Contents

: We begin with the notion of an algorithm, and we describe the cycle from problem statement to algorithmic solution to working code. We consider numerous examples that illustrate fundamental principles of algorithm design. In order to implement our algorithms, we introduce the functional programming language Haskell. Additionally, we explore the theoretical, technological, and organizational principles of computer systems. Topics covered include the binary representation of information in a computer, Boolean functions and their implementation as circuits, the design principles of CPUs, and the von-Neumann-architecture. Prerequisites: We recommend that participants of this class attend the Brückenkurs Mathematical Foundations of Computer Science and Bioinformatics.

• S. Thompson; Haskell: The Craft of Functional Programming; Addison-Wesley
• F. Rabhi, G. Lapalme; Algorithms: A Functional Proramming Approach; Addison-Wesley
• G. Hutton; Programming in Haskell; Cambridge University Press
• A. Tanenbaum, J. Goodman; Computerarchitektur; Pearson Studium

This lecture deals with: Exact and approximative string matching, dynamic programming and scoring schemata, finite automata and formal languages, pairwise und multiple alignment, multiple string matching, basics of Markov Chains und Hidden Markov Models, algorithms for fast search in sequence data bases.

In the exercises the above topics will be deepened, and techniques of analysis and evidence will be practised.

There is an associated practical course "Algorithmen und Datenstrukturen" (19401330). Please check there for further information!

Generelle Bücher/Basic reading:

• Neil C. Jones, Pavel A. Pevzner: An Introduction to Bioinformatics Algorithms. MIT Press, Cambridge, MA, 2004. ISBN 0-262-10106-8
• R. Durbin, S. Eddy, A. Krogh, G. Mitchison: Biological sequence analysis. Cambridge University Press, 1998. ISBN 0-521-62971-3
• David B. Mount: Bioinformatics. Sequence and Genome Analysis. Cold Spring Harbor Laboratory Press, New York, 2001. ISBN 0-87969-608-7
• Chao, Zhang: Sequence comparison, Theory and Methods: Springer, ISBN: 978-1-85800-319-4

Please see German description.

This practical course is associated with the lecture and exercise "Algorithmen und Datenstrukturen". It starts with an introduction to programming tools and the programming language applied in the lecture/exercise. After that, programming skills will be explained and taught using the algorithms discussed in the lecture.

See German text.

Contents:
Computers play an important role in (almost) all situations in life today. Computer-oriented mathematics provides basic knowledge in dealing with computers for solving mathematical problems and an introduction to algorithmic thinking. At the same time, typical mathematical software such as Matlab and Mathematica will be introduced. The motivation for the questions under consideration is provided by simple application examples from the aforementioned areas. The content of the first part includes fundamental terms of numerical calculation: number representation and rounding errors, condition, efficiency and stability.

Homepage: All current information on lectures and lectures

Literatur: R. Kornhuber, C. Schuette, A. Fest: Mit Zahlen Rechnen (Skript zur Vorlesung)

Content:

see German description

Please contact your advisor.

Further information on our homepage.

Block course during the semester break. Next available class: tba (two weeks, all day)

Location: Charité Campus Benjamin Franklin (Steglitz, Hindenburgdamm 30), Institut für Klinische Physiologie

For further information: http://klinphys.charite.de/bioinfo/

or mail to Dorothee Günzel

Within this course you will generate structural models of proteins by homology modelling. You will develop hypotheses which amino acids should be decisive for the structure.  These Hypotheses will be tested by carrying out molecular biologic experiments (such as site-directed mutagenesis by using two-step PCR). The construct will be cloned into expression vectors, transformed and amplified in bacteria, extracted, sequenced and overexpressed in cultured cells.

These cells will be analyzed in the confocal laser scanning microscope and by other techniques. The results will be evaluated and interpreted in the context of the original hypitheses.

The experimental part will be flanked by seminars introducing the theoretical background and the various techniques.

The exact program of this course depends on the actual research of the institute and is tightly connected to our actual projects.

Milatz S, Piontek J, Hempel C, Meoli L, Grohe C, Fromm A, Lee IM, El-Athman R, Günzel D (2017) Tight junction strand formation by claudin-10 isoforms and claudin-10a/-10b chimeras. Ann. N.Y. Acad. Sci. 1405: 102-115 (https://www.ncbi.nlm.nih.gov/pubmed/28633196)

Piontek J, Winkler L, Wolburg H, Müller SL, Zuleger N, Piehl C, Wiesner B, Krause G, Blasig IE (2008) Formation of tight junction: determinants of homophilic interaction between classic claudins. FASEB J. 22: 146-158 (https://www.ncbi.nlm.nih.gov/pubmed/17761522)

Please bring lab coat, if available!

