miércoles, 21 de diciembre de 2016

Artificial leaf as mini-factory for drugs (for Medicine)

(Nanowerk News) To produce drugs sustainably and cheaply, anywhere you want. Whether in the middle of the jungle or even on Mars. A 'mini-factory' whereby sunlight can be captured to make chemical products. Inspired by the art of nature where leaves are able to collect enough sunlight to produce food, chemical engineers at Eindhoven University of Technology (TU/e) have presented such a scenario.

They describe their prototype reactor - consciously shaped as a leaf -in today's journal Angewandte Chemie ("A leaf-inspired luminescent solar concentrator for energy efficient continuous-flow photochemistry").

Even with the naked eye the amount of light captured by the 'mini-factories' is visible, lit up bright red. The 'veins' through the leaves are the thin channels through which liquid can be pumped. The start products enter the one channel, light causes the reactions and the end product comes out via the other channels. (Image: Bart van Overbeeke)
Using sunlight to make chemical products has long been a dream of many a chemical engineer. The problem is that the available sunlight generates too little energy to kick off reactions. However, nature is able to do this. Antenna molecules in leaves capture energy from sunlight and collect it in the reaction centers of the leaf where enough solar energy is present for the chemical reactions that give the plant its food (photosynthesis).

Light capture
The researchers came across relatively new materials, known as luminescent solar concentrators (LSC's), which are able to capture sunlight in a similar way. Special light-sensitive molecules in these materials capture a large amount of the incoming light that they then convert into a specific color that is conducted to the edges via light conductivity. These LSC's are often used in practice in combination with solar cells to boost the yield.

Thin channels
The researchers, led by Dr. Timothy Noël, combined the idea of an LSC with their knowledge of microchannels, incorporating very thin channels in a silicon rubber LSC through which a liquid can be pumped. In this way they were able to bring the incoming sunlight into contact with the molecules in the liquid with high enough intensity to generate chemical reactions.

Watch an animation of the artificial leaf.
While the reaction they chose serves as an initial example, the results surpassed all their expectations, and not only in the lab.
"Even an experiment on a cloudy day demonstrated that the chemical production was 40 percent higher than in a similar experiment without LSC material", says research leader Noël. "We still see plenty of possibilities for improvement. We now have a powerful tool at our disposal that enables the sustainable, sunlight-based production of valuable chemical products like drugs or crop protection agents."

Paracetamol on Mars
For the production of drugs there is certainly a lot of potential. The chemical reactions for producing drugs currently require toxic chemicals and a lot of energy in the form of fossil fuels. By using visible light the same reactions become sustainable, cheap and, in theory, countless times faster. But Noël believes it should not have to stop there.

"Using a reactor like this means you can make drugs anywhere, in principle, whether malaria drugs in the jungle or paracetamol on Mars. All you need is sunlight and this mini-factory."

Source: Eindhoven University of Technology

Dec 21, 2016

martes, 29 de noviembre de 2016

The Robot Revolution Comes to Synthetic Biology

Automation allows thousands of possibilities when building weird new organisms

Photo: Ginkgo Bioworks Robots at Work: Ginkgo Bioworks’ lab automates genetic engineering.
Last month, synthetic biologists at Ginkgo Bioworks raised their glasses—filled with genetically modified beer—to cele­brate the launch of a new automated lab. By applying engineering principles to biology, and with the help of some nifty robotic equipment, Ginkgo has created a factory for churning out exotic life-forms, the likes of which have never before been seen on this planet.

The home brew they were drinking was an example of the potential applications of synthetic biology, a new field that builds on recent progress in genetic assembly methods. Scientists can now manufacture snippets of synthetic DNA and slip them into organisms, giving those critters strange capabilities. For example, the brewer’s yeast used to make the beer for the launch party had genes from an orange tree added to its own DNA. During the fermentation stage of the brewing process, those genes caused the yeast to produce ­valencene, an organic compound with a citrusy flavor. Speaking scientifically, it was delicious.

