Fisheye Perspective

Print may be dead, but paper is alive

abhishek.twr@gmail.com (Abhishek Tiwari) — Wed, 17 Mar 2010 05:14:00 +0000

Future of publishing is going to change, but not the way many people think. Two interesting videos encouraging people to fall in love with book reading again and suggest that publishing may be nearly dead but books are alive and kicking.

The first one created by UK branch of Dorling Kindersley Books, watch out the video at least the halfway point. (HT: Scholarly Kitchen)

Another lovely video from the New Zealand Book Council, an animated sequence based on Maurice Gee’s 1993 novel Going West, brings the experience of reading alive.

Non-PhD scientists are only hope if you want to battle floods of scientific data

abhishek.twr@gmail.com (Abhishek Tiwari) — Tue, 09 Mar 2010 10:04:00 +0000

This post may not surprise you if you already read the latest Nature editorial Do scientists really need a PhD? Before we talk about real issues, my first advice for aspiring PhD student - don't read too much into anti-PhD arguments presented here. In fact PhD can be uplifting, accomplishing, stimulating and a life long experience. For me this is really a complex issue, I love doing research as much as I hate the PhD.

I think it's safe to say that non-PhD scientists have always played an important role in scientific eco-system but they hardly get limelight and credit they deserve. What we are seeing here is a new role for non-PhD scientists, data saviors. Currently scientific community is battling with floods of scientific data especially the life sciences domain is generating unprecedented amount of data every day (may be more than 10 terabytes of sequencing data everyday). Community is facing a huge backlog in form of unprocessed data waiting for first hand analysis. I am not in a total aggrement with the interpretation that scientist without PhD are the way of the future, actuality I think that non-PhD researchers will play a key role as data scientists, a role which will be more instrumental in enabling the science than doing the science itself. Its just not about first hand analysis of data, field is being held back by several other key issues such curation and annotation. As data scientists non-PhD research can add tremendous value to the scientific output. What do you think?

Availability decay of Bioinformatics web resources : Yes widgets can change it

abhishek.twr@gmail.com (Abhishek Tiwari) — Sun, 07 Mar 2010 01:22:00 +0000

Quality and availability of bioinformatics resources is always a matter of great debate. HTTP 404 not found is quite frequent phenomenon for bioinformatics researchers looking to use some published web accessible database or analysis tool. A 4-year follow-up survey on the lack of persistence of bioinformatics resources was published in year 2008 by Jonathan D. Wren suggests that approximately 20% of URLs published in MEDLINE abstracts are now inaccessible, and the most common types of inaccessible content are computer programs (43%) and databases (19%). A similar kind of study was taken by Veretnik et. al where they analysed the availability of web resources published in the Nucleic Acids Research (NAR) Web server issues between 2004 to 2007. They found that a significant number web server goes offline after two years from their data of publication. I suspect the numbers are more higher than what we are seeing here and roughly 50% of published bioinformatics web resources have lifespan of only 2 years or less.

NAR Web server availability

Many people see this availability decay as huge problem in bioinformatics community but I suggest that sudden death of majority of bioinformatics resources simply implies their unpopularity among the potential users and it is part of solution to filter the reliable and quality resources. Jonathan D. Wren used term data tombs for the this kind of web resources, either they are rarely accessed due to there narrow scope

Data tombs, in large part, seem to have resulted from a ‘build it and they will come’ philosophy, which is OK as a means of justifying database creation, but not publication.

or there are major design flaws and lack of user perspective in which case users will fall on alternative option that is less complicated but easier to use. In simple words,

Developers often equate the power of their software with the number of options, but users usually equate the number of options with the number of barriers between them and their results.

One of the major problem with dead web resources is their ghost, aka citations for the published paper which solely describes the resource itself. Nonetheless, people keep citing the dead bioinformatics resources without any check and for all wrong reasons. We definitely need a mechanism to curb these ghost citations.

Apart from the quality and narrow scope there are several other reasons which may affect the accessibility of the web resources. Frequent change of URLs, funding issues, lack of time and resources to maintain the published applications are some of them. Time to time people have suggested various solutions, and two most compelling options are

1. Archiving of web resources in public repositories. This is can be done by a third party very much like Sourceforge, Google Code, Github or Bioconductor. For me the best example here is the BioModels database- a database of published systems biology models - for each release they keep a public dump in the Sourceforge repository. This is something resources developers should consider before they start developing the application especially how they can archive the database or tool. Again in recent past a lot of shops closed due to lack of funding, I think this is something which is inevitability for most of bioinformatics resources currently available except few very big ones such as NCBI, PDB etc. I said before and I will suggest again, rather than depending on funding agencies for a sustainable funding model for bioinformatics resources, project manager should consider these publicly available repositories and make regular dumps for their web resources.

2. Use of PURLs or Persistent uniform resource locators. This is a good alternative for resources with changing URLs. Whenever the URL changes, resources creator has to manually update the PURL to the new URL.

In a latest PLoS Computational Biology paper Bourne et. al argue that widgets centric web resources can change the way the computational biologist currently engage with online database and analysis tools (Thanks to Sushant for pointer to this article which was published very recently). Authors suggest that use of widgets is a compelling idea for long term availability of bioinformatics resources and I also think this can curb the growth of unwanted data tombs. A web widget is nothing but code snippet commonly a JavaScript chunk which can be embedded and executed within any separate HTML-based web page by an end user. Web widgets are around us for quite long time now, any one who maintains a blog knows very well about the widgets and the functionality they bring to the webpage. So what big fuss about widgets in bioinformatics? Well in simple words, if bioinforamtics is an Apple platform (iPhone or iPad) then widgets are just like iPhone application, extending the functionality of bioinforamtics resources in a modular way. Bourne et. al summarize it very well

First, it brings the application to you; it is an example of drop technology (simply drop the application into your Web page) and it facilitates use. You do not have to remember where to go and possibly be faced by a series of complex choices—the widget can offer a simplified interface to a subset of features. Second, and more importantly, assuming the use of widgets takes off, you can customize your own Web page to take advantage of work done by a variety of other scientists each producing widgets. So for example, you could aggregate a variety of remote methods that perform sequence and structure comparison using a variety of widgets from a variety of reputable sources, thereby creating a single point of reference. Taking this a step further, you can create and customize workflows composed of different widgets in a plug and play environment.

