This year's Lecture Series is organised by b-it Professor Holger Fröhlich and Professor Andreas Weber (Institute of Computer Science, University of Bonn). The talks will be held in Lecture hall at 5 p.m.

10 May 2012 

Dr. Markus Eiswirth Fritz-Haber-Institute of the Max-Planck-Society, Department of Physical Chemistry - Spatiotemporal Selforganization Group, Berlin, Germany 

More information about the lecturer and his work 

24 May 2012 

Dr. Christoph Dieterich Max-Delbrück-Centre for Molecular Medicine, Bioinformatics in Quantitative Biology Group, Berlin, Germany 
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Abstract 

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. 
I will discuss computational methods to identify regulators and their respective target sites. Novel experimental approaches to chart transcriptome-wide occupancy profiles will be outlined. Additionally, aspects of miRNA family evolution will be addressed as well as our solution to integrate these data. 

31 May 2012 

Professor Dr. Santo Motta Immunomics Group, Dept. Mathematics and Computer Science,University of Catania, Catania, Italy 

Abstract

The first part of the talk will discuss modeling activity in daily life as initial scenario for modeling in Physical Sciences and in Life Sciences. The different steps of the modeling process, purpose, data and mathematical representation will be discussed.

The second part of the talk will show examples on models developed by our group: i) model to reproduce the effect of an immuno-prevention cancer vaccine and find the optimal vaccination schedule; a model to represent the effect of a vaccine on lung metastases; a simple model for artherogenesis; a model for melanoma treatment.

14 June 2012 

Jun.-Prof. Dr. Nicole Radde Institute for Systems Theory and Automatic Control University of Stuttgart, Stuttgart, Germany

Abstract

Modeling the dynamic of intracellular networks with differential equations that are based on chemical reaction kinetics has become a standard approach in recent years. One of the main issues on the way to create predictive models is the identification of parameters from experimental data. Since these data are usually sparse, many parameters can usually only vaguably be defined and are sloppy. Furthermore, data are noisy due to intrinsic biological noise and measurement errors. Point estimates such as least squares or maximum likelihood estimates are not suited in those settings, and global approaches are needed. We use statistical Bayesian approaches for parameter estimation, which can deal with measurement uncertainties and hidden variables. Furthermore, sampling based Bayesian approaches are generally suited to handle sloppy and non-identifiable parameters. This talk introduces the general concepts of Bayesian learning of differential equation parameters and describes how the mentioned difficulties can be faced. We show applications to intracellular network models.

21 June 2012 

Professor Dr. Stefan Schuster Jena Centre for Bioinformatics, Group of Theoretical Systems Biology, University of Jena, Germany
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Abstract 

Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways [1]. This has manifold applications in bioengineering, such as predicting maximum yields and determining the roubustness to knockouts. Here, we give several illustrative examples of application of that analysis. They include the question whether fatty acids can be transformed into sugars in humans [2,3], whether vitamin B_3 can be substituted by tryptophan [4] and a successful theoretical prediction of a novel pathway in central metabolism in /Escherichia coli/ [1]. It is shown that a graph-theoretical connectivity analysis is insufficient here and that the topology (including bimolecular reactions) and mass flow should be taken into account, as is done in elementary-modes analysis. 

The basic ideas of algorithms for computing elementary modes are outlined. Elementary-modes analysis meets with the problem of combinatorial explosion, which has impeded scaling it up to genome-wide models. We present two approaches to overcoming this obstacle. One approach is by computing first the shortest elementary mode, then the second-shortest etc., up to a certain length [5]. This can be done by linear optimization. The second concept is called elementary flux patterns [3,6]. Within a large metabolic network, elementary flux patterns are defined as sets of reactions that represent the basic routes of any steady-state flux of the network through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. 

References

[1] S. Schuster, T. Dandekar, D.A. Fell: Detection of elementary flux modes in biochemical networks: A promising tool for pathway analysis and metabolic engineering, /Trends Biotechnol/. 17 (1999) 53-60. 
[2] L.F. de Figueiredo, S. Schuster, C. Kaleta, D.A. Fell: Can sugars be produced from fatty acids? A test case for pathway analysis tools. /Bioinformatics/ 25 (2009) 152-158. 
[3] C. Kaleta, L.F. de Figueiredo, S. Werner, R. Guthke, M. Ristow, S. Schuster: In silico evidence for gluconeogenesis from fatty acids in humans. /PLoS Comp. Biol./ 7 (2011) e1002116. 
[4] L.F. de Figueiredo, T.I. Gossmann, M. Ziegler, S. Schuster: Pathway Analysis of NAD^+ metabolism. /Biochem. J./439 (2011) 341–348. 
[5] L.F. de Figueiredo, A. Podhorski, A. Rubio, C. Kaleta, J.E. Beasley, S. Schuster, F.J. Planes: Computing the shortest elementary flux modes in genome-scale metabolic networks. /Bioinformatics/ 25 (2009) 3158-3165. 
[6] C. Kaleta, L.F. de Figueiredo, S. Schuster S.: Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns. /Genome Res./ 19 (2009) 1872-1883. 

05 July 2012 

Professor Dr. Ovidiu Radulescu Theoretical Biophysics and Systems Biology, University of Montpellier, France
More information about the lecturer and his work

Abstract 

A crucial question of developmental biology is how embryonic development has reliable outcome despite variability of genotype and environment. More than 60 years ago, Conrad H. Waddington,a visionary biologist, proposed theoretical concepts such as epigenetic landscape and canalization to describe the capacity of error correction of developmental systems. Recent quantitative studies of gene expression in Drosophila embryossupport the epigenetic landscape idea and elucidate some of the molecular mechanisms of canalization. Early development of the fruit fly is controlled by a gene network capable of error correction. This gene network can be modeled by differential equations describing the interaction among genes and the diffusion of the gene products. The model is a complex dynamical system whose properties reserves some surprises and broadens our view about mathematical principles of morphogenesis.