Search result: Catalogue data in Autumn Semester 2022

Computational Biology and Bioinformatics Master Information
More information at: Link
Advanced Courses
A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof at least 16 ECTS in the Theory and at least 10 ECTS in the Biology category.
Note that some of the lectures are being recorded: Link
Theory
At least 16 ECTS need to be acquired in this category.
NumberTitleTypeECTSHoursLecturers
252-0237-00LConcepts of Object-Oriented Programming Information W8 credits3V + 2U + 2AP. Müller
AbstractCourse that focuses on an in-depth understanding of object-oriented programming and compares designs of object-oriented programming languages. Topics include different flavors of type systems, inheritance models, encapsulation in the presence of aliasing, object and class initialization, program correctness, reflection
ObjectiveAfter this course, students will:
Have a deep understanding of advanced concepts of object-oriented programming and their support through various language features. Be able to understand language concepts on a semantic level and be able to compare and evaluate language designs.
Be able to learn new languages more rapidly.
Be aware of many subtle problems of object-oriented programming and know how to avoid them.
ContentThe main goal of this course is to convey a deep understanding of the key concepts of sequential object-oriented programming and their support in different programming languages. This is achieved by studying how important challenges are addressed through language features and programming idioms. In particular, the course discusses alternative language designs by contrasting solutions in languages such as C++, C#, Eiffel, Java, Python, and Scala. The course also introduces novel ideas from research languages that may influence the design of future mainstream languages.

The topics discussed in the course include among others:
The pros and cons of different flavors of type systems (for instance, static vs. dynamic typing, nominal vs. structural, syntactic vs. behavioral typing)
The key problems of single and multiple inheritance and how different languages address them
Generic type systems, in particular, Java generics, C# generics, and C++ templates
The situations in which object-oriented programming does not provide encapsulation, and how to avoid them
The pitfalls of object initialization, exemplified by a research type system that prevents null pointer dereferencing
How to maintain the consistency of data structures
LiteratureWill be announced in the lecture.
Prerequisites / NoticePrerequisites:
Mastering at least one object-oriented programming language (this course will NOT provide an introduction to object-oriented programming); programming experience
262-6140-00LRandom Processes: Theory and Applications from Physics to Finance Restricted registration - show details W4 credits3Gexternal organisers
AbstractBasics of probability theory; Random processes: General concepts; Markov processes: Master equation, Fokker-Planck equation, stochastic differential equations; Mathematical finance
ObjectiveBasics of the theory of stochastic processes and an overview of selected applications
262-6150-00LProgramming for Life Sciences Restricted registration - show details W4 credits2Pexternal organisers
AbstractThis course aims to train the students into the proficient use of programming in analyzing data derived from projects in life sciences. The format of the course includes 2 hour lectures, in which notions of software design and engineering will be discussed, and programming projects, done both in groups as well as individually.
ObjectiveBy the end of the course, students should be comfortable with accessing and analyzing a wide variety of biological data. Concepts such as reproducibility, modularity, interoperability, and scalability will be emphasised.
636-0015-00LAn Introduction to Probability Theory and Stochastic Processes with Applications to Biology
Does not take place this semester.
W4 credits3G
AbstractBiology is becoming increasingly quantitative and mathematical modeling is now an integral part of biological research. In many biological processes, ranging from gene-expression to evolution, randomness plays an important role that can only be understood using stochastic models. This course will provide the students with a theoretical foundation for developing such stochastic models and analyzing
ObjectiveThe aim of this course is to introduce certain topics in Probability Theory and Stochastic Processes that have been specifically selected with an eye on biological applications. This course will teach students the tools and techniques for modeling and analyzing random phenomena. Throughout the course, several biological applications will be discussed and students will be encouraged to do additional reading based on their research interests.
ContentThe first half of the course will cover the basics of Probability Theory while the second half will delve into the theory of Stochastic Processes. Below is the list of topics that will be covered in the course.

