Plug-and-play diagnostic devices

Jose Gomez-Marquez, co-director of MIT’s Little Devices Lab, holds a sheet of paper diagnostic blocks, which can be easily printed and then combined in various ways to create customized diagnostic devices. Image: Melanie Gonick/MIT

Modular blocks could enable labs around the world to cheaply and easily build their own diagnostics.

Watch Video

Researchers at MIT’s Little Devices Lab have developed a set of modular blocks that can be put together in different ways to produce diagnostic devices. These “plug-and-play” devices, which require little expertise to assemble, can test blood glucose levels in diabetic patients or detect viral infection, among other functions.

Ampli blocks. Image by Melanie Gonick, MIT. 

“Our long-term motivation is to enable small, low-resources laboratories to generate their own libraries of plug-and-play diagnostics to treat their local patient populations independently,” says Anna Young, co-director of MIT’s Little Devices Lab, lecturer at the Institute for Medical Engineering and Science, and one of the lead authors of the paper.

Using this system, called Ampli blocks, the MIT team is working on devices to detect cancer, as well as Zika virus and other infectious diseases. The blocks are inexpensive, costing about 6 cents for four blocks, and they do not require refrigeration or special handling, making them appealing for use in the developing world.

“We see these construction kits as a way of lowering the barriers to making medical technology,” says Jose Gomez-Marquez, co-director of the Little Devices Lab and the senior author of the paper.

Elizabeth Phillips ’13, a graduate student at Purdue University, is also a lead author of the paper, which appears in the journal Advanced Healthcare Materials on May 16. Other authors include Kimberly Hamad-Schifferli, an associate professor of engineering at the University of Massachusetts at Boston and a visiting scientist in MIT’s Department of Mechanical Engineering; Nikolas Albarran, a senior engineer in the Little Devices Lab; Jonah Butler, an MIT junior; and Kaira Lujan, a former visiting student in the Little Devices Lab.

Advanced Healthcare Materials Paper (click to enlarge)

Customized diagnostics

Over the past decade, many researchers have been working on small, portable diagnostic devices based on chemical reactions that occur on paper strips. Many of these tests make use of lateral flow technology, which is the same approach used in home pregnancy tests.

Despite these efforts, such tests have not been widely deployed. One obstacle, says Gomez-Marquez, is that many of these devices are not designed with large-scale manufacturability in mind. Another is that companies may not be interested in mass-producing a diagnostic for a disease that doesn’t affect a large number of people.

The Little Devices Lab researchers realized that they could get these diagnostics into the hands of many more people if they created a kit of modular components that can be put together to generate exactly what the user needs. To that end, they have created about 40 different building blocks that lab workers around the world could easily assemble on their own, just as people began assembling their own radios and other electronic devices from commercially available electronic “breadboards” in the 1970s.

“When the electronic breadboard came out, that meant people didn’t have to worry about building their own resistors or capacitors. They could worry about what they actually wanted to use electronics for, which is to make the entire circuit,” Gomez-Marquez says.

In this case, the components consist of a sheet of paper or glass fiber sandwiched between a plastic or metal block and a glass cover. The blocks, which are about half an inch on each edge, can snap together along any edge.

• Some of the blocks contain channels for samples to flow straight through, 
• some have turns, and 
• some can receive a sample from a pipette or 
• mix multiple reagents together.

The blocks can also perform different biochemical functions. Many contain antibodies that can detect a specific molecule in a blood or urine sample. Those antibodies are attached to nanoparticles that change color when the target molecule is present, indicating a positive result.

These blocks can be aligned in different ways, allowing the user to create diagnostics based on one reaction or a series of reactions. In one example, the researchers combined blocks that detect three different molecules to create a test for isonicotinic acid, which can reveal whether tuberculosis patients are taking their medication.

The blocks are color-coded by function, making it easier to assemble predesigned devices using instructions that the researchers plan to put online. They also hope that users will develop and contribute their own specifications to the online guide.

Better performance

The researchers also showed that in some ways, these blocks can outperform previous versions of paper diagnostic devices. For example, they found that they could run a sample back and forth over a test strip multiple times, enhancing the signal. This could make it easier to get reliable results from urine and saliva samples, which are usually more dilute than blood samples, but are easier to obtain from patients.

“These are things that cannot be done with standard lateral flow tests, because those are not modular — you only get to run those once,” says Hamad-Schifferli.

The team is now working on tests for human papilloma virus, malaria, and Lyme disease, among others. They are also working on blocks that can synthesize useful compounds, including drugs, as well as blocks that incorporate electrical components such as LEDs.

The ultimate goal is to get the technology into the hands of small labs in both industrialized and developing countries, so they can create their own diagnostics. The MIT team has already sent them to labs in Chile and Nicaragua, where they have been used to develop devices to monitor patient adherence to TB treatment and to test for a genetic variant that makes malaria more difficult to treat.

Catherine Klapperich, associate dean for research and an associate professor of biomedical engineering at Boston University, says the MIT team’s work will help to make the diagnostic design process more inclusive.

“By reducing the barriers to designing new point-of-care paperfluidics, the work invites nonexperts in and will certainly result in new ideas and collaborations in settings all around the world,” says Klapperich, who was not involved in the research. “The practical demonstrations of the system presented here are poised to be immediately useful, while the possibilities for others to build on the tool are large.”

