This course (TiCB) is running for the 2nd time.  Each year, I plan to drop a subtopic with 4 lectures and add a new subtopic.  This year ADVANCED PHYLOGENETICS was dropped and COMPUTATIONAL NEUROSCIENCE was added.  In 2020, I plan to drop COMPUTATIONAL EVOLUTION and add MACHINE/DEEP LEARNING in the BIOSCIENCES.   When I have given the same lectures 3-4 times, I polish them and make them into audio-lectures, that can be found on the lecture page.  I hope to have 20 hours of BIOLOGICAL SEQUENCE ANALYSIS there by late April 2019.

Computational Evolution

1. Statistical Alignment (Tue 15.01.2019 12am) – Preparatory material / Lecture
2. Phylogeny Enumeration (Wed 16.01.2019 2pm) – Preparatory material / Lecture
3. Protein Evolution (Tue 22.01.2019 12am) – Preparatory material / Lecture
4. Network Evolution (Wed 23.01.2019 2pm) –  Preparatory material / Lecture

Problem Sheet (Tree Counting and Ancestral Analysis. Hand-in: Tuesday 29.1 10AM . Msc Session:  Thur 31.01.2019 12am – 1pm – Undergraduate Session:  Friday 1.2.2019 3.00pm – 4.30pm)

Molecular Dynamics

1. Physics of Molecules: QM and MM (Tue 29.01.2019 12am)  Prep. material / Lecture
2. Integrators and Approximations (Wed 30.01.2019 2pm) Lecture
3. History and Applications of MD (Tue 5.02.2019 12am) Prep material / Lecture
4. Protein Folding (Wed 6.02.2019 2pm) Preparatory material / Lecture

Problem Sheet (Molecular Dynamics. Hand-in:Tuesday 12.2 10AM. Session:  Thur 14.02.2019 12am – 1pm – Undergraduate Session:  Friday 13.2.2019 3.00pm – 4.30pm)

Chemoinformatics and Combinatorics of Molecules 

1. Mathematical Models of the Origins of Life (Tue 12.02.2019 12am) Prep. mat Lecture
2. Molecule Enumeration (Wed 13.02.2019 12pm) Preparatory material Lecture
3. Chemical Space (Tue 19.02.2019 12am) Preparatory material Lecture
4. Chemical Reactions (Wed 20.02.2019 2pm) Preparatory material Lecture

Problem Sheet (Molecular Combinatorics Hand-in: Tuesday 26.2 10AM   Undergraduate Session:  Friday 29.2.2019 3.00pm – 4.30pm)
Solutions (code: ipython/jupyter notebook, or pure python)

Computational Neuroscience

1. Biological and Artificial Networks  (Tue 26.02.2019 12am) Prep. material Lecture
2. The Coalescent and Recombination (Wed 27.02.2019 2pm)  Prep. material Lecture
3. Modelling Neurons and Networks (Tue 5.03.2019 12am)  Lecture
4. Large Scale Brain Modelling (Wed 6.03.2019 2pm) Preparatory material Lecture

Problem Sheet (Neuroscience Hand-in: Wednesday 6.3 6PM. Undergraduate Session:  Friday 8.3.2019 3.00pm – 4.30pm)

This course is examined by mini-projects. Anything that legitimately could be called COMPUTATIONAL BIOLOGY is acceptable as a topic.  Mini-projects will be released to student March 9th 2018.  They will include examples of what could be a topic, but also the possibility of chosing your own topic. Reports should be handed in April  at noon 2019

Examples of reports from an earlier similar course can be found here:

• Stochastic Context Free Grammars in RNA Secondary Structure Prediction                                        
• Substitution Models with Rate Heterogeneity       
• Clustering Biological Networks
• Quantum algorithms for Protein folding    
• Probability Theory of Networks                                          
• Inference of Gene Regulatory Networks using Differential Equations                                                    
• Hidden Markov Models and their Applications to Bioinformatics
• Computational Models for Gene prediction
• Alignment-based De Novo Prediction of Non-coding RNA
• Automated Annotation of Genes                                           
• Network flow and its applications to the analysis  
• Identification of Regulatory Elements – Phylogenetic Footprinting
• Probable and Improbable Paths in Sequence Evolution            
• Simulative Approach to Alignment    
• Sequence RAFs                   
• Birth and Death Processes
• Haplotype Imputation
• Prediction of drug targets
• Computational Approaches to Nanopore Sequencing            
• Molecular Dynamics for Modelling Dimers    
• Persistent Homology                   
• One Algorithm Fits All     
• Spiking Neural Networks