Fisheye Perspective

What you call this? Linguistic morphology of chemical names and lost in translation

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 05 Nov 2009 14:44:00 +0000

Here is a real world example how Linguistic morphology of chemical names may have unwanted secondary effects

For example, English search engines such as Google and Yahoo! are unable to find "chlorobenzene" by searching for "benzene". Interestingly, in other languages such as Chinese, Japanese, or Korean (CJK languages), this is less of a problem, where for example the Japanese "

(chlorobenzene) can usually be found by querying for"
(benzene).

So next time you visit Japan and wish to buy an Aspirin be sure what you are asking for. Similarly when chemical names fluorescin and fluorescein transliterated to Japanese their distinction is totally lost. Phenylacetate and phenyl acetate are different compounds but again indistinguishable in Japanese because it does not retain a notion of whitespace. In fact when it comes to chemical naming there is an endless list for this type of linguistic confusions.

Indeed Mr Bob may not be only one who is lost in translation

Director [in Japanese, to the interpreter]: The translation is very important, O.K.? The translation.
Interpreter [in Japanese, to the director]: Yes, of course. I understand.
Director [in Japanese, to Bob]: Mr. Bob. You are sitting quietly in your study. And then there is a bottle of Suntory whisky on top of the table. You understand, right? With wholehearted feeling, slowly, look at the camera, tenderly, and as if you are meeting old friends, say the words. As if you are Bogie in Casablanca, saying, "Here's looking at you, kid," -- Suntory time!
Interpreter [In English, to Bob]: He wants you to turn, look in camera. O.K.?
Bob: Is that all he said?

Reference:
Sayle, R. (2009). Foreign Language Translation of Chemical Nomenclature by Computer Journal of Chemical Information and Modeling, 49 (3), 519-530 DOI: 10.1021/ci800243w

Lowering Pharma firewalls: Just for Bioinformatics or Chemoinformatics also

abhishek.twr@gmail.com (Abhishek Tiwari) — Wed, 04 Nov 2009 16:02:00 +0000

Notion of pre-competitive collaboration has been in under experiment steadily for quite sometime now. Notable examples are the Airbus consortium of European aircraft manufacturers, the Sematech consortium of US semiconductor manufacturers, banks working together to launch Visa and Mastercard, our recent moon lust and many more. But this was never a case for pharmaceutical industry until now which is now lowering industry firewalls to shift funding and focus from early- to late-stage projects by developing cooperation in the areas with little potential for differentiation most notably a shared informatics infrastructure through public–private partnerships. Pre-competitive collaboration in this process means that everyone will have same common pool of data and resources. Competition will be still there but for better ideas, for better models and to discover first.

Pre-competitive informatics initiatives
A very interesting opinion piece appeared in September issue of Nature Reviews Drug Discovery discussing the importance of pre-competitive informatics initiatives in drug discovery. Article suggest that many companies are already beginning to embrace this idea, and that for some companies

the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain.

This was a very timely review in the wake of several initiatives such a Innovative Medicines Initiative (IMI), EBI industry programme, Pistoia Alliance and many others. The idea of lowering industry firewalls caught more attention after the announcement of Sage Bionetworks, a non-profit medical research organization established this year with initiatives of Merck duo Eric Schadt and Stephen Friend. A similar kind of effort Open Source Drug Discovery(OSDD) was launched by CSIR, India earlier this year with a initial investment of US $38 million. OSDD consortium is trying to implement open source model for Drug Discovery and public-private partnership is one of the major focus of this initiative. Exciting isn't it? But wait there is a twist in story, there are no definitive answers for what type of data is pre-competitive and what is not? The definition of of pre-competitive is fluidic and it depends on several factors, one of them is whether data belongs to biology or chemistry. Article suggests that any data and tools used by biologists should be under consideration for pre-competitive sharing but those used by chemist should remain the competitive or proprietary (which is very much according to current trends). I could not find any rational reason behind this argument except the fact that there is overwhelming amount of public data in biology domain and day by day companies and institution are finding it hard to manage, integrate and use them for drug discovery. I will go further and suggest that much of these initiatives serves no benefits unless otherwise the data and tools belonging to chemistry domain is also considered as pre-competitive. Ironically much of the data and tools released by pharmaceutical companies under these initiatives are yet to proof their importance. For instance much hyped Life Science Grid released by Eli Lilly (which went open source in year 2008) failed to attract even an average user base. Lilly released only the biology side of the grid which includes a selected group of non proprietary plug-ins, including those for Gene Browser, NCBI Entrez, and Gene Ontology. Forgive me but there are already better tools for the biology in public domain. In my opinion unhindered access to data and tool is prerequisite for the success of the pre-competitive landscape which require more active contributions from the industry participants. Currently the systems is evolving and for now something is better than nothing.
Apart from the issues related to definition of pre-competitive boundaries there are several other bottlenecks, for instance the who will fund the long-term maintainability of such an infrastructure, those remain unresolved .

