bio+tech

The Day-long Think Tank

Mo — Mon, 14 May 2012 02:07:25 +0000

With the summer blockbuster film season starting we are getting to see teams of superheroes come together and save the earth by putting aside their differences and working together for brief periods of time. The traditional view of the life sciences is one where slow, steady progress is made over the lifespan of ones career. This is how innovation and discoveries are made, or so we were taught [1].

Manhattan Project Scientists in 1940 Meeting at UC Berkeley

However, recent advances in other fields, have shown us that this is not the only plausible model. Hackathons, are usually a day or two day long gatherings where software engineers come together to either work on a guided problem or on their own projects. These gatherings have had very notable successes of late, spinning off many companies, new tools, and significant discoveries [2]. It has been said, more frequently of late, that more than just in technology the information, consumer, and social web have advanced the way technical organizations operate. In many ways not only are the products of these companies evolving at a faster rate than the traditional life sciences, the organizational structures and operational protocols themselves leave much to be desired in the traditional sciences.

The Assemblathon is a set of periodic collaborative efforts that all help improve methods of genome assembly.

Hackathons are just the tip of this iceberg, one which we discuss to a relative depth in this piece. As mentioned above, the idea of bringing together the best minds in a particular field for a day or two in order to solve guided or open-ended problems is a stark contrast to the gradual nature of scientific research as usual. Of course the most obvious counter-point to applying this form of problem solving structure to life science research is the nature of working with hardware, and materials, “wet science” as it has been dubbed. Here it is prudent to distinguish and acknowledge data generation and analysis as the bottlenecks in life science research today. Whether we are speaking of genomics, pharmacology, or the large hadron collider, researchers are being drowned by a tsunami of data [3]. Additionally, the previously adequate research dissemination structure of scientific publishing within established journals is being exposed as a highly monopolized and inaccessible medium [4]. This is but a glimpse into the problems we are faced with. However, it is exactly the information related nature of these obstacles that make them ideal for the concentrated and unpredictable undertakings of a hackathon.

SHDH is a party for hackers and thinkers, combining serious and not-so-serious productivity

There are two approaches in guiding solutions via this method, hands on and hands off, just like most other things. The hands on approach involves setting goals, determining metrics of success, and stratifying the participant population. At the polar end is a laissez-faire approach, where a time, place, and beer are provided, the rest is left up to probability [5]. The key in both approaches is gathering the right people for the right problem. Though in the latter approach it is of upmost importance, in addition to assuring those involved have a thorough understanding of the nuances of the particular field of focus. In some ways it can be argued that the Manhattan Project was a progenitor of what may yet be the science hackathon, although much more dramatic in scope and time, but perhaps not in what is at stake.

Closing The Gap Between Computational & Pharmaceutical Innovation

Mo — Tue, 03 Apr 2012 18:39:48 +0000

When confronted with the mortality of life, it becomes painfully clear that medicine has not been able to keep up with information and computational innovations. At the heart of the problem stands the drug development process, where an average of 5 to 10 years of research and billions of dollars worth of investment often fails to produce a product.

Figure 1 | Probability of success to market from key milestones. Data: cohort of 14 companies.

In the past few years, molecules in development have seen a frightening rate of attrition. The most capital and resource intensive period comes during the clinical trials, which can be broken-down into the following stages: Phase I trials evaluate if a new drug is safe, Phase II and Phase III trials assess a drug’s efficacy, monitor side effects, and compare the drug to similar compounds already on market. Recent studies by the Centre for Medicines Research, places Phase II success rates at 18%, lower than at any other time during drug development [1]. Spending on average of $300 million to $1 Billion up until this point of research is par for the course [2].

Figure 2 | Computer-assisted screenings and traditional discovery strategy distributions of new molecular entities (NME). Followers are in the same class as previously approved drugs.