Just think about potential implications of this technology, for instance a bioinformatics papers describing a database or analysis tool can be embedded in online version of paper itself, while reading and citing user can check current accessibility or availability of the resources described in the publication and if the resources is not available without further wasting their time to read the whole paper they can just move to alternative option.

Widgets not just to people, but to programs also
If used in its purest form another advantage with widget centric approach will be ability of widgets to communicate with other widgets or programs which is nothing but an API centric model. For that we will need a standards for widget development both on the server side as well as on the client side, may be we need a community wide effort something like The minimum information required for publishing a computer application. I genuinely believe this is a very nice suggestion from Bourne et. al.

References:
Bourne, P., Beran, B., Bi, C., Bluhm, W., Dunbrack, R., Prlić, A., Quinn, G., Rose, P., Shah, R., Tao, W., Weitzner, B., & Yukich, B. (2010). Will Widgets and Semantic Tagging Change Computational Biology? PLoS Computational Biology, 6 (2) DOI: 10.1371/journal.pcbi.1000673

Wren, J., & Bateman, A. (2008). Databases, data tombs and dust in the wind Bioinformatics, 24 (19), 2127-2128 DOI: 10.1093/bioinformatics/btn464

Veretnik, S., Fink, J., & Bourne, P. (2008). Computational Biology Resources Lack Persistence and Usability PLoS Computational Biology, 4 (7) DOI: 10.1371/journal.pcbi.1000136

Wren, J. (2008). URL decay in MEDLINE--a 4-year follow-up study Bioinformatics, 24 (11), 1381-1385 DOI: 10.1093/bioinformatics/btn127

NodeXL for visualizing biological network data in Excel and some other serious Microsoft ventures in Bioinformatics

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 05 Mar 2010 10:28:00 +0000

Lately Microsoft has released few interesting add-ins for their Office products. Yesterday I wrote about a Word add-in that enables the annotation of scientific documents using bio-ontologies and controlled vocabularies. Today I learned about their latest add-in for Excel, NodeXL, which enables user to create and analyze network visualizations without any hassle. Like the ontology add-in NodeXL is absolutely free and code base is open sourced. NodeXL is primarily designed for extracting, analyzing and visualizing social media networks, in fact it can connect directly to social networking website such as Twitter to import the network data for analysis in Excel. For instance you can import network data of people who have recently tweeted a certain term which might interest you. Coming back to the main motive behind this post, I feel there is nothing which stops us to use NodeXL with biological data. It will be quite easy to extend the NodeXL to import the data from biological databases such as BIND. I know there are plenty of tools already available for the biological networks visualization, interested readers can find a detailed review of major biological network visualization tools by Pavlopoulos et. al. But considering the fact that most of biologist are more familiar with Excel than any other tool out there for network visualization, using NodeXL will make more sense to them. Even in current form itself NodeXL can be used to visualize the biological data, user can import graph data in any major file formats such as GraphML, UCINet, Pajek, and matrix.

Another thing which caught my attention is Microsoft is working with two more bioinformatics extensions, first one is Microsoft Biology Foundation (MBF) a language-neutral bioinformatics toolkit built as an extension to the Microsoft .NET framework and the second one is Biology Extension for Excel which is built on top of MBF and enables a simple and flexible way to work with genomic sequences, metadata, and interval data in an Excel. Both of these products are currently in their beta release and developed by a dedicated Microsoft development team. In current release MBF and its SDK include several features,

Microsoft Biology Foundation:
Core MBF system library (bio.dll).
Core MBF web service library (WebServicesHandler.dll).
Advanced algorithms for use in performing Multiple Sequence Alignment as well as whole genome de novo assembly, included both as core library features as well as examples of how to add modular functionality to the core via the Addin auto-registration mechanism (Addin\PaDeNa.dll, Addin\Pamsam.dll).
Introductory as well as advanced technical documentation to better inform those interested in learning more about the technology and capabilities (Doc).
SDK:
Complete technical reference documentation explaining all available capabilities of the MBF framework (BioDotNet.chm).
Trident activity samples for generating genomics workflows using the Trident workflow workbench (bio.workflow.dll).
IronPython project to show how the MBF library of functional can be used with the Python scripting language (BioDemo.py).
Sequence Simulator, a small sample application which allows for splitting of large sequences into multiple shorter “reads” – intended to replicate what might come off a next-generation sequencing machine.

Despite being a .Net centric project I think MBF is going to offer a serious competition to OBF projects such as BioPerl, BioPython etc in terms of quality, optimization and stability. A final release of MBF is expected around June this year, after that project will be more open for external contribution which currently seems quite restricted.

IBM's latest anomaly-finding algorithm crunches 9TB of numbers in 20 minutes

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 04 Mar 2010 11:55:00 +0000

This is one of many surprises you can always expect from big blues, in fact IBM has always focused to develop the efficient computational algorithms in order to reduce the computational complexity and cost related to the processing of the enormous pools of data. What is new about this latest breakthrough from IBM is the development of energy efficient method to crunch the big data. The mathematical algorithm, developed by IBM's laboratories in Zurich, is tricky and quite general in nature, in fact it can be used to solve several domain specific problems including bioinformatics one. According this IBM news announcement:

In a record-breaking experiment, IBM researchers used the fourth most powerful supercomputer in the world -- a Blue Gene/P system at the Forschungszentrum Julich in Germany -- to validate nine terabytes of data (nine million million or a number with 12 zeros) in less than 20 minutes, without compromising accuracy. Ordinarily, using the same system, this would take more than a day. Additionally, the process used just one percent of the energy that would typically be required.

Mathematical description of algorithm written on the back of the glass
Image Credits IBM Research Zurich Flickr stream

In a post-Hadoop scenario this work has major implications especially when most of Hadoop implementations are tuned for availability and computational efficiency, not for the energy efficiency.

Bio-ontologies for everyone with new Microsoft Word Add-in

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 04 Mar 2010 05:13:00 +0000

A latest paper in BMC Bioinformatics describes a Microsoft Word Add-in for ontology recognition. Tool is freely available from Codeplex portal and as prerequisite you will need Microsoft Word 2007. This add-in enables the annotation of scientific documents based on terms that appear in ontologies and controlled vocabularies. I am sure this tool is going to fuel the debate on the article of the future as like everyone else I think that the next generation of scientific articles will be semantic rich and there is urgent need for semantic enrichment of scientific articles. Adding semantic data to scientific articles during the authoring process is one of the option discussed on several occasions but lack of noticeable tools to assist authors in these efforts remained biggest bottleneck in this process. Now this new add-in for one of the most widely used authoring program gives a new opportunity to authors to add semantic data to a scientific document as it is being written.