1. The mathematical representation of random phenomena: The probability space, properties of the probability measure, Independence of events, Conditional probability and Bayes formula, applications to parameter inference.

2. Random Variables and their distributions: Discrete and continuous random variables, Expectation and Variance, Important Examples of Random Variables, Independent random variables and their sums, Conditional Distribution and Conditional Expectation, Markov and Chebyshev inequalities. Law of total variation, estimation of intrinsic and extrinsic noise in biological systems.

3. Convergence of Random Variables: Modes of convergence, Laws of large numbers, the central limit theorem, the law of the iterated logarithm, Applications to the analysis of cell population data.

4. Generating functions and their applications: Definition and important examples, Random Walks, Branching processes, Coalescent processes, Modeling epidemic processes and stem-cell differentiation.

5. Markov chains: Transition functions and related computations, Classification of states and classification of chains. Concepts of recurrence, transience, irreducibility and periodicity, Stationary distributions, Continuous time Markov Chain model of a biochemical reaction network.

6. Stochastic Processes: Existence and Construction, Stationary Processes, Renewal Processes, The Wiener Process, The Ergodic Theorem, Leveraging experimental techniques in Biology.

7. Introduction to the theory of Martingales: Basic definitions, Martingale differences and Hoeffding's inequality, Martingale Convergence Theorem, Crossings and convergence, Stopping times and the optional sampling theorem, Doob's maximal inequalities, Applications to the analysis of stochastic biochemical reaction networks.
LiteratureWhile no specific textbook will be followed, much of the material and homework problems will be taken from the following books:
An Introduction to Stochastic Processes with Applications to Biology, Linda Allen, Second Edition, Chapman and Hall, 2010.
Probability And Random Processes, Grimmett and Stirzaker, Third Edition, Oxford University Press, 2001.
Prerequisites / NoticeThe course will involve a healthy balance between mathematical rigor (theorem proving) and biological applications. Students are expected to have a good grasp of Linear Algebra and Multivariable Calculus. Basic knowledge of set theory will also be needed. Students should
be prepared for abstract reasoning.
263-3010-00LBig Data Information Restricted registration - show details W10 credits3V + 2U + 4AG. Fourny
AbstractThe key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations.
ObjectiveDo you want to be able to query your own data productively and efficiently in your future semester projects, master thesis, or PhD thesis? Are you looking for something beyond the Python+Pandas hype? This courses teaches you how to do so as well as the dos and don'ts.

"Big Data" refers to the case when the amount of data is very large (100 GB and more), or when the data is not completely structured (or messy). The Big Data revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm".

Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it are needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small.

The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof.

After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each one of your projects efficiently and consistently.
ContentThis course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem.

No data is harmed during this course, however, please be psychologically prepared that our data may not always be in third normal form.

- physical storage: distributed file systems (HDFS), object storage(S3), key-value stores

- logical storage: document stores (MongoDB), column stores (HBase), graph databases (neo4j), data warehouses (ROLAP)

- data formats and syntaxes (XML, JSON, RDF, Turtle, CSV, XBRL, YAML, protocol buffers, Avro)

- data shapes and models (tables, trees, graphs, cubes)

- type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +)

- an overview of functional, declarative programming languages across data shapes (SQL, XQuery, JSONiq, Cypher, MDX)

- the most important query paradigms (selection, projection, joining, grouping, ordering, windowing)

- paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark)

- resource management (YARN)

- what a data center is made of and why it matters (racks, nodes, ...)

- underlying architectures (internal machinery of HDFS, HBase, Spark, neo4j)

- optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing)

- applications.

Large scale analytics and machine learning are outside of the scope of this course.
LiteratureCourse textbook: Link

Papers from scientific conferences and journals. References will be given as part of the course material during the semester.
Prerequisites / NoticeThe lecture is hybrid, meaning you can attend with us in the lecture hall, or on Zoom, or watch the recordings on YouTube later. Exercise sessions are in presence.