Practical Course: Using open source software and libraries for data analysis and computer-aided drug design

This practical course introduces the students to diverse software tools and libraries which can support data generation and analysis in various areas of computer-aided drug design.  The course will sensitize the students to the ease and the profit of using bio-/cheminformatics methods.

The course shall enable the students to set-up their own drug design pipeline including protein target preparation, compound library set-up and filtering (i.e. using drug-like and toxicity filters), docking of the compounds to the target and evaluation of the results. The course will include the following tasks:

• There will be a short introductory part into python programming using the novel and interactive IPython notebooks.
• The students will write their own scripts using, e.g., python libraries for data handling (pandas), protein (BioPython) and compound (RDKit) processing as well as machine learning (scikit-learn) to prepare their compound libraries.  
• Open access tools for protein structure preparation and visual inspection (PyMol) will be used.
• AutoDock software will be engaged for docking of the compounds into the target protein and the results will be evaluated.   

Each day, we will perform a small task from the described computer-aided drug design pipeline (morning lecture followed by practical work asn presentation). The students can work individually or in groups, depending on the total number of participants.

Practical course date: 18.03.-22.03.2019, 5 days, daily 9 am - 5 pm.
Preliminary meeting: In February, exact date will be announced soon.

Place: Campus Mitte, Vorchowweg 6 (or surrounding Charité Campus), 10117 Berlin

Research AG Volkamer

The practical course gives the student a chance to test his or her understanding of the material taught in the theory course. A short introduction on each topic will be given on this course for individual topics. The students will be assigned to a small project/task which they have to perform. The students will be assigned into groups of two/three/max four members, depending on the total number of students registered for the course.

Website: https://bioinformatics.charite.de/main/course_practical.php

Structure of the course:

Everyday students will be assigned projects in groups for each topic. They work together to solve some interesting scientific problem. Each group will be closely supervised by the respective tutors. At the end of the lecture series, students (individual group) will be assigned one of the topic of the course, which they have to present in front of the wider audience. This will provide students, a way to practice presentation skills and can help them to develop the expertise needed to discuss their research in a clear and meaningful way.

Learning how to answer specific questions and present data to a range of individuals, will help students in other endeavors, including future conference presentations, masters or dissertation defenses.

Topic for practical lectures are:

1. Introductory session
2. Homology modeling
3. 3D molecular superimposition of small molecules
4. In silico screening, molecular fingerprints and chemical similarity
5. Natural products and fragments based drug discovery
6. In silico toxicity prediction
7. Personalized medicine

Important: Material from the theory course will be intensively used in the practical course. It is advisable, that the students need to attend the theory course before participating in the practical course. Both the courses are interlinked.

The goal of this course is to give an introduction to the most important mathematical and algorithmic ideas underlying the constraint-based analysis of genome-scale metabolic networks, with applications to systems biology and biotechnology.

We will discuss the steady-state flux cone, flux balance analysis (FBA), flux variability analysis (FVA), flux coupling analysis (FCA), thermodynamic constraints, elementary flux modes (EFMs), and optimality principles for metabolic regulation. On the algorithmic side, the focus will be on linear and mixed-integer optimisation methods.

Homepage:

http://www.mi.fu-berlin.de/w/AgMathLife/MolnetzASS15

Content:

In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their "Fortgeschrittenenpraktikum". Credits are only awarded for the presentation of papers.

Please sign up on the KVV pages (open "Site Browser" and look forJournal Club).

aktuelle Publikationen aus der Forschung

Open for:

Master and PhD students

The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

Planned topics are as follows:

• Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
• Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
• Laboratory information management systems: sample management, integration of instruments and application, data exchange
• Bioinformatic workflows: Galaxy, Snakemake and KNIME
• Containerized frameworks: Advantages of using Docker, Bioconda etc.
• Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
• Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.

Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations or Boolean Networks.

This course will introduce advanced biometric methods used in clinical and observational studies. Topics covered include complex study designs and advanced modeling. Students should have a solid background in statistics and an interest in medical applications of statistics.