The configuration panel allows a user to download ontologies of interest from NCBO

or provide custom ontologies from the local machine or a remote source. Database ID

recognition can also be activated or deactivated via this panel.

Some of the features currently supported by the tool

*Inline Syntax Coloring of Informative Words
*Built-in Knowledge of Ontologies and Controlled Vocabularies maintained and delivered by NCBO
*Built-in Knowledge of Biological Databases (Protein Data Bank, UniProtKB, NCBI GenBank/RefSeq)
*Automatic Detection of Identifiers
*Custom Semantic Markup

Reference:
Fink, J., Fernicola, P., Chandran, R., Parastitidas, S., Wade, A., Naim, O., Quinn, G., & Bourne, P. (2010). Word add-in for ontology recognition: semantic enrichment of scientific literature BMC Bioinformatics, 11 (1) DOI: 10.1186/1471-2105-11-103

The whole is more than the sum of its parts

abhishek.twr@gmail.com (Abhishek Tiwari) — Wed, 03 Mar 2010 20:40:00 +0000

Systems biology is supposed to revolutionise the biology by going beyond reductionism of molecular biology which has dominated biology for decades. The reductionist approach mostly relie on breaking a larger biological system down into pieces, analysing smaller pieces seperately and further determining the connections between them. For nearly 30 years (1970s to the early 1990s) this kind of approach was quite popular and successful in the biology. For a long time a particular type of reductionism – methodological reductionism - has been particularly evident in biology with a mojor focus to understand the physiochemical basis of biological phenomenon. Unfortunately reductionism has its own problems, one of the major concern is the lack of complexity or ability to capture the system level behviour. One of the excerpt from Margaret Drabble's book The Sea Lady portrayes the harsh realities of reductionism,

You can't learn everything from the laboratory, that's what he used to say. The whole is more than the sum of its parts, he told us. The whole behaves differently from the parts, and has different properties. That's what he taught us, and he was right. It's out of fashion to say these days, when we spend our time scrutinizing the interactions of eukaryotic microbes, but it's true, nevertheless. It's still true.

MapReduce goes evolutionary

abhishek.twr@gmail.com (Abhishek Tiwari) — Mon, 08 Feb 2010 09:51:00 +0000

Scientists from Texas A&M University have developed a new algorithm MrsRF (MapReduce Speeds up Robinson-Foulds) for analyzing large collection of evolutionary trees using MapReduce framework. Matthews et. al, have used their MapReduce algorithm to compute all-to-all Robinson-Foulds (RF) distance matrix on multi-core computing platforms. Calculation of all possible Robinson-Foulds distance pairs is a computationally intensive task. The results show that a significant speedup can be achieved using MrsRF compared to the fastest sequential algorithms.

We studied the performance of our MrsRF algorithm on two large biological trees sets consisting of 20,000 trees of 150 taxa each and 33,306 trees of 567 taxa each. Our experiments show that MrsRF is a scalable approach reaching a speedup of over 18 on 32 total cores.

Phase 1 of the MrsRF algorithm. Two mappers and two reducers are used to process the input files.

Apart from speeding up the phylogenetic analysis, this study presents a new type of MapReducible problem where "the size of the input (t evolutionary trees) is much smaller than the size of the output (t × t RF matrix)". Generally in MapReduce implementations the final output is smaller in size than the initial input. Another important thing which authors point out is getting best performance out of MapReduce implementation on a multi-core cluster depends on the cluster configuration. For instance, they tried their problem set with 32 total cores, a 16 nodes by 2 cores (16 × 2) cluster configuration which outperformed 8 × 4, 4 × 8, and 32 × 1 cluster configuration.
Overall their research makes a strong case for using MapReduce framework to design high-performance phylogenetic applications and it can be best for tackling the large evolutionary computational problems such as summarizing the big collections of evolutionary trees. An open-source implementation of MrsRF algorithm is freely available from the Google code.

Reference:
Matthews, S., & Williams, T. (2010). MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees BMC Bioinformatics, 11 (Suppl 1) DOI: 10.1186/1471-2105-11-S1-S15

Science changes the world, sometimes the world changes the science, we are overdue

abhishek.twr@gmail.com (Abhishek Tiwari) — Mon, 08 Feb 2010 04:33:00 +0000

Dr. Monica Anderson talks about reductionism vs holism in science. She also suggests that artificial intelligence failed because intelligence is too holistic and by its nature it always attracted the hardcore reductionists. One of my favorite quote from this talk is "Science changes the world, sometimes the world changes the science, we are overdue", which is significant in each and every sense. Check out the whole lecture it is worthwhile.

After phylogenetics Microsoft patents personal data mining

abhishek.twr@gmail.com (Abhishek Tiwari) — Sat, 06 Feb 2010 08:23:00 +0000

I hope you remember that some time back Microsoft tried to patent clustering phylogenetics methods which was a socking news for the bioinformatics community as community used these methods for a long time without any restriction. Now Microsoft had patented the personal data mining system. According to patent abstract which was accepted just last week

Personal data mining mechanisms and methods are employed to identify relevant information that otherwise would likely remain undiscovered. Users supply personal data that can be analyzed in conjunction with data associated with a plurality of other users to provide useful information that can improve business operations and/or quality of life. Personal data can be mined alone or in conjunction with third party data to identify correlations amongst the data and associated users. Applications or services can interact with such data and present it to users in a myriad of manners, for instance as notifications of opportunities.

The patents includes some heavy weight names from Microsoft including Bill Gates. System tries to answer personal queries such as "What is the best digital camera and where I can find the cheapest one?". What really troubling is the claims like the one below,

Furthermore, as will be appreciated, various portions of the disclosed systems and methods may include or consist of artificial intelligence, machine learning, or knowledge or rule based components, sub-components, processes, means, methodologies, or mechanisms (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, classifiers . . . ).

Also I am also not sure how a patent was granted purely based on data streams without the looking on the background of algorithm used.