This course, in the autumn semester, is only intended for:
- Computer Science students
- Data Science students
- CBB students with a Computer Science background

Mobility students in CS are also welcome and encouraged to attend. If you experience any issue while registering, please contact the study administration and you will be gladly added.

For students of all other departements interested in this fascinating topic: I would love to have you visit my lectures as well! So there is a series of two courses specially designed for you:
- "Information Systems for Engineers" (SQL, relational databases): this Fall
- "Big Data for Engineers" (similar to Big Data, but adapted for non Computer Scientists): Spring 2023
There is no hard dependency, so you can either them in any order, but it may be more enjoyable to start with Information Systems for Engineers.

Students who successfully completed Big Data for Engineers are not allowed to enrol in the course Big Data.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingfostered
Social CompetenciesCommunicationfostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
261-5112-00LAlgorithms and Data Structures for Population Scale Genomics Restricted registration - show details
Number of participants limited to 30.
W3 credits2GA. Kahles
AbstractResearch in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions.
ObjectiveThis interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it.
ContentOver the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content.
1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants.
2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures.
3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data.
252-0834-00LInformation Systems for Engineers Information W4 credits2V + 1UG. Fourny
AbstractThis course provides the basics of relational databases from the perspective of the user.

We will discover why tables are so incredibly powerful to express relations, learn the SQL query language, and how to make the most of it. The course also covers support for data cubes (analytics).
ObjectiveDo you want to be able to query your own data productively and efficiently in your future semester projects, bachelor's thesis, master thesis, or PhD thesis? Are you looking for something beyond the Python+Pandas hype? This courses teaches you how to do so as well as the dos and don'ts.

This lesson is complementary with Big Data for Engineers as they cover different time periods of database history and practices -- you can take them in any order, even though it might be more enjoyable to take this lecture first.

After visiting this course, you will be capable to:

1. Explain, in the big picture, how a relational database works and what it can do in your own words.

2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc).

3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data.

4. Design new schemas to store data in accordance to the real world's constraints, such as relationship cardinality

5. Explain what bad design is and why it matters.

6. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of what is called "normal forms".

7. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster.

8. Access an existing relational database from a host language such as Java, using bridges such as JDBC.

9. Explain what data independence is all about and didn't age a bit since the 1970s.

10. Explain, in the big picture, how a relational database is physically implemented.

11. Know and deal with the natural syntax for relational data, CSV.

12. Explain the data cube model including slicing and dicing.

13. Store data cubes in a relational database.

14. Map cube queries to SQL.

15. Slice and dice cubes in a UI.

And of course, you will think that tables are the most wonderful object in the world.
ContentUsing a relational database
=================
1. Introduction
2. The relational model
3. Data definition with SQL
4. The relational algebra
5. Queries with SQL

Taking a relational database to the next level
=================
6. Database design theory
7. Databases and host languages
8. Databases and host languages
9. Indices and optimization
10. Database architecture and storage

Analytics on top of a relational database
=================
12. Data cubes

Outlook
=================
13. Outlook
Literature- Lecture material (slides).

- Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom
(It is not required to buy the book, as the library has it)
Prerequisites / NoticeThe lecture is hybrid, meaning you can attend with us in the lecture hall, or on Zoom, or watch the recordings on YouTube later. Exercise sessions are in presence.