By contrast, computational drug design strategies have made tremendous advances in the new millennia with new tools to identify targets and virtual screening assays. These include structure-based tools to lead identification and optimization utilizing X-ray crystallography. As well as, high-throughput target-based screenings of key protein families like G protein-coupled receptors. Promising indicators of computational drug designs are encouraging new companies to court Big Pharma, who to-date have relied on academia or internal projects for computation. For a company like GeneDrop, even a fraction of the development budget would be adequate to deliver favorable results.

Drug development’s addressable market-size for global corporations such as Novartis or Roche, which have between 20-100 molecules in the pipeline at a given time, is estimated at $1.11 Trillion in 2011; down from $1.24 Trillion in 2001 [2]. There are approximately ten large pharmaceutical companies and many small ones with one or two late-stage molecules in development.

Fig 3 | Early-stage computational drug design flow

To-date, most computation in the space has been limited to early-stage research on the discovery of molecules prior to the clinical trial phases. However, the fall in market cap has sent drug companies scrambling as patents on existing blockbuster drugs near expiration, and those in development see increasingly high failure rates. This begs the question: why are computational resources being spent in the early-stage, when most failures occur in the late-stage, during Phase II

Fig 4 | Pharmacogenomics attempts to correlate how individuals will respond to drugs based genomic variability.

As always, cost has been a primary factor. Late-stage computation has meant analysis of bio-metric data, which has been limited to blood-work and questionnaires of trial subjects. The pie in the sky of course, has always been genomics, the price of which was deemed too high. Even up to a couple of years ago, it would cost over $10,000 to sequence an individual. With Phase II and III trials consisting of hundreds to thousands of patients, the method was rarely used. As of the last few months this is no longer the case, with the cost hovering around $5,000 and quickly approaching $1000 per patient.

So, we are faced with an enticing opportunity for information technology to rescue a high-capital, old-world industry. Threading this needle however is no easy task; entrenched industries with high quarterly revenues are notoriously conservative when adopting innovation, especially from the outside. Adding to this is the high barrier of the technical languages of the hard-sciences and the networking culture of global corporations. Luckily both are boundaries which have been broken before in other industries and we can be optimistic; if anyone can break it, it is the passionate and talented.

A cross-post by Mo from petridishtalk.com

Citations:

[1] Trial watch: Phase II failures: 2008–2010 by J. Arrowsmith – Nature Reviews Drug Discovery 10, 328-329 (May 2011) | doi:10.1038/nrd3439

[2] - Fig 1- A decade of change by J. Arrowsmith – Nature Reviews Drug Discovery 11, 17-18 (January 2012) | doi:10.1038/nrd3630

[3] – Fig 2- How were new medicines discovered? by David C. Swinney & Jason Anthony - Nature Reviews Drug Discovery 10, 507-519 (July 2011) | doi:10.1038/nrd3480

[4] – Fig 4 - Genomics in drug discovery and development by Dimitri Semizarov, Eric Blomme (2008) ISBN 0470096047, 9780470096048

DNA & Iterated Function Systems

Mo — Mon, 27 Feb 2012 19:00:17 +0000

H. Sapiens

Genomic code that makes us is made up of four letters, ATGC. Billions of these letters together creates a lifeform. Iterated function systems (IFS) are anything that can be made by repeating the same simple rules over and over. The easiest example being tree branches, add a simple structure repeatedly ad-infinitum and before you know it we have complex and beautiful systems; the popular example being the Sierpinski Triangle or “triforce” for the Zelda fans. As the cost of DNA sequencing becomes cheaper day by day we are confronted with a tsunami of data and it has become exceedingly difficult to derive meaningful answers from all the information contained within us.

Triforce Power

Finding any advantage in ways to organize and view the data helps us discover minute differences between individuals or say a normal cell versus a cancer cell. This is where Chaos Game Representation (CGR) becomes helpful, CGR is just a form of IFS that is helpful in mapping seemingly random information, that we suspect or know to have some sort of underlying structure.

