Ed's Big Plans

Brief: That IEEE November conference paper Stefan and I had been working on was finally submitted in the wee hours of last night. The newborn weighed in at the IEEE specified 6 pages, single spaced.

Just for fun, here’s a really zoomed out view of pages 1, 4 and 6.

A Monet Isopentenol c/o Autotrace

Brief: Happy accidents make me happy– here’s isopentenol after grabbing it as an SDF out of PubChem, dumping it out into a PNG with Bioclipse, grayscaling it with GIMP– then converting it to an SVG with Autotrace (RO IT Systems)… It’s just pretty… like a Monet. Of course, I have to go back and make it look like a molecule again for a paper… but I’m going to admire the pretty little alcohol for a bit.

Okay– I managed to finish that 3-layer neural network implementation the other day– actually, it was a while ago but I didn’t post about it from being busy. It’s a pretty standard network, but I’m proud to say it’s small and works for OSX and Win32. I have to put in a few #define directives to have it work with Linux as well.

I will have to document it too when I get a chance. The reason why I made a brand new executable (instead of using the source from my previous projects) is because I needed something that would take in launch-time parameters so that it didn’t need to be recompiled each time someone decides to use the binary on a new dataset with a different number of inputs. Right now, the thing has barely any solid parameters that can’t be touched at launch-time.

The NNcmk (Neural Network – Cameron, Ma, Kremer) package is C compilable, uses the previously developed in-house library for the NGN and will be available shortly after I’m satisfied that I’ve squashed all the bugs, fixed the output and have documented the thing completely. I think Chris has difficulty with it right now mostly because I didn’t specify exactly what parameters do what– I did at least provide a (DOS) batch file with an example run-in-train-mode / run-in-test-mode sequence…

Back to work on that paper right now though…

Brief: Python normalizer (norm.py) — normalizes each real value column of a comma separated value (CSV) file.

Matthew also has a post about Recombinatron & Big Bang Day here.

‘Big Bang Day’ was this awesome Saturday morning where a bunch of the UWiGEM modeling folks came together and integrated our modules together. We ended up delegating more work, understanding the problem better and fixing up some of the logic in the big picture. After everyone had filed in and we managed to figure out how to synchronize with the SVN…. and after I managed to break the SVN and fix it again (thankfully!), it was time to get to work. I think the project as it stands right now is more or less done unless Giant Scaffold manages to find something that needs fixing.

The big picture was simplified to three giant objects that passes a big bag of DNA to one another in sequence.

The DNA bag is actually a list of DNAClass Objects (DNAObjects)– We decided not to create our own collection… there’s already a Python list. The Giant Scaffold module controls the movement of the DNA bag or subset there of from storage to Operators to Filters.

I was working with the Operators team– Basically, Matthew finished our team’s work because I managed to get swamped with thesis defense preparations, and Andre managed to take down the UWiGEM server.

(Andre incidentally has a post about taking down servers and making backups here.)

The Operators team ended up producing two big functions (and their little internal functions) and one support function.

reactOneStrand(DNAObject) – Produces a list of resultant DNAObjects when the integrase enzyme is used on a single strand of DNA– this function may produce a one-list, two-list or three-list of DNAObjects. One-lists result from inversion (indirect) reactions, two-lists result from excision (direct) reactions, and three-lists result from palindromic operators.

reactTwoStrands(DNAObject, DNAOther) – Produces a list of resultant DNAObjects when two strands of DNA are reacted together with integrase.

The Filters team split their filters into three big enclosing functions– these three functions are equivalent to categories based on the likelihood that a given event would happen in a cell (Frequent, Moderate, Infrequent).

I unfortunately had to leave roughly 2.5 hours into Big Bang Day on other business but was happy to continue the madness online and on a subsequent Monday.

And now… some more photos…

Look! Everyone’s on their laptops– gee, I didn’t know they had Python on computers now!

Wylee, Brandon and Jordan are all part of the Filters team.

Chong is part of the Giant Scaffold team. Matthew and Andre are part of the Operators team.