NFYA09016U Biological Networks (BioNet)

• 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 timescales 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 shock responses and conditions for obtaining oscillations inside living cells. This include feedback from stress to the regulation of proteins that counteracts the stress.
• Understand networks, and what makes networks relevant for dealing with biological systems. This include both transcription regulatory networks, and their coupling to the production line networks of metabolism.
• Know about evolution in terms of models for how it could have formed the living world. In particular to know about models for evolving molecular networks inside an evolving species, as well as a minimal model for co-evolving animal species.
• Knowledge about tools 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 and dynamics of evolution. The gained understanding will center around the following fundamental equations and algorithms:

• Monods Growth law
• gene activity as function of protein regulatory input
• time for a protein to find specific site in a cell
• event driven simulation for gene expression
• equations for bi-stability using positive feedback
• equations for metabolic fluxes and their regulation
• equations for signal spreading and robustness in sbstract networks
• equation for push-pull reactions in signalling cascades
• equation for adaptive responses
• agent based simulation of excitable media
• agent based model for punctuated co-evolution of life

Competences
This course makes the student able to adapt knowledge from both introductory biophysics and cellbiology into a new, specialized topic, the physics of life viewed as biological network.
The course gives the student competencies to make and evaluate dynamical models, a useful tool in this topic, but which also has applications in other branches of physics and science in general.