Lecturers: |
dr. ir. Jeroen de Ridder, University Medical Center Utrecht / Delft University of Technologyprof. dr. ir. Dick de Ridder, Wageningen Universitydr. K. Anton Feenstra, Vrije Universiteit, Amsterdamdr. Aalt-Jan van Dijk, Wageningen University & Research Center |

Contact: |
dr. ir. Jeroen de Ridder e-mail: j.deridder@tudelft.nl telephone: +31 15 2783418 |

prof. dr. ir. Dick de Ridder e-mail: dick.deridder@wur.nl telephone: +31 317 48074 |

The next course will be given May 23-27 2016, at the Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, Delft, The Netherlands (Building 36, the tallest building on the campus). Travel directions can be found here.

**NOTE:** The lectures will be in the v/d Poelzaal (LB01.220), on the 1st floor of the low rise building (room 220; take a right when arriving on the first floor and take the 'bridge' to the low-rise building)

The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. The NBIC Technology Track course "Pattern recognition" and the ASCI course "Advanced pattern recognition" (a1) discuss many of the tools used in this course, but it is not required to have followed these. Prior knowledge of molecular biology is a bonus, but also not strictly required.

Preparation material on probability theory, linear algebra and molecular biology can be found below and should be read by all students before the course starts.

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. A number of alternative network models more suited for high-level simulation of cellular behaviour will also be introduced. 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.

In preparation for the course, please read the following primers on

Not all topics discussed in these primers will be used extensively in the course, but if you find yourself severely lacking in a certain area it may be wise to look up additional texts.

A folder containing handouts of the slides, papers to be discussed and a lab course manual will be distributed at the start of the course. Electronic copies of the course material can be found here.

After the course, you will write a short research proposal (e.g. for an MSc student) on the application of one or more of the algorithms discussed during the course to a problem you encounter in your own research. The proposal should clearly state the background, the motivation, the problem statement and a proposed solution. Most importantly, it should be realistic, i.e. not require many years of work, large investments or magic to finish. You can discuss your idea for the proposal with the lecturers during the course. An example proposal (by Peter van Nes) and a set of guidelines are available for download.

Please mail your finished proposal to Jeroen de Ridder (as a PDF). The deadline for submission is June 18, 2016. We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.

You can register for this course through the BioSB or ASCI websites. The maximum number of participants is 40, so register soon to be sure of a course seat! Should the course be overbooked, PhD-students in the BioRange programme or in the ASCI research school will be allowed access first.

Please refer to the BioSB/ASCI sites for fees.

The fee includes:

- Course material: Handouts, papers to be discussed and a lab course manual will be handed out at the start of the course. Software required for the lab course will be made available online.
- Catering: Coffee, tea and soft drinks and lunch will be provided. Drinks will be organized in the afternoon of Monday, in conjuntion with the RSG BioCafe.

Information about hotel accommodation in Delft during this week can be found here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.

One full week, followed by a final assignment. Most days are laid out uniformly, roughly as follows:

09.30 - 12.30 |
Pitches and Lectures |

12.30 - 13.30 |
Lunch |

15.30 - 17.30 |
A hands-on computer lab course on the algorithms discussed. |

13.30 - 15.30 |
Participants read a scientific paper on the topics of the next day, in small groups. A couple of salient keywords is distributed among the participants. Next day, these keywords will be discussed. |

A detailed schedule can be found here.

1. Monday 23-05-2016 |
Networks in biology |

Room | v/d Poelzaal (LB01.220) |

Lecturer | Jeroen de Ridder, Dick de Ridder |

Subjects | A brief overview of moleculary biology: DNA, RNA, proteins and metabolites. High-throughput measurement techniques and databases available. The role of networks in molecular biology. Examples of biological networks: regulatory programmes, signalling pathways and metabolic pathways. Networks as graphs, as steady-state descriptions and as dynamical systems. Network properties (small world properties, hub; dynamic properties, stability). Network visualization (Cytoscape, and some well-known plugins). |

2. Tuesday 24-05-2016 |
Network inference and enhancement |

Room | v/d Poelzaal (LB01.220) |

Lecturer | Dick de Ridder, Jeroen de Ridder |

Subjects | Inferring various network models (linear, Boolean, Bayesian) from measurement data. Frequently used network models, derivation of networks from high-throughput data. |

3. Wednesday 25-05-2016 |
Network-based data analysis |

Room | v/d Poelzaal (LB01.220) |

Lecturer | Jeroen de Ridder, Dick de Ridder |

Subjects | Network clustering. Network flow, random walk and diffusion algorithms. Network-based classification and enrichment testing. |

4. Thursday 26-05-2016 |
Network validation and execution |

Lecturer | Aalt-Jan van Dijk |

Subjects | Analysing protein interaction networks in combination with protein sequences. Interaction network evolution, reconstruction of ancestral networks, network alignment for cross-species comparisons. Interaction specificity, predicting protein interaction sites using network data and sequence data, correlated motif mining (interaction-driven vs motif-driven approaches). |

Lecturer | Anton Feenstra |

Subjects | A discrete approach to network modeling. Using Petri-nets as a formal network modeling tool, discrete and coarse-grained levels of cell constituents can be modeled in a discrete event fashion to understand network properties and behaviour at an abstract level. Applications to signalling and regulatory networks is discussed using 'real-life' examples. |

5. Friday 27-05-2016 |
Network mining |

Lecturer | Jaap Heringa |

Subjects | Active subnetworks - integrative approaches for finding active, deregulated subnetworks. Basics of combinatorial optimization. Comparison of two methods (jActiveModules, Heinz) on a large protein-protein interaction network and a realistic cancer microarray dataset: Compute modules, perform GO enrichment, visualize using eXamine, compare, discuss! |