Ginkgo Bioworks, a hip young company based in Boston, recently raised US $100 million on the promise of finding many such useful applications for synthetic biology. It used some of that cash to build Bioworks2, the company’s vast new lab that uses robotic systems to form an assembly line for organisms.

Ginkgo needs to make microbes on a grand scale in order to find those that can function as tiny biological factories for its customers. Many of the altered organisms will be duds, but through highly organized trial and error, the bioengineers will eventually devise a microbe that turns out a desired substance—like a chemical ingredient used for perfumes, beverages, pesticides, or laundry detergents.

The company’s business model centers on the microbes themselves, not the end products. “We’re not in the business of manufacturing chemicals, flavors, or fragrances,” explains Ginkgo creative director Christina Agapakis. “We specialize in the organisms, and we partner with our customers, who will make the product.” Ginkgo licenses organisms to its customers, she says, and gets royalties if they’re used.

But building an organism to spec is no easy task. Genetics still isn’t well understood; there’s no universal catalog of genes that details each one’s characteristics. Even if researchers know what a particular gene does in an orange tree, for example, when they add it to a yeast cell, it might interact with the native DNA in unexpected ways. If they’re adding several genes from different species to that yeast cell, things get even more complicated.

That’s why Ginkgo takes an engineering approach to biology, applying a rigorous design-build-test cycle to the creation of living organisms. The new lab’s extreme automation is critical to this approach, says Patrick Boyle, ­Ginkgo’s head of organism design. “In grad school, I might have taken my five best ideas and tried them out,” Boyle says. “Here we take our 1,000 best ideas, try them all out, and see which works best.

Engineering Life
How Ginkgo Bioworks Makes Customized Critters
1: DESIGN Organism designers choose genes from a wide range of animals and plants and combine them in hundreds of different ways. Each mashup is a unique fragment of DNA. Illustration: Erik Vrielink
Design: Ginkgo designers search the scientific literature looking for genes that would cause the yeast to produce useful enzymes. The aim: When the designers feed sugar to the yeast, these enzymes should cause it to carry out chemical reactions that ultimately result in rose oil. But there is a dizzying array of genes and enzymes to consider. “If you have 100 possible enzymes that can serve as a step in a four-step pathway, that’s a lot of design space to explore,” Boyle says.

2: BUILD Robotic instruments take all these fragments and insert them into separate yeast cells. They’re integrated into the cells’ DNA and can change how the yeast function. Illustration: Erik Vrielink
Build: Ginkgo outsources the actual manufacturing of synthetic DNA. When a batch of manufactured DNA arrives at Ginkgo, liquid-handling robots build the new organisms by adding the various snippets to yeast cells. “During my Ph.D., I spent a lot of time moving tiny amounts of fluid around,” says creative director Agapakis. “When we started Ginkgo, a lot of the robots looked like eight-armed grad students—there were a lot of pipettes.” The process has ramped up as these robots have gotten more capable. Ginkgo now has liquid-handling robots that quickly move nanoliters of fluid using targeted pulses of sound.

3: TEST Ginkgo’s customers want the altered yeast to churn out specific products. Spectrometry machines analyze all the yeast cells to see if any are producing the desired output. Illustration: Erik Vrielink
Test: Once the robots have created a thousand yeast variants containing different mashups of genes, it’s time to see if the cells are making their rose oil. Mass spectrometry machines crack open the cells and examine all the molecules inside, checking for the product and also determining whether the yeast is healthy. But success on both counts doesn’t necessarily mean the organism will meet the customer’s needs. Boyle says that in the case of rose oil, Gingko studies each yeast’s overall “fragrance profile.” While a cell may be making certain useful fragrance molecules, it may be making others that are not. “I like the fresh-baked-bread smell, but it’s not great when you’re trying to sell a perfume,” Boyle says.

4: SCALE UP Ginkgo puts its best producers into bioreactors to see how they fare at industrial scale. Lessons learned help the organism designers make choices for the next round of experiments. Illustration: Erik Vrielink
Scale Up: Ginkgo adds one extra step to the typical engineering cycle, since a modified yeast cell that looks like a winner in the lab might not perform as well in the customer’s fermentation vats. In one corner of the lab, robotic systems fill and monitor rows of benchtop bioreactors, using a variety of sensors to watch the processes inside.