Reference:
Barnes, M., Harland, L., Foord, S., Hall, M., Dix, I., Thomas, S., Williams-Jones, B., & Brouwer, C. (2009). Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery Nature Reviews Drug Discovery, 8 (9), 701-708 DOI: 10.1038/nrd2944

Gladwell states as guidlines for a better omics data management

abhishek.twr@gmail.com (Abhishek Tiwari) — Sat, 31 Oct 2009 16:45:00 +0000

Universal application of high throughput omics technologies have enabled scientists to measure tens of thousands of data points in a single experiment. As a result of this scientific world has become deluged with data. This has greater implications the way science will be done in coming years. There is a general accord that science has turned more into a data management problem. Put the technical aspect of scientific data management aside, and ask can we depict useful and practically relevant conclusions from our past experiences in scientific data management, and what makes few data management strategies/efforts more popular than others? I am talking about appropriate philosophical foundations for scientific data and knowledge management. Although there is no universal agreement what is best way to manage heterogeneous scientific data which keeps evolving over the time, simplicity and abstraction have always appealed data pundits. Generally most of the scientific data management strategies have not conceived from the social perspective, they are always technology driven. You may call it by any name Big Data, Open Data, Linked Data or you can be in love with XML or RDF, but I am not impressed there will be any ultimate solution to this problem. So is there any social perspective for scientific data management at all? Well I guess so. Masanori Arita has recently published an interesting review article about what can metabolomics learn from genomics and proteomics? What I really liked about this article is the Masanori's analogy of omics data management with Gladwell states. Many of us will be aware about the Malcolm Gladwell highly praised book The Tipping Point: How Little Things Can Make a Big Difference, where based on sociological observation Gladwell describes the three rules of social epidemics: the law of the few, the stickiness factor, the power of context. Tipping points are nothing but the critical point at which the momentum for change becomes unstoppable or viral. From omics data management perspective lets just consider the first and foremost important, the law of the few which states that a small number of influential people called mavens (information specialists), salesmen (persuaders, charismatic), and connectors (truly extraordinary knack for connecting with people) play a crucial role in staging the tipping point. What Masanori really wanted to stage was the viral success for the metabolomics as a fundamental data-driven science like its counterparts, genomics and proteomics. But there is big massage for every other omics, Masanori draw three simple conclusions,

Mavens: large public databases with focus on information quantity
Salesmen: data appeal by simple formats/standards
Connectors: wiki-based community/knowledge portals

First point is large public databases with early focus on information quantity. Data must be readily and freely accessible through large, stable public repositories such as Genebank, SwissProts. Information quantity is no longer an issue in genomics and proteomics community, although they are suffering from several other issues such as high proportion of unannotated data (Out of 4000 bacterial genome projects only a handful are publicly well annotated) , high error rate (20 to 45% for high-throughput protein–protein interaction data) and low information content. On a rather small note stability need long term funding and I guess most of large public repositories are quite comfortable with that.
Second point is data appeal by simple formats/standards. Data format/standards played a important role in success and popularity of certain research areas. Masanori notes that

In biology, the readability of raw data affects popularity. In fact, metabolism, the primary research topic in metabolomics, is notorious for its incomprehensibility and many researchers stayed away from metabolic networks containing lengthy structural and stoichiometric information. The KEGG database gained popularity for its oversimplified representation of metabolic networks: each metabolite is represented as a node without structure, and each reaction as a binary relationship without stoichiometry. Although its oversimplification resulted in considerable misunderstandings , the KEGG database boosted the graph-oriented analysis of metabolic pathways, and consequently, it awakened the interest of the research community in metabolism. Many popular databases containing gene expression or protein–protein interaction data also use simple notations.

Third point is use of wikis as major platform for hosting the biological information. As matter of fact major biology databases are in the process of transferring to wiki-based sites and use of wiki as sites is getting momenta. Further

We, as scientists, should pay more attention to the evolution of web information because wiki embodies the quintessence of all sciences: the acquisition of knowledge through open discussion.

Openness is not only reason in favor of wiki over traditional databases. Situation is quite complicated for curated and annotated non-wiki databases where evolution of data remains intractable. Whether there was any updated in data base, and if yes why it was updated and was it discussed in appropriated forum before update, these issues remains gray area for non-wiki type sites. For instance, take the example of BioModels database which expose the systems biology models as releases, in each release there are few new models but it also includes old models which may or may not be modified after the previous release. In current BioModels implementation there is no clear mechanism to track the changes related to evolution of a given model. From a user perspective tracking of revisions and edits is really important. The other issue is whether or not curation projects have a backup mechanism in place. I am raising this issues because funding sources for biological databases are quite limited, which means sooner or later few or many projects will be out of fund (A recent example of this is the arabidopsis resource, TAIR). Rather than asking to funding agencies for sustainable model for biological databases funding, I would suggest that project manager should be asked to include the additional details in their project proposal such as how they will keep the data stream alive if funding run out at first place. I think there are several options to keep data stream alive for short lived projects, just dump the whole database in sourceforge or any other repository. Best option is make Wikipedia your new home. In all fairness I am not against long term funding of the database projects, but this should be a goal oriented merit based decision and even then very few will be succeed.
Not everything is well with wiki option also, like absence of incentives for participating in crowd sourcing efforts. But there is better chance and hope.

Reference:
Arita, M. (2009). What can metabolomics learn from genomics and proteomics? Current Opinion in Biotechnology DOI: 10.1016/j.copbio.2009.09.011

Optical Cell 2 Duo lac operon: Bridging the gap between Bacteria and Yeast

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 29 Oct 2009 00:47:00 +0000

Students from iGEM Harward team are using the optical communication to create a physically distributed lac operon between a bacteria and an yeast cell which normally occur within the same cell. Idea is to use the principles of synthetic biology to decouple the single cell lac operon events such as de-repression and transcription into two different cells in order to create a spatially separation between these events. In this new system bacteria send optical signal to yeast that the operon has been de-repressed and in response the yeast complete the operon’s function and express beta-galactosidase.