The researchers are now investigating large-scale manufacturing techniques, and they hope to launch a company to manufacture and distribute the kits around the world.

“We are excited to open the platform to other researchers so they can use the blocks and generate their own reactions,” Young says.

The research was funded by a gift from Autodesk and the U.S. Public Health Service.

ORIGINAL: MIT News

By Anne Trafton | MIT News Office 

May 16, 2018

MIT Just Created Living Plants That Glow Like A Lamp, And Could Grow Glowing Trees To Replace Streetlights

Roads of the future could be lit by glowing trees instead of streetlamps, thanks to a breakthrough in creating bioluminescent plants. Experts injected specialized nanoparticles into the leaves of a watercress plant, which caused it to give off a dim light for nearly four hours. This could solve lots of problems.

The chemical involved, which produced enough light to read a book by, is the same as is used by fireflies to create their characteristic shine. To create their glowing plants, engineers from the Massachusetts Institute of Technology (MIT) turned to an enzyme called luciferase. Luciferase acts on a molecule called luciferin, causing it to emit light.

 

Roads of the future could be lit by glowing trees instead of streetlamps, thanks to a breakthrough in creating bioluminescent plants. Experts created a watercress plant which caused it to glow for nearly four hours and gave off enough light to illuminate this book

Another molecule called Co-enzyme A helps the process along by removing a reaction byproduct that can inhibit luciferase activity. The MIT team packaged each of these components into a different type of nanoparticle carrier.

The nanoparticles help them to get to the right part of the plant and also prevent them from building to concentrations that could be toxic to the plants. The result was a watercress plant that functioned like a desk lamp.

Researchers believe with further tweaking, the technology could also be used to provide lights bright enough to illuminate a workspace or even an entire street, as well as low-intensity indoor lighting. 

Michael Strano, professor of chemical engineering at MIT and the senior author of the study, said: 'The vision is to make a plant that will function as a desk lamp — a lamp that you don't have to plug in. The light is ultimately powered by the energy metabolism of the plant itself. Our work very seriously opens up the doorway to streetlamps that are nothing but treated trees, and to indirect lighting around homes.'

Luciferases make up a class of oxidative enzymes found in several species that enable them to 'bioluminesce', or emit light. Fireflies are able to emit light via a chemical reaction.

In the chemical reaction luciferin is converted to oxyluciferin by the luciferase enzyme. Some of the energy released by this reaction is in the form of light. The reaction is highly efficient, meaning nearly all the energy put into the reaction is rapidly converted to light.

Lighting accounts for around 20 per cent of worldwide energy consumption, so replacing them with naturally bioluminescent plants would represent a significant cut to CO2 emissions. The researchers’ early efforts at the start of the project yielded plants that could glow for about 45 minutes, which they have since improved to 3.5 hours.

The light generated by one ten centimetre (four inch) watercress seedling is currently about one-thousandth of the amount needed to properly read by, but it was enough to illuminate the words on a page of John Milton's Paradise Lost.

The MIT team believes it can boost the light emitted, as well as the duration of light, by further optimising the concentration and release rates of the chemical components. For future versions of this technology, the team hopes to develop a way to paint or spray the nanoparticles onto plant leaves, which could make it possible to transform trees and other large plants into light sources. 

The researchers have also demonstrated that they can turn the light off by adding nanoparticles carrying a luciferase inhibitor. This could enable them to eventually create plants that shut off their light emission in response to environmental conditions such as sunlight, they say.

The full findings of the study were published in the American Chemical Society journal Nano Letters.

ORIGINAL: The Space Academy
December 18, 2017

The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe

Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics.

In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players.

But there is a problem. There is no mathematical reason why networks arranged in layers should be so good at these challenges. Mathematicians are flummoxed. Despite the huge success of deep neural networks, nobody is quite sure how they achieve their success.

Today that changes thanks to the work of Henry Lin at Harvard University and Max Tegmark at MIT. These guys say the reason why mathematicians have been so embarrassed is that the answer depends on the nature of the universe. In other words, the answer lies in the regime of physics rather than mathematics.

First, let’s set up the problem using the example of classifying a megabit grayscale image to determine whether it shows a cat or a dog.

Such an image consists of a million pixels that can each take one of 256 grayscale values. So in theory, there can be 2561000000 possible images, and for each one it is necessary to compute whether it shows a cat or dog. And yet neural networks, with merely thousands or millions of parameters, somehow manage this classification task with ease.

In the language of mathematics, neural networks work by approximating complex mathematical functions with simpler ones. When it comes to classifying images of cats and dogs, the neural network must implement a function that takes as an input a million grayscale pixels and outputs the probability distribution of what it might represent.

The problem is that there are orders of magnitude more mathematical functions than possible networks to approximate them. And yet deep neural networks somehow get the right answer.

Now Lin and Tegmark say they’ve worked out why. The answer is that the universe is governed by a tiny subset of all possible functions. In other words, when the laws of physics are written down mathematically, they can all be described by functions that have a remarkable set of simple properties.

So deep neural networks don’t have to approximate any possible mathematical function, only a tiny subset of them.

To put this in perspective, consider the order of a polynomial function, which is the size of its highest exponent. So a quadratic equation like y=x2 has order 2, the equation y=x24 has order 24, and so on.