Bullet-Shaped Vesicular Stomatitis Virus

abhishek.twr@gmail.com (Abhishek Tiwari) — Sat, 06 Feb 2010 06:24:00 +0000

In a latest online article in journal Science Ge et al. report a cryo-electron microscopy structure of a model rhabdovirus, a bullet-shaped vesicular stomatitis virus (VSV). Using their structural data they suggest a mechanism of VSV assembly in which the nucleocapsid spirals from the tip to become the helical trunk.

Cryo-EM structure showing the putative cytoplasmic tail of G protein binding to an M subunit through a thin linker. Image credits Science

Proposed mechanism by which the nucleocapsid ribbon generates the virion head, starting with its bullet tip.Image credits Science

Architectural representation of the virion trunk, rendered in 3D animation.

Computational Biology: Is reproducibility overrated?

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 05 Feb 2010 12:04:00 +0000

Recently I was reading a bunch of articles on computational reproducibility in scientific research including one from my fellow blogger Grant Jacobs of Code for life blog. The term reproducibility in computational science is not monolithic - what seems essential for one scientific domain may not matter to another. Finding out what it takes is a difficult, but necessary effort. According to Randy LeVeque, a prominent mathematician

Scientific and mathematical journals are filled with pretty pictures these days of computational experiments that the reader has no hope of repeating. Even brilliant and well intentioned computational scientists often do a poor job of presenting their work in a reproducible manner. The methods are often very vaguely defined, and even if they are carefully defined, they would normally have to be implemented from scratch by the reader in order to test them.

Latest series of debate originated from an article in journal Science written by Jill P. Mesirov on accessible reproducible research in computer science. The center of argument was Jill's own experience with GenePattern-Word RRS system with intention to universalize the described approach. After reading the whole paper I found nothing novel about the kind of reproducibility Jill is proposing in the article, in fact there are plenty of ongoing efforts to support the computational reproducibility by developing niche markup languages and associated tools. Many of these efforts such as SBML and CellML are well in advanced stage, and again most of journals now demand their perspective authors to submit their data and model in domain specific formats. I can't see why these efforts are not a step further towards accessible computational reproducibility. They are totally qualified as mean to achieve the computational reproducibility. Suggestions such as computational research need a paradigm shift or calls out for a new model for the way we publish our results is already in place, may be in primitive form but we are coming there. We talk and talk but never appreciate what communities are doing, this new drive for reproducibility evangelism is faulty and incomplete unless this gives a due consideration for ongoing community wide efforts to tackle this issue.

From this whole debate I was forced to ask few questions,

1. Computational reproducibility is important but to what extent? Reproduction and repetation of scientific protocol is never a straightforward affair. By no means scientific reproducibility should be considered as replacement for scientific integrity and learning. It also reminds me an argument presented by James Bassingthwaighte who thinks that computational reproducibility might be overrated. According to James

•Better ideas will emerge when other investigators attack a model (hypothesis) and attempt to improve on it.
•Redevelopment is the best way to penetrate a model or hypothesis –i.e. no waste of effort!
This
-is the real test of reproducibility of the original authors’ views
-really validates it by doing it totally independently
-makes it less likely to incorporate the original’s errors

Copy-cat errors are like mutations in a phylogenetic sequence, so it’s better to start at the beginning each time.

Although this is not very universal argument but I am sure readers are well aware about the fact that debugging can be a rewarding experience when you are learning the programing language. Debugging in computational protocols depends on fine tuning of parameters which requires a close understanding of algorithm in use. Same genome analysis pipeline may require different sets of BLAST parameters for the unlike sequence families. This kind of learning is possible only when you challenge the user to come up with a better solution.

(Check out the full presentation below, it is worthwhile)

2. Are development process (for computer softwares, pipeline, models) and end product "reproducible research" are two different things? They look slightly unrelated but they depend on each other. Employing best practice such as use of domain specific standards (languages and guidelines) along with software design principles in the development process results in provenance-aware computer softwares, pipeline and models. Provenance refer to the process of tracing and recording of the origins and evolution of the data. The analysis workflow or pipeline itself can be captured by process definition or workflow description languages while the data remains seated in the wrapper of domain specific markup languages completing the full circle of provenance-aware tools. You want total reproducibility, do this I promise nothing will come in between. Unfortunately its hard to implement most or part of above suggestion, because when it comes to science getting things done (GTD) is major concern than quality of codes we write. Workflow solutions like Taverna, Galaxy, KNIME, Keplar, Pipeline Pilot, Inforsense KDE are designed to track the trace and history of scientific data and analysis workflow in the same way I suggested above. These tools are available for a long time and scientist mostly failed to embrace these ideas and tools. Why? For the same reasons they don't embrace the very idea of web 2.0. Because in science advertisement of scholarship (read it peer reviewed publications) is more important than scholarship itself (I call it the learning experience).

3. What is the role of standards and guidelines? In last few years scientific community has seen a flurry of activity over standards and guidelines development. Are they any good for reproducibility? Yes, indeed. Let me explain this with one example, can you understand the essence of the image published in a peer reviewed scientific article without reading the following text? Well answer is pretty no. Albeit it might be easy to understand the circuit diagram published in a electrical book just because most of notation of circuit schematics are well standardized. The people in scientific community have realized this problem and result is a community driven effort Systems Biology Graphical Notation (SBGN) to standardize the graphical notations in biology. Although SBGN is in its early stage it can play an important role the way we communicate knowledge in biology. Similary in machine learning and data mining community PMML (Predictive Model Markup Language) is doing a fantastic job by enabling users to reuse, post-process, and utilize data mining models irrespective of the tool that generated them. In my humble opinion, notion of putting the reproducibility into computational code is just magical unless we have better underlaying standards and guidelines to support this.

4. What we can learn from Unix and Open source model? Using a "make" inspired computaional reproducibility systems is not new. "make" is a populatr utility program for automatically building executable programs and other non-source files from source code. In past "make" based tools such as SCons has been used as platform for reproducible research in scientific computing. Secondly scientific community should avoid any reliance on proprietary softwares, they make a big gap in reproducibility workflow due to closed source nature.

Closing thoughts
The troubles of computational reproducbility are primarily those of ideology and practice. This may be a slightly controversial assertion, but I would argue that technically reproducibility in computational biology is not a big issue. The adoption of reproducible research practice has been slow. The focuse of reproducbility related discussions in computaional biology should be more on how to inspire the scientific communities to embrace a better reproducible research.