In our case this would be the human genome. Although when looking at the letters coming from our DNA it seems like billions of random babbles, it is of course organized in a manner to give the blueprint for our bodies. So let’s roll the dice- do we get any sort of meaningful structure when applying CGR to DNA? If you are so inclined, something fun to try is the following:

genome = Import["c:\data\sequence.fasta", "Sequence"];
genome = StringReplace[ToString[genome], {"{" -> "", "}" -> ""}];
chars = StringCases[genome, "G" | "C" | "T" | "A"];
f[x_, "A"] := x/2;
f[x_, "T"] := x/2 + {1/2, 0};
f[x_, "G"] := x/2 + {1/2, 1/2};
f[x_, "C"] := x/2 + {0, 1/2};
pts = FoldList[f, {0.5, 0.5}, chars];
Graphics[{PointSize[Tiny], Point[pts]}]

g1346a094 on Chromosome 7

For example, reading the sequence in order, apply T1 whenever C is encountered, apply T2 whenever A is encountered, apply T3 whenever T is encountered, and apply T4 whenever G is encountered. Really though any transformations to C, A, T, and G can be used and multiple methods can be compared. Self-similarity is immediately noticeable in these maps, which isn’t all that surprising since fractals are abundant in nature and DNA after all, is a natural syntax. Being aware that these patterns exist within our data, opens us up to some new questions to evaluate if IFS, CGR and fractals in general are helpful tools in the interpretation of genomic data.

Signal transducer 5B (STAT5B), on chromosome 17

Since the mapping is 1-1 and we see patterns emerge, we are hinted that there may be biological relevance; especially because different genes yield different patterns. But what exactly are the correlations between the patterns and the biological functions? It would also be very interesting to see mappings of introns/exons colored differently or color amino acids and various codons. One thing is for sure, genomes aren’t just endless columns and rows of letters, they are pictures. It is much easier to compare pictures and discover variations, which can ultimately allow us to find meaningful interpretations from this invaluable data.

A cross-post by Mo from petridishtalk.com

Citations:

Jeffrey, H. J., “Chaos game visualization of sequences,” Computers & Graphics 16 (1992), 25-33.

Ashlock, D. Golden, J.B., III. Iterated function system fractals for the detection and display of DNA reading frame (2000) ISBN: 0-7803-6375-2

VV Nair, K Vijayan, DP Gopinath ANN based Genome Classifier using Frequency Chaos Game Representation (2010)

Mo — Wed, 22 Feb 2012 02:25:56 +0000

Biotech for Hackers

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. 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).

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? 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.

A cross-post by Mo from petridishtalk.com

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

Try It, You’ll Like It

Mo — Tue, 21 Feb 2012 02:31:47 +0000

Normal Conference VS Developer Conference. SHDH Illustrated by Derek Yu

Attending gatherings for software developers in silicon valley, their hackathons leave much to be desired at bio events like Sagecon, the least of which being the beer.

Hopefully this will be a fun tool for folks not well acquainted with genomics/programming to sandbox and explore in.

The National Center for Biotechnology Information (NCBI) provides a command line based standalone Basic Local Alignment Search Tool (BLAST) package known as BLAST+ to analyze and play with genomic sequence data. Although, the legacy web based BLAST can perform a range of functions, BLAST+ as a command line tool is much better to understand and analyze large amounts of nucleotide data. It may be best to get an idea of what sort of data we’re dealing with by getting into the government’s database:

mokas$ ftp ftp.ncbi.nlm.nih.gov
Connected to ftp.wip.ncbi.nlm.nih.gov.
220-
 Warning Notice!

 This is a U.S. Government computer system, which may be accessed and used
 only for authorized Government business by authorized personnel.
 Unauthorized access or use of this computer system may subject violators to
 criminal, civil, and/or administrative action.

 All information on this computer system may be intercepted, recorded, read,
 copied... There is no right of privacy in this system.