Obviously, the number of orders is infinite and yet only a tiny subset of polynomials appear in the laws of physics. “For reasons that are still not fully understood, our universe can be accurately described by polynomial Hamiltonians of low order,” say Lin and Tegmark. Typically, the polynomials that describe laws of physics have orders ranging from 2 to 4.

The laws of physics have other important properties. For example, they are usually symmetrical when it comes to rotation and translation. Rotate a cat or dog through 360 degrees and it looks the same; translate it by 10 meters or 100 meters or a kilometer and it will look the same. That also simplifies the task of approximating the process of cat or dog recognition.

These properties mean that neural networks do not need to approximate an infinitude of possible mathematical functions but only a tiny subset of the simplest ones.

There is another property of the universe that neural networks exploit. This is the hierarchy of its structure. “Elementary particles form atoms which in turn form molecules, cells, organisms, planets, solar systems, galaxies, etc.,” say Lin and Tegmark. And complex structures are often formed through a sequence of simpler steps.

This is why the structure of neural networks is important too: the layers in these networks can approximate each step in the causal sequence.

Lin and Tegmark give the example of the cosmic microwave background radiation, the echo of the Big Bang that permeates the universe. In recent years, various spacecraft have mapped this radiation in ever higher resolution. And of course, physicists have puzzled over why these maps take the form they do.

Tegmark and Lin point out that whatever the reason, it is undoubtedly the result of a causal hierarchy. “A set of cosmological parameters (the density of dark matter, etc.) determines the power spectrum of density fluctuations in our universe, which in turn determines the pattern of cosmic microwave background radiation reaching us from our early universe, which gets combined with foreground radio noise from our galaxy to produce the frequency-dependent sky maps that are recorded by a satellite-based telescope,” they say.

Each of these causal layers contains progressively more data. There are only a handful of cosmological parameters but the maps and the noise they contain are made up of billions of numbers. The goal of physics is to analyze the big numbers in a way that reveals the smaller ones.

And when phenomena have this hierarchical structure, neural networks make the process of analyzing it significantly easier.

“We have shown that the success of deep and cheap learning depends not only on mathematics but also on physics, which favors certain classes of exceptionally simple probability distributions that deep learning is uniquely suited to model,” conclude Lin and Tegmark.

That’s interesting and important work with significant implications. Artificial neural networks are famously based on biological ones. So not only do Lin and Tegmark’s ideas explain why deep learning machines work so well, they also explain why human brains can make sense of the universe. Evolution has somehow settled on a brain structure that is ideally suited to teasing apart the complexity of the universe.

This work opens the way for significant progress in artificial intelligence. Now that we finally understand why deep neural networks work so well, mathematicians can get to work exploring the specific mathematical properties that allow them to perform so well. “Strengthening the analytic understanding of deep learning may suggest ways of improving it,” say Lin and Tegmark.

Deep learning has taken giant strides in recent years. With this improved understanding, the rate of advancement is bound to accelerate.

Ref: arxiv.org/abs/1608.08225: Why Does Deep and Cheap Learning Work So Well?

ORIGINAL: TechnologyReview

by Emerging Technology from the arXiv

September 9, 2016

Scientists Built a Biological Computer Inside a Cell

Add caption

MIT engineers have developed biological computational circuits capable of both remembering and responding to sequential input data.

The group's work, which is described in this week's issue of Science, represents a critical step in the progression of synthetic biology with the integration of DNA-based memory, in particular, pointing the way toward building large computational systems from biological components—computing devices that are living cells—and, ultimately, programming complex biological functions.

More specifically, Nathaniel Roquet and colleagues at MIT's Synthetic Biology Group were able to implement within a living cell what's known as a state machine: an abstract mathematical model describing computation as a list of of distinct internal states paired with an associated list of operations (or machine inputs) required to transition from state to state. So: a new state is always the result of an old state taken in combination with new inputs (history matters). State machines happen to describe a very large number of different things, from natural language processing algorithms to neurological systems to something as simple as a vending machine.

In a living cell, DNA is the natural candidate for storing state information. After all, that's what DNA does: store information. What Roquet and co. have created is a framework for chemically manipulating DNA such that states are encoded in DNA sequences. As a storage mechanism, this allows for both conveniently reading out a given state via genetic sequencing and also regulating gene expression via state transitions. In other words, the states can be linked to cellular behavior. The DNA serves as the memory for the state machine. The rest is in how, specifically, the DNA is manipulated and what effect that has on cellular behavior.

This could mean integrating biological state machines into tumor models, where they may be used to genetically surveil the activation of genes that may cause cancer

In their experiments, Roquet and co. programmed E. coli cells to react to several substances commonly used in biological laboratory experiments, including an analogue of the antibiotic tetracycline, a sugar called arabinose, and a chemical called DAPG that helps plants protect their roots from pathogens. The cells could be reprogrammed to other inputs as needed, however.

The actual cell behavior being programmed by the researchers was the expression of genes coding for the production of different fluorescent proteins representing different colors. With three different inputs they were able to produce 16 different combinations of colors.

