NFYA09016U  Biological Networks (BioNet)

Volume 2017/2018
Education

BSc Programme in Nanoscience
BSc Programme in Physics
BSc Programme in Molecular Biomedicine

Content

This course should give the student an understanding of life as a physical system. A system that can be modeled in much the same way as classical physics systems, but with the additional complications that a living system is build of many different parts. Parts which are put together in a network of interacting units. Emphasis is on quantitative modeling, with the aim to teach the students how to construct and analyse dynamic models by use of simple programming.

The course discuss dynamics on biological networks through introduction of classical model organisms from molecular biology. Topics include physics of gene regulation, models for decision processes inside cells, bi-stability in genetic systems, response dynamics, oscillations, virus-bacteria games and and how these play out in microbial ecosystems.

Learning Outcome

Skills

  • Understand the central dogma, DNA-->RNA-->Proteins and will be able to write equations and algorithms associated to each of the steps. Such models involves stochastic molecular events and knowledge of time-scales associated to basic processes inside a living cell.
  • Understand how the same genome can give rise to widely different genetic activity. Such epigenetics is an important part of life-death decisions in the microbial world, as well as for the development of multicellularity.
  • Understand principles for gene regulation, meaning that the central dogma is supplemented with numerous back-arrows. An important part of being alive is feedback, and the student should know about feedback associated to both metabolism, and feedback associated to stress.
  • Understand and model oscillations of gene expression inside living cells. This include feedback from stress to the regulation of proteins that counteracts the stress.
  • Ability to construct quantitative models for living systems, and thereby increase the students intuition about modeling as a scientific method.

 

Knowledge

The student will obtain knowledge about networks and information processing in living systems, including physics of gene regulation, dynamics on biological networks. The gained understanding will center around the following fundamental equations and algorithms:

  • Models of biological transcription and translation.
  • Monods Growth law.
  • Gene activity as function of protein regulatory input.
  • Event driven simulation for gene expression.
  • Dynamical models of gene regulatory circuis.
  • Equations for bi-stability using positive feedback.
  • Epigenetics and read-write enzymes.
  • Equations for metabolic fluxes and their regulation.
  • Equations for push-pull reactions in signalling cascades.
  • Equations for adaptive responses.
  • Gene expression and spatio-temporal patterns.
  • Predator-prey modeling and its application to microbial ecology.

 

Competences

This course give the student a quantitative understanding of cellular and microbial biology.
The course gives the student competences to make and evaluate dynamical models.

Kim Sneppen: "Models of Life: Dynamics and Regulation in Biological Systems" Cambridge University Press 2014. The course curriculum is defined as chapter 1 to 8, chapter 10 and part of chapter 12 in the book.

Each week will have 2 times two hours of lectures, and 2 times two hours
of exercises a week. Answers to these exercises will subsequently
be made available on course homepage: cmol.nbi.dk/kursus/
Course homepage: cmol.nbi.dk/kursus/
Restricted elective course for specialisations in "bio- og medicinsk fysik" "gymnasierettet specialisering" and "fysik".
Credit
7,5 ECTS
Type of assessment
Oral examination, 25 min
No preparation time
Aid
Without aids
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

samme som ordinær eksamen

Criteria for exam assesment

See "learning outcome"

  • Category
  • Hours
  • Lectures
  • 28
  • Practical exercises
  • 28
  • Exam
  • 0,5
  • Theory exercises
  • 149,5
  • Total
  • 206,0