Fall 2011 – Spring 2012


Spring 2012

Biology

Molecular Cell Biology II

Carl-Philipp Heisenberg

Molecular Cell Biology II is an introductory course on all molecular and cellular concepts from biology. We will discuss the structure and function of cells and tissues from a molecular and cell biological point of view. This course builds on basic knowledge from MCB I and provides an introduction to Neuroscience I and II.

     
Course typeBreadth
ECTS credits3
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Biology

Quantitative Biology

Calin Guet, Gasper Tkacik, Michael Sixt

The course will cover advanced topics in biology based on recent research results with an emphasis on theoretical and experimental techniques. Topics to be discussed are: bacterial chemotaxis and signal transduction in biochemical networks, inference of regulatory interactions from data, zeroth order ultrasensitivity, the role of diffusion processes in biology and measurements of diffusion using fluorescence correlation spectroscopy, random walks as tools to characterize macromolecules, application of beam theory to describe molecular and cellular architecture.

     
Course typeBreadth
ECTS credits3
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Biology

Biological Physics

Gasper Tkacik, Mark Bollenbach

This course will introduce concepts and techniques from statistical physics, thermodynamics, and dynamical systems theory, and apply them to key problems in biology. Questions we will address include: What distinguishes biological processes from equilibrium phenomena? How do biological molecules and cells move? What are the physical principles underlying gene regulation? What is the role of fluctuations in biology and what are the tools for describing them? We will illustrate these topics with specific examples, including some from synthetic biology.

     
Course typeBreadth
ECTS credits3
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Computer Science

Computational Complexity

Krishnendu Chatterjee, Krzysztof Pietrzak

Computational Complexity: Complexity theory is a field on the border of mathematics and computer science with a remarkable list of celebrated achievements as well as vibrant present research activity. Complexity theory is the basic foundation of computer science, and it is concerne with classifying computational problems according to the resource (such as computation time and space) that are needed to solve them. This course is aimed at exposing students to the basic models of computation and the basic results and notion of complexity theory. Possible topics of the course are: 1. Automata theory and regular languages. 2. Turing machine model of computation. 3. Diagonalization and undecidability. 4. Non-determinism in computation, and the class of NP problems. 5. Reductions and completeness.

     
Course typeBreadth
ECTS credits3
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Mathematics

Computational Geometry and Topology

Herbert Edelsbrunner

Geometry and Topology are old fields in mathematics which have been turned into computational disciplines relatively recently. In this course, we teach a small but important subset of topics in the new field of Computational Geometry and Topology that are connected to applications in science and engineering more than others. I. Voronoi diagrams and Delaunay triangulations. II. Alpha shapes. III. Applications in structural molecular biology. IV. Homology. V. Persistent Homology.

     
Course typeBreadth
ECTS credits3
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Mathematics

Differential Equations

Chris Wojtan

This course will focus on building an intuitive, high-level understanding of calculus, differential equations, and dynamical systems, with applications to biology, neuroscience, and physics. We begin with a review of basic tools for differentiation and integration, and then we discuss multivariate calculus concepts like the gradient, divergence, curl, and Laplacian operators. Next, we draw attention to the special topics of optimization, Taylor series, and Fourier series. Afterward, we spend a few lectures introducing the concepts of ordinary differential equations and partial differential equations, with an emphasis on understanding their general behavior instead of drilling specific methods for solving them. In the last leg of this course, we introduce methods for solving differential equations in a computer. We introduce the ideas of accuracy and stability, and we analyze the behavior of a few basic numerical integrators. We end by building an intuition for common problems that arise within computer simulations in practice, and we discuss strategies for avoiding these difficulties.

     
Course typeBreadth
ECTS credits3
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Neuroscience

Introduction to Neuroscience I

Peter Jonas

In Neuroscience 1, molecular and cellular neuroscience is covered. We will address properties of ion channels, neurons and other excitable cells, subcellular processes (presynaptic terminals and dendrites), synaptic signaling in different microcircuits (hippocampus, cerebellum, basal ganglia, olfactory bulb), and synaptic plasticity.