"Synthetic state machines that record and respond to sequences of signaling and gene regulatory events within a cell could be transformative tools in the study and engineering of complex living systems," Roquet writes. In other words, by implementing a state machine (a computer) in a living cell, it's possible to use that state machine to surveil otherwise impossible-to-observe cellular happenings.

For example, progenitor cells (similar to stem cells) develop into differentiated cell types with specific functions thanks to transcription factors, proteins that help regulate gene expression in cells. Transcription factors have allowed researchers to program both progenitor cells to become certain specific types of functional cells—and also to do the opposite, programming functional cells to behave as undifferentiated cells. However, much about the process remains mysterious. A state machine that could record the DNA transitions resulting from TF activation could go a long way toward not only understanding these processes, but manipulating them as well. 

The circuits in the biological state machine are dependent on enzymes called recombinases. These enzymes are activated by various inputs into a cell, such as chemical signals, and act to tweak that cell's DNA. But the tweak that actually occurs depends on the orientation of two DNA sequences known as recognition sites. The important thing is that the effect of changing any two recognition sites (the resulting cellular behavior) depends on how other recognition sites have been altered previously. Hence, memory.

There's really no shortage of potential applications here. The example Roquet gives is in integrating biological state machines into tumor models, where they may be used to genetically surveil the activation of oncogenes (genes that may cause cancer) and deactivation of tumor suppression mechanisms in individual cells.

"This idea that we can record and respond to not just combinations of biological events but also their orders opens up a lot of potential applications," Roquet offers in a statement. "A lot is known about what factors regulate differentiation of specific cell types or lead to the progression of certain diseases, but not much is known about the temporal organization of those factors. That's one of the areas we hope to dive into with our device."

Computers have become "alive," but perhaps not in the way that many of us anticipated. A unicellular organism itself won't ever be packing much computational horsepower, but considered as a building block, the potential is pretty wild.

ORIGINAL: Vice

by MICHAEL BYRNE EDITOR 
July 21, 2016

Ten institutions that dominated science in 2015

Getty Images/iStockphoto/Thinkstock

The University of Oxford is the top research UK university in the Nature Index for the first time.

The top 10 institutions in the Nature Index are the largest contributors to papers published in 68 leading journals in 2015.

Full list of institutions

2016 tables

How the Nature Index works

1. Chinese Academy of Sciences (CAS), China

Weighted fractional count (WFC): 1357.82

Established in 1949 in Beijing, the Chinese Academy of Sciences (CAS) is the world’s largest scientific organisation, comprising 114 institutes and 48,500 researchers. In 2015 its scientists made the largest contribution to high-quality research included in the index, a contribution that’s grown by a compound annual growth rate of 6.8% since 2012. Last year, one of the oldest CAS research centres, the Institute of Chemistry, founded in 1956, was also one of the largest contributing departments to the institute’s weighted fractional count. Lei Jiang, from the Institute of Chemistry, says: “Chemistry and materials science are currently strong in China because they were relatively easy subjects to start researching back in the 1980s. They didn’t need expensive equipment. If you look at nanoscience as an example, around half of the top scientists in global nanoscience are Chinese scientists who were educated during this period.”

2. Harvard University, United States

WFC: 772.33

As the second most prolific institution in the Index, and the most prolific university, Harvard University owes two-thirds of its research output to contributions made in the life sciences. Recognising the growth of interdisciplinary research areas such as translational medicine in the life sciences, Harvard has responded by developing an integrated PhD programme that facilitates cross-disciplinary academic and research collaboration. The Harvard Medical School (HMS), established in 1782, is one of the discipline’s high-fliers. Since the 1930s, fifteen researchers from HMS have shared in nine Nobel Prizes. For the most recent of them, in 2009, Jack Szostak discovered how telomeres cap the ends of chromosomes and protect them from degradation, opening up new lines of enquiry into ageing and cancer research. While last year, a popular piece of research from Harvard’s sleep medicine department highlighted the negative effects of light-emitting e-readers on sleep quality.

3. French National Centre for Scientific Research (CNRS), France

WFC: 699.45

The French National Centre for Scientific Research (CNRS) is the largest fundamental research organisation in Europe, comprising ten institutions with more than 32,000 researchers, engineers and technicians. The physical sciences made up more than a third of the contributions to the Index in 2015. In recent years the CNRS has become a key player in planetary science. CNRS engineers have been remotely operating and maintaining instruments on board NASA’s Curiosity Rover, which has been exploring Mars since 2012. The mission lead to the confirmation late last year that liquid water currently flows on the planet. This year CNRS researcher Franck Montmessin is leading the European Space Agency’s mission ExoMars 2016 to investigate the planet’s atmosphere and find evidence of past life beneath its surface.

4. Max Planck Society, Germany

WFC: 655.67

Ranked fourth in the Nature Index, the Max Planck Society has the physical sciences to thank for its high position. Since 1914, physics researchers at Max Planck, a government-funded association of research institute, have won nine Nobel Prizes. The society is named after the quantum theorist, Max Planck, the institute’s second Nobel recipient in 1918, four years after Max von Laue won for his pioneering work in X-ray crystallography. During the Second World War, Laue’s gold medal was dissolved in acid to prevent discovery by the Nazis, and then recast by the Nobel Society after the war. Today, two institutes stand out in terms of research output in the index: the polymer and solid state research units. Last year, nanochemistry scientists focusing on solid-state research published a paper in Advanced Materials that explored moisture-sensitive technology and its potential use in touchless screens.

