Research in molecular biology and biochemistry (LÍF114F)

This course is important for all graduate students in molecular biology and related fields. It is divided into two main parts: 1) presentation of a research article (journal club) and presentation of the research project (work in progress). For the research article students select a recent research article of interest and give a presentation on the aims and results. The goal is to train reading research articles in a critical way and present to others. The students have also as an assignment to bring up questions when other students present articles. The aim is to enhance critical approaches concerning scientific aims and methods used to reach the aims. In the project presentations students are expected to concentrate on the aims give a backgrounds and details of the methods used, results and the planned continuation. The students are expected to train in getting the message of the project in a clear way. The project presentation can lead to suggestion from other students that are useful for the approach.

This course is in English and has been on both spring and autumn term. The student can take this course four times giving max 8 ECTS.

Attendance is obligatory.

Final project (LUF441L)

• The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The supervisor of the thesis project can be a researcher outside the University of Iceland. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
• The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
• Final project exam is divided into two parts: Oral examination and open lecture
• Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
• According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
• All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
• According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.

Learning Outcomes:

Upon completion of an MS thesis, the student should be able to:

• Formulate research questions / hypothesis
• Use an appropriate theoretical framework to shed light on his / her topic
• Use economic methodology to answer a specific research question
• Competently discuss the current knowledge within the field and contribute to it with own research
• Gather, critically analyze and interpret data
• Assess the scope of a research project and plan the work accordingly
• Effectively display results and discuss their meaning within the context of the established literature
• Draw conclusions from incomplete data and explain the limitations of the research

Algorithms in Bioinformatics (TÖL504M)

This course will cover the algorithmic aspects of bioinformatic. We  will start with an introduction to genomics and algorithms for students new to the field. The course is divided into modules, each module covers a single problem in bioinformatics that will be motivated based on current research. The module will then cover algorithms that solve the problem and variations on the problem. The problems covered are motif finding, edit distance in strings, sequence alignment, clustering, sequencing and assembly and finally computational methods for high throughput sequencing (HTS).

Introduction to Research Studies and the Scientific Community (LÍF128F)

Introduction to Research Studies and the Scientific Community for M.sc. and Ph.D. students. The scientific community. Ethical, professional and practical information for research students. The research student's rights and responsibilities. Career opportunities. Lab safety and professionalism. Scientific method, conflict of interest and proper scientific conduct. What you can expect and not expect from supervisors. Duties and responsibilities of graduate students. Experimental design and how to write and publish results. Bibliographic software, tables and figure presentation. Techniques for poster and oral presentations. Writing scientific papers. Writing science proposals.

Grant writing and opportunities, cover letters, publishing environment and options. Thesis completion and responsibilities around graduation.

Format. Lectures, practicals, student projects and reviewing. Indvidual and group projects.

The course is run over 11 weeks in the fall.

Computational Intelligence (IÐN102M)

Basic aspects of computational intelligence, which is the study of algorithms that improve automatically through experience.

Human Genetics (LÍF513M)

Lectures: Mendelian genetics, organization of the human genome, structure of chromosomes, chromosomal changes and syndromes, gene mapping via association and whole genome sequencing methods, genetic analysis, genetic screening, genetics of simple and complex traits, genes and environment, cancer genetics, gene therapy, human and primate evolution, ethical issues concerning human genetics, informed consent and private information. Students are expected to have prior knowledge of the principles genetics.

Practical: Analyses of genetic data, study of chromosomal labelling, analyses of genetic associations and transcriptomes.

Biostatistics II (Clinical Prediction Models ) (LÝÐ301F)

This course is a continuation of Biostatistics I and constitutes a practical guide to statistical analyses of student's own research projects. The course covers the following topics. Estimation of relative risk/odds ratios and adjusted estimation of relative risk/odds ratios, correlation and simple linear regression, multiple linear regression and logistic regression. The course is based on lectures and practical sessions using R for statistical analyses.

Genomics and bioinformatics (LÍF659M)

Genomics and bioinformatics are intertwinned in many ways. Technological advances enabled the sequencing of for instance genomes, transcriptomes and proteomes. Complete genome sequences of thousands of organisms enables study of this flood of information for gaining knowledge and deeper understanding of biological phenomena. Comparative studies, in one way or another, building on Darwininan thought provide the theoretical underpinnings for analyzing this information and it applications. Characters and features conserved among organisms are based in conserved parts of genomes and conversely, new and unique phenotypes are affected by variable parts of genomes. This applies equally to animals, plants and microbes, and cells, enzymatic and developmental systems.