In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.

The goal of this course is to give an introduction to the most important mathematical and algorithmic ideas underlying the constraint-based analysis of genome-scale metabolic networks, with applications to systems biology and biotechnology.

We will discuss the steady-state flux cone, flux balance analysis (FBA), flux variability analysis (FVA), flux coupling analysis (FCA), thermodynamic constraints, elementary flux modes (EFMs), and optimality principles for metabolic regulation. On the algorithmic side, the focus will be on linear and mixed-integer optimisation methods.

Homepage:

http://www.mi.fu-berlin.de/w/AgMathLife/MolnetzASS15

Content:

In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their "Fortgeschrittenenpraktikum". Credits are only awarded for the presentation of papers.

Please sign up on the KVV pages (open "Site Browser" and look forJournal Club).

aktuelle Publikationen aus der Forschung

Open for:

Master and PhD students

The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

Planned topics are as follows:

• Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
• Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
• Laboratory information management systems: sample management, integration of instruments and application, data exchange
• Bioinformatic workflows: Galaxy, Snakemake and KNIME
• Containerized frameworks: Advantages of using Docker, Bioconda etc.
• Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
• Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.

Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations or Boolean Networks.

This course will introduce advanced biometric methods used in clinical and observational studies. Topics covered include complex study designs and advanced modeling. Students should have a solid background in statistics and an interest in medical applications of statistics.

In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.

The goal of this course is to acquire the background of spatial databases, the kernel of Geographic Systems. The major aspects that will be handled are: modeling and querying geospatial information, spatial access methods (SAMs), data representation, basic operations (mostly from computational geometry), and optimization. Insights into current applications such as location-based services (e.g., navigation systems) will also be given. Knowledge in databases is necessary. This course encompasses: formal lectures, exercises, as well as a practical project with PostGIS.  

Handouts are enough to understand the course. The following book will be mostly used: P. Rigaux, M. Scholl, A. Voisard.Spatial Databases - With Application to GIS. Morgan Kaufmann, May 2001. 432 p. (copies in the main library)

Zielgruppe:

Studierende im Masterstudiengang Voraussetzungen: Vorlesung: Einf. in Datenbanksysteme

This class focuses on the design and analysis of algorithms, with topics including:

• general principles of algorithm design and analysis,
• flow problems on graphs,
• amortized analysis and advanced data structures,
• theory of NP-completeness,
• approximation methods for hard problems,
• other topics.

Prerequisites are basic knowledge of algorithms and relevant mathematics. All Bachelor and Master students interested in advanced algorithmic techniques are welcome.

• Cormen, Leiserson, Rivest, Stein: Introduction to Algorithms, 2nd Ed. McGraw-Hill 2001
• Kleinberg, Tardos: Algorithm Design Addison-Wesley 2005.

Target audience

All Master and Bachelor students who are interested in algorithms.

Prerequisites

Basic familiarity with the design and analysis of algorithms.

The goal of this course is to give an introduction to the most important mathematical and algorithmic ideas underlying the constraint-based analysis of genome-scale metabolic networks, with applications to systems biology and biotechnology.

We will discuss the steady-state flux cone, flux balance analysis (FBA), flux variability analysis (FVA), flux coupling analysis (FCA), thermodynamic constraints, elementary flux modes (EFMs), and optimality principles for metabolic regulation. On the algorithmic side, the focus will be on linear and mixed-integer optimisation methods.

Homepage:

http://www.mi.fu-berlin.de/w/AgMathLife/MolnetzASS15

Practical Course: Using open source software and libraries for data analysis and computer-aided drug design

This practical course introduces the students to diverse software tools and libraries which can support data generation and analysis in various areas of computer-aided drug design.  The course will sensitize the students to the ease and the profit of using bio-/cheminformatics methods.

The course shall enable the students to set-up their own drug design pipeline including protein target preparation, compound library set-up and filtering (i.e. using drug-like and toxicity filters), docking of the compounds to the target and evaluation of the results. The course will include the following tasks:

• There will be a short introductory part into python programming using the novel and interactive IPython notebooks.
• The students will write their own scripts using, e.g., python libraries for data handling (pandas), protein (BioPython) and compound (RDKit) processing as well as machine learning (scikit-learn) to prepare their compound libraries.  
• Open access tools for protein structure preparation and visual inspection (PyMol) will be used.
• AutoDock software will be engaged for docking of the compounds into the target protein and the results will be evaluated.   