5. Stanford University, United States

WFC: 530.83

When Stanford University opened its doors to students in 1891, it became one of America’s first non-sectarian, co-educational private colleges. Stanford’s first president, David Starr Jordan, said: “Work in applied science is to be carried out side-by-side with the pure sciences and humanities, and to be equally fostered.” This attitude remains true today as research output is equally balanced between all the disciplines. The university is also credited for its entrepreneurial spirit. During the 1950s, the University leased land to a new industrial park that went on to become Silicon Valley. To this day, Stanford researchers continue this legacy of real-world influence. Howard Rose, a Stanford residential fellow and CEO of Deep Stream VR, is currently developing a virtual reality app called Cool! that is designed to immerse patients in a world that enables them to manage chronic pain without drugs.

6. University of Tokyo, Japan

WFC: 487.03

Last year’s Nobel Prize for Physics went to Takaaki Kajita, the director of the University of Tokyo’s Institute for Cosmic Ray Research. But UTokyo is not resting on its laurels. The university is focused on opening its doors to the outside world by welcoming international collaborations, foreign staff and students, and closer relationships with business. The institute’s latest high-profile research reflects this gearshift. UTokyo’s Graduate School of Frontier Sciences was involved in a decade-long project with four countries to establish the presence of a giant ocean that wraps Saturn’s moon, Encheladus, beneath its crust.

7. Massachusetts Institute of Technology, United States

WFC: 483.62

This year marks a hundred years since the Massachusetts Institute of Technology (MIT) moved from Boston to Cambridge in the US. The move ushered in an age of achievement, from the first chemical synthesis of penicillin to building the magnetic core memory that made digital computers possible. The university’s is known for its hands-on approach to teaching and research - students are given a practical-based education in science, technology and related areas of scholarship - appears to be on the money. A 2015 report suggested that 30,000 companies founded by MIT alumni were active as of 2014, employing 4.6 million people and producing annual revenues of $1.9 trillion, equivalent to the world’s 10th largest economy. Eighty-five present and former members of the MIT community have won the Nobel Prize, including nine current faculty members.

8. Helmholtz Association of German Research Centres, Germany

WFC: 413.71

The Helmholtz Association of German Research Centres is the country’s largest scientific organisation with almost 38,000 employees and an annual budget of nearly €4 billion. Their research output is dominated by the physical sciences, and last year the Karlsruhe Institute of Technology (KIT) had the biggest impact on the association’s overall ranking in the index. KIT was officially founded in 2009 when the Karlsruhe Research Center and University of Karlsruhe merged. In a recent paper in Nature Photonics, KIT researchers demonstrated a way to store optical information that could dramatically reduce the power consumption and increase the speed of optical communication networks.

9. University of Oxford, United Kingdom

WFC: 398.38

Having produced 16 Nobel Laureates in medicine since 1932, the University of Oxford has a long pedigree in the life sciences. More than 30% of contributions to the most recent Nature Index come from this discipline. In the 21st century, the university focuses on a wide range of fascinating aspects of the life sciences from clinical practice in modern medicine to epidemiological and genetic studies. For example, researchers are currently exploring the negative effects of loud noise in an intensive care unit. Another group of scientists whose research is soon to published, have recently sequenced seven genomes of the Brazilian Zika virus taken from seven different individuals infected with the virus to determine when and where it entered the Americas.

10. University of Cambridge, United Kingdom

WFC: 390.54

A strong showing in the physical sciences has helped put the University of Cambridge in the Nature Index top ten. One of the key contributors to the university’s research output is the Cavendish Laboratory, which is the largest physics department in the UK and birthplace of ground-breaking discoveries such as the structure of DNA, and the splitting the atom. According to Professor Andy Parker, head of the Cavendish Laboratory: “The laboratory’s strength comes from the very wide range of research performed, from cosmology, through solid state and nanoscience, to the physics of medicine, backed up by world-class facilities, and the freedom given to its staff to pursue their own directions. This bottom-up approach to research strategy has proved its worth over many decades.”

How the Nature Index works

Since 2012, every paper published in 68 top-tier journals has been included in the Nature Index database. The index ranks the contribution of research institutions to these articles, by a metric called weighted fractional count (WFC), which divides credit for each article by the affiliations of contributing authors. This measure is weighted to account for the disproportionate number of astronomy articles in the index.

ORIGINAL: Nature Index

By Sarah O'Meara

20 April 2016

Gene circuits in live cells can perform complex computations.

Technique combines analogue and digital processes in engineered cells.

Stock image of mitotic cells

Living cells are capable of performing complex computations on the environmental signals they encounter.

These computations can be continuous, or analogue, in nature — the way eyes adjust to gradual changes in the light levels. They can also be digital, involving simple on or off processes, such as a cell’s initiation of its own death.

Synthetic biological systems, in contrast, have tended to focus on either analogue or digital processing, limiting the range of applications for which they can be used.

But now a team of researchers at MIT has developed a technique to integrate both analogue and digital computation in living cells, allowing them to form gene circuits capable of carrying out complex processing operations.

