Here we collect information for all four parts (lecture, special lecture, seminar, and practical course) of the module Graphen und Biologische Netze.

Registration

For the oral exam you need to register.
Please register here.
The information you provide will be for the Pruefungsprotokoll.

Main Lecture

The lecture is a blackboard-style lecture. There is no TeX script!
Am 2.12.2016 (Dies Academicus) findet die Vorlesung statt! Das Thema ist 'Co-Graphen'.

Special Lecture

Our topic will be a introduction into phylogenetic combinatorics with an emphasis on distance-based methods.
Die 3. Spezialvorlesung findet am 21.12. um 11 Uhr statt!

Seminar

13.12. -- 15.12.2016, jeweils 11:30 bis 13:00 und 14:00 - 15:40
During the seminar, every participant will present recent work from the scientific literature. Stay tuned until friday.
Current deadline for finding a paper is November, 11th.

  • papers with graph-theoretic content from the past 12 months (approximately): graph theory papers or bioinformatics with graph theory content
  • not obviously trivial. Will not be checked by us. Use 'common sense'. I.e. don't choose papers where the only graph-theoretical content is a drawing of a circle.
  • first email in (*according to my inbox time stamp*) counts at choener@bioinf
  • send me one paper title, I won't select for you. Email body should contain: your name, paper title, year, journal, authors, link to paper (see below for examples in the program)
  • use a meaningful email subject header, maybe "Graphen + BioNetze, Seminar, 2016, Paper"
  • if your choice doesn't show up after November, 11th, send a gentle reminder


    Seminar Program

    IMPORTANT: STICK TO 15 min presentations!

    Session 1: Applications

  • [Ulrike Klotz] Graph Theory Enables Drug Repurposing - How a Mathematical Model Can Drive the Discovery of Hidden Mechanisms of Action; 2014 ; PloS One ; Gramatica et al link
  • [Anastasia Wolschewski] "APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks." , 2016, Bioinformatics, Bonnici, Vincenzo, et al. , link
  • [Kathleen Wende] Calderone, Alberto, et al. "Comparing Alzheimers and Parkinsons diseases networks using graph communities structure." BMC systems, 2016
  • [Sebastian Luhnburg] Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution; 2016 Mar; Elsevier; Eduardo Corel, Philippe Lopez, Raphaël Méheust, and Eric Bapteste; link

    • Session 2: Algorithms

  • [Marius Brunnert] New Biology Inspired Anonymous Distributed Algorithms to Compute Dominating and Total Dominating Sets in Network Graphs ; 2016 ; PDPS workshop 2016 ; Luo et al link
  • [Marcel Winter] Algorithms for Visualizing Phylogenetic Networks, 2016 , Ioannis G. Tollis, Konstantinos G. Kakoulis Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016) link
  • [Alexander Scholz] L-GRAAL: Lagrangian graphlet-based network aligner, 2015, Oxford University Press, Noel Malod-Dognin and Natasa Przulj, link
  • [Christian Heide] Fimmel, E., Michel, C. J., Struengmann, L. n -Nucleotide circular codes in graph theory. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, 20150058 (2016) link
  • [Markus Michaelis] "Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale", 2016, PLOS ONE, Emmons et al link

    • Session 3

  • [Sebastian Lange] Elhesha, R. & Kahveci, T., Identification of large disjoint motifs in biological networks, BMC Bioinformatics, 2016 link
  • [Sophie Wolf] A random graph model of density thresholds in swarming cells, 2016, Journal of Cellular and Molecular Medicine, S.G. Jena, link
  • [Stefan Kraemer] The unrooted set covering connected subgraph problem differentiating between HIV envelope sequences, European Journal of Operational Research, 2016, Stephen J. Maher, John M. Murray, link
  • [Maik Froebe] SYNC or ASYNC: Time to Fuse for Distributed Graph-Parallel Computation, 2015, ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, link

    • Session 4: Gene Expression

  • [Michael Rode] Learning from Co-expression Networks: Possibilities and Challenges, 2016, frontiers in plant science, Elise A. R. Serin, Harm Nijveen, Henk W. M. Hilhorst and Wilco Ligterink link
  • [Alexander Engler], Proteome rearrangements after auditory learning: high-resolution profiling of synapse-enriched protein fractions from mouse brain., 2016, Journal of Neurochemistry, Thilo Kaehne et al., link
  • [Stephan Thoenes] Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model, Lam C. Tsoi, 2015, Bioinformatics, link
  • [Florian Maeschle] Theoretical knock-outs on biological networks,2016, Journal of Theoretical Biology , Pedro J. Mirandaa et al link
  • [Adarelys Andrades] Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory, 2015, PLoS ONE, Stavrakas et al., link [spaeter!]