To understand how this works in practice, take Ginkgo’s first efforts in the perfume business. The company is working with the French fragrance maker Robertet on a yeast that spits out rose oil, because extracting the substance from rose petals is expensive.

If even their best oil-producing yeast isn’t up to spec, the company’s organism designers go back to the drawing board, using the results of the experiment to inform their next 1,000 best guesses. One of these days, Ginkgo’s bioengineers say, they’ll make the perfect batch—one that comes out smelling like a rose.

Posted 29 Nov 2016

jueves, 24 de noviembre de 2016

Bringing Silicon to Life

Scientists persuade nature to make silicon-carbon bonds

A new study is the first to show that living organisms can be persuaded to make silicon-carbon bonds—something only chemists had done before. Scientists at Caltech "bred" a bacterial protein to have the ability to make the man-made bonds, a finding that has applications in several industries.

Molecules with silicon-carbon, or organosilicon, compounds are found in pharmaceuticals as well as in many other products, including agricultural chemicals, paints, semiconductors, and computer and TV screens. Currently, these products are made synthetically, since the silicon-carbon bonds are not found in nature.

The new research, which recently won Caltech's Dow Sustainability Innovation Student Challenge Award (SISCA) grand prize, demonstrates that biology can instead be used to manufacture these bonds in ways that are more environmentally friendly and potentially much less expensive.

"We decided to get nature to do what only chemists could do—only better," says Frances Arnold, Caltech's Dick and Barbara Dickinson Professor of Chemical Engineering, Bioengineering and Biochemistry, and principal investigator of the new research, published in the Nov. 24 issue of the journal Science.

The study is also the first to show that nature can adapt to incorporate silicon into carbon-based molecules, the building blocks of life. Scientists have long wondered if life on Earth could have evolved to be based on silicon instead of carbon. Science-fiction authors likewise have imagined alien worlds with silicon-based life, like the lumpy Horta creatures portrayed in an episode of the 1960s TV series Star Trek. Carbon and silicon are chemically very similar. They both can form bonds to four atoms simultaneously, making them well suited to form the long chains of molecules found in life, such as proteins and DNA.

"No living organism is known to put silicon-carbon bonds together, even though silicon is so abundant, all around us, in rocks and all over the beach," says Jennifer Kan, a postdoctoral scholar in Arnold's lab and lead author of the new study. Silicon is the second most abundant element in Earth's crust.

The researchers used a method called directed evolution, pioneered by Arnold in the early 1990s, in which new and better enzymes are created in labs by artificial selection, similar to the way that breeders modify corn, cows, or cats. Enzymes are a class of proteins that catalyze, or facilitate, chemical reactions. The directed evolution process begins with an enzyme that scientists want to enhance. The DNA coding for the enzyme is mutated in more-or-less random ways, and the resulting enzymes are tested for a desired trait. The top-performing enzyme is then mutated again, and the process is repeated until an enzyme that performs much better than the original is created.

Directed evolution has been used for years to make enzymes for household products, like detergents; and for "green" sustainable routes to making pharmaceuticals, agricultural chemicals, and fuels.

In the new study, the goal was not just to improve an enzyme's biological function but to actually persuade it to do something that it had not done before. The researchers' first step was to find a suitable candidate, an enzyme showing potential for making the silicon-carbon bonds.

Bringing Silicon to Life: Scientists Persuade Nature to Make Silicon-Carbon Bonds

Researchers in Frances Arnold’s lab at Caltech have persuaded living organisms to make chemical bonds not found in nature. The finding may change how medicines and other chemicals are made in the future.
Credit: Caltech

"It's like breeding a racehorse," says Arnold, who is also the director of the Donna and Benjamin M. Rosen Bioengineering Center at Caltech. "A good breeder recognizes the inherent ability of a horse to become a racer and has to bring that out in successive generations. We just do it with proteins."

