There are several semantic sources that can be found in the Web that are either explicit, e.g. Wikipedia, or implicit, e.g. derived from Web usage data. Most of them are related to user generated content (UGC) or what is called today the Web 2.0. In this talk we show several applications of mining the wisdom of crowds behind UGC to improve search. We will show live demos to find relations in the Wikipedia or to improve image search as well as our current research in the topic. Our final goal is to produce a virtuous data feedback circuit to leverage the Web itself.

"On-the-Fly Model Checking for Regular Alternation-Free Mu-Calculus”:

Increasing read length is viewed as the crucial condition for fragment assembly with next-generation sequencing technologies. However, introducing mate-paired reads (separated by a gap of length GapLength) opens a possibility to transform short mate-pairs into long mate-reads of length approximately GapLength, and thus raises the question as to whether the read length (as opposed to GapLength) even matters. We describe a new tool for assembling mate-paired short reads and use it to analyze the question of whether the read length matters.

It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show very little effect individually but strong effect if they occur jointly, a phenomenon known as epistasis or multilocus interaction. In this seminar, we explore the applicability of decision trees to this problem. A case-control study was performed, composed of 164 controls and 94 cases with 32 SNPs available from the BRCA1, BRCA2 and TP53 genes. There was also information about tobacco and alcohol consumption.

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We present the basics of the new high-throughput sequencing technologies and discuss some of its applications and associated research problems from a bioinformatics perspective.

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The installation of software packages (on Linux as well as in other package-driven platforms as eclipse plugins) depends on the correct resolution of dependencies and conflicts between packages. As an NP-complete problem, this is an hard task which todays technology does not address in an acceptable way. This seminar introduces a new approach to solving the software dependency problem in a Linux environment, devising a way for solving dependencies according to available packages and user preferences.

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Optimization problems arise in a wide variety of scientific and engineering applications. It is computationally challenging when optimization procedures have to be performed in real time to optimize the performance of dynamical systems. For such applications, classical optimization techniques may not be competent due to the problem dimensionality and stringent requirement on computational time. One very promising approach to dynamic optimization is to apply artificial neural networks.

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Molecular hydrogen (H2) is an environmentally clean energy carrier that can be a valuable alternative to the limited fossil fuel resources of today. The BioModularH2 project aims at designing reusable, standardized molecular building blocks that integrated into a “chassis” will result in a photosynthetic bacterium containing engineered chemical pathways for competitive, clean and sustainable hydrogen production. For this project the unicellular cyanobacterium Synechocystis sp. PCC 6803 (Synechocystis) is being used as the photoautotrophic “chassis” for this project.

Synthetic biology is a new field of research that combines computer models of biological systems with DNA synthesis and genetic engineering techniques in order to design and build new biological functions, systems and organisms. While still in its infancy, this area of research is expected to develop rapidly, so that very soon researchers, companies and hackers will be able to design, build and release in the wild new organisms.

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The increasing availability of metabolic network models and data poses new challenges in what concerns optimization. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models.