matters more complicated but will allow us to get an even deeper understanding of living organisms.
Bio: Dr. Björn Usadel studied in Biochemistry in Berlin and New York where he worked in Prof Ulrike Gaul's lab on the development on the visual system of Drosophila. During this time he got interested in Bioinformatics and then went on to Golm where he did his PhD in the group of Dr. Markus Pauly on the identification and characterization of novel cell wall genes. He then worked as a Postdoc in Prof. Mark Stitt's lab on the visualization and evaluation of high throughput data. Since 2008 he was a group leader at the Max Planck Institute and since then worked on data visualization, analysis as well as sugar status and cell wall biosynthesis. Since 2011 he is a full pofessor at the RWTH Aachen university and a co-director at the Forschungszentrum Jülich.

 

REGISTRATION AND ACCOMMODATION

Registration deadline: September 2nd, 2012.
Registration fee: EUR 100 (*)

(*) The registration is free for students and staff of the University of Udine, University of Trieste, and SISSA.

We can provide accommodation for 40 participants, assigned on a first-come first-served basis. To apply use the online registration form. Acceptance of more participants will be evaluated by the organizers.

The registration fee covers participation at all lectures, course materials, coffee break, and lunches. Accomodation is not included. Please contact the organization or visit the web site for additional information.

LOCATION

The school will take place in Udine, in Friuli Venezia Giulia, Italy. Lessons will be held at the congress center of ERDISU, in Viale Ungheria, I-33100, Udine. The congress center is 15 minutes walking from the train station.

WEBSITE AND CONTACT

For all additional information, please visit the website: http://phdsummerschools.uniud.it/bci/second-joint-summer-school-biology-computation-and-information

SPONSORS

  • Regione Autonoma del Friuli Venezia Giulia
  • University of Trieste
  • University of Udine.
  • SISSA, Trieste.

ORGANIZING AND SCIENTIFIC COMMITTEE

  • Alberto Policriti, University of Udine (school co-director)
  • Luca Bortolussi, University of Trieste (school co-director)
  • Claudio Altafini, SISSA, Trieste (school co-director)
  • Nicola Vitacolonna, University of Udine