Each day, we will perform a small task from the described computer-aided drug design pipeline (morning lecture followed by practical work asn presentation). The students can work individually or in groups, depending on the total number of participants.

Practical course date: 18.03.-22.03.2019, 5 days, daily 9 am - 5 pm.
Preliminary meeting: In February, exact date will be announced soon.

Place: Campus Mitte, Vorchowweg 6 (or surrounding Charité Campus), 10117 Berlin

Research AG Volkamer

Block course during the semester break. Next available class: tba (two weeks, all day)

Location: Charité Campus Benjamin Franklin (Steglitz, Hindenburgdamm 30), Institut für Klinische Physiologie

For further information: http://klinphys.charite.de/bioinfo/

or mail to Dorothee Günzel

Within this course you will generate structural models of proteins by homology modelling. You will develop hypotheses which amino acids should be decisive for the structure.  These Hypotheses will be tested by carrying out molecular biologic experiments (such as site-directed mutagenesis by using two-step PCR). The construct will be cloned into expression vectors, transformed and amplified in bacteria, extracted, sequenced and overexpressed in cultured cells.

These cells will be analyzed in the confocal laser scanning microscope and by other techniques. The results will be evaluated and interpreted in the context of the original hypitheses.

The experimental part will be flanked by seminars introducing the theoretical background and the various techniques.

The exact program of this course depends on the actual research of the institute and is tightly connected to our actual projects.

Milatz S, Piontek J, Hempel C, Meoli L, Grohe C, Fromm A, Lee IM, El-Athman R, Günzel D (2017) Tight junction strand formation by claudin-10 isoforms and claudin-10a/-10b chimeras. Ann. N.Y. Acad. Sci. 1405: 102-115 (https://www.ncbi.nlm.nih.gov/pubmed/28633196)

Piontek J, Winkler L, Wolburg H, Müller SL, Zuleger N, Piehl C, Wiesner B, Krause G, Blasig IE (2008) Formation of tight junction: determinants of homophilic interaction between classic claudins. FASEB J. 22: 146-158 (https://www.ncbi.nlm.nih.gov/pubmed/17761522)

Please bring lab coat, if available!

This course will introduce advanced biometric methods used in clinical and observational studies. Topics covered include complex study designs and advanced modeling. Students should have a solid background in statistics and an interest in medical applications of statistics.

In this course, we introduce and discuss statistical methods and study design applied in clinical trials. Basic design aspects like randomization, blinding, the definition of control groups and endpoints will be discussed as well as different study types such as efficacy trials, equivalence and bioequivalence trials, phase I, II and III trials and principles of meta analyses. The related statistical models and test will be introduced as well. The aim of the lectures is to learn about biometrical thinking in the context of clinical trials which includes the application of statistical methods, but also critical thinking on the experimental setting.

The practical course gives the student a chance to test his or her understanding of the material taught in the theory course. A short introduction on each topic will be given on this course for individual topics. The students will be assigned to a small project/task which they have to perform. The students will be assigned into groups of two/three/max four members, depending on the total number of students registered for the course.

Website: https://bioinformatics.charite.de/main/course_practical.php

Structure of the course:

Everyday students will be assigned projects in groups for each topic. They work together to solve some interesting scientific problem. Each group will be closely supervised by the respective tutors. At the end of the lecture series, students (individual group) will be assigned one of the topic of the course, which they have to present in front of the wider audience. This will provide students, a way to practice presentation skills and can help them to develop the expertise needed to discuss their research in a clear and meaningful way.

Learning how to answer specific questions and present data to a range of individuals, will help students in other endeavors, including future conference presentations, masters or dissertation defenses.

Topic for practical lectures are:

1. Introductory session
2. Homology modeling
3. 3D molecular superimposition of small molecules
4. In silico screening, molecular fingerprints and chemical similarity
5. Natural products and fragments based drug discovery
6. In silico toxicity prediction
7. Personalized medicine

Important: Material from the theory course will be intensively used in the practical course. It is advisable, that the students need to attend the theory course before participating in the practical course. Both the courses are interlinked.

Content:

In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their "Fortgeschrittenenpraktikum". Credits are only awarded for the presentation of papers.

