Class Discussion – 2/3/10
1.2.1 Cellular Components – Proteins formed from amino acids. Carbohydrates (C, H, O) store and transport energy. Lipids used in cell signaling. Nucleic Acids structural units of DNA and RNA. Hydrolysis is essential to digestion.
1.3.4 Cell Cycle -Cell grows to accommodate a split, duplicates chromosomes and checks for errors before going into mitosis, the actual split of the cell into two replicated cells. Cell cycle may block progression if certain conditions are not met. Cell monitors DNA replication until complete before proceeding to mitosis. CDK conserved during evolution and drives cell cycle by chemical modification.
1.4.2 System biology markup language – SBML used to represent models of cell signaling pathways, metabolic pathways, biochemical reactions, gene regulation, etc. Complex code that is able to be used on many different machines; perhaps the specific code makes the program run more universally?
1.4.3 KEGG – Kyoto Encyclopedia of Genes and Genomes. Database of biological information, highly technical.
Class Discussions – 2/1/10
1.1.2 Small World Networks – Mostly a review of last semester Graph Theory. These networks are intriguing because they have properties of both regular lattices and random graphs which gives them high clustering coefficients but low diameter.
1.2.2 Eukaryotic Cells – This is everything I ran away from back in High School! These cells have lots of small structures contained within the cell wall (nucleus, golgi apparatus, mitochondria, etc.), and it is this structure that dictates what moves where, when, how, and why.
1.2.3 Cellular Organisms – A lot of this material went over my head. The one thing I managed to get down was that prokaryotes don’t have a nucleus, and eukaryotes do.
1.4.1 Ontologies – Show relationships between concepts that may or may not exist. This concept blew my mind until we discussed specific examples of it. When the presentation first started, my first thought of ontologies to mathematics would be the idea of variables like x, y, z whose influence we analyze through calculation, although I don’t know if this is right. The concept became much clearer though, when I realized its applications to video games, religion, and a religion’s mythology.
Class Discussions – 1/29/10
Random Networks – Review of graph theory, low inter – connectivity, low span.
Scale-Free Network Model
Single node/hub = constant (more single nodes = more hubs)
Preferential attachment – Proteins want to be connected to other highly-connected proteins, but proximity is more important. Vertex degree has a normal distribution with fat tail. Leads to highly connected hubs.
Removal of one big hub can dismantle whole network.
