Bench Press

In order to deal with the global outbreak of swine flu effectively, tracking the number of swine flu cases is imperative. Having as much accurate data as possible regarding the epidemic is essential for evaluating what moves the global community ought to start taking to make it through this outbreak. Thus, using quick and accurate tools to evaluate the countless samples  being collected around the world is an absolute necessity. Luckily scientists at the University of Colorado and InDevR, a small biotech in Colorado, may have exactly what the world needs in a microarray chip dubbed the FluChip.

In 2005 Dr. Kathy Rowlen, CEO of InDevR, led a team at the University of Colorado working with the Centers for Disease Control and Prevention (CDC) in developing the FluChip in order to allow labs across the world to quickly distinguish samples between 72 different influenza strains. Her group’s work produced a viable testing platform that produced results in less than 12 hours with impressive accuracy.

Now Dr. Kathy Rowlen and InDevR have licensed the FluChip technology from the University of Colorado. InDevR has arranged to begin testing samples of the swine flu on a M-gene variant of the FluChip while also working on improving the initial design by incorporating new technologies, hopefully making a new assay basic enough that any lab with PCR capabilities will be able to utilize it. Here’s to hoping the FluChip will help us get a better picture of the current state of the swine flu epidemic.

InDevR Press Release:

InDevR, a small biotech company in Boulder, CO, announced today that they have licensed the FluChip technology from the University of Colorado.  The FluChip was invented by a joint team of scientists at the University of Colorado and the Centers for Disease Control and Prevention in an NIH sponsored effort led by Professor Kathy Rowlen.  Rowlen, now the CEO of InDevR, said that InDevR has arranged to test genetic material from the recent swine H1N1 virus on the MChip as well as other versions of the FluChip which are under development.  According to Rowlen “Based on work we conducted a couple of years ago, it appears that the M-gene version of the FluChip will be able to distinguish human H1N1 viruses from the new swine H1N1 virus.  If that proves to be the case, the FluChip will be a much needed and powerful new tool for surveillance since all of the current influenza diagnostics on the market are unable to subtype this virus.” The most popular diagnostic tests for influenza include rapid immunoassays, which are only able to identify the type (A or B) of influenza virus, and reverse-transcriptase polymerase chain reaction assays, which were designed for human-adapted influenza viruses and are not able to identify the swine H1N1 subtype.  State Public Health Laboratories must now send any influenza A viruses that cannot be subtyped using existing diagnostics to the CDC for analysis by genome sequencing or viral isolation.  The CDC must select viruses to analyze since it is not possible to run every sample collected from a large number of Public Health Labs.

The M-gene based FluChip has been demonstrated to delineate human-adapted viruses from non-human viruses, such as the H1N1 virus that caused the 1918 “Spanish Flu”.  “Since the FluChip assay can be conducted within a single day it could be employed in State Public Health Laboratories to greatly enhance influenza surveillance and our ability to track the virus,” Rowlen said.  InDevR will combine the FluChip technology with an innovative detection technology (NESATM), which InDevR also licensed from the University of Colorado and further developed with NIH sponsorship, to make the FluChip assay inexpensive and easy to use in any lab that has basic PCR capabilities.  “Kathy and her team have been engaged with this and similar diagnostic technology for many years,” said Mary Tapolsky, Senior Licensing Manager at the University of Colorado Technology Transfer Office. “CU TTO is excited about this experienced and motivated group developing and commercializing this promising technology.

Thankfully food scares like the Salmonella contamination of peanut products from the Peanut Corporation of America are fairly rare in the US the health risk posed by contaminated food and water must be taken extremely seriously and effective means to test and certify food and water safety are of the utmost importance. Despite this even with modern advances some tests can currently take days to verify the safety of food and water samples.

This lag time can ultimately prove disastrous in certain scenarios. For example, the rural
community of Walkerton, Canada experienced E. coli contamination in May, 2000 resulting in seven deaths and hundreds sickened by the contaminated water. Therefore, testing of critical supplies like water ought to be as near real time as possible in order to minimize potential harm.

This brings me to research being conducted by Dr. Shacham-Diamand’s group at Tel Aviv University. Speaking about the dangers of water poisoning Dr. Shacham-Diamand warns “You don’t want hospitals to be sensors for toxicity. That’s too late”. This desire to provide a more rapid and effective testing apparatus propelled Shacham-Diamand’s group to design a “lab on a chip” capable of accurately detecting a wide range of contaminants in water within minutes of simply adding water to the chip.

This “lab on a chip” is built upon genetically engineered E. coli in reaction chambers on a chip as seen in the diagram below (Subfigures A + B). When exposed to nL samples the E. coli luminesce in the presence toxins which are then detected and quantified by the signal strength. Initial experiments done with E. coli containing the lac promoter (activated by IPTG) fused to lux-CDABE genes of V. Fischeri proved the feasibility of utilizing whole cell bacteria in order to generate luminescent signal that could be detected utilizing a solid-state photodetector¹. Other experiments conducted by Dr. Shacham-Diamond’s group have proved the feasibility of detecting a variety of contaminants with genetically engineered E. coli².

Currently, Dr. Shacham-Diamand’s group has worked on further modeling of their chip design (above Subfigure C) in order to optimize the detection of the luminescent signal. The flexibility generated by using genetically engineered E. coli has Dr. Shacham-Diamand’s group looking into alternative applications of their chip such as screening potential cancer drugs.

