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Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

Courses in previous years: [ 2006 ]

T-61.6070 Special course in bioinformatics I:
Modeling of biological networks V P, (3-7 cr)

Lecturer Prof. Sami Kaski, Laboratory of Computer and Information Science, Helsinki University of Technology
Assistant M.Sc. Arto Klami
Credits (ECTS) 5 or 7
Semester Spring 2007 (periods III and IV)
Sessions On Fridays at 10-12 in room A328 (at the Computer Science and Engineering building).
Registration TKK students: WebTopi, others: send mail to t616070@cis.hut.fi. The course started on Friday 19.1. 2007, but it is still possible to join (please send e-mail if you are planning to do this).
E-mail t616070@cis.hut.fi


Modeling of networks is a rich research area joining modern computational modeling approaches with timely applications in diverse fields including the WWW and biological networks. The common denominator is that the goal is to model the link structure between items. In biological systems, which is the application area of this course, the items are typically genes or biological molecules, and links are interactions between them. The data may be in the form of direct measurements of the links, and the task is then to directly model the link structure, for instance searching for hubs or clusters. Alternatively, the data may be about the nodes, and the task is to infer the underlying link structure. This has been studied a lot when inferring gene regulatory networks from gene expression data, for instance.

In this course we will discuss the modeling methods and the biological applications together, by studying a set of recent papers and relevant background material. The applications give the necessary context for appreciating the models, and the methods give a rigorous background for modeling of biological networks. From the modeling perspective, modeling and inference of graphs is a very active research area in which spectral methods, kernel methods, co-occurrence data, Bayesian networks, graphical models, and causal models are some of the relevant keywords. In bioinformatics some relevant research areas are protein interaction networks (interactomics), regulation of gene expression, and more generally systems biology.

The course will be most useful for graduate-level (after bachelor) or doctoral students of bioinformatics or related fields. Mathematically oriented biology and medical students are very welcome as well. The modeling methodologies are very general, and useful also for other students of computer science, mathematics and physics.

Course format

The course is held as a seminar course, where every participant gives one lecture/presentation of a chosen topic. Passing the course with 7 credit points requires performing the following tasks:

  1. Give a presentation of an article
  2. Devise a small exercise task and a model solution for it
  3. Solve the exercises given by the other participants
  4. Perform a small project work on the topic of the presentation

Instructions for the individual tasks are given here, and the exercise problems are also on a separate page. Leaving out the project work but passing the first three requirements results to 5 credit points. The course will be graded so that 60% of the grade is based on the presentation (including the exercise task) and 40% on the project work. If one solves almost all (90%) exercise problems then they have a weight of 10% towards the best grade, and solving at least half of them is required for passing.


Some basic course on machine learning helps significantly in understanding the models, but sufficient knowledge of mathematics (probabilities, statistics, linear algebra etc) should also be enough. Basic knowledge of bioinformatics or computational biology is strongly advisable.


Below is a preliminary schedule for the course. The topics and the material of the remaining presentations will be added when they have been fixed. The schedule may still change if there are new participants. There will be e-mail notification if a significant change is made.

The first two presentations are tutorials given by the course staff, and the papers mentioned should be read before the sessions.

The slides of the presentations have restricted access. The password required for downloading them has been sent to the course participants.

Time Lecturer Subject and material
19.1. Sami Kaski
  • Administrative issues
  • Introduction
16.2. Sami Kaski Overview of biological network modeling
  • Obligatory: Nir Friedman. Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303:5659, 799-805, 2004 (PDF)
  • If new to bioinformatics: Hunter: Molecular Biology for Computer Scientists (PDF)
  • Recommended reading: Brent: Genomic Biology, Cell 100:169-183 (HTML) and Endy & Brent: Modelling cellular behavior. Nature 409:391-395 (PDF)
  • Recommended additionally (not easy! need some background but are very current!): Qi Y, Ge H (2006) Modularity and Dynamics of Cellular Networks. PLoS Comput Biol 2(12): e174 (PDF) and Tegner and Björkegren: Perturbations to uncover gene networks. TRENDS in Genetics, 23:1, 2006 (HTML) and Schitt and Brazma. Modelling in molecular biology: describing transcription regulatory networks at different scales. Phil. Trans. R. Soc. B (2006) 361:483-494 (PDF)
  • Slides (PDF).
23.2. Arto Klami Tutorial on Bayes networks
  • Obligatory: David Heckerman: A tutorial on learning with Bayesian networks (PDF)
  • Other sources: Chistopher M. Bishop: Pattern Recognition and Machine Learning (2006), chapter 8.
  • Slides (PDF).
2.3. Cancelled No session
9.3. Jarkko Miettinen Kernel methods for link prediction
  • Yamanishi, Vert, Kanehisa: Protein network inference from multiple genomic data: a supervised approach (HTML)
  • Kato, Tsuda, Asai: Selective integration of multiple biological data for supervised network inference (HTML)
  • Slides (PDF).
16.3. Mikko Kivelä Analysis of network structure
  • Barabasi, Oltvai: Network biology: Understanding the cell's functional organization (PDF)
  • Slides (PDF).
23.3. Erno Lindfors Topology analysis of complex biological networks
  • Khanin, Wit: How scale-free are biological networks? (HTML)
  • Przulj: Biological network comparison using graphlet degree distribution (HTML)
  • Slides (PDF).
30.3. Jenni Hulkkonen Advanced Bayes networks
  • Segal, Pe'er, Regev, Koller, and Friedman. Learning module networks. (PDF)
  • Pe'er, Tanay, and Regev. MinReg: A scalable algorithm for learning parsimonious regulatory networks in yeast and mammals. (PDF)
  • Slides (PDF).
6.4. No meeting Easter
13.4. Janne Toivola Dynamic Bayes networks
  • Zou, Conzen: A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. (HTML)
  • Murphy, Mian: Modelling gene expression data using dynamic Bayesian networks. (PDF)
  • Slides (PDF).
20.4. Antti Ajanki & Abhishek Tripathi Recent advances in network modeling
  • E. M. Airoldi, D. M. Blei, S. E. Fienberg and E. P. Xing1: Mixed Membership Stochastic Block Models for Relational Data with Application to Protein-Protein Interactions (PDF)
  • M. E. J. Newman, E. A. Leicht: Mixture models and exploratory data analysis in networks (HTML)
  • Slides 1 (PDF).
  • Slides 2 (PDF).
27.4. Ilkka Huopaniemi & Andrey Erlmolov Metabolic networks
  • Förster et al.: Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network (HTML)
  • Uwe Sauer: Metabolic networks in motion: 13C-based flux analysis (HTML)
  • Slides 1 (PDF).
  • Slides 2 (PDF).
4.5. Cancelled Visualization of biological networks

Papers and topics

The topics and material for the presentations will be decided directly with each participant. Background material will be added here to the degree that it is needed.

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