This week in synthetic biology

abhishek.twr@gmail.com (Abhishek Tiwari) — Mon, 01 Feb 2010 02:50:00 +0000

This was another incredible week for synthetic biology community, again several peer reviewed papers made their way to This week in synthetic biology (TWiSB). This was a week of riboswitches, biofuels and biological nanofactories which we will be discussing in next section. For now (as usual) if you have a synthetic biology story which you want to share with us you can send an email to post@syntheticfuture.posterous.com.

Engineered orthogonal selectivity of the riboswitches
Riboswitches are strctural element typically found in the 5′-UTRs (untranslated regions) of bacterial or eukaryotic mRNAs. These riboswitches generally dictate the the gene expression of corresponding genes by interacting or binding with the small target molecules or metabolites. Binding of naturally occuring metabolite to aptamer domain of riboswitch mRNA cause the structural change in the gene expression machinery leading to regulation of gene expression levels. Now researchers from The University of Manchester have developed riboswitches that allow orthogonally selective, tuneable, and dose-dependent control of gene expression in response to nonnatural synthetic small molecule. A news release from the university office suggest that this is very first rewire of genetic switches, although not everyone is convinced with that and it can be considered as first rewire of genetic riboswitches. Christina Agapakis has an interesting review for this paper on her blog. The findings are reported by the group let by Dr Neil Dixon in the latest edition of Proceedings of the National Academy of Sciences (PNAS). As Dr Neil Dixon suggest that the next big thing will be to realise the real potential of this research by tweaking the important biological pathway and replicate this technology for human cells.

Combining the best of synthetic biology and nanotechnology
Seems like my picks from futurama of science are coming true. George Church assertion that nano-tech in combination with synthetic biology will be offering unseen endeavors can not wait for 2020, it's already here. Scientists at the University of Maryland (UMD) have now demonstrated how engineered biological nanofactories can trigger quorum sensing responses in targeted bacterial population which will help us to combat the bacterial infections without antibiotics by hijacking bacterial communication network. In a recent research published in advance online edition of journal Nature Nanotechnology William E. Bentley and team report self-assembled, self-guided and self-destructible biological nanofactories which not only target a specific population in bacterial co-cultures selectively but also trigger communication between two bacterial populations that do not communicate in normal conditions. Not all bacteria are bad some are beneficial for us and potentially this kind of differential behavior of nanofactories can help us selectively protect the good bacteria while same time targeting the harmful bacteria (pathogen) by switching off their triggering activities. Normally this kind of selective is missing in antibiotics and hence they have unwanted side effects. These nanofactories are functinally modular, they have targeting module , and sensing, synthesis and assembly modules (last 3 modules make a single unit, a fusion protein). One important aspect of research was use of biofabrication to assemble antibodies on to the fusion protein unit, which enables targeting. Interestingly these nanofactories can self-destruct upon completion of the task.

Image credits Nature Nanotechnology

Following their addition to bacterial cultures, nanofactories bind specifically to the targeted bacteria (green circle), synthesize and deliver AI-2 (yellow circles) at their cell surfaces and trigger the quorum sensing response.

Synthetically engineered bacterium can make biofuel directly from biomass
Since the article has already been reviewed to death in mainstream media, I am just going to give a quick overview. Researchers throughout the world are working to produce biofuel using synthetic engineered microbes in a cost effective way. Scaling up these different solution holds the key for their success. To this end scientists are trying different novel approaches, for example very recently the Lionetti et. al reported that genetic modification of plant cell wall may scale-up biofuel production and now researchers from the University of California, Berkeley, and the biotech firm LS9 of South San Francisco, California report another cost-effective route for direct biofuel production from grass or crop waste.

A more scalable, controllable and economic route to this important class of chemicals would be through the microbial conversion of renewable feedstocks, such as biomass-derived carbohydrates. Here we demonstrate the engineering of Escherichia coli to produce structurally tailored fatty esters (biodiesel), fatty alcohols, and waxes directly from simple sugars. Furthermore, we show engineering of the biodiesel-producing cells to express hemicellulases, a step towards producing these compounds directly from hemicellulose, a major component of plant-derived biomass.

Image credits Nature

By using an array of genetic modifications they not only diverted the metabolic flux in desired direction to produce non-native products but they also improved the yields of these products within an order of magnitude of that required for commercial production. Currently this approach converts the simple sugar or hemicellulose component of biomass into biofuesl, they are targetting to scale up this process by producing biofuels directly from cellulose. This will eventually require the synthetically engineered cells those can express cellulases- a diverse group of enzymes that hydrolyze or degrade celluloses into simple forms.

Solazyme in Red Herring's Global 100 of Tech Startups

Solazyme, a renewable oil production company in algal synthetic biology, received the Red Herring Global 100 award, placing the company among a list of startups.

Minimizing the Risks of Synthetic DNA

On January 11, 2010, the AAAS CSTSP hosted a meeting to discuss the draft U.S. Sequence Framework for Synthesis of Double Stranded DNA providers with the scientific community and gene synthesis providers. Stakeholders from academia, private industry, and the gene synthesis industry all support the voluntary guidance. Although meeting participants identified specific concerns and offered specific recommendations to address some of those concerns, the overall sentiment was that the guidance is well thought out, facilitates advances in scientific knowledge, and allows for international engagement.

That's all for this week. Next week TWiSB will be back with few more interesting stories.

Latest Cryptogram Challenge

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 28 Jan 2010 04:48:00 +0000

4th round of Cryptogram Challenge is open now, and in the latest version of cipher-based puzzle participants have to unravel the message coded into the image of a 96-well plate from a RNAi experiment. The contest will run from January 18 until it is solved and the first person who will solve the puzzle will receive $1,500 plus opportunity to choose one of five benchtop devices from Invitrogen. Now more about the puzzle, participants have to find a statement containing four words hidden behind the pattern of following image.

To decode the cipher hidden in this 96-well plate image participant will be provided a proper background information and additional clues every Monday until it is solved.