The ideal candidate turned out to be a protein from a bacterium that grows in hot springs in Iceland. That protein, called cytochrome c, normally shuttles electrons to other proteins, but the researchers found that it also happens to act like an enzyme to create silicon-carbon bonds at low levels. The scientists then mutated the DNA coding for that protein within a region that specifies an iron-containing portion of the protein thought to be responsible for its silicon-carbon bond-forming activity. Next, they tested these mutant enzymes for their ability to make organosilicon compounds better than the original.

After only three rounds, they had created an enzyme that can selectively make silicon-carbon bonds 15 times more efficiently than the best catalyst invented by chemists. Furthermore, the enzyme is highly selective, which means that it makes fewer unwanted byproducts that have to be chemically separated out.

"This iron-based, genetically encoded catalyst is nontoxic, cheaper, and easier to modify compared to other catalysts used in chemical synthesis," says Kan. "The new reaction can also be done at room temperature and in water."

The synthetic process for making silicon-carbon bonds often uses precious metals and toxic solvents, and requires extra processing to remove unwanted byproducts, all of which add to the cost of making these compounds.

As to the question of whether life can evolve to use silicon on its own, Arnold says that is up to nature. "This study shows how quickly nature can adapt to new challenges," she says. "The DNA-encoded catalytic machinery of the cell can rapidly learn to promote new chemical reactions when we provide new reagents and the appropriate incentive in the form of artificial selection. Nature could have done this herself if she cared to."

The Science paper, titled "Directed Evolution of Cytochrome c for Carbon-Silicon Bond Formation: Bringing Silicon to Life," is also authored by Russell Lewis and Kai Chen of Caltech. The research is funded by the National Science Foundation, the Caltech Innovation Initiative program, and the Jacobs Institute for Molecular Engineering for Medicine at Caltech.

Written by Whitney Clavin

Contact: Whitney Clavin
(626) 395-1856

Site content Copyright © 2016 California Institute of Technology

jueves, 10 de noviembre de 2016

The Competitive Landscape for Machine Intelligence

Three years ago, our venture capital firm began studying startups in artificial intelligence. AI felt misunderstood, burdened by expectations from science fiction, and so for the last two years we’ve tried to capture the most-important startups in the space in a one-page landscape. (We prefer the more neutral term “machine intelligence” over “AI.”)

In past years, we heard mostly from startup founders and academics — people who pay attention to early, far-reaching trends in technology. But this year was different. This year we’ve heard more from Fortune 500 executives with questions about machine intelligence than from startup founders.

These executives are asking themselves what to do. Over the past year, machine intelligence has exploded, with $5 billion in venture investment, a few big acquisitions, and hundreds of thousands of people reading our earlier research. As with the internet in the 1990s, executives are realizing that this new technology could change everything, but nobody knows exactly how or when.

If this year’s landscape shows anything, it’s that the impact of machine intelligence is already here. Almost every industry is already being affected, from agriculture to transportation. Every employee can use machine intelligence to become more productive with tools that exist today. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses.

And unlike with the internet, where latecomers often bested those who were first to market, the companies that get started immediately with machine intelligence could enjoy a lasting advantage.

So what should the Fortune 500 and other companies be doing to get started?

Make Talent More Productive
One way to immediately begin getting the value of machine intelligence is to support your talent with readily available machine intelligence productivity tools. Some of the earliest wins have been productivity tools tuned to specific areas of knowledge work — what we call “Enterprise Functions” in our landscape. With these tools, every employee can get some of the powers previously available only to CEOs.

These tools can aid with monitoring and predicting (e.g., companies like Clari forecasting client-by-client sales to help prioritize deals) and with coaching and training (Textio’s* predictive text-editing platform to help employees write more-effective documents).

Find Entirely New Sources of Data
The next step is to use machine intelligence to realize value from new sources of data, which we highlight in the “Enterprise Intelligence” section of the landscape. These new sources are now accessible because machine intelligence software can rapidly review enormous amounts of data in a way that would have been too difficult and expensive for people to do.

