Ed's Big Plans

Modeling Team Selection with Flush();

A modeling meeting occurred on Wednesday. Andre headed off the discussion and revisited the entire program layout in a nice chalkboard cartoon. Unfortunately, Andre generally doesn’t push down hard enough or make wide enough lines with the chalk in order to make a high enough contrast image against the black board for photography (i.e. faint drawing => no photos, sorry).

The discussion saw the formalization and division of the programming problem into three distinct software components as follows.

• Genetic Fragment Operators
• Genetic Fragment Filters
• Overall Program Logic

Genetic Fragment Operators

These are the functions that represent reverse-complementation, enzyme activity etc..

Genetic Fragment Filters

These are functions that represent removing uninteresting, ‘inert’, undesirable and fatal fragments of DNA. This definition will become more precise once we’ve worked on the project a bit and better understand the philosophical correctness of each of these notions.

Overall Program Logic

The overall program logic will constitute producing some structure that represents a Big Bag of DNA (as opposed to a cell), communication between this Big Bag, the Operator module and the Filter module and of course– our main program loop.

What I’m doing…

I’ve been tasked with producing a universal representation of DNA which includes a circular iterator on a loop of DNA with an arbitrary starting position. This is OK to do in Python with the use of the ‘yield’ operator. I will be borrowing from Jordan / Brendan / My own previous ideas for this representation– we want to have an easy single-letter-token system and for the moment are happy with the single byte space ascii has to offer.

I met with Andre today — we spent most of the time discussing idly about programming languages.

Before that, he disclosed that many in the modeling team were building their own independent components of the Recombinatron project that were perfectly isolated from one another.

Most of the effort however was used to model the problem so that each of us has a good working idea of what we need to consider, what could be tough and what could be easy.

I recommeded to everyone that we all share our little bubble projects so we can critique what techniques and approaches we like the best. Apparently there was a meeting after I left between Andre and everyone else wherein a poll was taken with respect to what programming language to use and what versionning system to use.

I’m looking forward to seeing everyone else’s code snippets, the language and cvs chosen– as well as what API we’ll bang out and delegate to each person on the team.

My prototype of the project (incomplete).

I might put notes up in my wiki for this item, time depending.

Met with Danielle Nash, coordinator at iGEM Waterloo today. There are three branches of projects this year, first the completion of last year’s project and the introduction of one new project in two parts.

Last year’s project consists of a delivery system, wherein one introduces bacteria that have been modified so that all genomic DNA is lost. The bacteria thus function as subcellular-sized vehicles that are broken down, so that some arbitrary payload is released into a patient. The focus of the team working on this subproject is its completion; elucidation and final characterization of the system behaviour.

This year’s project consists first of a “foundational advance” submission which formally defines a consistent means to create cassettes for the exchange of genetic material between some vector and a target bacterial chromosome. This involves the definition of a new mutant strain with the homologous recombination sites, suitable for the integrase used; a well defined and consistent cassette, which one would use to enclose the biobricks or other genes of interest; and a short integrase plasmid, likely with just the gene and a promoter of some arbitrary strength. The second is an extension to this project, which defines a different chasis (target organism); this time a plant, likely arabidopsis.

David Monje Johnston immediately comes to mind; he oversaw iGem Guelph last year, in his plant agriculture lab. His advice could likely benefit the team.

What am I doing?

Well, I’ve contacted Andre Masella who currently lives and reigns at Laurier. He’s the head of modeling this year for Waterloo and has summarized the objectives as the creation of some software that will anticipate the best sequence of sites needed to ensure the highest probability of consistent exchange between the cassette and the chasis chromosome.

We’ve all settled on the idea that I would be assisting Andre, with the likelihood of coaching an undergraduate in bioinformatics software design, deployment and utility all the while.

I’m very interested in seeing the background work on this, since I have little idea of what the problem constraints are. What makes a given sequence a good sequence (high in stability, high in predictability), what makes a given sequence bad (high in variance, low likelihood of working)? I’ve written back to ask about related papers he’s worked with, seen or written.

This should be a BLAST.