Tons of Richard Suggestions (OWW / Private wiki), Biggest thing is to make simulated data from random (or special) sequences and see how well SDM works. Probably like 4 more graphs
Images are better (fly egg cytoplasm)
Kinesin modeling paper, Requires lots of reading, Not too much programming, but re-running simulations, making graphs, Middle author for Anthony
Andy Isotope Paper, A bit of work for Steve
SDM, Requires some more programming, If lots of new programming and graphs (maybe some of our data), then first author for Anthony), New node
Crumley, Notebook / Figshare
Make list of figures/experiments to make
Get code running
Get code running
Find important sequences, These are probably in Anthony OWW notebook, Once found, SJK put into github repository as sensible text files (best way I can see to do it quickly)
This is probably not a good idea. The advantage is using fully-open-source (such as R). The disadvantage, of course, is it would take extra time. Must...resist...urge
After lunch, Find "old" simulation of pCP681 or pBR322, make sure "new" simulation provides same numbers, Add sequences and "development distribution" and EXE file to same directory; start github project(s)
find yeast genome, download, note location and details
find alternative sequences for hiding in yeast genomic data, human, fungi similar to yeast, drosophila, koch repetitive unzipping data, other plasmids, puc19, pbluescript, pals, lambda dna, e coli
finish experiments mindmap, read original paper
Find or create XhoI finding software
Get "automatic" simulator working
Find / get match scoring stuff working
Find yeast genome, Worth checking Larry's notebook private wiki to see if you can see where he got it, His notebook on private wiki has "all pages" view, But there may be a better source
If not where is data so I can throw up on FigShare
If so how long will this take? Can we make it faster? Is it worth it to make it faster?
Can we use other genomes on the XhoI software?, I'd like to at least do the other yeast species and simulate some random fragments to try and match., And also lambda DNA, and pALS
I guess we want some probability that each data point will be adjusted and there is some spread of noise that each point can be adjusted up to the max value?, Does this make sense?
What about thermal noise? ie some factor of kT?
Is there anything better than just adding like 1pN noise?