Note:

Each of the 96 blocks in the image contains two cells: one in the upper left and one in the lower right corner. All cells are expressing the gene "R" that encodes for a protein that appears red as a function of the intensity of expression. The cells in the upper left have not been exposed to an siRNA that suppresses gene R. The cells in the lower right exhibit the degree of gene R expression after transfection with a subtle dose of the specific siRNA. Just as in biology, expression of gene R varies somewhat in intensity between the cells in the upper left corners. Similarly, after siRNA transfection a variable suppression of gene R is seen among the cells in the lower right hand corners.

iPad: That's what it look like

abhishek.twr@gmail.com (Abhishek Tiwari) — Thu, 28 Jan 2010 00:59:00 +0000

The Interactorium visualization

abhishek.twr@gmail.com (Abhishek Tiwari) — Wed, 27 Jan 2010 16:17:00 +0000

The Interactorium is a platform developed at University of New South Wales to visualise very large interactome datasets such as one incorporating up to 40,000 proteins or 6000 multiprotein complexes, in context of 3D virtual cell. It is built on the Skyrails Visualisation Engine and it permits multi-level viewing of the molecular biology of the cell.

HT(Bioclues)

Futurama of science: My Picks

abhishek.twr@gmail.com (Abhishek Tiwari) — Sun, 24 Jan 2010 23:12:00 +0000

In a recent issue, Scientific journal Nature ran a story about how different scientific disciplines will evolve over next ten years (for interested readers article is freely available here). Nature asked a selected groups of leading researchers and science policy makers to forecast scientific endeavour in a range of fields such as synthetic biology, the microbiome, metabolomics, personalised medicine, drug discovery etc. For me most of predictions were interesting although many of them sounded like science fiction but thats what these predictions are all about.

Wordl of vision 2020

Google Search
On the future of search technologies Peter Norvig idea to connect the next decade search engine with brains signals is a fascinating proposition. In his own desctiption

The majority of search queries will be spoken, not typed, and an experimental minority will be through direct monitoring of brain signals. Users will decide how much of their lives they want to share with search engines, and in what ways.

Imagine what can be done with such a technology (well I will expect a brain Dropbox utility where I can synchronize the latest news in my brain directly from Google search engine). People will neither bother to type nor remember, eventually everyone will be living in a web-matrix designed by Google.

Synthetic biology
Second in our list is the synthetic biology. George Church point outs the myriad technologies such as nano-tech in combination with synthetic biology will be offering unseen endeavors such as

nano-memory devices that harness the ability of certain bacteria to navigate Earth's weak magnetic field using magnetite nanoparticles.

In my opinion in coming ten years synthetic biology will be mostly doing what it is supposed to do - production of chemicals, bioremediation, synthetic biofuels and food.

Drug discovery, Informatics and Personalized medicine
On the future of the drug discovery Gary P. Pisano suggest that "no one should be surprised to see the emergence of a major Chinese multinational drug company". Fair enough, globalization of drug innovation will be major theme in coming decade. As matter of fact in recent years China has emerged as one of the fastest-growing pharmaceutical markets in the world, increasing by 39 percent to $24.5 billion in year 2008. Pharmaceutical giant like Pfizer are seeking partnerships with Chinese drugmakers.
One other growing area of pharmaceutical interest will be personalized medicine more especially the development of tailored therapeutic drugs based on genetic differences and identification of high-impact risk factors behind many diseases. What really needed is the informatics innovation to translate the data deluge into actionable results that can impact drug discovery process - a factor which is slightly ignored in the article. According to Eric Schadt of Sage Bionetworks, advances of the last decade will demand an informatics revolution in the coming years and "real challenge in the next decade will be informatics based"

Only by marrying information technology to the life sciences and biotechnology will we realize the astonishing potential of the vast amounts of biological data we will be capable of generating. Such data, if properly integrated and analyzed, will enable personalized medicine strategies that lead to every one of us making better choices on how we not only treat disease, but prevent it altogether.

A similar kind of view was presented by David Walt

Data processing and bioinformatics will become the bottleneck as the need grows to assemble and compare large numbers of genomes. Moore’s Law just can’t keep up.

Decline in funding and rise of multidisciplinarity
Other major concern which was raised by several experts from varying scientific domains is the sustainability. R&D funding for most of scientific domain is facing a sudden decline. For instance in year 2009 total funding for the European Bioinformatics Institute dipped about 10 percent. Similarly in

United States, for example, budget deficits have caused many states to reduce their funding for public universities, and at the federal level, there is likely to be no growth or a cut in funding for research programmes.

Scientific community is under tremendous presssure from the funding bodies and to avoid a decline of funding levels community is looking towards a more collaborative, multidisciplinary approach. Research themes with broader perspective may have higher priority over narrow scope. Further the significant decline in funding may be affecting more to countries with great preeminence in scientific research. Developed countries such China are vigorously pushing forward science and technology innovation through increased funding even during hard times of recession.

Developing world
One of the suggestions that the developing world will be more benefited with scientific development in this period is quite genuine and remarkable. For instance countries like China are thinking to bypass 3G communication technologies altogether, and move straight to LTE or 4G. In same way

As costs drop, such technology will allow developing nations to leapfrog fertilizer-wasting, fossil-fuel-intensive and disease-rife farming for cleaner, more efficient systems, just as they are leapfrogging costly landlines in favour of mobile-phone networks.

This week in synthetic biology

abhishek.twr@gmail.com (Abhishek Tiwari) — Sun, 24 Jan 2010 00:06:00 +0000

This was an amazing week for synthetic biology community, part of the reason is the synthetic biology has completed 10 years since the very first synthetically engineered biological oscillator and toggle switch were introduced in journal Nature in year 2000. So on this eve I have decided that the title of this blog post "This week in synthetic biology" is going to be a regular section of Fisheye Perspective blog appearing on every weekend. Initially I will be posting a link round up for a week which will include excerpts from peer reviewed articles, news items and interesting tweets. But I have few interesting future plans including a podcast series TWISB (covering both Systems and Synthetic Biology) in close association of fellow synthetic and systems biology bloggers. If you have a synthetic biology story which you want to share you can send an email to post@syntheticfuture.posterous.com.

Ten years of synergy
The latest issues of Nature has an editorial on last ten years of synthetic biology.

As it develops along this and other paths, synthetic biology itself will demand more by way of new fundamental biological knowledge — quantitative, systematic, computational and biophysical. And conversely, one of the deepest lessons from these first ten years is that biological knowledge will require synthetic approaches if it is to become a mature and reasonably predictive science.