In summary,

In this system, bacteria to communicate to yeast the presence of IPTG, which results in transcription of lacZ in the yeast cells. To permit bacteria to send an optical signal, we expressed in E. coli a red firefly luciferase under IPTG induction. To allow yeast to receive the signal, we used a two-hybrid-system based on the interaction between the red-light-sensitive Arabidopsis thaliana phytochrome PhyB and its interacting factor PIF3. Interaction between PhyB and PIF3 is induced by the red light from the bacteria, resulting in transcription of the lacZ gene.

Although optical communication between higher multicellular organisms is very common what you seeing here is the an optical communication system between a prokaryote and a eukaryote. By spatial seperation of the control and production units coupled with complementary features of bacteria and yeast makes this kind of setup more useful for the industrial applications such as large scale production of enzymes.

Mackenzie Cowell's Fascination with Synthetic Biology

abhishek.twr@gmail.com (Abhishek Tiwari) — Tue, 27 Oct 2009 15:31:00 +0000

Mackenzie Cowell is one of the founders of DIYbio.org, an organization that aims to help make biology a worthwhile pursuit for citizen scientists, amateur biologists, and DIY biological engineers who value openness and safety. Mackenzie was recently interviewed by MAKE magazine for an ongoing series of video interviews with notable working scientists and technologists. These interviews were recorded at SciFoo, an un-conference on Science and Technology organized by O'Reilly Media along with Nature Publishing Group and Google. Mac is fascinated about emerging discipline of synthetic biology,

In the future there is a really neat opportunity for open source science to be driven by amateurs.

A Conversation with Tim Berners-Lee

abhishek.twr@gmail.com (Abhishek Tiwari) — Mon, 26 Oct 2009 13:03:00 +0000

Here Comes Science

abhishek.twr@gmail.com (Abhishek Tiwari) — Sat, 24 Oct 2009 11:47:00 +0000

They Might Be Giants' album, Here Comes Science, is collection of thought provoking audio/video content for kids. A musical melody which covers a broad range of topics in science starting from why science is real and why it is so important? Here are two videos from the collection:

Science is real, the kick-off song which which explains how scientific beliefs formed with the process of rigorous testing.

Meet the Elements, an animated upbeat ode of elements and how they form our world.

Story of "Synthia" the (theoretical) human-made synthetic microbe

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 23 Oct 2009 12:21:00 +0000

"The Story of Synthia" explains -- in a cartoon -- how some scientists are attempting to create synthetic "life." By replacing the genome of a natural microbe with a human-made genome constructed from synthetic DNA, they hope to give birth to a new, synthetic species -- mycloplasma laboratorium.

Google's future scale computing

abhishek.twr@gmail.com (Abhishek Tiwari) — Wed, 21 Oct 2009 13:17:00 +0000

Forget about the Internet scale, seriously that is not enough for Google. Google engineers are now talking about future scale which means company is preparing to manage as many as 10 million servers in the future. Google fellow Jeff Dean describes Google's Future scale as,

~10⁶ to 10⁷ machines, ~10¹³ directories,~10¹⁸ bytes of storage, spread at 100s to 1000s of locations around the world, ~10⁹ client machines

Woo! Jeff don't stop here, he further presents Google's MapReduce usage statistics over time, check out the incredible numbers of input data read (500+ petabyte) .

SynBioWave: Google Wave extension for synthetic biology

abhishek.twr@gmail.com (Abhishek Tiwari) — Wed, 21 Oct 2009 11:55:00 +0000

As I mentioned in my previous posts that for next few days we are going to cover various interesting activities related to iGEM 2009 and selected student projects will be featured on the Fisheye Perspective blog. Next in our list is project SynBioWave, the Google Wave extension for synthetic biology developed by a team of students from Albert Ludwigs University Freiburg. I must accept that team has done excellent job with wave and it is one of the best wave extensions for scientists out there (see a list of wave applications for the scientist at the end of the article). Concept is to develop a Google wave based collaborative environment for synthetic biology which will enable multiple distributed users to analyze and construct genetic parts in real time. Apart from Google Wave environment SynBioWave is using two other technologies: BioJava and Qooxdoo. In following image you can see the whole architecture of the SynBioWave.

SynBioWave Conceptual Architecture Flowchart

In order to create a basic wave functionality for the different molecular biology tasks such as cloning, team is using BioJava library. BioJava enables the processing of biological data such as sequences and 3D structures, while Qooxdoo is used to to extend Wave's graphical user interfaces (GUI) with custom tool bars, buttons, forms, context-menus for SynBioWave application. Team has also introduced a qooxWave protocol for creating custom client side GUI inside a wave from a server side robot.

SynBioWave's qooxWave protocol

Sequence manipulation from Google Wave
So what type things you can do with SynBioWave? Development of SynBioWave is currently in early stage, but still you can to several things such as visualizing your sequence data, compare your sequence data or multiple sequence alignment. SynBioWave supports multiple views for visualize the sequence information in a intuitive way, not to mention the automated sequence coloring feature.