Imagine if you could afford to have someone listen to every audio recording of your salespeople and predict their performance, or have a team look at every satellite image taken from space and determine what macroeconomic indicators could be gleaned from them. These data sources might already be owned by your company (e.g., transcripts of customer service conversations or sensor data predicting outages and required maintenance), or they might be newly available in the outside world (data on the open web providing competitive information).

Rethink How You Build Software
Let’s say you’ve tried some new productivity tools and started to mine new sources of data for insight. The next frontier in capturing machine intelligence’s value is building a lasting competitive advantage based on this new kind of software.

But machine intelligence is not just about better software; it requires entirely new processes and a different mindset. Machine intelligence is a new discipline for managers to learn, one that demands a new class of software talent and a new organizational structure.

Most IT groups think in terms of applications and data. New machine intelligence IT groups will think about applications, data, and models. Think of software as the combination of code, data, and a model. “Model” here means business rules, like rules for approving loans or adjusting power consumption in data centers. In traditional software, programmers created these rules by hand. Today machine intelligence can use data and new algorithms to generate a model too complex for any human programmer to write.

With traditional software, the model changes only when programmers explicitly rewrite it. With machine intelligence, companies can create models that evolve much more regularly, allowing you to build a lasting advantage that strengthens over time as the model “learns.”

Think of these models as narrowly focused employees with great memories and not-so-great social skills — idiot savants. They can predict how best to grow the business, make customers happier, or cut costs. But they’ll often fail miserably if you try to apply them to something new, or, worse, they may degrade invisibly as your business and data change.

All of this means that the discipline of creating machine intelligence software differs from traditional software, and companies need to staff accordingly. Luckily, though finding the right talent may be hard, the tools that developers need to build this software is readily available.

How robotics and machine learning are changing business.
For the first time, there is a maturing “Stack” (see our landscape) of building blocks that companies can use to practice the new discipline of machine intelligence. Many of these tools are available as free, open-source libraries from technology companies such as

  • Google (TensorFlow), 
  • Microsoft (CNTK), or 
  • Amazon (DSSTNE). 
Others make it easier for data scientists to collaborate(see “Data Science”) and manage machine intelligence models (“Machine Learning”).

If your CEO is struggling to answer the question of how machine intelligence will change your industry, take a look at the range of markets in our landscape. The startups in these sections give a sense of how different industries may be altered. Machine intelligence’s first useful applications in an industry tend to use data that previously had lain dormant. Health care is a prime example: We’re seeing predictive models that run on patient data and computer vision that diagnoses disease from medical images and gleans lifesaving insights from genomic data. Next up will be finance, transportation, and agriculture because of the volume of data available and their sheer economic value.

Your company will still need to decide how much to trust these models and how much power to grant them in making business decisions. In some cases the risk of an error will be too great to justify the speed and new capabilities. Your company will also need to decide how often and with how much oversight to revise your models. But the companies that decide to invest in the right models and successfully embed machine intelligence in their organization will improve by default as their models learn from experience.

Economists have long wondered why the so-called computing revolution has failed to deliver productivity gains. Machine intelligence will finally realize computing’s promise. The C-suites and boardrooms that recognize that fact first — and transform their ways of working accordingly — will outrun and outlast their competitors.

*The authors’ fund has invested in this company.

Shivon Zilis is a partner and founding member of Bloomberg Beta, which invests heavily in the future of work. She focuses on early-stage data and machine intelligence investments.

James Cham is a Partner at Bloomberg Beta where he invests in data-centric and machine learning-related companies.

viernes, 4 de noviembre de 2016

A Conductor of Evolution’s Subtle Symphony

At first, the biologist Richard Lenski thought his long-term experiment on evolution might last for 2,000 generations. Nearly three decades and over 65,000 generations later, he’s still amazed by evolution’s “awesome inventiveness.”

Logan Zillmer for Quanta Magazine
Early in his career, the decorated biologist Richard Lenski thought he might be forced to evolve. After his postdoctoral research grant was canceled, Lenski began to look tentatively at other options. With one child and a second on the way, Lenski attended a seminar about using specific types of data in an actuarial context — the same type of data he had worked with as a graduate student. Lenski collected a business card from the speaker, thinking he might be able to make use of his background in a new career.