Please sign up on the KVV pages (open "Site Browser" and look forJournal Club).

aktuelle Publikationen aus der Forschung

Open for:

Master and PhD students

The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

Planned topics are as follows:

• Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
• Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
• Laboratory information management systems: sample management, integration of instruments and application, data exchange
• Bioinformatic workflows: Galaxy, Snakemake and KNIME
• Containerized frameworks: Advantages of using Docker, Bioconda etc.
• Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
• Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.

Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations or Boolean Networks.

Content:

In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their "Fortgeschrittenenpraktikum". Credits are only awarded for the presentation of papers.

Please sign up on the KVV pages (open "Site Browser" and look forJournal Club).

aktuelle Publikationen aus der Forschung

Open for:

Master and PhD students

The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

Planned topics are as follows:

• Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
• Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
• Laboratory information management systems: sample management, integration of instruments and application, data exchange
• Bioinformatic workflows: Galaxy, Snakemake and KNIME
• Containerized frameworks: Advantages of using Docker, Bioconda etc.
• Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
• Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.

Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations or Boolean Networks.

Content:

In this seminar we will present original work in Computational biology as well as progress reports from PhD students. Master students will either be assigend a paper, or present their MSc thesis plans and results, or report about their "Fortgeschrittenenpraktikum". Credits are only awarded for the presentation of papers.

Please sign up on the KVV pages (open "Site Browser" and look forJournal Club).

aktuelle Publikationen aus der Forschung

Open for:

Master and PhD students

The main objectives of this seminar are (1) to introduce fundamental methods of data handling in bioinformatics research with a particular focus on lab management systems, containerized frameworks and software workflow systems, (2) to provide a general overview of open science, sustainable software development, information security and data protection and (3) to discuss ethical issues, chances and risks with regard to fundamental research and personalized medicine.

Planned topics are as follows:

• Open vs. closed Science: chances and risks of open science. Open access and traditional publishing.
• Sustainable software development and reproducible research: open data and open source, code repositories, maintainability of software and code in the sciences
• Laboratory information management systems: sample management, integration of instruments and application, data exchange
• Bioinformatic workflows: Galaxy, Snakemake and KNIME
• Containerized frameworks: Advantages of using Docker, Bioconda etc.
• Information security and data protection: securing personalized data, relevance for data analyses in research, efficient handling guidelines of data security, potential issues with open science
• Ethical issues, chances and risks of omics research: immense opportunities when manipulating genome information, novel ethical questions need to be asked, „right not to know“, danger of discrimination against individuals or groups based on genetic differences

Organisational note: During the first course at the beginning of the semester, the aforementioned topics will be briefly introduced and course material (e.g. relevant publications) will be provided. In the second course, topics will be assigned to the attendees of the seminar.

Molecular metabolic, signaling and gene-regulatory networks form complex networks that underly the normal physiological functioning of the cell. Various perturbations within these networks have been described in diseases. We will here use original papers to study and discuss how perturbations can be implemented in models and how they change the network characteristics. We will focus on dynamic models described by ordinary differential equations or Boolean Networks.

Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

• Unique decodability and prefix codes
• Entropy and entropy rate as theoretical limits of lossless compression
• Optimal codes, Huffman codes
• Arithmetic coding
• Lempel-Ziv coding
• Linear prediction
• Examples from text, image and audio compression

In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

• Scalar quantization, optimal scalar quantization
• Theoretical limits of lossy compression: Rate distortion functions
• Vector quantization
• Predictive quantization
• Transform coding
• Examples from audio, image, and video compression

• Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
• Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
• Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
• Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
• Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.

The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations of data. This course aims to familiarize students with the principles, techniques, and methods in data visualization and provide practical skills for designing and implementing data visualizations.

This course gives students a solid introduction to the fundamentals of data visualization with current insights from research and practice. By the end of the course, students will

1. Be able to select and apply methods for designing visualizations based on a problem,
2. know essential theoretical basics of visualization for graphical perception and cognition,
3. know and be able to select visualization approaches and their advantages and disadvantages,
4. be able to evaluate visualization solutions critically, and
5. have acquired practical skills for implementing visualizations.

This course is intended for students interested in using data visualization in their work and students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

Textbuch

Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

Zusätzliche Literatur

Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2021_22/course_data_visualization.html

Course overview:

Website: https://bioinformatics.charite.de/main/course_theoretical.php

The Structural Bioinformatics Group offers a broad and comprehensive set of lectures in the areas of structural bioinformatics and drug design. The course begins with a general introduction of the drug design pipeline and an introductory course on the structural bioinformatics research field. These lead on to more specialized topics, amongst others in chemo-informatics, molecular docking, homology modeling and more detailed aspects of toxicity prediction models.