For non-CS/DS students only, BSc and MSc
Elementary knowledge of set theory and logics
Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingfostered
Social CompetenciesCommunicationfostered
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
752-5500-00LApplied Bioinformatics: Microbiomes Restricted registration - show details W5 credits2V + 2UN. Bokulich
AbstractLearn to apply practical bioinformatics/computational skills for analysis of microbiomes in foods and human health! Students will apply basic programming skills for scientific computing and bioinformatics, and learn and discuss the importance of microbiomes to foods and human health, through recognition and comparison of ecological theory, methodology, and experimental design across systems.
ObjectiveLearn to apply bioinformatics and computational methods for analysis of microbiome next-generation sequencing data. A secondary goal is to critically examine the relevance of microbiomes to food quality, safety, and human health, through application of theory and appropriate experimental design. Students completing this course will thus be able to both apply appropriate methodology to study microbiomes (or other high-dimensional data) in different systems, as well as evaluate and interpret bioinformatics results.
Content1. Introduction to microbiomes and microbial bioinformatics toolkit. Python, Pandas, Jupyter, GitHub, visualization libraries for Python.
2. Analysis of marker-gene sequence data for microbiome profiling. QIIME 2, database searching, taxonomic classification, phylogenetics.
3. Microbial diversity, function, and ecology. Molecular ecology, diversity metrics, ordination methods.
4. Advanced topics in microbial bioinformatics. Metagenomics, machine learning, functional analysis, data visualization, et cetera.

This course requires extensive engagement in learning outside of the classroom (using online resources and practical exercises), with a focus on active learning in the classroom.
Prerequisites / NoticeNo specific pre-requisites, but students should have some familiarity with microbiology, molecular biology, programming (Python), bioinformatics, and statistics.

Students will bring and work on their own laptop computers.

All software used in the course is free and open-source.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationassessed
Cooperation and Teamworkfostered
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingassessed
Self-direction and Self-management fostered
Biology
At least 10 ECTS need to be acquired in this category.
NumberTitleTypeECTSHoursLecturers
551-0309-00LConcepts in Modern Genetics
Information for UZH students:
Enrolment to this course unit only possible at ETH. No enrolment to module BIO348 at UZH.

Please mind the ETH enrolment deadlines for UZH students: Link
W6 credits4VY. Barral, D. Bopp, A. Hajnal, O. Voinnet
AbstractConcepts of modern genetics and genomics, including principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference.
ObjectiveThis course focuses on the concepts of classical and modern genetics and genomics.
ContentThe topics include principles of classical genetics; yeast genetics; gene mapping; forward and reverse genetics; structure and function of eukaryotic chromosomes; molecular mechanisms and regulation of transcription, replication, DNA-repair and recombination; analysis of developmental processes; epigenetics and RNA interference.
Lecture notesScripts and additional material will be provided during the semester.
551-0313-00LMicrobiology (Part I)W3 credits2VW.‑D. Hardt, L. Eberl, B. Nguyen, J. Piel, M. Pilhofer, A. Vagstad
AbstractAdvanced lecture class providing a broad overview on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis.
ObjectiveThis concept class will be based on common concepts and introduce to the enormous diversity among bacteria and archaea. It will cover the current research on bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis.
ContentAdvanced class covering the state of the research in bacterial cell structure, genetics, metabolism, symbiosis and pathogenesis.
Lecture notesUpdated handouts will be provided during the class.
LiteratureCurrent literature references will be provided during the lectures.
Prerequisites / NoticeEnglish
The lecture "Grundlagen der Biologie II: Mikrobiologie" is the basis for this advanced lecture.
551-0317-00LImmunology IW3 credits2VM. Kopf, A. Oxenius
AbstractIntroduction into structural and functional aspects of the immune system.
Basic knowledge of the mechanisms and the regulation of an immune response.
ObjectiveIntroduction into structural and functional aspects of the immune system.
Basic knowledge of the mechanisms and the regulation of an immune response.
Content- Introduction and historical background
- Innate and adaptive immunity, Cells and organs of the immune system
- B cells and antibodies
- Generation of diversity
- Antigen presentation and Major Histoincompatibility (MHC) antigens
- Thymus and T cell selection
- Autoimmunity
- Cytotoxic T cells and NK cells
- Th1 and Th2 cells, regulatory T cells
- Allergies
- Hypersensitivities
- Vaccines, immune-therapeutic interventions
Lecture notesElectronic access to the documentation will be provided. The link can be found at "Lernmaterialien"
Literature- Kuby, Immunology, 9th edition, Freemen + Co., New York, 2020
Prerequisites / NoticeFor D-BIOL students Immunology I (WS) and Immunology II (SS) will be examined as one learning entity in a "Sessionsprüfung". All other students write separate exams for Immunology I and Immunology II. All exams (combined exam Immunology I and II, individual exams) are offered in each exam session.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesfostered
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityassessed
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection assessed
Self-direction and Self-management assessed
636-0105-00LIntroduction to Biological ComputersW4 credits3GY. Benenson
AbstractBiological computers are man-made biological networks that interrogate and control cells and organisms in which they operate. Their key features, inspired by computer science, are programmability, modularity, and versatility. The course will show how to rationally design, implement and test biological computers using molecular engineering, DNA nanothechnology and synthetic biology.
ObjectiveThe course has the following objectives:

