Invited Session Tue.1.H 2033

Tuesday, 10:30 - 12:00 h, Room: H 2033

Cluster 12: Life sciences & healthcare [...]

Bioinformatics and combinatorial optimization I


Chair: Rumen Andonov and Carlile Lavor



Tuesday, 10:30 - 10:55 h, Room: H 2033, Talk 1

Zachary Voller
An optimal solution to the generalized distance geometry problem

Coauthor: Zhijun Wu


NMR experiments on a protein yield a set of inter-atomic distance ranges. A number of structures satisfying the distance constraints, derived from distance range and bond information, are then generated. This ensemble of structures is often under represented and inaccurately represents the protein's structural fluctuations. In this presentation we present an alternative problem where its solution, derived from interior point optimization, provides a single representation for a protein's conformation and its ensemble of possible structures.



Tuesday, 11:00 - 11:25 h, Room: H 2033, Talk 2

Antonio Mucherino
Re-ordering protein side chains for the discretization of MDGPs

Coauthors: Luiz M. Carvalho, Virginia Costa, Carlile Lavor, Nelson Maculan


We consider a class of Molecular Distance Geometry Problems (MDGPs) that can be discretized in the hypothesis some assumptions are satisfied. We refer to this class of problems as the Discretizable MDGP (DMDGP). The discretization assumptions are strongly depend upon the ordering that is associated to the atoms of the considered molecules. In a recent work, we proved that any MDGP related to protein backbones can be discretized if the backbone atoms are re-arranged by considering a special ordering we identified. In this work, we investigate the possibility to find such discretization orderings for the side chains of the amino acids involved in the protein synthesis.



Tuesday, 11:30 - 11:55 h, Room: H 2033, Talk 3

Martin Gebser
Repair and prediction (under inconsistency) in large biological networks with answer set programming

Coauthors: Carito Guziolowski, Mihail Ivanchev, Torsten Schaub, Anne Siegel, Sven Thiele, Philippe Veber


We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we propose a range of different operations for altering experimental data and/or a biological network in order to re-establish their mutual consistency and thus to enable automated prediction. For accomplishing repair and prediction, we take advantage of the distinguished modeling and reasoning capacities of Answer Set Programming. We validate our framework by an empirical study on the widely investigated organism Escherichia coli.


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