The course centers on the theoretical and practical aspects of comparative analysis, about analyses of genomes, metagenomes and transcriptomes to study biological, medical and applied questions. The lectures cover structure and sequencing of genomes, transcriptomes and proteomes, molecular evolution, different types of bioinformatic data, shell scripts, intro to R and Python scripting and applications. The practicals include, retrieval of data from databases, blast and alignment, assembly and annotation, comparison of genomes, population data analyses. Students will work with databases, such as Flybase, Genebank and ENSEMBL. Data will be retrived with Biomart and Bioconductor, and data quality discussed. Algorithms for search tools and alignments, read counts and comparisons of groups and treatments. Also elements of python scripting, open linux software, installation of linux programs, analyses of data from RNA-seq, RADseq and genome sequencing.

Students are required to turn in a few small and one big group project and present the large project with a lecture. In discussion session primary literature will be presented.

Final project (LUF441L)

• The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The supervisor of the thesis project can be a researcher outside the University of Iceland. The thesis represents 30 or 60 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
• The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
• Final project exam is divided into two parts: Oral examination and open lecture
• Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
• According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
• All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
• According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.

Learning Outcomes:

Upon completion of an MS thesis, the student should be able to:

• Formulate research questions / hypothesis
• Use an appropriate theoretical framework to shed light on his / her topic
• Use economic methodology to answer a specific research question
• Competently discuss the current knowledge within the field and contribute to it with own research
• Gather, critically analyze and interpret data
• Assess the scope of a research project and plan the work accordingly
• Effectively display results and discuss their meaning within the context of the established literature
• Draw conclusions from incomplete data and explain the limitations of the research

Research in molecular biology and biochemistry (LÍF223F)

This seminar course is an important contribution to the education of all continuing students in the field of molecular biology. It is divided into two parts: journal club and work in progress. For the journal club students will present a recent article about an interesting area of research. The aim is to learn to read articles and to be able to present them to others. This should encourage students to be critical and ask questions about the methods used and the general approach of the research described. The work in progress part includes a detailed description of the project the student is working on, including aims, introduction, materials and methods and discussion with future work. The aim of this is to teach the student to present their work in a concise and organized manner. This also often helps to solve problems in the study due to the input of the other students and group leaders present.

The course is taught in english both in the autumn and spring semester. Students can take the course at least four times giving a total of 8 ECTS.

Cell Biology II (LÍF614M)

The emphasis is on research articles. Resent research in various field with links to cell biology are included but can vary between years. For each lecture max three research articles are included.

Each student gives a seminar on one research article with details on methods and results. The students write a report (essay) on the article and discusses the results in a critical way.

Examples of topics included in the course: innate immunity, prions, the proteins pontin and reptin, polarized epithelium, development of trachea, data analyses and gene expression, autophagy, the origin of the nucleus.

Introduction to Systems Biology (LVF601M)

Systems biology is an interdisciplinary field that studies the biological phenomena that emerge from multiple interacting biological elements. Understanding how biological systems change across time is a particular focus of systems biology. In this course, we will prioritize aspects of systems biology relevant to human health and disease.

This course provides an introduction to 1) basic principles in modelling molecular networks, both gene regulatory and metabolic networks; 2) cellular phenomena that support homeostasis like tissue morphogenesis and microbiome resilience, and 3) analysis of molecular patterns found in genomics data at population scale relevant to human disease such as patient classification and biomarker discovery. In this manner, the course covers the three major scales in systems biology: molecules, cells and organisms.

The course activities include reading and interpreting scientific papers, implementation of computational algorithms, working on a research project and presentation of scientific results.

Lectures will comprise of both (1) presentations on foundational concepts and (2) hands-on sessions using Python as the programming language. The course will be taught in English.

High Performance Computing (REI204M)

Design of parallel computers and parallel programming models. Shared memory architecture. Message passing and distributed memory architecture. Parallel programming of computer clusters using MPI and multicore programming using OpenMP. Parallel algorithms for sorting, searching, linear algebra, and various graph problems.

Course topics will be very similar like HPC in Fall 2019:

http://www.morrisriedel.de/hpc-course-fall-2019

Positioning in the Field of High-Performance Computing (HPC)

• Consists of techniques for programming & using large-scale HPC Systems
• Approach: Get a broad understanding of what HPC is and what can be done
• Goal: Train general HPC techniques and systems and selected details of domain-specific applications

Course Motivation

Parallel processing and distributed computing

• Matured over the past three decades
• Both emerged as a well-developed field in computer science
• Still a lot of innovation, e.g. from hardware/software

‘Scientific computing‘ with Maple, Matlab, etc.

• Performed on small (‘serial‘) computing machines like Desktop PCs or Laptops
• An increasing number of cores enables ‘better scientific computing‘ today
• Good for small & fewer complex applications, quickly reach memory limits

‘Advanced scientific computing‘

• Used with computational simulations and large-scale machine & deep learning
• Performed on large parallel computers; often scientific domain-specific approaches
• Use orders of magnitude multi-core chips & large memory & specific many-core chips
• Enables ‘simulations of reality‘ - often based on known physical laws and numerical methods