Monthly Archives: March 2011

Biotech for Hackers: Computational Genomics 1 of 2

A low hurdle to entry along with the ability to iterate rapidly is key to taking on problems & creating solutions. What do these solutions look like in genomics and why can hackers lead the way? Fig 1 shows something very similar to social interaction maps one comes across at places like Facebook.

Fig 1: Interaction map of genes implicated in Alzheimer's. Genes were grouped by those that have similar functions (squares) and those with different functions (circles). Modules with a red border have high confidence interactions. While the weight of the connecting green lines corresponds to the number of interactions between two sets.

The map above is of individual gene relationships where an algorithm began with 12 seed genes that previous experiments have shown to play a role in Alzheimer’s disease. These seeds were compared with 185 new candidate genes from regions deemed susceptible to carrying Alzheimer’s genes. From here, both experimental and computational data was combined to generate Fig 1, which the authors dubbed AD-PIN (Alzheimer’s Disease Protein Interaction Network).

Fig 2: Interactions discovered by the Hig-Confidence (HC) set generated by this study in context to known relationships in the Human Interactome (created in past studies).

What we learn by simply tracking genes already known to play a role in Alzheimer’s is the discovery of new regions of genetic code that are  also participating in the expression of related functions, in this case those being affected by the disease, such as memory. In Fig 2 we see that between seeds this algorithm produced 7 high confidence interaction results, of which 3 were  in common with previous studies. In addition to almost 200 new interactions, which can each lead to new therapies, blockbuster drugs and better understanding of the disease itself.

Many software developers have extensive experience and interest in dealing with large data sets, finding correlations  and creating meaningful solutions. However, much of our generation has had little exposure to these problems. Often resulting in the bandwagon effect, as one recent article put it “the latest fucking location fucking based fucking mobile fucking app.” Progress has often been linked to literacy, from books to programming, being able to read and write in life-code just might be the next stage.

Original published study: Interactome mapping suggests new mechanistic details underlying Alzheimer’s disease by Soler-Lopez et al.

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Live @ Future of Genomic Medicine IV: Juan Enriquez – Understanding Life Codes

Managing Director at Excel Medical Ventures and CEO of Biotechonomy. Begins with talk of economy, dangers of bank leverage. BofA lends out $47 per $1 in-hand. Banks fail, gov tries to help, banks bring gov down as well. Dancing in the flames, this is our [researchers] arena, technology is a larger wave than any financial crisis, you will either be washed away or surf  it. Moving from digital code to life code. Man vs mouse 5% difference. Humans transmit code through time. Tribe vs empire as result of standardized code. Changes in code matter, the dominant code is changing, from “01” to “ATGC”. Life is imperfectly transmitted code, imperfection is important. Life code will be the big driver of the global economy. Aileron, protein stabilizers, $1.1B deal. You can order bioreactor and organ printers from Harvard Regenerative Medicine magazine. Community matters, if you’re around happy people you are happy, if you are around obesity than you are inclined to be obese, importance of startup culture to medicine. Can’t genomics be dangerous? Prove to me it will never hurt anyone and we’ll allow you to sell it, in this scheme table salt would not pass FDA clinical trial. Must innovate information infrastructure to handle genomic data.

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