Structure of the course:

This course consists of eight lecture series. A detailed lecture for a period of 2 hrs on individual topics is delivered in the morning session. Afternoon session consists of an assignment for each topic. This part of the lecture course is assessed by examination, which will take place at the end of the course. Students are asked to delivered a seminar/presentation for 25 minutes, on the topic assigned to them. The material for the examination will be provided in advance, ensuring that the students have required time for preparation.

Topics for lectures are:

• 1. Introductory session
• 2. Peptide design
• 3. Homology modeling
• 4. 3D Molecular superimposition of small molecules
• 5. In silico screening, molecular fingerprints and chemical similarity
• 6. Molecular docking
• 7. Natural products and fragments based drug discovery
• 8. In silico toxicity prediction

Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

• Unique decodability and prefix codes
• Entropy and entropy rate as theoretical limits of lossless compression
• Optimal codes, Huffman codes
• Arithmetic coding
• Lempel-Ziv coding
• Linear prediction
• Examples from text, image and audio compression

In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

• Scalar quantization, optimal scalar quantization
• Theoretical limits of lossy compression: Rate distortion functions
• Vector quantization
• Predictive quantization
• Transform coding
• Examples from audio, image, and video compression

• Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
• Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
• Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
• Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
• Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.

The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations of data. This course aims to familiarize students with the principles, techniques, and methods in data visualization and provide practical skills for designing and implementing data visualizations.

This course gives students a solid introduction to the fundamentals of data visualization with current insights from research and practice. By the end of the course, students will

1. Be able to select and apply methods for designing visualizations based on a problem,
2. know essential theoretical basics of visualization for graphical perception and cognition,
3. know and be able to select visualization approaches and their advantages and disadvantages,
4. be able to evaluate visualization solutions critically, and
5. have acquired practical skills for implementing visualizations.

This course is intended for students interested in using data visualization in their work and students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

Textbuch

Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

Zusätzliche Literatur

Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2021_22/course_data_visualization.html

Course overview:

Website: https://bioinformatics.charite.de/main/course_theoretical.php

The Structural Bioinformatics Group offers a broad and comprehensive set of lectures in the areas of structural bioinformatics and drug design. The course begins with a general introduction of the drug design pipeline and an introductory course on the structural bioinformatics research field. These lead on to more specialized topics, amongst others in chemo-informatics, molecular docking, homology modeling and more detailed aspects of toxicity prediction models.

Structure of the course:

This course consists of eight lecture series. A detailed lecture for a period of 2 hrs on individual topics is delivered in the morning session. Afternoon session consists of an assignment for each topic. This part of the lecture course is assessed by examination, which will take place at the end of the course. Students are asked to delivered a seminar/presentation for 25 minutes, on the topic assigned to them. The material for the examination will be provided in advance, ensuring that the students have required time for preparation.

Topics for lectures are:

• 1. Introductory session
• 2. Peptide design
• 3. Homology modeling
• 4. 3D Molecular superimposition of small molecules
• 5. In silico screening, molecular fingerprints and chemical similarity
• 6. Molecular docking
• 7. Natural products and fragments based drug discovery
• 8. In silico toxicity prediction

Data compression is a technology, which only enables a variety of applications in our information age. Even though the underlying technology is often hidden from the end user, we use data compression every day when we hear music, watch images and videos, or use applications on our smartphone.

In this course, the fundamental and most often used approaches for data compression are introduced.  We discuss theoretical foundations as well as methods used in practice.

