Quantitativ Biology and Integrative Biology, 7.5 credits

Contents

The course focuses on basic mathematical and statistical methods that are widely used within modern bioinformatics. The course starts with linear algebra (matrices, vectors, eigenvalues) that are then applied to markov chains (observable and hidden). The wide utility of linear algebra is studied through testing alternative bases, with examples from, e.g., image and signal analysis. To be able to study high dimensional data, the course also introduce coordinate transformations and dimensional reduction (PCA and OLS).
The course introduced Bayesian statistics and solved related complex equations (e.g., RSTAN). The focus is on statistical models common within bioinformatics (e.g., the poisson and negative binomial distribution). Common statistical models for analyzing large dimensional data will be introduced. This includes multilinear models and nonlinear latent space models. This will be exemplified by applications in chemometrics and single cell analysis.

Expected learning outcomes

Knowledge and Understanding:
The student should be able to:
• explain the origin of common statistical distributions
• describe the difference between Bayesian and frequentist statistics
• explain the solution space for overdetermined and underdetermined systems
• explain the concept of "memoryless processes"
• explain the difference between linear and nonlinear systems
• explain what a latent space is

Skills and Abilities:
The student should be able to:
• solve systems of linear equations
• formulate biological problems using observable and hidden Markov chains, and analyze them
• analyze large-scale data in terms of latent spaces, using both linear and nonlinear methods
• model data using hierarchical multiparametric models with alternative statistical distributions, with emphasis on the Poisson and negative binomial distributions
• apply methods from chemometrics and single cell analysis to large scale data

Judgement and approach:
The student should be able to:
• evaluate the validity of the mathematical formulations used in the course for describing biological systems

Required Knowledge

90 ECTS from completed courses in the fields of biology, biomedicine, molecular biology, molecular ecology, molecular evolution, genetics, or other related subjects; 5 credits in programming in R or Python.

Form of instruction

The course is campus based and consists of lectures (pre recorded or on site) and teacher led sessions where students can obtain guidance while working on assignments. The course is given in English but may be given in Swedish if English is not required.
Participants need access to their own computer.

Examination modes

The course is examined through an individual written on campus exam. The grading scale for the course is Fail (U), Pass (G), and Pass with Distinction (VG). To receive a grade of Pass (G) for the entire course, the student must achieve Pass (G) on the written exam. To receive Pass with Distinction (VG), the student must achieve Pass with Distinction (VG) on the written exam.

The course coordinator may decide that active participation in non mandatory tasks, cases, or discussion forums can award bonus points added to the exam result, not exceeding 10% of the total exam score. The course coordinator must inform students at the course start which activities award bonus points and the requirements for obtaining them.

A student who has failed two examination attempts for the course or part of the course is entitled to have another examiner appointed, unless special reasons argue against it (Higher Education Ordinance, Chapter 6, Section 22). Requests for a new examiner should be addressed to the Deputy Head of the Department of Molecular Biology.

The examiner may decide to deviate from the examination format stated in the syllabus. Individual adaptations of the examination format must be considered based on the student’s needs. Adaptations must remain within the framework of the intended learning outcomes. A student in need of adapted examination must request this no later than 10 working days before the examination. The examiner decides on adapted examination and informs the student.

Transitional provisions

If the syllabus ceases to apply or undergoes major revisions, students are guaranteed at least three examination opportunities (including the regular exam) under the regulations of the syllabus in which they were originally registered, for a maximum of two years after the syllabus ceased to apply or the course was discontinued.