Three Sequence Views: Simple, Gadget and Circular

There is a simple view for short sequences typed or copied directly to the wave and a embedded gadget view for longer sequences and sequence comparisons like for example in multiple sequence alignments. Both views providing a clearly represented scaling and increase readability by automatically colorizing the sequences according to the sequence type. And in the end there is a circular view for displaying fully featured circular dna as needed for example in displaying vectors and plasmids.

BLAST from Google Wave
Further there is a BLAST robot which can BLAST the biological sequence information against available resources and further process the received Blast-hits.
and many more
There is another BioBrick robot which integrates different assembly-algorithms and enables the DAS communication with BioBrick database for genetic part import and upload in compliance with BioBrick standards. Further team is developing an Eclipse-plugin for SynBioWave-developers and several other robots.

So where will this lead us? Google Wave development for life scientists is now moving into next level. SynBioWave and other extensions (listed in the end) are making their way for the next generation of scientific engagement and collaboration.

List of wave applications for the scientists

SynBioWave Robot for Synthetic Biology (synbiowave@appspot.com)
BLAST Robot (blastrobot@appspot.com)
ChemSpidey- A Google Wave Robot for displying chemical structures and molecular weight calculation (chemspidey@appspot.com)
CDKitty -A Google Wave robot for CDK functionality (chemdevelkit@appspot.com)
Igor - A Google Wave robot to manage your references (helpmeigor@appspot.com )
Watexy- A Google wave robot for Latex writing (watexy@appspot.com)
A proteomics robot for Google Wave (systems-biology-data@appspot.com)
CodeBot – A Coding Robot for Google Wave (codebot-wave@appspot.com)

Public perception and Synthetic Reality

abhishek.twr@gmail.com (Abhishek Tiwari) — Sat, 17 Oct 2009 17:05:00 +0000

If synthetic biology fulfills its promise it has potential to replace the world created by Darwinian evolution with one created by us. At least that's what Michael Specter's recent story published in September 28th issue of The New Yorker suggests. In last few months the synthetic biology has drawn increased media attention, and as a matter of fact the The New Yorker story is one of the most comprehensive collections about this very nascent field. Despite several major breakthroughs in last few years the public awareness about the synthetic biology is very poor. A recent groundbreaking survey of 1,001 U.S. adults conducted by Peter D. Hart Research Associates and the Project on Emerging Nanotechnologies has found that only 22 percent of Americans have heard of synthetic biology.

Image Credits PEN

A further digging of the survey report hints that regardless of their awareness of synthetic biology, a strong majority of adults think that the risks will outweigh the benefits especially individuals who regularly attend religious services. I can also understand the feelings of those who believe in god because if synthetic biology truly succeeds everyone of us will be their own Darwins. Critics of synthetic biology generally raise the concern that scientist might be playing with God when they create new life forms, that's where public awareness really matters. If the public does not realize mankind can use synthetic biology to make new drugs or renewable energy, it will look like we are playing with god. No doubt there are serious concerns especially possibilities for deliberate abuse like bioterrorism risk which many suggest as extremely unlikely scenario. However to accomplish a dream of synthetic reality, we will also need an education system such as International Genetically Engineered Machine (iGEM) competition, an undergraduate synthetic biology contest run by the MIT, that will encourage skepticism and the study of science. What really worries me is lack of participation from New Zealand, like previous years this year again there is no team from New Zealand. Although last year two members of Auckland Bioengineering Institute, Dr Michael Cooling and James Lawson, were the mentors of gold medal winning team of the Newcastle University. CellML technology developed at the Auckland Bioengineering Institute played a major role in their endeavor which also suggests we have sufficient core expertise to participate and win iGEM contests. Yesterday I was surprised to learn that 12 year olds Indu Voruganti, Will Allen and 11 year olds Ahmad Rana, Ashley Kim had developed an innovative synthetic biological approach to cure the nasal allergy. I am sure we have equally talented undergrads, all they need is more awareness with a little guidance. This year 2 teams from Australia are participating in iGEM and we should hope to send at least one NZ team for next year's iGEM.

PS: Drop me an email or a comment if you might be interested to form a NZ team. I have few brainstorming ideas to start with!!!!

We essentially want to create a drug factory in your nose

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 16 Oct 2009 15:19:00 +0000

The International Genetically Engineered Machine (iGEM) 2009 Jamboree dates are coming closer and for next few days we are going to look around the wikis to learn more about what different teams are doing this year. iGEM 2009 is bigger than ever, in fact more than 110 teams competing this year's Jamboree falls on Halloween (October 31) weekend. The 2009 Brown iGEM team is taking an innovative synthetic biological approach to create self-regulating drug factory in the nose to treat the allergic rhinitis which is the most common type of nasal allergy with symptoms such as nasal congestion, itching, burning, sneezing, etc . Team has engineered a new effective strain of Staphylococcus epidermidis that can cure allergy without any side effect.

iGEM team worked to treat allergic rhinitis by engineering Staphylococcus epidermidis, a microbe endogenous to the human nasal flora, to secrete a recombinant histamine-binding protein in response to the elevated histamine concentrations of an allergic response. The engineered strain of S. epidermidis will function as a self-regulating drug factory in the nose, providing relief, without any negative side effects.