     
Course typeBreadth
ECTS credits3
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Neuroscience

Introduction to Neuroscience II

Gasper Tkacik, Jozsef Csicsvari

In Neuroscience 2, systems neuroscience and computational neuroscience will be addressed. We will provide an overview over rhythmic activity in the brain, information coding in neuronal circuits, analysis of complex neuronal activity patterns, experimental techniques to analyze brain function (e.g. Ca2+ imaging, voltage imaging, superresolution techniques, and FRET), and computational techniques to model signaling in the brain (modeling action potential patterns, receptor kinetics, and dendritic integration).

     
Course typeBreadth
ECTS credits3
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Other

Scientific Presentation and Conduct

Sylvia Cremer, Thomas Henzinger

How to present your results to the scientific community? We discuss how to best prepare a scientific manuscript, conference contribution or grant application. We also provide information on literature search and citations, publishing decisions, job applications and good scientific practice and highlight differences in procedures between theoretical and experimental research fields.

     
Course typeRequired
ECTS credits0
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Fall 2011/2012

Biology

Problems in Evolutionary Genetics

Nick Barton

Population and quantitative genetics help us to understand and predict how populations evolve. This course will be based on a set of quantitative exercises - both pencil and paper, and simulation. The aim is to understand how we can model the evolution of populations, and how we use such models to make inferences from variation in DNA sequence and in quantitative traits. 1) Describing populations 2) Quantitative genetics 3) Neutral theory of molecular evolution 4) Selection 5) Interactions between evolutionary forces 6) Speciation

     
Course typeBreadth
ECTS credits3
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Biology

Molecular Cell Biology I

Harald Janovjak

Molecular Cell Biology I is an introductory course on all molecular concepts from biology. We will discuss the structure and function of the cell's building blocks, proteins, lipids and nucleic acids, from a chemical and physical point of view. This course introduces basic knowledge for MCB II and Introduction to Neuroscience I and II.

     
Course typeBreadth
ECTS credits3
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Computer Science

Algorithms I

Krzysztof Pietrzak, Vladimir Kolmogorov

This course covers techniques for the design and analysis of algorithms, with a special emphasis on methods useful in bioinformatics. The topics covered include: basic data structures; algorithms on graphs, trees and strings; techniques like dynamic programming, divide-and-conquer and amortized analysis. This course is a prerequisite for the course Computational Complexity.

     
Course typeBreadth
ECTS credits3
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Computer Science

Formal Methods

Krishnendu Chatterjee, Thomas Henzinger

We present formal modeling languages and analysis tools for discrete-event dynamical systems, with special emphasis on applications from computer science and biology. The languages we discuss are based on mathematical logic, rewrite rules, automata, circuits, and programming constructs. The analysis methods include model checking, theorem proving, and abstract interpretation. We give brief introductions to advanced models incorporating time, probabilities, game-theoretic aspects, and continuous behavior.

     
Course typeBreadth
ECTS credits3
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Mathematics

Discrete Mathematics & Probability

Herbert Edelsbrunner, Nick Barton

Discrete mathematics and probability provide concepts that are fundamental to computer science, biology, and other disciplines. This course emphasizes the connection to computer science and biology through the selection and motivation of topics: I. Fundamentals: counting, logic. II. Discrete Probability: in-exclusion, random variables. III. Processes: graphs, matrices, Markov, branching. IV. Continuous Probability: distributions, central limit, diffusion.

     
Course typeBreadth
ECTS credits3
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Mathematics

Linear Algebra

Christoph Lampert

Linear Algebra (from a Data Analysis point of view) introduces the most important concepts of Linear Algebra: vectors, matrices, linear functions, eigenvalues/-vectors, and how to solve linear equations. For this, it takes a route motivated by the problems that occur in scientific data analysis, such as linear regression and dimensionality reduction. Linear Algebra is a prerequisite for the follow-up courses "Differential equations" and "Geometry and Topology".

     
Course typeBreadth
ECTS credits3
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Other

Introduction to Research at IST Austria

 

This course gives an introduction to the research of the IST faculty. The intent is to foster the interdisciplinary spirit at IST Austria and provide first-year PhD students with an overview to aid them in choosing labs for rotations and their doctoral advisor. Also IST students who have transferred from other places are strongly encouraged to attend the course and external students are welcome. Each class will feature one of the faculty presenting her/his work. Each faculty will provide background reading the week prior to teaching.

     
Course typeRequired
ECTS credits1
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