Quorum of bacteria genetic clocks
I already wrote a short post about this, as such article already got superb response in all type of media circles. For interested readers I will suggest these two articles (available free to read) and this video which explains everything about the work of Jeff Hasty and colleagues. One thing which is mostly overlooked in the hype is the the role of micurofludics devices, in this case microfluidic devices were used to investigate the collective synchronization properties along with spatiotemporal waves occurring at millimetre scales. In Hasty's system,

the bacteria are lodged on a microfluidic device that contains tiny channels to allow nutrients to flow to the cells and waste products to flow away. The team reports that the timing and strength of the synchronized clock's oscillations depend on how quickly nutrients and waste are pumped through the channels of the device.

Five hard truths for synthetic biology and bioengineering
Again I blogged this one here, Roberta Kwok explores five challenges for the synthetic biology and how they might be resolved. I would add two more challenges in this list: a missing systems level understanding and lack of technology to tackle the large assembly problem for DNA synthesis.

Synthetic biology drives the design of strong and optimized promoters
Researchers from Howard Hughes Medical Institute have developed a synthetic biology approach for the designe and optimization of promoters. These synthetic promoters could help to improve the transcription levels in cellular contexts in which current promoter don't work very well or have little or no expression in many cell lineages. Stephen J. Elledge and colleagues have implemented this approach for the generation and screening of transcription factor binding sites in human cells.

Using an unbiased screen of a synthetic 10-bp repeat sequence library we identified many synthetic enhancer elements capable of increasing transcription from a minimal CMV promoter. In several cases, the recovered synthetic enhancers had transcriptional activation activity on par with the WT CMV enhancer with only two rounds of enrichment. Thus a 100-bp synthetic enhancer can be engineered to match or exceed the promoter strength of the strongest known mammalian promoters.

A NSF Funded Bio-fabrication Project
This week a new International Open Facility Advancing Biotechnology (BIOFAB) production facility was lunched which will be producing free standardized DNA parts and methods. Facility will be lead by bioengineers from UC Berkeley and Stanford University and it has already secured two years of funding from National Science Foundation (NSF). Project has already secured commitments from Lawrence Berkeley National Laboratory (LBNL), the BioBricks Foundation (BBF), and the Synthetic Biology Engineering Research Center (SynBERC). A following news release says

Today, a single designer microbe can take years to create and cost tens of millions of dollars, since each control element – a promoter or transcription factor – has to be identified, characterized and tweaked in order to be reused. One UC Berkeley project to engineer microbes to produce the anti-malarial drug artemisinin took 10 years to get out of the lab into small-scale production, at a cost of $25 million.

Thats all for this week.
BONUS: A bit old article on the challenges of informatics in synthetic biology.

Symphony and synchrony of bacterial time keeping machines

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 22 Jan 2010 14:48:00 +0000

One of the coolest videos I ever seen, demonstrating the enormous potential of synthetic biology. Its nearly 10 years since the advent of the synthetic biology and the very first synthetic biological oscillator. Despite all challenges and hypes synthetic biology is now in delivery mode. Now researchers from University of California, San Diego have programmed the bacterial cells using engineered genetic circuits in such a way that it allows entire bacterial colony to syncrinze their time keeping clocks in a same fashion as we humans do. Each bacterial cell has its own time keeping clock and normally these cells use the quorum sensing to communicate with each other which involves cell secreted chemical signaling molecules called autoinducers and receptor that can specifically detect the signaling molecule (inducer). Bacterial cells use quorum sensing to coordinate certain behaviors based on the local density of the bacterial population such as symbiosis, virulence, competence, conjugation, antibiotic production, motility, sporulation, and biofilm formation. In this ground breaking work which is published in recent issue of Nature Jeff Hasty and colleagues have synchronized clocks of bacterial using various quorum-sensing factors and inhibitors such as acyl-homoserine lactone (AHL).

Nothing wrong with synthetic biology, It's just B-I-O-L-O-G-Y and the rest is silence

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 22 Jan 2010 05:11:00 +0000

Recent issue of Nature magazine has few very interesting articles about synthetic biology and one of them is "Five hard truths for synthetic biology" written by Roberta Kwok. When I first read this article my first reaction was- nothing wrong with synthetic biology, it's just biology gets in the way of the engineering.

Nothing to do with their hearts
Nothing to do with their heads
Nothing to do with their homes
Nothing to do with their beds

It's just B-I-O-L-O-G-Y
Can't you see
It's just Biology
Biology - coming in between you and me

I guess that was the same reaction from Christina Agapakis, "There's a lot of biology that gets in the way of the engineering", well I could not agree more. Most of our current understanding and knowledge about the biological systems generally fall short when it comes to manipulate them in the same way as engineers does with transistors and switches to create circuits such as radio. Unfortunately biological systems are more complicated than one can think, even a fully functional single cell can be more complex than Boeing 747. As Martin Fussenegger suggests "This is the type of complexity that is very difficult to capture by standardized characterization". Terms such as circuitry, modularity, redundancy and robustness which are also used to describe engineered systems like cars, electronics, robots and airplanes are now adopted to understand and manipulate the complexity of living systems as well. But biological systems are not like plug n play devices so they don't work the way we engineers expect because there are several reason and Roberta review captures five key challenges

Many of the parts are undefined (and mechanism also)
The circuitry is unpredictable
The complexity is unwieldy
Many parts are incompatible
Variability crashes the system

Apart from that biological system have the gestalt property of a whole and it cannot be described merely as a sum of its parts which leads us to another important conclusion that digital systems anology may not work unless we have holistic view of the system. Now scientist have realized these limitations and to overcome this they are looking towards the alternative pathways where they can exploit best of both, biological and digital systems. One option could be creating orthogonal or parallel circuitry without touching the natural machinery of the cell, alternatively physically isolation of the synthetic network from the rest of the cell could be a possible solution.

Reference:
Kwok, R. (2010). Five hard truths for synthetic biology Nature, 463 (7279), 288-290 DOI: 10.1038/463288a

Lightful Principles: Future of Graphical User Interfaces

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 22 Jan 2010 03:51:00 +0000

Jayson Elliot talks about a new Lightful model for graphical user interface development. Jayson Elliot is a user experience designer, writer and speaker.

Jayson's Lightful model is based on following principles

MODELESS
The same action, performed on the same object, never results in a different outcome.