The first part of the course deals with lossless compression, in which the original data can be reconstructed exactly. This part includes the following topics:

• Unique decodability and prefix codes
• Entropy and entropy rate as theoretical limits of lossless compression
• Optimal codes, Huffman codes
• Arithmetic coding
• Lempel-Ziv coding
• Linear prediction
• Examples from text, image and audio compression

In the second part of the course, we consider lossy compression, by which only an approximation of the original data can be reconstructed. This type of compression enables much higher compression rates and is the dominant form of compression for audio, image and video data. The second part of the course includes the following topics:

• Scalar quantization, optimal scalar quantization
• Theoretical limits of lossy compression: Rate distortion functions
• Vector quantization
• Predictive quantization
• Transform coding
• Examples from audio, image, and video compression

• Sayood, K. (2018), “Introduction to Data Compression,” Morgan Kaufmann, Cambridge, MA.
• Cover, T. M. and Thomas, J. A. (2006), “Elements of Information Theory,” John Wiley & Sons, New York.
• Gersho, A. and Gray, R. M. (1992), “Vector Quantization and Signal Compression,” Kluwer Academic Publishers, Boston, Dordrecht, London.
• Jayant, N. S. and Noll, P. (1994), “Digital Coding of Waveforms,” Prentice-Hall, Englewood Cliffs, NJ, USA.
• Wiegand, T. and Schwarz, H. (2010), “Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, vol. 4, no. 1-2.

The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations of data. This course aims to familiarize students with the principles, techniques, and methods in data visualization and provide practical skills for designing and implementing data visualizations.

This course gives students a solid introduction to the fundamentals of data visualization with current insights from research and practice. By the end of the course, students will

1. Be able to select and apply methods for designing visualizations based on a problem,
2. know essential theoretical basics of visualization for graphical perception and cognition,
3. know and be able to select visualization approaches and their advantages and disadvantages,
4. be able to evaluate visualization solutions critically, and
5. have acquired practical skills for implementing visualizations.

This course is intended for students interested in using data visualization in their work and students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

Textbuch

Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

Zusätzliche Literatur

Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2021_22/course_data_visualization.html

Course overview:

Website: https://bioinformatics.charite.de/main/course_theoretical.php

The Structural Bioinformatics Group offers a broad and comprehensive set of lectures in the areas of structural bioinformatics and drug design. The course begins with a general introduction of the drug design pipeline and an introductory course on the structural bioinformatics research field. These lead on to more specialized topics, amongst others in chemo-informatics, molecular docking, homology modeling and more detailed aspects of toxicity prediction models.

Structure of the course:

This course consists of eight lecture series. A detailed lecture for a period of 2 hrs on individual topics is delivered in the morning session. Afternoon session consists of an assignment for each topic. This part of the lecture course is assessed by examination, which will take place at the end of the course. Students are asked to delivered a seminar/presentation for 25 minutes, on the topic assigned to them. The material for the examination will be provided in advance, ensuring that the students have required time for preparation.

Topics for lectures are:

• 1. Introductory session
• 2. Peptide design
• 3. Homology modeling
• 4. 3D Molecular superimposition of small molecules
• 5. In silico screening, molecular fingerprints and chemical similarity
• 6. Molecular docking
• 7. Natural products and fragments based drug discovery
• 8. In silico toxicity prediction

Joint class taught by the Institute of Clinical Physiology and the Institute of Physiology at the Charité.

Contents:

Using selected, up-to-date examples from biology and physiology, the work steps from data acquisition, data processing, data preparation, data assessment to the modeling of complex physiological relationships are studied theoretically and practically. Models from the following areas are dealt with in more detail:

- Basic biophysical and biochemical processes (e.g. free and facilitated diffusion through channel and transport proteins, active ion transport through membrane transporters, receptor-ligand interaction, interaction of structural and motor proteins)

- Structure-function analysis of transport proteins

- Biological networks (e.g. signal networks, metabolic networks, transportome models, feedback-mechanisms)

- Modeling physiological functions of an organism (e.g. mass transfer to the kidney, blood- and immune-function, muscle movement, temperature regulation, circadian rhythm, cardiac- and circulatory function, autonomic regulation / heart-rate-variability, body-composition)

The course will be split into two segments: the first seven appointments in the semester will take place at the Institute of Physiology, while the second seven appointments will take place at the Institute of Clinical Physiology.

Please contact your advisor.

Further information on our homepage.

The goal of this course is to give an introduction to the most important mathematical and algorithmic ideas underlying the constraint-based analysis of genome-scale metabolic networks, with applications to systems biology and biotechnology.

We will discuss the steady-state flux cone, flux balance analysis (FBA), flux variability analysis (FVA), flux coupling analysis (FCA), thermodynamic constraints, elementary flux modes (EFMs), and optimality principles for metabolic regulation. On the algorithmic side, the focus will be on linear and mixed-integer optimisation methods.

Homepage:

http://www.mi.fu-berlin.de/w/AgMathLife/MolnetzASS15