Don’t worry about the scary message, this is all public data… well until the funding stops. Take a look in the blast/db directory for many pre-formatted databases NCBI has provided, i.e. genomic & protein reference sequences, patent nucleotide sequence databases from USPTO & EU/Japan Patent Agencies. Get yourself the latest BLAST+ from blast/executables/LATEST , I used ncbi-blast-2.2.25+-universal-macosx.tar.gz .

Installation:

mokas$ tar zxvpf ncbi-blast-2.2.25+-universal-macosx.tar.gz 
mokas$ PATH=/Users/mokas/Desktop/ncbi-blast-2.2.25+/bin
mokas$ export PATH
mokas$ echo $PATH
/Users/mokas/Desktop/ncbi-blast-2.2.25+/bin
mokas$ mkdir ./blast-2.2.25+/db
mokas$ blastn -help
USAGE
  blastn [-h] [-help] [-import_search_strategy filename]
...

Databases should be loaded directly into /db directory created above with the mkdir command. The last thing that needs to be done is to make a “.ncbirc” text file in the main directory containing the following:

[BLAST]
BLASTDB=/Users/mokas/Desktop/ncbi-blast-2.2.25+/db

This will guide the program to where data is being kept. At the end of the day we should hope to get something like this:

mokas$ blastn -query Homo_sapiens.NCBI36.apr.rna.fa -db refseq_rna
BLASTN 2.2.25+
...
Query=  ENST00000361359 ncrna:Mt_rRNA chromosome:NCBI36:MT:650:1603:1
gene:ENSG00000198714
Length=954
                                                                      Score     E
Sequences producing significant alignments:                          (Bits)  Value

ref|XR_109154.1|  PREDICTED: Homo sapiens hypothetical LOC1005054...   464    5e-128

>ref|XR_109154.1| PREDICTED: Homo sapiens hypothetical LOC100505479 (LOC100505479),
partial miscRNA
Length=266

 Score =  464 bits (251),  Expect = 5e-128
 Identities = 255/257 (99%), Gaps = 0/257 (0%)
 Strand=Plus/Minus

Query  334  CACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGACTACGAAAGTGGCTTTAACAT  393
            |||||||||||||||||||||||||||| |||||||| ||||||||||||||||||||||
Sbjct  257  CACCTGAGTTGTAAAAAACTCCAGTTGATACAAAATAAACTACGAAAGTGGCTTTAACAT  198

BLAST+ in action.

Much thanks are in order to Dr. Tao Tao of NCBI

A cross-post by Mo from petridishtalk.com

Citations: Standalone BLAST Setup for Unix – BLAST® Help – NCBI Bookshelf

October 2011 bio+tech

Brad — Wed, 19 Oct 2011 22:19:50 +0000

Tuesday, October 18, 2010
Time: 7:00pm
Event Page: Eventbrite
Location: Giordano Bros. 3108 16th St, SF, CA (note, this is a brand new Giordano Bros – NOT the one on Columbus Ave in North Beach!)
Contact: windmiller[at]gmail[dot]com
Please RSVP!

The Scoop: On September 13th we had a fantastic kick-off for Bio+Tech at Giordano Bros in the Mission in SF! What a great turnout, and from eveyone I interacted with, I’d have to say that I was very impressed with the diversity of ideas, skill sets and people. Once again in October we’ll be teaming with our friends from Hackers and Founders to bring even more great people in to the mix! We hope you can make it – come, grab a beer and meet some fantastic new folks or even future collaborators!

As a note, many of you asked if there could be a medical and/or healhtcare slant to the meet up – of course! It’s all one big ecosystem – from consumer health, to new biopharma, to delivery models, and genomics – there’s tons of room for all.

We’ll be hosting this at the new Giordano Bros in the Mission – they have a shop in North Beach already, but this place is brand new! Nothing like having an entrepreneur event actually hosted in a start-up!

Please RSVP here at Eventbrite so we know how many people to expect! Thanks!