MONOTONOUS
Rather than choosing among multiple ways to issue a command, there is exactly one way to achieve any given task.

SUBSERVIENT
Assume your users are intelligent. Assume your users are busy.

INTERRUPTIBLE
Nothing your computer is doing is more important that you. Every process must be instantly interruptible.

PERSISTENT
The system should remain in the exact state it was when the user last left it.

FORGIVING
No error should be irreversible. All actions should be undoable, no matter how trivial or large, or how far in the past.

VISIBLE
System status should always be visible for all actions, even if the information seems too "low level" for the user.

INVISIBLE
The interface only exists to support the user's actual task. Never let the interface become the focus of the user's attention

Even though there is an issue of when there isn't the question of if

abhishek.twr@gmail.com (Abhishek Tiwari) — Tue, 19 Jan 2010 23:14:00 +0000

Sherlock Holmes and Proteomics

abhishek.twr@gmail.com (Abhishek Tiwari) — Tue, 19 Jan 2010 06:06:00 +0000

By reading the title of the post you may be curious that what remote similarity proteomics can share with Sherlock Holmes? Well not much at this moment except the complexity of storyline. An excerpt from a bit old article, interested readers will find it useful to understand the complexity of proteome

Modern proteome analysis is a very complex 'detective story', which might baffle even the most famous investigator, Sherlock Holmes [1]. The reason is that, in any proteome, a few proteins dominate the landscape and often obliterate the signal of the rare ones, so that, when the police reach the scene of the crime, the thin thread of evidence remains hidden. In addition, proteomes of any origin can be extremely complex, impervious to even the most sophisticated analytical tools. For instance, according to Anderson et al., the human plasma should contain most, if not all, human proteins, as well as proteins derived from viruses, bacteria and fungi. Also, numerous post-translationally modified forms of each protein are present, along with, possibly, millions of distinct clonal immunoglobulin sequences. To this intrinsic complexity, one can add the enormous dynamic range, encompassing some 10 orders of magnitude between the least abundant (e.g. interleukins, at concentrations of < 1 ng·mL−1) and the most abundant (e.g. albumin, ≈ 50 mg·mL−1). For these reasons, any scientist working on any proteomic project deserves the title 'detective', be it the most famous Sherlock Holmes, the illustrious Hercule Poirot, or even the clumsy inspecteur Jacques Clouseau, de la Sûreté de Paris.

Reference:
Righetti, P., & Boschetti, E. (2007). Sherlock Holmes and the proteome − a detective story FEBS Journal, 274 (4), 897-905 DOI: 10.1111/j.1742-4658.2007.05648.x

"Voodoo science" rocks the boat of IPCC

abhishek.twr@gmail.com (Abhishek Tiwari) — Mon, 18 Jan 2010 04:15:00 +0000

I generally don't write about climate science and global warming. For me this is a highly controversial topic and I always keep a distance from the discussions related to global warming. Climate change has supporters and skeptics. I am neither a supporter nor a skeptic but I am not in denial. The phenomenon of global warming is self evident and it is happening. Indeed glaciers are melting and it does not matter if they disappear in 25 years or 300 years. What really matters is we failed to act together very badly. In my humble opinion all type of groups, supporters or skeptics, should use rational scientific arguments to defend their side of story. Should Inter-governmental Panel on Climate Change (IPCC) scientists have done their homework properly they could have escaped from such an embarrassment. The lack of seriousness and profound effort to tackle the whole issue on the IPCC side is totally unacceptable. IPCC's central claim that world's glaciers were melting so fast that those in the Himalayas could vanish by 2035 is nothing but example of "voodoo science". It turns out that the much-hyped claim was merely a speculation without any evidence.

As Geoffrey Lean in Telegraph UK writes

This is serious, as the authority of the IPCC rests on meticulously basing its reports on peer-reviewed literature and, indeed, on taking a conservative view.

The latest IPCC scandal can be most serious failures in climate research, in fact much-hyped "Climategate" emails will not come anywhere close to it. Interestingly the IPCC's claim about Himalayan glaciers was exposed from within the climate change supporter community. According to Sunday Times

The lead role in that process was played by Graham Cogley, a geographer from Trent University in Ontario, Canada, who had long been unhappy with the IPCC's finding.

He traced the IPCC claim back to the New Scientist and then contacted Pearce. Pearce then re-interviewed Hasnain, who confirmed that his 1999 comments had been "speculative", and published the update in the New Scientist.

Cogley said: "The reality, that the glaciers are wasting away, is bad enough. But they are not wasting away at the rate suggested by this speculative remark and the IPCC report. The problem is that nobody who studied this material bothered chasing the trail back to the original point when the claim first arose. It is ultimately a trail that leads back to a magazine article and that is not the sort of thing you want to end up in an IPCC report."

Iroonacally in a reaction to an Indian climate research paper last year which undermined IPCC's Himalayas claim Rajendra Pachauri, the IPCC chairman , has dismissed the paper as "voodoo science". Now the same "voodoo science" rocks the boat of IPCC.

Update:
As my fellow bloggers asked, here goes the controversial claim about the Himalayan glaciers

Glaciers in the Himalaya are receding faster than in any other part of the world and, if the present rate continues, the likelihood of them disappearing by the year 2035 and perhaps sooner is very high if the Earth keeps warming at the current rate. Its total area will likely shrink from the present 500,000 to 100,000 km2 by the year 2035 (WWF, 2005).

SBML: Let the crowdsourcing begin!

abhishek.twr@gmail.com (Abhishek Tiwari) — Fri, 15 Jan 2010 04:18:00 +0000

Well SBML editors have came out with this nice idea of crowd-sourcing or community sourcing the debugging process of SBML Level 3 by organizing a contest to find the bugs in the L3 Core specification. Here goes the announcement

In order to help debug the SBML L3 Core final candidate specification, we're going to run a contest. The winner will receive a US$ 100 Amazon gift certificate. The rules are simple:

1. Look for errors in the L3v1 Core final candidate specification document released December 31, 2009, available from http://sbml.org/Documents/Specifications ("Errors" can be typographical, grammatical, content, or consistency in nature.)

2. Report EACH error SEPARATELY using the short entry form at http:/sbml.org/sbml-l3v1core-contest

3. The contest starts immediately and ends midnight, Pacific Standard Time, on Feb. 28.