Expected prior knowledge
The student is expected to have a basic knowledge of molecular biology, statistics and linear algebra. It is advisable to have followed IN4085 (Pattern Recognition).
Molecular biology is concerned with the study of the presence of and interactions between molecules, at the cellular and sub-cellular level. In bioinformatics and systems biology, algorithms and tools are developed to model these interactions, with various goals: predicting yet unobserved interactions, assigning functions to yet unknown molecules through their relations with known molecules; predicting certain phenotypes such as diseases; or just to build up biological knowledge in a structured way.
Such interaction models are often best modelled as networks or graphs, which opens up the possibility of using a large number of readily available algorithms for inferring networks, performing simulations of biology, optimising paths or flows through networks, graph-based data integration and graph mining. Many of these algorithms can be applied (sometimes with slight alterations) to solve a particular biological problem, such as modeling transcriptional regulation or predicting protein interaction/complex formation, but also to derive systems behaviour by breaking down networks into modules or motifs with certain characteristics.
In this course, we will first give a brief overview of molecular biology, the advent of high-throughput measurement techniques and large databases containing biological knowledge, and the importance of networks to model all this. We will highlight a number of peculiar features of biological networks. Next, a number of basic network models (linear, Boolean, Bayesian) will be discussed, as well as methods of inferring these from observed measurement data. Building on the network inference methods, a number of ways of integrating various data sources and databases to refine biological networks will be discussed, with specific attention to the use of sequence information to refine transcription regulation networks. Finally, we will give some examples of algorithms exploiting the networks found to learn about biology, specifically for inspecting protein interaction networks and for finding active subnetworks.
After succesfully completing this course, a student is able to: list the basic elements of a living cell and their interactions, and describe how these can be measured; explain what type of mathematical model is applicable to what measurement(s), at what level(s), in a given systems biology problem; read and comment upon recent network-based computational biology literature; discuss the state-of-the-art in systems biology and integrative bioinformatics, and future challenges.
See for the course, exam and resit schedule, the TU Delft timetable page.
The course consists of a mixture of lectures by the teachers and paper presentations by one or more of the students. Each paper presentation will be followed by a in-depth discussion. There will also be a practical session allowing students to get hands-on experience with network models.
Literature and Study Materials
Slides, collection of papers and lab course manual (Blackboard).
Students are required to write a proposal for a research project in which they clearly state the biological problem to be solved, the necessary data, the computational approach as well as the innovative parts of the approach. This proposal will be graded. Next, the paper presentations as well as discussions will be graded. The final grade for the course will be based on all these grades.
As students depend on each other (to present the material to the class), a commitment to follow the course through to the end is required.
See also the course description in the TU Delft study guide 2018-2019.