* Familiarize students with parallels between theories in computer science and engineering and information-processing in live cells and organisms

* Introduce basic theories of computation

* Introduce approaches to creating novel biological computing systems in non-living environment and in living cells including bacteria, yeast and mammalian/human cells.

The covered approaches will include
- Nucleic acids engineering
- DNA and RNA nanotechnology
- Synthetic biology and gene circuit engineering
- High-throughput genome engineering and gene circuit assembly

* Equip the students with computer-aided design (CAD) tools for biocomputing circuit engineering. A number of tutorials will introduce MATLAB SimBiology toolbox for circuit design and simulations

* Foster creativity, research and communication skills through semester-long "Design challenge" assignment in the broad field of biological computing and biological circuit engineering.
ContentNote: the exact subjects can change, the details below should only serve for general orientation

Lecture 1. Introduction: what is molecular computation (part I)?

* What is computing in general?
* What is computing in the biological context (examples from development, chemotaxis and gene regulation)
* The difference between natural computing and engineered biocomputing systems

Lecture 2: What is molecular computation (part II) + State machines

1st hour

* Detailed definition of an engineered biocomputing system
* Basics of characterization
* Design challenge presentation

2nd hour

* Theories of computation: state machines (finite automata and Turing machines)

Lecture 3: Additional models of computation

* Logic circuits
* Analog circuits
* RAM machines

Basic approaches to computer science notions relevant to molecular computation. (i) State machines; (ii) Boolean networks; (iii) analog computing; (iv) distributed computing. Design Challenge presentation.

Lecture 4. Classical DNA computing

* Adleman experiment
* Maximal clique problem
* SAT problem

Lecture 5: Molecular State machines through self-assembly

* Tiling implementation of state machine
* DNA-based tiling system
* DNA/RNA origami as a spin-off of self-assembling state machines

Lecture 6: Molecular State machines that use DNA-encoded tapes

* Early theoretical work
* Tape extension system
* DNA and enzyme-based finite automata for diagnostic applications

Lecture 7: Introduction to cell-based logic and analog circuits

* Computing with (bio)chemical reaction networks
* Tuning computation with ultrasensitivity and cooperativity
* Specific examples

Lecture 8: Transcriptional circuits I

* Introducing transcription-based circuits
* General features and considerations
* Guidelines for large circuit construction

Lecture 9: Transcriptional circuits II

* Large-scale distributed logic circuits in bacteria
* Toward large-scale circuits in mammalian cells

Lecture 10: RNA circuits I

* General principles of RNA-centered circuit design
* Riboswitches and sRNA regulation in bacteria
* Riboswitches in yeast and mammalian cells
* General approach to RNAi-based computing

Lecture 11: RNA circuits II

* RNAi logic circuits
* RNAi-based cell type classifiers
* Hybrid transcriptional/posttranscriptional approaches

