Altklausur

Question 1

What is Systems Biology?
In what ways does it differ from ’classical’ biology? (Brief answer!)

Answer 1

Systems Biology investigates the dynamic interactions if proteins and genes using systems theoretic methods. It stresses that only through the study of the dynamic interactions alone, one can find the emergent processes that make up life.

Question 2

A model in systems biology is usually not
– simplified
– a mathematical representation of biological processes
– the central element of systems biology research
– valid under all conceivable environmental conditions

Answer 2

The last one is correct. A model is not valid under all conditions.

Question 3

Which are the main factors shaping the composition of the gut microbiota? Give a definition of alpha and beta diversity!
What is the main difference between them?

Answer 3

Among others, the following influence the gut microbiome: Age, diet (e.g. Western diet, Vegeterian, Vegan, Non-western), stress, inflammation (cause and/or consequence), environment, medication (e.g. antibiotics),…

Alpha diversity refers to the richness/diversity of a community (local species pool), whereas beta diversity defines the total species diversity (dissimilarity between communities).

Alpha diversity is measured within a community, and beta diversity among communities

Question 4

What is a positive and negative auto regulation, and how are they used by the cell in dynamic regulation?

Answer 4

Positive Autoregulation (PAR) occurs when the product of a gene activates its own production. PAR is a common network motif in transcription networks but occurs less often in the E.coli network than negative auto regulation. Positive auto regulation can lead to instability.

Negative Autoregulation (NAR) occurs when the product of a gene represses its own production. NAR is a common network motif in transcription networks. Circuits with negative auto regulation speed up the response time and show increased robustness against parameter fluctuations.

Question 5

Name and describe the main features of Genetic Algorithms (GA)?
Which optimization strategy should you at the beginning of the optimization, and which strategy do you use at the end and why?
What problems are GA used for, and give an example of such a problem?

Answer 5

A GA consists of a population that reproduces according to its fitness to create offspring with a mutated /recombined genome. The child generation is evaluated against the solution using some fitness function, and the process of reproduction is started over until some cutoff criterium is met.
At the beginning of optimization, you should allow for crossing-over and mutations likewise to better explore the parameter space. At the end of the optimization, changes to the offspring should mainly come from mutations, avoiding destroying a solution that has settled close to a local minimum.
Usually, a GA is used to solve complex optimization problems, i.e., the traveling salesman problem