Stunning images from Wellcome Image Awards

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 16 Oct 2009 14:30:00 +0000

Yesterday Wellcome Trust announced the winners of the tenth Wellcome Image Awards, featuring 19 stunning images. These images are simply incredible, many of them are technically very difficult to achieve and require specific coloring skills. I have just selected a few remarkable images from the slide show. Click here for the full slide show of images along with different background information.

Microparticle drug delivery

This image shows the synthetic polymers used to coat a drug, either to target the release of the drug in a specific part of the digestive tract or to allow the drug to be released slowly. Polymers play an important role in reducing side-effects of drugs, as well as the number of times a patient needs to take a medication. Scanning electron micrograph images are taken in black and white and are coloured later. The orange spheres contain the drug and the encapsulating co-polymers are coloured blue.

Villi in the small intestine

3D reconstruction of villi in the small intestine of the mouse. Villi are small finger like projection that cover the internal surface of the small intestine to increase the surface area and assist in digestion. This images was created from a wholemount that had a fluorescent stain applied to mark different cellular components. The tips of the villi have been cropped away to show the internal morphology. The red shows the F-actin (stained with rhodamine phalloidin), which cover the surface of each villus. Cell nuclei are labelled blue (stained with DAPI).

Lung cancer cell

A single cell grown from a culture of lung epithelial cancer cells. The purple spheres are 'blebs': irregular bulges where the cell's internal scaffolding - its cytoskeleton - becomes unlinked from the surface membrane. Scanning electron micrograph.

Sensory nerve ending of hair follicle

This image highlights the sensory nerve endings at the end of a hair follicle. Sensory nerves are nerves that receive sensory stimuli, which sense movement, pressure and pain. This colourisation in this image was effected by treating the tissue with silver nitrate. The nerve axons are stained black.

Compact bone

These circular structures are regions of compact bone from a human femur. Compact bone forms a hard outer shell around the spongy bone that makes up the marrow space in the centre.

This image is naturally striking. No colour has been added to the specimen, yet the vascular canals almost appear as though they are bleeding

In vitro fertilisation

This image shows sperm and an egg (or ovum) at the moment of conception by in vitro fertilisation (IVF). The egg is surrounded by protective cumulus cells around the outside surface, coloured yellow. The sperm need to penetrate the membrane surrounding the egg, called the zona pellucida, if successful fertilisation is to occur. Light micrograph.

Guaranteeing broadband access

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 15 Oct 2009 10:58:00 +0000

Although this news is already hot in air I could not resist to post it again. According to reports Finland is the first country to make broadband access a legal right.

Starting next July, every person in Finland will have the right to a one-megabit broadband connection, says the Ministry of Transport and Communications. Finland is the world's first country to create laws guaranteeing broadband access.
The government had already decided to make a 100 Mb broadband connection a legal right by the end of 2015. On Wednesday, the Ministry announced the new goal as an intermediary step.

Can we expect something like this from John Key administration? Seriously speaking current broadband infrastructure in New Zealand really sucks, may be pigeon can be faster than broadband.

Big data, Big challenges

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 15 Oct 2009 04:00:00 +0000

Image by Photo Blog 0001 via Flickr

Recently New York Times has published a tech article about big data. According the article some of the largest technology companies like I.B.M. and Google think that students from elite American universities students are not trained well to work on Internet scale problems of tomorrow. If you remember last month a similar story was published in Wall Street Journal highlighting how data deluge has swamped science historians. For example Sloan Digital Sky Survey project has generated 140 terabytes of digital data cataloging 230 million celestial objects, encompassing 930,000 galaxies, 120,000 quasars and 225,000 stars. Another Large Synoptic Survey Telescope will be producing 30 terabytes of data each night (15+ petabytes, in fact every year). Similarly third generation of DNA sequencers will generate many petabytes of information a year. Sooner or later researcher working in data intensive scientific areas such as genomics and astronomy will find themselves overwhelmed with petabytes scale data outputs which will outstrips their ability to maintain them. Jimmy Lin an associate professor at the University of Maryland quoted in the article as "Science these days has basically turned into a data-management problem". Although we are talking about petabytes of data, ironically so called big data (Internet scale or mega-scale) is not a storage problem. Problems associated with big data are primarily those of analysis. The problem is to actually capture predictable characteristics of this data or how to interact with this kind of data on regular basis. It is hard to imagine what a petabytes scale data will look like which Facebook or Google are dealing on daily basis.

The big question is whether the person on the other side of that machine will have the wherewithal to do something interesting with an almost limitless supply of genetic information.

Well those are not only problems we have, most of our freshly minted university graduates are not prepared to face this kind of data deluge. Why?

For the most part, university students have used rather modest computing systems to support their studies. They are learning to collect and manipulate information on personal computers or what are known as clusters, where computer servers are cabled together to form a larger computer. But even these machines fail to churn through enough data to really challenge and train a young mind meant to ponder the mega-scale problems of tomorrow.

