Printed Dots Detect Ebola (and More) Without a Lab – IEEE Spectrum

Posted on by cathywu

A “toehold hairpin RNA” sensor is a key to the process. If a single strand of RNA includes complementary sequences at separated stretches along its length, it can fold back upon itself to form a hairpin. The Wyss researchers engineer an RNA sequence so that it includes: a stretch of detector RNA that will bind to messenger RNA produced by the target (a transcript Ebola virus produces to build coat proteins for new viruses, for example); a ribosome binding site sequence, which will prompt the ribosome to grab the molecule and start reading its instructions to make protein; a “closure” sequence that binds to the detector RNA, hiding the ribosome binding site in the loop of the hairpin; and mRNA instructions for an enzyme (such as beta-galactosidase) that will alter the structure of a reporter molecule (such as a yellow form of galactose) to change its color (say, to a purple).

The design leaves the toehold, a short strip of detector RNA, dangling free at the bottom of the hairpin. The target RNA latches onto the toehold and starts zipping up along the rest of the detector sequence—and unzipping the closure sequence. This opens up the ribosome binding site in the hairpin. The unfolded mRNA then passes through the ribosome, and the ribosome produces the enzyme. The enzyme reacts with the reporter and, voila, the color changes.

Cost. Paper-based diagnostics could cost as little as US $0.02 to $0.04 per sensor, they say. This is dramatically lower than the $0.45 to $1.40 for familiar antibody-based rapid diagnostics tests (RDTs) like home pregnancy and glucose kits, and the $1.50 to $4.00 cost of the reagents used in a PCR (polymerase chain reaction) DNA assay.

Speed. The paper-based synthetic gene network diagnostics the Harvard team produced are about as fast as antibody-based home tests, a little faster than PCR, and much faster than the bacterial and viral culture methods that have been a diagnostic mainstay. The Wyss group’s paper diagnostics produce detectable color changes in 20 to 40 minutes (or perhaps longer, depending on the assay and the concentration of the target molecule). Antibody-based RDTs show results in about 20 minutes. And PCR assays require at least 60 minutes…and a well-equipped laboratory.

via Printed Dots Detect Ebola (and More) Without a Lab – IEEE Spectrum.

Paper-Based Synthetic Gene Networks
Pardee, Keith et al.
Cell , Volume 159 , Issue 4 , 940 – 954

Abstract:

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts – IEEE Spectrum

Posted on by cathywu

On neural nets (deep learning):

Spectrum: If you could, would you declare a ban on using the biology of the brain as a model in computation?

Michael Jordan: No. You should get inspiration from wherever you can get it. As I alluded to before, back in the 1980s, it was actually helpful to say, “Let’s move out of the sequential, von Neumann paradigm and think more about highly parallel systems.” But in this current era, where it’s clear that the detailed processing the brain is doing is not informing algorithmic process, I think it’s inappropriate to use the brain to make claims about what we’ve achieved. We don’t know how the brain processes visual information.

On big data:

Michael Jordan: So it’s like having billions of monkeys typing. One of them will write Shakespeare.

Spectrum: Do we currently have the tools to provide those error bars [on predictions]?

Michael Jordan: We are just getting this engineering science assembled. We have many ideas that come from hundreds of years of statistics and computer science. And we’re working on putting them together, making them scalable. A lot of the ideas for controlling what are called familywise errors, where I have many hypotheses and want to know my error rate, have emerged over the last 30 years. But many of them haven’t been studied computationally. It’s hard mathematics and engineering to work all this out, and it will take time.

It’s not a year or two. It will take decades to get right. We are still learning how to do big data well.

Spectrum: What adverse consequences might await the big-data field if we remain on the trajectory you’re describing?

Michael Jordan: The main one will be a “big-data winter.” After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.” I am predicting that. It’s what happens in these cycles when there is too much hype, i.e., assertions not based on an understanding of what the real problems are or on an understanding that solving the problems will take decades, that we will make steady progress but that we haven’t had a major leap in technical progress. And then there will be a period during which it will be very hard to get resources to do data analysis. The field will continue to go forward, because it’s real, and it’s needed. But the backlash will hurt a large number of important projects.

On $1bil:

Spectrum:So are you saying that for a billion dollars, you could, at least as far as natural language is concerned, solve the problem of generalized knowledge and end up with the big enchilada of AI: machines that think like people?

Michael Jordan: So you’d want to carve off a smaller problem that is not about everything, but which nonetheless allows you to make progress. That’s what we do in research. I might take a specific domain. In fact, we worked on question-answering in geography. That would allow me to focus on certain kinds of relationships and certain kinds of data, but not everything in the world.

via Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts – IEEE Spectrum.