The synthetic circuits, presented in a paper published today in the journal Nature Communications, are capable of measuring the level of an analogue input, such as a particular chemical relevant to a disease, and deciding whether the level is in the right range to turn on an output, such as a drug that treats the disease.

In this way they act like electronic devices known as comparators, which take analogue input signals and convert them into a digital output, according to Timothy Lu, an associate professor of electrical engineering and computer science and of biological engineering, and head of the Synthetic Biology Group at MIT’s Research Laboratory of Electronics, who led the research alongside former microbiology PhD student Jacob Rubens.

“Most of the work in synthetic biology has focused on the digital approach, because [digital systems] are much easier to program,” Lu says.

However, since digital systems are based on a simple binary output such as 0 or 1, performing complex computational operations requires the use of a large number of parts, which is difficult to achieve in synthetic biological systems.

“Digital is basically a way of computing in which you get intelligence out of very simple parts, because each part only does a very simple thing, but when you put them all together you get something that is very smart,” Lu says. “But that requires you to be able to put many of these parts together, and the challenge in biology, at least currently, is that you can’t assemble billions of transistors like you can on a piece of silicon,” he says.

The mixed signal device the researchers have developed is based on multiple elements. A threshold module consists of a sensor that detects analogue levels of a particular chemical.

This threshold module controls the expression of the second component, a recombinase gene, which can in turn switch on or off a segment of DNA by inverting it, thereby converting it into a digital output.

If the concentration of the chemical reaches a certain level, the threshold module expresses the recombinase gene, causing it to flip the DNA segment. This DNA segment itself contains a gene or gene-regulatory element that then alters the expression of a desired output.

“So this is how we take an analogue input, such as a concentration of a chemical, and convert it into a 0 or 1 signal,” Lu says. “And once that is done, and you have a piece of DNA that can be flipped upside down, then you can put together any of those pieces of DNA to perform digital computing,” he says.

The team has already built an analogue-to-digital converter circuit that implements ternary logic, a device that will only switch on in response to either a high or low concentration range of an input, and which is capable of producing two different outputs.

In the future, the circuit could be used to detect glucose levels in the blood and respond in one of three ways depending on the concentration, he says.

“If the glucose level was too high you might want your cells to produce insulin, if the glucose was too low you might want them to make glucagon, and if it was in the middle you wouldn’t want them to do anything,” he says.

Similar analogue-to-digital converter circuits could also be used to detect a variety of chemicals, simply by changing the sensor, Lu says.

The researchers are investigating the idea of using analogue-to-digital converters to detect levels of inflammation in the gut caused by inflammatory bowel disease, for example, and releasing different amounts of a drug in response.

Immune cells used in cancer treatment could also be engineered to detect different environmental inputs, such as oxygen or tumor lysis levels, and vary their therapeutic activity in response.

Other research groups are also interested in using the devices for environmental applications, such as engineering cells that detect concentrations of water pollutants, Lu says.

Ahmad Khalil, an assistant professor of biomedical engineering at Boston University, who was not involved in the work, says the researchers have expanded the repertoire of computation in cells.

“Developing these foundational tools and computational primitives is important as researchers try to build additional layers of sophistication for precisely controlling how cells interact with their environment,” Khalil says.

The research team recently created a spinout company, called Synlogic, which is now attempting to use simple versions of the circuits to engineer probiotic bacteria that can treat diseases in the gut.

The company hopes to begin clinical trials of these bacteria-based treatments within the next 12 months.

ORIGINAL: MIT News

Helen Knight | MIT News correspondent
June 3, 2016

A programming language for living cells

MIT biological engineers have devised a programming language that can be used to give new functions to E. coli bacteria.
Image: Janet Iwasa

New language lets researchers design novel biological circuits.

MIT biological engineers have created a programming language that allows them to rapidly design complex, DNA-encoded circuits that give new functions to living cells.

Using this language, anyone can write a program for the function they want, such as detecting and responding to certain environmental conditions. They can then generate a DNA sequence that will achieve it.

“It is literally a programming language for bacteria,” says Christopher Voigt, an MIT professor of biological engineering. “You use a text-based language, just like you’re programming a computer. Then you take that text and you compile it and it turns it into a DNA sequence that you put into the cell, and the circuit runs inside the cell.”

Voigt and colleagues at Boston University and the National Institute of Standards and Technology have used this language, which they describe in the April 1 issue of Science, to build circuits that can detect up to three inputs and respond in different ways. Future applications for this kind of programming include designing bacterial cells that can produce a cancer drug when they detect a tumor, or creating yeast cells that can halt their own fermentation process if too many toxic byproducts build up.

The researchers plan to make the user design interface available on the Web.

No experience needed

Over the past 15 years, biologists and engineers have designed many genetic parts, such as sensors, memory switches, and biological clocks, that can be combined to modify existing cell functions and add new ones.

However, designing each circuit is a laborious process that requires great expertise and often a lot of trial and error. “You have to have this really intimate knowledge of how those pieces are going to work and how they’re going to come together,” Voigt says.

Users of the new programming language, however, need no special knowledge of genetic engineering.

“You could be completely naive as to how any of it works. That’s what’s really different about this,” Voigt says. “You could be a student in high school and go onto the Web-based server and type out the program you want, and it spits back the DNA sequence.”