I guess this is something for which we can not blame students alone. The lack of resources and exposer to new technologies is the one of the reasons. To tackle this issue Google and IBM are now promoting Internet-scale research at places like the University of Washington and Purdue by giving students wide access to their powerful computational infrastructure. Idea is to encourage the students to churn the data with the help of open-source tools like Hadoop used for processing Internet-scale data sets. Hadoop which is open source implementation of MapReduce, a software framework introduced by Google to support distributed computing on mega-scale data sets on clusters of computers. By the start of 2008 Google was processing over 20 petabytes of data per day through an average of 100,000 MapReduce jobs spread across its massive computing clusters which gives a glimpse of Google's Internet-scale capabilities. In a similar kind of initiative to promote the cloud based distributed computing learning, Amazon Web Services (AWS) is providing their on-demand infrastructure to the educational purposes for free.
So far we have talked about the next generation of data which is coming out of high throughput technologies in different scientific disciplines, and we all agree that this will have greater impact on the infrastructure of research, research funding and beyond (if and only if this is managed properly). On a further note, this data will need to be annotated with metadata, then archived and curated. Each of these seems to be mammoth task which means focus should not be only on onetime analysis but also on future reusability and interoperability.
In following video Roger Magoulas (Director of Research at O'Reilly) talks about the Big Data in general and gives a glimpse into future technologies and general advice to organizations interested in improving their proficiency in handling web-scale data.

Hell – is sitting on a hot stone reading your own scientific publications

abhishek.twr@gmail.com (Abhishek Tiwari) — Wed, 14 Oct 2009 13:00:00 +0000

A top-10 list of recommendations for how to write consistently boring scientific publications by Kaj Sand-Jensen
• Avoid focus
• Avoid originality and personality
• Write l o n g contributions
• Remove implications and speculations
• Leave out illustrations
• Omit necessary steps of reasoning
• Use many abbreviations and terms
• Suppress humor and flowery language
• Degrade biology to statistics
• Quote numerous papers for trivial statements

Interview with Nobel Laureate Venki Ramakrishnan

abhishek.twr@gmail.com (Abhishek Tiwari) — Tue, 13 Oct 2009 13:01:00 +0000

Venki Ramakrishnan was awarded the 2009 Nobel Prize in Chemistry for 'studies of the structure and function of the ribosome', the cell's protein-making factory. In this interview, he talks about his surprise at winning the prize, and what it meant to see the atomic-level structure of the ribosome for the first time.

Monkeys exhibit the uncanny valley effect

abhishek.twr@gmail.com (Abhishek Tiwari) — Tue, 13 Oct 2009 12:28:00 +0000

Image via Wikipedia

A recent peer reviewed research published in journal PNAS suggests that monkey visual behavior falls into the uncanny valley. The idea of uncanny valley was introduced by Japanese roboticist Masahiro Mori in 1970. In simple words, as virtual humans approach photorealism or human likeness, they risk making real humans uncomfortable or may evoke negative feelings in human observers. Although uncanny valley is a valid and widely accepted psychological phenomenon in human supported by several controlled perceptual experiments, it was never tested for a nonhuman species. A phenomenon which was also subscribed as reason for poor box office performance of highly realistic computer-animated films such as the Polar Express. Study suggest that humans are not the only ones to exploit this fascinating psychological peculiarity, monkey too have photorealism issues and there may be evolutionary origins behind the uncanny valley. Basically computer/synthetic generated/animated facial rendering can be categorized in three classes: unrealistic, realistic and real, and this is the interaction of realistic features which fall in the valley. In this case monkeys looked longer at real faces and unrealistic synthetic faces than at realistic synthetic faces. Although there is no clear explanation for the uncanny valley effect, but according to current understanding it is not the increased realism that evokes the uncanny valley effect, but rather that the increased realism lowers the tolerance for abnormalities. Last year Image Metrics who also developed the game Grand Theft Auto created a animation character Emily which is supposed to be one of the finest animations to have crossed a long-standing roadblock of uncanny valley (see the video below Emily's introduction about the technology).
Render-types and hypothetical outcomes. (A) The facial expressions and render-types used in the experiment. These images were extracted from the dynamic videos and represent the point of maximal expression. These frames were also used as the static stimuli. (B) Five hypothetical outcomes for the monkeys’ visual behavioral response.

Reference:
Steckenfinger, S., & Ghazanfar, A. (2009). Monkey visual behavior falls into the uncanny valley Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0910063106

What synthetic biology can learn from programming languages

abhishek.twr@gmail.com (Abhishek Tiwari) — Sun, 11 Oct 2009 04:28:00 +0000

What is synthetic biology? In simple words Synthetic biology is nothing but putting engineering into biology. An engineered genetic toggle switch developed by Tim Gardner and Jim Collins is a good example of how engineering principles are driving the boat of synthetic biology. Researchers are now trying to adapt concepts developed in area of programming language development & software engineering for synthetic biology applications. A latest paper in PLoS Computational Biology shows how methods used by computer scientists to develop programming languages can be applied to DNA sequences. They report an attribute grammar based formalism to model the structure-function relationships in synthetic DNA sequences. An attribute grammar is constructed as an extension of a context-free grammar and in computer science it is commonly used to translate the text of a program source code or the syntax tree directly into the computational operations or machine level instructions. Further

The translation of a gene network model from a genetic sequence is very similar to the compilation of the source code of a computer program into an object code that can be executed by a microprocessor (Figure 1). The first step consists in breaking down the DNA sequence into a series of genetic parts by a program called the lexer or scanner. Since the sequence of a part may be contained in the sequence of another part, the lexer is capable of backtracking to generate all the possible interpretations of the input DNA sequences as a series of parts. All possible combinations of parts generated by the lexer are sent to a second program called the parser to analyze if they are structurally consistent with the language syntax. The structure of a valid series of parts is represented by a parse tree (Figure 2). The semantic evaluation takes advantage of the parse tree to translate the DNA sequence into a different representation such as a chemical reaction network. The translation process requires attributes and semantic actions. Attributes are properties of individual genetic parts or combinations of parts. Semantic actions are associated with the grammar production rules. They specify how attributes are computed. Specifically, the translation process relies on the semantic actions associated with parse tree nodes to synthesize the attributes of the construct from the attributes of its child nodes, or to inherit the attributes from its parental node.