Lecture 12: In vitro DNA-based logic circuits

* DNAzyme circuits playing tic-tac-toe against human opponents
* DNA brain


Lecture 13: Advanced topics

* Engineered cellular memory
* Counting and sequential logic
* The role of evolution
* Fail-safe design principles

Lecture 14: Design challenge presentation
Lecture notesLecture notes will be available online
LiteratureAs a way of general introduction, the following two review papers could be useful:

Benenson, Y. RNA-based computation in live cells. Current Opinion in Biotechnology 2009, 20:471:478

Benenson, Y. Biocomputers: from test tubes to live cells. Molecular Biosystems 2009, 5:675:685

Benenson, Y. Biomolecular computing systems: principles, progress and potential (Review). Nature Reviews Genetics 13, 445-468 (2012).
Prerequisites / NoticeBasic knowledge of molecular biology is assumed.
636-0511-00LDevelopmental Neuroscience Restricted registration - show details
Does not take place this semester.
W2 credits2Vexternal organisers
AbstractDevelopment of cerebral cortex, hippocampus, motor system, olfactory system, visual system, auditory system, somatosensory system, navigation and memory systems, developmental disorders (autism, stem cells in the developing, adult and diseased brain.
Objective
636-0515-00LMolecular Medicine I Restricted registration - show details W2 credits2Vexternal organisers
AbstractThis lecture series will introduce biologists to the mechanisms that cause human diseases. Emphasis will be on the genetic and environmental factors that lead to diseases, and how this knowledge can be used to develop diagnostic and therapeutic procedures.
Objective
262-6170-00LMolecular Mechanisms of Development Restricted registration - show details
Does not take place this semester.
W2 credits2Vexternal organisers
AbstractThis lecture will present examples for developmental switches in a variety of systems, including single-cell organisms, plants, nematodes, flies and vertebrates. The lecture will illustrate a way of thinking rather than attempt to cover single details of the issues discussed.
Objective
262-6180-00LMolecular Control of Vertebrate Development and Organogenesis Restricted registration - show details W2 credits2Vexternal organisers
AbstractThis course will introduce the participants to the basic principles, genetic tools and vertebrate model systems used to study developmental processes
Objective
262-5130-00LEvolutionary Medicine: Morphological Changes and Pathologies (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: BIO440

Mind the enrolment deadlines at UZH:
Link
W6 credits5GUniversity lecturers
AbstractThe course addresses aspects of this transdisciplinary research in lectures and provides opportunity to perform small research projects in the fields of paleogenetics, palaeopathology, imaging technologies, long- and short- term morphological changes.
ObjectiveThe goal of the course is to familiarize the students with state-of-the- art topics and methodologies in evolutionary medicine.
262-6101-00LNew Approaches to Tackle Antibiotic ResistanceW1 credit1Vexternal organisers
AbstractThis lecture series was redesigned in the scope of the recently established Swiss-wide research network on antibiotics called AntiResist.Internationally renowned scientists from different disciplinespresent their latest findings and discuss how their work relates to a better understanding of infection processes and antibiotic therapy.
Objective
262-6102-00LFunctional Organization of the Cell Nucleus Restricted registration - show details W2 credits2Vexternal organisers
AbstractThis course will describe the structure and function of the eukaryotic nucleus. In this course, all features of the nucleus from regulated import/export through pores, to the compartmentalization of transcription, splicing, replication and repair will be covered. The lectures will give an up-to-date overview of a complex structure-function problem that touches on crucial aspects of cell identity
Objective
262-6103-00LCellular Signalling Restricted registration - show details W2 credits2Vexternal organisers
AbstractThis course gives an introduction into cellular signalling mechanisms, followed by specific topics covering tyrosine and serine/threonine kinase growth factor receptors, protein/protein and protein/lipid interaction modules, signalling by Ras family G proteins, lipid kinases, phospholipid-coupled transduction systems, protein kinase C, G protein-coupled receptors, and other topics.
Objective
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