    • Session 5: Brains and Ecology

  • [Yves Annanias] Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery, 2016, Journal of Neurosurgery, Michael G. Hart, MBChB, Rolf J. F. Ypma, PhD, Rafael Romero-Garcia, PhD, Stephen J. Price, FRCS (Neuro.Surg) and John Suckling, PhD1, link
  • [Felix Helfer] Graph theory illustrates spatial and temporal features that structure elephant rest locations and reflect risk perception, 2016, Ecography, George Wittemyer, Laura M. Keating, Fritz Vollrath and Iain Douglas-Hamilton, link
  • [Thilo Muehl-Benninghaus] Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach; Anna Manelis, Jorge R. C. Almeida, Richelle Stiffler, Jeanette C. Lockovich, Haris A. Aslam and Mary L. Phillips; 2016 link
  • [Akber Sarchaddi] Small World architecture in brain connectivity and hippocampal volume in Alzheimerâ**s disease: a study via graph theory from EEG data; 2016; Springer Science; Fabrizio Vecchio, Francesca Miraglia, Francesca Piludu, Giuseppe Granata, Roberto Romanello, Massimo Caulo, Valeria Onofrj, Placido Bramanti, Cesare Colosimo, Paolo Maria Rossini link

    • Session 5: Social Networks and related topics

  • [Marcel Gauglitz] Ion aggregation in high salt solutions. V. Graph entropy analyses of ion aggregate structure and water hydrogen bonding network, 2016, The Journal of Chemical Physics, link
  • [Joerg Walter] A novel analysis on the efficiency of hierarchy among leader-following systems, 2016, Automatica, J.Shao et al., link
  • [Jeremias Schebera] Ungewissheit versus Unsicherheit in Sozialen Netzwerken, 2016, Diskussionsbeitraege der Fakultaet fuer Wirtschaftswissenschaft der FernUniversitaet in Hagen , Wilhelm Raedder et al, link
  • [Jan Hendrik Witte] Distributed leader selection, 2015, CDC, Sergio Pequito, George J. Pappas, Victor Preciado, link

    • Practical Course

    Die Praktikumsnachbesprechung ist am Donnerstag 9.3.17 um 16:30 Uhr im Raum 109.
    20.02. -- 03.03..2017, jeweils 10:00 h -- 17:00 h

    Background

    Gene clusters evolve through tandem duplication of single or sometimes multiple genes. These tandem duplications often arise through non-homologous crossover. This can lead to the introduction of new copies of genes that are recombinants of their neighbors.

    Together with the uneven rates of evolution often exhibited by paralogous genes with divergent functions and the possible action of concerted evolutions it becomes difficult to disentangle the details of their evolutionary history of a gene cluster.

    Research Questions

    In this year's lab course we are going to model the evolution of a gene cluster with simplified distance-based model and compare the predictions to real-life data of gene clusters.

    Tasks

  • To prepare for the lab:
  • Read up on the molecular mechanisms of non-homologous crossover and concerted evolution
  • Read the following rough outline of the research paper that we will try to complete with this course

    [PDF]

  • Read some backgroud information on important gene clusters, e.g. [ Hox clusters ] [ Globin gene cluster ]
  • Read the paper [ Algebraic Dynamic Programming over general data structures ], we will be using the Hamiltonian path method to analyze our data
  • Artificial Life Framework (ALF) and the paper
  • example file here
  • example output (for humanB45D.dat at -t 0.01) here here here
  • In the lab
  • Implement a simulation of Type R Distance Matrices without and with noise
  • as an iteration on distance matrices using equ.(4) of the manuscript draft
  • modelling sequence evolution with non-homologous crossover, gene conversion, and mutations (with artificial life framework)
  • Use Algorithm 2 from the draft to reconstruct the sequence of events
  • Retrieve sequence data for several gene clusters that are made up of paralogs and construct genetic distance matrices for them
  • Investigate whether they fit the R Distance Matrix model
  • Analyse the clusters with the Hamiltonian path method
    •
  • At the end
  • we will as usual have a presentation/discussion session on our results
  • Die minimal anfornderung zum bestehen des Prakikums