Figure. 1

Figure 2.

Proposed formalism can be quite useful to understand how a set of genetic components relates to a function with potential to assemble a new biological systems of desired functionality or phenotype using BioBricks standard biological parts. It will be implemented into GenoCAD, a web-based tool used for genetic engineering of cells.

Reference:
Cai, Y., Lux, M., Adam, L., & Peccoud, J. (2009). Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars PLoS Computational Biology, 5 (10) DOI: 10.1371/journal.pcbi.1000529

BioEngineered personalized human bone grafts created from bone marrow stem cells

abhishek.twr@gmail.com (Abhishek Tiwari) — Sun, 11 Oct 2009 02:24:00 +0000

Scale up of engineered bone grafts towards clinical applications is always a central challenge in regenerative medicine. In latest article PNAS Grayson et. al report an approach to develop clinically sized viable bone grafts of human temporomandibular joint (TMJ) condylar or Jaw bone seeded with human bone marrow stem/progenitor cells (hMSCs). This study was carried out by a research group led by Dr Gordana Vunjak-Novakovic at the Columbia University and it appears in the online issue of journal PNAS. This is first time that anatomically shaped bone graft has been reported and this work has major implications. As these grafts are engineered from the patient’s own stem cells hence there is a low risk of graft rejection. This work opens door for many possible clinical applications such bone reconstructions after congenital defects, cancer resections, bone grafts for spinal fusion (also called arthrodesis) and many more. In following figure B is the a real jaw while C is the bioengineered one.

Image Credits PNAS

The whole approach is described below

Anatomically shaped scaffolds were generated from fully decellularized trabecular bone by using digitized clinical images, seeded with hMSCs, and cultured with interstitial flow of culture medium. A bioreactor with a chamber in the exact shape of a human TMJ was designed for controllable perfusion throughout the engineered construct. By 5 weeks of cultivation, tissue growth was evidenced by the formation of confluent layers of lamellar bone (by scanning electron microscopy), markedly increased volume of mineralized matrix (by quantitative microcomputer tomography), and the formation of osteoids (histologically). Within bone grafts of this size and complexity cells were fully viable at a physiologic density, likely an important factor of graft function. Moreover, the density and architecture of bone matrix correlated with the intensity and pattern of the interstitial flow, as determined in experimental and modeling studies.

However the engineered graft does not replicate the whole system, for instance it does not include other tissues such as cartilage layer which is often damaged in TMJ disorders. Future challenges include engineering of vascularized bone that can be immediately connected to the blood supply of the host and hybrid grafts including bone and tissues.

Reference:
Grayson, W., Frohlich, M., Yeager, K., Bhumiratana, S., Chan, M., Cannizzaro, C., Wan, L., Liu, X., Guo, X., & Vunjak-Novakovic, G. (2009). Regenerative Medicine Special Feature: Engineering anatomically shaped human bone grafts Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0905439106

Top NZ entry for Nikon Small World Photomicrography Competition 2009

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 09 Oct 2009 15:17:00 +0000

Small World is regarded as the leading forum for showcasing the beauty and complexity of life as seen through the light microscope. Confocal fluorescence image taken by Dr. Lloyd Donaldson from Scion, Next Generation Biomaterials Te Papa Tipu Innovation Park Rotorua stands on 8th. Nice work Dr. Lloyd.

Cotton fibers stained with berberine sulphate and color depth shaded (200x)

This year Arabidopsis thaliana image taken by Dr. Heiti Paves (Tallinn University of Technology Tallinn, Estonia) got first place (image below).

Get your sample scanned for free under electron microscope

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 09 Oct 2009 13:13:00 +0000

A small endorsement which I think will be useful for the readers of this blog. ASPEX, a leading producer of Scanning Electron Microscopes (SEM) and Microanalysis software is offering free SEM scan of any sample. Anyone can send in their sample for scan and it could be anything such as your hair, any insect or whatever you like (check out this SEM Image Gallery). Just download the sample submission form by clicking here and follow the instructions as described below.

PS: and if you don't have any sample ask to your children :-)

A Polymer analog of Hilbert's curve at the megabase scale

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 09 Oct 2009 12:00:00 +0000

Hilbert curve or Hilbert space-filling curve is a continuous fractal space-filling curve that densely fills higher-dimensional space without crossing itself. It was first described by the German mathematician David Hilbert in 1891. In a recent article Aiden et al. describe a new method called as Hi-C for reconstructing the three-dimensional architecture of the human genome which not only reveals folding principles of the human genome but also resembles a polymer analog of Hilbert's curve at the megabase scale. Their finding suggest that Chromosomes are organized in a fractal knot-free conformation or fractal globule that is densely packed while easily folded and unfolded contrary to what was previously hypothesized as an equilibrium globule. This study has shown that each Chromosome is organized into two separate compartments, keeping active genes accessible while sequestering unused DNA in a denser storage compartment. Each chromosome alternates between regions of active, gene-rich DNA and inactive, gene-poor stretches.

