Bioinformatics and Dynamical Modeling

Course goals

Upon successful completion of this course, students will be able to: 

1. Analyze, interpret, and form an opinion on given Ordinary Differential Equation (ODE) models.
2. Translate simple biological / chemical systems into mathematical models (ODEs).
3. Discuss the suitability of different model formalisms according to the scientific question asked.
4. Access, evaluate and employ various biological databases and bioinformatic tools on the internet
5. Discuss how different levels of conservation can be determined and used in various sequence alignment algorithms and interpret their results
6. Reconstruct and interpret phylogenetic gene or species trees based on (alignment) data
7. Interpret large omics data sets using data visualization, hierarchical data clustering and machine learning

Content

This course introduces students to the research fields of bioinformatics and biological modeling. Central themes are the use of data to extract underlying patterns on function and evolution, and the use of models to test hypotheses and make predictions for biological systems.

Introduction:
Biological processes are notoriously complex, and studying their dynamics through modeling and simulation is an effective way to understanding their key properties. In the first part, the students will learn to model the dynamics of biological processes such as cell signaling, gene regulation, interaction between bacteria and phages. Students will be trained to read and interpret ordinary differential equations models, as well as write such models themselves. They will analyse the models using phase spaces and stability analysis of equilibria to predict the behaviour of these dynamical systems and make use of simulation techniques. Furthermore, they learn to give a biological interpretation of the results of such analyses to answer biological questions and critically assess the model on its validity.

Large-scale data collection (omics) is ubiquitous and highly informative in the field of life sciences. To appropriately use and interpret omics data, the new wave of life scientists needs to understand bioinformatics methods. In the second part of this course, students learn about fundamental bioinformatics algorithms to study biological sequences. They will use the results to predict protein function or reconstruct the evolutionary history of genes, proteins and species. Furthermore, they will interpret high-dimensional omics-data through visualization, hierarchical clustering and machine-learning.

Set-up of this course:
The student prepares each lecture by reading the indicated parts of the syllabus. Each lecture is followed by a tutorial session to get a deeper understanding of the content and to practice the required skills. Most weeks, in an additional tutorial, students work individually or in small groups on a graded assignment (six in total). Some tutorials are on pen and paper, others require the students to bring their own laptop. Both parts of the course are concluded by a written exam.

Acquired skills:

1. Using pen and paper, and the computer to answer biological questions
2. Abstract thinking in multiple dimensions
3. Creative and critical thinking and problem-solving skills
4. Visualizing complex problems using graphic tools, both with pen and paper and on the computer
5. Analyzing an Ordinary Differential Equation (ODE) system
6. Using the terminal to interact with folders and files
7. Applying bioinformatics tools
8. Increased experience with mathematics & Python programming

Relation to other courses:
The course will build on the first-year computer and mathematical skills (MBLS-102 & MBLS-106) to answer biological questions in systems such as those seen during the first-year cell biology (MBLS-101) and functional biology (MBLS-107) course. The skill of translating between biology and models developed here, will be further extended in the courses ‘Computational biology’ (B-B3COMB10) and ‘Modeling life’ (B-B3MDLI25). The knowledge on bioinformatic methodology to answer biological questions related to function and evolution is required for the course ‘Genome bioinformatics ’ (B-B3BCG20), and is also relevant for the course ‘Omics for Life Sciences’.

Pre-requisites
Successfully completed MBLS-101, MBLS-102 and having followed MBLS-107

Teaching format course (estimation):
Lectures 30%
Tutorials 45%
Self study 25%

Grade (check the course manual for details):

1. Written exams (part-1, 40% and part-2, 40%) 
2. Individual/small group assignments, (part-1 10%, part-2 10%)

For both parts a 5.0 is the minimal grade to pass