The language is based on Verilog, which is commonly used to program computer chips. To create a version of the language that would work for cells, the researchers designed computing elements such as logic gates and sensors that can be encoded in a bacterial cell’s DNA. The sensors can detect different compounds, such as oxygen or glucose, as well as light, temperature, acidity, and other environmental conditions. Users can also add their own sensors. “It’s very customizable,” Voigt says.

The biggest challenge, he says, was designing the 14 logic gates used in the circuits so that they wouldn’t interfere with each other once placed in the complex environment of a living cell.

In the current version of the programming language, these genetic parts are optimized for E. coli, but the researchers are working on expanding the language for other strains of bacteria, including Bacteroides, commonly found in the human gut, and Pseudomonas, which often lives in plant roots, as well as the yeast Saccharomyces cerevisiae. This would allow users to write a single program and then compile it for different organisms to get the right DNA sequence for each one.

Biological circuits

Using this language, the researchers programmed 60 circuits with different functions, and 45 of them worked correctly the first time they were tested. Many of the circuits were designed to measure one or more environmental conditions, such as oxygen level or glucose concentration, and respond accordingly. Another circuit was designed to rank three different inputs and then respond based on the priority of each one.

One of the new circuits is the largest biological circuit ever built, containing seven logic gates and about 12,000 base pairs of DNA.

Another advantage of this technique is its speed. Until now, “it would take years to build these types of circuits. Now you just hit the button and immediately get a DNA sequence to test,” Voigt says.

His team plans to work on several different applications using this approach: bacteria that can be swallowed to aid in digestion of lactose; bacteria that can live on plant roots and produce insecticide if they sense the plant is under attack; and yeast that can be engineered to shut off when they are producing too many toxic byproducts in a fermentation reactor.

The lead author of the Science paper is MIT graduate student Alec Nielsen. Other authors are former MIT postdoc Bryan Der, MIT postdoc Jonghyeon Shin, Boston University graduate student Prashant Vaidyanathan, Boston University associate professor Douglas Densmore, and National Institute of Standards and Technology researchers Vanya Paralanov, Elizabeth Strychalski, and David Ross.

ORIGINAL: MIT

Anne Trafton | MIT News Office 

March 31, 2016

Acoustic tweezers manipulate cells with sound waves

An illustration of the surface acoustic wave generators, with the generated 3-D trapping nodes. The inset indicates a single particle within a 3-D trapping node, which can be manipulated independently along x, y, or z axes.

Technique could enable 3-D printing of cellular structures for tissue engineering.

Engineers at MIT, Penn State University, and Carnegie Mellon University have devised a way to manipulate cells in three dimensions using sound waves. These “acoustic tweezers” could make possible 3-D printing of cell structures for tissue engineering and other applications, the researchers say.

Designing tissue implants that can be used to treat human disease requires precisely recreating the natural tissue architecture, but so far it has proven difficult to develop a single method that can achieve that while keeping cells viable and functional.

“The results presented in this paper provide a unique pathway to manipulate biological cells accurately and in three dimensions, without the need for any invasive contact, tagging, or biochemical labeling,” says Subra Suresh, president of Carnegie Mellon and former dean of engineering at MIT. “This approach could lead to new possibilities for research and applications in such areas as regenerative medicine, neuroscience, tissue engineering, biomanufacturing, and cancer metastasis.”

Suresh, Ming Dao, a principal research scientist in MIT’s Department of Materials Science and Engineering, and Tony Jun Huang, a professor of engineering science and mechanics at Penn State, are senior authors of a paper describing the device, published the week of Jan. 25. in the Proceedings of the National Academy of Sciences.

The paper’s lead author is Penn State graduate student Feng Guo. The team also includes Penn State researchers Zhangming Mao, Yuchao Chen, James Lata, Peng Li, Liqiang Ren, Jiayang Liu, Zhiwei Xie, and Jian Yang.

3-D control

The new acoustic tweezers are based on a microfluidic device that the researchers previously developed to manipulate cells in two dimensions. This device produces two acoustic standing waves, which are waves with a constant height. Where the two waves meet, they create a “pressure node” that can trap single cells. By altering the wavelength and another wave property known as the phase, the researchers can move the node and the cell trapped within it.

The research team previously used a similar approach to separate cancer cells from healthy cells, which could be useful for detecting rare tumor cells in a patient’s bloodstream and predicting whether the tumor will spread.

In the new study, the researchers added a third dimension of control: Once the cells are trapped in a horizontal plane, they can be moved up and down by altering the acoustic waves’ power, that is, the rate at which sound energy is emitted. Boosting the power allows the researchers to lift the cells from the surface in a type of “acoustic levitation,” then place them in a specific location, Dao says.

The researchers also developed equations that allow them to accurately predict how changes in the wavelength, phase, and acoustic power will affect cells’ positions.

“We now have a good idea of what to expect and how to control the 3-D positioning of the acoustic waves and the pressure nodes, enabling validation of the method as well as system optimization,” Dao says.

“Innovative approach”

In this study, the researchers demonstrated their device on polystyrene particles as well as mouse fibroblast cells. They were able to move the cells, one at a time, into specific positions on a surface and create patterns. They could also stack cells on top of each other.