In the end innovative nanoscale devices like this “lab on a chip” and the DNA-coated nanowire device we blogged about previously show tremendous promise for improving our ability to detect and diagnose a wide range of problems be they contaminated water or diseases.

(Sources: Tel Aviv University , 1 – Towards toxicity detection using a lab-on-chip based on the integration of MOEMS and whole-cell sensors , 2 - Novel Integrated Electrochemical Nano-Biochip for Toxicity Detection in Water)

Although digital modeling may eventually lead to the creation of the Matrix, I think it's safe to take our chances.

The digital age is upon us! SLR cameras are now DSLRs, VHS tapes are now Blu-ray discs, CRT monitors are now OLED screens, and every two years, our chips double in computational power (as predicted by Moore’s Law). With all these rapid advancements in technology, I believe the next step will be a dramatic increase in the use of computer modeling.

We see the signs everywhere. More and more, people are relying on computers to help them gather and process information. But why stop there? Why not have computers be the primary engine of design and testing? Obviously it’d be remiss to ignore the human factor in completing any project, at least until the apocalypse foretold by the Matrix or the Terminator series comes true. However, I’m convinced that the more we incorporate computer programs into our decisions and our design flows, the better, for three basic reasons:

1. Objectivity: To quote Dr. Gregory House, “people always lie.” I don’t mean this in a cynical way, but humans “lie” even when they don’t intend to. We’ve all heard of the placebo affect and how manipulable and inconsistent human memory is. Computers, on the other hand, aren’t designed to lie or to be irrational. Give a computer program an input, and you can rest assured that it will faithfully return the output it calculates is best. No matter what the circumstance, you can rely on computer programs to provide unbiased, objective results.
2. Consistency: Try shooting 1000 free throws in a row. Impossible? Maybe, for a human (unless you’re Steve Nash). However, program a robot to shoot a basketball the same way, with the same stroke, and the same force, and you’re looking at a free-throw shooting machine (literally). Computers only know how to do what they’re programmed to do. Given an algorithm, a computer will duplicate it consistently, no matter how many times the user asks.
3. Speed: How fast can you calculate the roots of the polynomial 23x³ + 17x² – 31x + 71? With advancements in transistor speed and computing algorithm, the complexity of problems a computer can solve (and the speed at which they can be solved) is astounding — and growing. Some calculations that are considered intractable for humans are done in seconds on a computer.

These three points underscore my main argument: we shouldn’t use computers only when it’s necessary, but whenever it’s possible. Computers are fast, efficient, and reliable. With careful ingenuity and good programming, there’s no limit to what computers can accomplish. That’s why, to me, the ability to model things digitally is such an appealing prospect. The benefits include:

1. Thorough testing: As a computer scientist myself, I can attest to the fact that there is no such thing as too much testing, and especially when it comes to safety concerns. Using computers, builders of things ranging from automobiles to buildings will be able to run many tests on a product or a feature before release and, potentially, before a single tool is touched. This enhances our ability to design effective and safe products.
2. Lower costs: Instead of spending millions on supplies and materials for multiple physical prototypes, virtual trials will enable cheap testing of new designs and features. Obviously, physical trials cannot (and should not) be omitted, but digital models which are intrinsically easily re-tested and modified, are clearly the cost-effective approach to designing/testing/tweaking. Ask yourself what’s cheaper: designing four prototypes to test four possible features, or running four computer models of the different features and building out only two prototypes with the two best features?
3. Better understanding of design: Some scientists joke that engineers can only understand things that they can take apart and put it back together. While that subject is worth a post on its own, it’s hard to argue that one level of understanding comes from being able to model. Comparing these computer models to reality and seeing the differences, then, allows the engineer/designer to test their understanding. A product which succeeds on the computer monitor but fails in reality suggests that there is some aspect of the design that isn’t being modeled correctly — and that insight not only furthers scientific understanding, but can also help designers quickly design the next product or feature.

This is the power of virtual modeling. Using our computers to do the dirty work while we reap the benefits of inexpensive and efficient computing. We’ve already seen glimpses of this happening, and I believe that these trends will not only continue, but are good things for us to embrace. Imagine the FDA requiring drug trials to be first run in a computer model: testing its efficacy on a simulation of a human body. Imagine regulations requiring architects and contractors to digitally test the earthquake safety of a building before they even lay the first beam. The possibilities, and possible benefits, are endless.

To reach that end stage, I see several things needing to happen:

1. Greater emphasis on developing good mathematical models: In order to model things rigorously on a computer, we need a solid understanding of how things work, and we need to ingrain the “art” of computer modeling into a new generation of designers and engineers. Computer models should not be a luxury that only specialty shops engage in, they should be the norm, and something every architect, engineer, and product designer is aware of.
2. Developing better computing technologies: Although processors today are operating at unprecedented speeds, in order to realize large-scale digital modeling, we still need our computers to be faster and more efficient. This may mean designing faster computer chips (or using GPUs and/or gaming processors) or looking into innovative new technologies such as distributed computing.
3. Developing/sharing numerical computing algorithms: While faster computers means being able to physically process more data, this is only half of the equation. On the software side, computer scientists still need to find and deploy innovative solutions to solving these challenging modeling problems or else no amount of computing power will ever be enough. Hopefully, the scientific computing community will move to develop and share new (ideally open source) innovations oriented around solving these computing challenges, and an open environment of collaboration will drive the greatest innovation and adoption of these techniques.

If you have any thoughts on this subject, would love to hear them in the comments!