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T-61.5110 Intensive course on modeling biological networks (5 cr ECTS)

PASCAL and Hecse special course

Lecturers	Prof. Sami Kaski, TKK; Prof. Tommi Jaakkola, MIT Tutorial to cell biology: Doc. Petri Auvinen, University of Helsinki
Assistant	Arto Klami, TKK
Credits (ECTS)	5
Course code	T-61.5110 (TKK) and 582620 (Univ. of Helsinki)
Location	Otaniemi (Espoo/Finland) (how to get there)
Time	13.-17.8.2007
Lectures	10:30-12, 13-14:30, lecture hall T2 (Computer science building is number 30 on local map)
Exercises	15-17, Maari-C (number 17 in the same map)
E-mail	t615110@cis.hut.fi

Registration:

Register by sending email to t615110@cis.hut.fi. If you are a Hecse and/or PASCAL student, please tell that in the email.

The course attracted sufficiently many participants and will be arranged. You should receive a confirmation email about your registration; if not, please send email to the above address. The course has also reached the maximum number of participants (due to limited size of the exercise hall). You may still register, but unless you give very good reasons you are not that likely to fit in. The first priority is for PASCAL/Hecse students, and secondary priority for students majoring in Computer and information science (TKK) or Computer science (UH). The lecture hall is bigger, so it is possible to attend the lectures even if the course is otherwise full. If you want to attend just the lectures, please tell that when registering.

TKK students should additionally register in WebTopi, but that will not be needed before the actual course starts. Instructions will be added here when the registration for this course opens in WebTopi. Students from the Department of Computer Science (Univ Helsinki) should correspondingly register through the department registration system when it opens.

Note: The course has two organizing institutes, TKK and the Department of Computer Science at the University of Helsinki. Students from both can attend the course without any special arrangements (such as the JOO study right). The same holds for all PASCAL and Hecse students. If you are unsure about practicalities regarding the attendance, please tell that when registering.

Prerequisites:

Basic knowledge of probability, statistics, linear algebra, calculus; some experience on programming and algorithms; basics on cellular biology (or: read a crash course beforehand).

Readings before the course:

If you have not studied biology, you should at least get familiar with the basic concepts and terminology. Below is a list of possible sources, which are largely overlapping. There is no reason to read all of them; the purpose of the list is to simply provide different alternatives so you can read the one that seems best for you.

L. Hunter: Molecular Biology for Computer Scientists : Slightly oldish, but the fundamental biological things are still current
Brown: Genomes : Chapters 1 and 3
NCBI Science Primer

It is also a good idea to know something about graphical models beforehand. Below is a list of possible sources of information, and again the purpose is not to read everything but pick out some of the sources.

Part I and Part II of Bishop's tutorial (slides) on probabilistic graphical models.
Jordan: Graphical models
D. Heckerman: A tutorial on learning with Bayesian networks
Murphy: A brief introduction to graphical models and Bayesian networks
Websites of courses about graphical models:
- Course by T. Jaakkola: lectures 18-23
- Course by P. Myllymäki and W. Buntine
Several new machine learning books treat the same topics, and can be read instead if you have access to one of those

It is not necessary to understand everything, but you should know at least the basic concepts to be able to follow the lectures.

Material:

brief lecture notes; given as handouts in the beginning of the lectures, and also distributed on the web-page (see contents)
for the lectures of the first day there are only slides, but the background material covers much of the topic

Grading and passing the course:

To pass the course you need to:

Participate actively in the lectures and exercise sessions (no need to return the solutions)
Do a project work

The course will be graded solely based on the project work, and the range of accepted grades is 1-5.

Description:

This course is about computational methods for solving key problems in cellular biology and medicine. In particular, we discuss how to understand and elucidate interacting sets of molecules that give rise to and regulate cellular processes, from basic metabolism to growth and diseases. The ability to infer such molecular processes from measurements requires a combination of advanced modeling tools and biological knowledge. The development of such tools, probabilistic graphical models in particular, is an active area of research.

Our goal in this course is to bring biological and medical research problems together with appropriate analysis tools and methods. The course starts from basics but will rapidly reach contemporary research questions. Ideally, students with stronger computational backgrounds will get deeper understanding of the core modeling strategies in this area and a sense of open modeling and biological questions, whereas students already familiar with biological problems will understand limitations of current modeling approaches, where they do and don't apply, and will learn to match problems with computational approaches.

The course is taught through interactive lectures that are based on lecture notes and recent papers. The students are expected to complete a class project and attend exercise sessions. The project can be done in groups, ideally with students from different disciplines.

We expect the course to be most useful for graduate-level (advanced Master's students), doctoral students in computer science, applied mathematics, statistics, bioinformatics, bioinformation technology as well as methodologically inclined students in biology and medicine.

Monday	Petri Auvinen	tutorial to cell biology and measurement of cellular processes Slides part 1 and Slides part 2 (note that both files are quite large, around 20Mb)
Tuesday	Samuel Kaski	gene expression, analysis of differential expression, co-expression Lecture notes and Differential expression (by Jaakkola) Slides
Wednesday	Tommi Jaakkola	gene regulation, protein-DNA binding, expression programs Slides
Thursday	Samuel Kaski	protein-protein interaction networks Lecture notes and slides
Friday	Tommi Jaakkola	genetic regulatory networks Slides

Exercises

Monday, updated with "solutions"
Tuesday
Thursday

PASCAL students:

There is no registration fee but accommodation and food is on your own expense. PASCAL students may apply for a stipend in the conference and workshop attendance programme of PASCAL, and we may have limited travel support (send a brief letter about your background and motivation if you wish to apply).