Abstract

We describe Hi-C, a method that probes the three-dimensionalarchitecture of whole genomes by coupling proximity-based ligationwith massively parallel sequencing. We constructed spatial proximitymaps of the human genome with Hi-C at a resolution of 1 megabase.These maps confirm the presence of chromosome territories andthe spatial proximity of small, gene-rich chromosomes. We identifiedan additional level of genome organization that is characterizedby the spatial segregation of open and closed chromatin to formtwo genome-wide compartments. At the megabase scale, the chromatinconformation is consistent with a fractal globule, a knot-free,polymer conformation that enables maximally dense packing whilepreserving the ability to easily fold and unfold any genomiclocus. The fractal globule is distinct from the more commonlyused globular equilibrium model. Our results demonstrate thepower of Hi-C to map the dynamic conformations of whole genomes.

Reference:
Lieberman-Aiden, E., van Berkum, N., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B., Sabo, P., Dorschner, M., Sandstrom, R., Bernstein, B., Bender, M., Groudine, M., Gnirke, A., Stamatoyannopoulos, J., Mirny, L., Lander, E., & Dekker, J. (2009). Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome Science, 326 (5950), 289-293 DOI: 10.1126/science.1181369

Convergence and confluence of data sharing efforts

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 09 Oct 2009 07:35:00 +0000

Most of data sharing policies share some common principles such as protecting the cumulative data outputs, recognizing data as a public good and data sharing as strong value chains of innovation for subsequent scientific exploitation. A improved data access and sharing not only helps to maximize the research potential but it also reinforces open scientific research which encourages diversity of analysis and opinion. In a latest article published in October 9 issue of journal Science, Dawn Field, John Wilbanks and others highlight several issues related to convergence and confluence of data sharing efforts-
1. Data managing versus data sharing
Funding agencies are more focused on data managing efforts such creating institutional infrastructure and repositories rather than data sharing. Authors suggest that although such centralizationefforts provides economy of scale, institutional memory, and reusablecapability, in long term it also finds a substantial direct cost thatmay compete with research funding.
2. Heterogeneity and seamless interoperability
Despite the fact that most funding agencies are committed to data sharing efforts they can hardly avoid the heterogeneity and overlap of these kind of efforts due to different types of communities servedby each funder and the data types they generate. Seamlessinteroperability should be prime focus in this kind of matters.

Image credits Biosharing

3. Challenges due to proliferation of standardization efforts
There is an exponential increase in number of community-driven efforts to create minimal information guidelines,ontologies and file-format projects. On one side it seems a positive sign of communityengagement, but on the other side this proliferation brings new challenges because most of these projects are focused on a particular biological domain and due to their domain specific virtue most of these projects are fragmentedand not suitable for the interoperability. Also on several occasions one can observe unnecessary overlaps and duplication of these efforts.
4. Intellectual and ethical issues
Not all but few researchers' are concerned about intellectual ownership and the commercial aspects of their research work. Ethical issues such as whether or not patient specific data should be open especially to avoid the potential misuses (For instance a recent UK government scheme to establish nationality through DNA testing using recent development of large SNP databases has sparked a intense debate)
5. Lack of funding
Last but not least lack of funding may be detrimental for these substantial and long term undertakings.

Recommendations
Further authors recommend that there should be a single detailed multi-level data sharing guidelinewhich can serve as a template for policy documents at the various levels such as funder, community,and project level. For enforcement of policy they suggest that funding agency should bring a clear mandate for the inclusion of data-sharing plans in grant applications including the compulsory deposition of data generated due to funding of research in appropriatedatabases within a specified time line adhering with best standards.
They also emphasize the requirement of a centralize hub which can build essential linkages between various topdown(guided by public and private funding agencies) and buttom-up (grassroots developments such as Science Commons, MIBBI, OBO, BioPortal, FUGE etc) data sharing initiatives to serve a "one-stop shop" for those who are in pursuit of data sharing policies and information.

Reference:
Field, D., Sansone, S., Collis, A., Booth, T., Dukes, P., Gregurick, S., Kennedy, K., Kolar, P., Kolker, E., Maxon, M., Millard, S., Mugabushaka, A., Perrin, N., Remacle, J., Remington, K., Rocca-Serra, P., Taylor, C., Thorley, M., Tiwari, B., & Wilbanks, J. (2009). 'Omics Data Sharing Science, 326 (5950), 234-236 DOI: 10.1126/science.1180598

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 08 Oct 2009 08:57:00 +0000

Christopher Collins has developed this new visualization technique which can reveal interesting set relations in data using isocontours over existing visualizations. Following is the abstract of the Bubble Sets paper accepted in InfoVis '09

While many data sets contain multiple relationships, depicting more than one data relationship within a single visualization is challenging. We introduce Bubble Sets as a visualization technique for data that has both a primary data relation with a semantically significant spatial organization and a significant set membership relation in which members of the same set are not necessarily adjacent in the primary layout. In order to maintain the spatial rights of the primary data relation, we avoid layout adjustment techniques that improve set cluster continuity and density. Instead, we use a continuous, possibly concave, isocontour to delineate set membership, without disrupting the primary layout. Optimizations minimize cluster overlap and provide for calculation of the isocontours at interactive speeds. Case studies show how this technique can be used to indicate multiple sets on a variety of common visualizations.