Integrative Systems Biology Exercise
Discussion questions for integrative systems biology:
1. What technological advances have facilitated systems biology? (i.e.
What can we do now that we couldn’t do 20 years ago?)
High throughput DNA sequencing, microarrays, genetic interaction screens,
better tissue and stain collections
2. What microarray technologies are helpful in this context?
Expression analysis, chIP-chip, SNP/genotyping, array CGH, peptide arrays
3. What different types of information can be used to build biological
networks?
Protein-protein interactions, protein-DNA interactions, metabolic reactions
(connecting metabolites)
4. Discuss the differences between protein interaction networks and
metabolic networks. What are the differences between the network
representations?
Metabolic networks have metabolites as nodes/vertices and edges as
enzymes (proteins) while protein interaction networks have proteins as nodes
5. What are some of the characteristics (statistics) that are calculated for
biological networks?
Node degree distribution, all-pairs shortest paths distribution, clustering
coefficient distribution
6. What network statistic can be used to help find protein complexes?
Clustering coefficient measure
7. What do we need to know to estimate how significant the distance is
between two nodes in a network? Consider the path length as the
distance.
All pairs shortest paths distribution (the shortest path between any two
different nodes)
8. Which type of interaction data might be most informative when
integrated with microarray expression data?
ChIP-chip data measuring protein-DNA (transcription factor – promoter)
interactions as these often control transcriptional regulation
Integrative Systems Biology Exercise
9. What are the challenges with visualizing networks?
Layout is computationally difficult. Projecting the graph/network into a 2-D
plane (the figure) may create lots of overlapping edges, maybe you want to
minimize edge crossings.
10. What are network motifs and what is the limitation to finding large
motifs?
Network motifs are over-represented subgraphs (graph isomorphs) i.e. that
have the same number of nodes and configuration of edges. Finding motifs is
computationally difficult (finding graph isomorphs is O(n!) in the size of the
motif) and can only be done for small motif sizes (n<10).
11. What distinguishes protein-protein interaction networks from scale free
networks?
Hmm. Not a very clear question but what I must have had in mind is,
biological networks made of protein-protein interactions tend to be modular
(also referred to as hierarchical) thus have a correlation coefficient that
depends on the node degree.