“This is an exceptionally innovative approach of manipulating particles and single cells in 3-D in fluids,” says Taher Saif, a professor of mechanical science and engineering at the University of Illinois at Urbana-Champaign, who was not part of the research team. “Since acoustic energy is used for this manipulation, the approach is noninvasive and the cells maintain their viability. Overall, the method presented will be of significant interest for a broad community, from biologists to bioengineers.”

The researchers have filed for a patent on the technology and plan to continue developing it for tissue engineering and other applications.

ORIGINAL: MIT News

Anne Trafton | MIT News Office 

January 25, 2016

This Millenial Might Be The New Einstein

Now 22, Pasterski is getting her Ph.D. at Harvard.SOURCE: Michael Noble Jr. / Landov

WHY YOU SHOULD CARE

Her research could change our understanding of the fundamentals as we know them.

One of the things the brilliant minds at MIT do — besides ponder the nature of the universe and build sci-fi gizmos, of course — is notarize aircraft airworthiness for the federal government. So when Sabrina Gonzalez Pasterski walked into the campus offices one cold January morning seeking the OK for a single-engine plane she had built, it might have been business as usual. Except that the shaggy-haired, wide-eyed plane builder before them was just 14 and had already flown solo. “I couldn’t believe it,” recalls Peggy Udden, an executive secretary at MIT, “not only because she was so young, but a girl.”

OK, it’s 2016, and gifted females are not exactly rare at MIT; nearly half the undergrads are women. But something about Pasterski led Udden not just to help get her plane approved, but to get the attention of the university’s top professors. Now, eight years later, the lanky, 22-year-old Pasterski is already an MIT graduate and Harvard Ph.D. candidate who has the world of physics abuzz. She’s exploring some of the most challenging and complex issues in physics, much as Stephen Hawking and Albert Einstein (whose theory of relativity just turned 100 years old) did early in their careers. Her research delves into black holes, the nature of gravity and spacetime. A particular focus is trying to better understand “quantum gravity,” which seeks to explain the phenomenon of gravity within the context of quantum mechanics. Discoveries in that area could dramatically change our understanding of the workings of the universe.

Among the many skills she lists on her no-frills website: “spotting elegance within the chaos.”

She’s also caught the attention of some of America’s brightest working at NASA. Also? Jeff Bezos, founder of Amazon.com and aerospace developer and manufacturer Blue Origin, who’s promised her a job whenever she’s ready. Asked by e-mail recently whether his offer still stands, Bezos told OZY: “God, yes!”

But unless you’re the kind of rabid physics fan who’s seen her papers on semiclassical Virasoro symmetry of the quantum gravity S-matrix and Low’s subleading soft theorem as a symmetry of QED (both on approaches to understanding the shape of space and gravity and the first two papers she ever authored), you may not have heard of Pasterski. A first-generation Cuban-American born and bred in the suburbs of Chicago, she’s not on Facebook, LinkedIn or Instagram and doesn’t own a smartphone. She does, however, regularly update a no-frills website called PhysicsGirl, which features a long catalog of achievements and proficiencies. Among them: “spotting elegance within the chaos.”

Pasterski stands out among a growing number of newly minted physics grads in the U.S. There were 7,329 in 2013, double the four-decade low of 3,178 in 1999, according to the American Institute of Physics. Nima Arkani-Hamed, a Princeton professor and winner of the inaugural $3 million Fundamental Physics Prize, told OZY he’s heard “terrific things” about Pasterski from her adviser, Harvard professor Andrew Strominger, who is about to publish a paper with physics rock star Hawking. She’s also received hundreds of thousands of dollars in grants from the Hertz Foundation, the Smith Foundation and the National Science Foundation.

Pasterski, who speaks in frenetic bursts, says she has always been drawn to challenging what’s possible. “Years of pushing the bounds of what I could achieve led me to physics,” she says from her dorm room at Harvard. Yet she doesn’t make it sound like work at all: She calls physics “elegant” but also full of “utility.”

Despite her impressive résumé, MIT wait-listed Pasterski when she first applied. Professors Allen Haggerty and Earll Murman were aghast. Thanks to Udden, the pair had seen a video of Pasterski building her airplane. “Our mouths were hanging open after we looked at it,” Haggerty said. “Her potential is off the charts.” The two went to bat for her, and she was ultimately accepted, later graduating with a grade average of 5.00, the school’s highest score possible.

An only child, Pasterski speaks with some awkwardness and punctuates her e-mails with smiley faces and exclamation marks. She says she has a handful of close friends but has never had a boyfriend, an alcoholic drink or a cigarette. Pasterski says: “I’d rather stay alert, and hopefully I’m known for what I do and not what I don’t do.”

While mentors offer predictions of physics fame, Pasterski appears well grounded. “A theorist saying he will figure out something in particular over a long time frame almost guarantees that he will not do it,” she says. And Bezos’s pledge notwithstanding, the big picture for science grads in the U.S. is challenging: The U.S. Census Bureau’s most recent American Community Survey shows that only about 26 percent of science grads in the U.S. had jobs in their chosen fields, while nearly 30 percent of physics and chemistry post-docs are unemployed. Pasterski seems unperturbed. “Physics itself is exciting enough,” she says. ”It’s not like a 9-to-5 thing. When you’re tired you sleep, and when you’re not, you do physics.”

ORIGINAL: MIT News

BY Farah Halimejan