But then, as it sometimes does — and I was very lucky — the tide turned,” Lenski told Quanta Magazine in his high-rise office at Michigan State University. “We got the grant renewed, and soon thereafter, I started getting faculty offers.

Lenski, a professor of microbial ecology at Michigan State, is best known for his work on what’s known as the long-term evolution experiment. The project, started in 1988, examines evolution in action. He and his lab members have been growing 12 populations of E. coli continuously for over 65,000 generations, tracking the development and mutations of the 12 separate strains.

The results have garnered attention and accolades — including a MacArthur “genius” grant, which Lenski received in 1996 — both for the enormity of the undertaking and for the intriguing findings the study has yielded. Most notably, in 2003, Lenski and his collaborators realized that one strain of E. coli had evolved the ability to use citrate as an energy source, something no previous population of E. coli was able to do.

Lenski is also interested in digital organisms, computer programs that have been designed to mimic the process of evolution. He was instrumental in the push to open the Beacon Center at Michigan State, which gives computer scientists and evolutionary biologists the opportunity to forge unique collaborations.

Quanta Magazine met with Lenski in his office to talk about his own evolving interests in the field of evolutionary biology — and about the time he almost pulled the plug on the long-term experiment. An edited and condensed version of the conversation follows.

Logan Zillmer for Quanta Magazine. Vials containing the E. coli strains that make up the long-term evolution experiment.

QUANTA MAGAZINE: What sort of questions have been driving forces in your career?

RICHARD LENSKI: One question that has always intrigued me is about the reproducibility or repeatability of evolution. Stephen Jay Gould, the paleontologist and historian of science, posed this question: If we could rewind the tape of life on Earth, how similar or dissimilar would it be if we watched the whole process play out again? The long-term experiment that we do has allowed us to gather a lot of data about this question.

So is evolution repeatable?
Yes and no! I sometimes tell people it’s been a fascinating motivating question, but on one level, it is a terrible question, and one you would never tell a graduate student to go after. That’s because it is very open-ended, and it does not have a very clear-cut answer.

From the long-term experiment, we’ve seen

  • some really beautiful examples of things that are remarkably reproducible, and 
  • on the other hand some other crazy things where one population goes off and does things that are entirely different from the other 11 populations in the experiment.
How did you first come up with the idea for the long-term experiment?
I had been working already for several years on experimental evolution with bacteria, as well as viruses that infect bacteria. Those were fascinating, but everything became so complicated so quickly that I said, “Let’s reduce evolution down to its bare bones.In particular, I wanted to go after this question of reproducibility or repeatability of evolution. And if I wanted to be able to look at the reproducibility of evolution, I wanted a system that was very simple. When I started the long-term experiment, my original goal was that I would call it the long-term experiment when I got to 2,000 generations.

How long did that take you?
The actual running of the experiment was about 10 or 11 months, but by the time we had collected data, wrote it up, and got the paper published, it was more like two and a half years or so. By then the experiment had already passed 5,000 generations, and I realized we should keep it going.
Logan Zillmer for Quanta Magazine. Richard Lenski in his office.

Did you anticipate the experiment going on for as long as it has?
No. No, I didn’t. There was a five-year period, maybe from the late ’90s into the early 2000s, where I thought about possibly stopping the experiment. This was for a couple of different reasons. One was that I was getting hooked on this other way of studying evolution, which involved looking at evolution in self-replicating computer programs, which was absolutely fascinating. Suddenly I saw this even shinier way of studying evolution, where it could go even more generations and do even more, seemingly neater, experiments.

How have your views on studying evolution via these digital organisms changed over time?
I had this sort of “puppy love” when I first learned about it. At first, it was just so extraordinarily interesting and exciting to be able to watch self-replicating programs, to be able to change their environments, and to watch evolution happen.

One of the really exciting things about digital evolution is that it shows that we think of evolution as being about stuff with blood and guts and DNA and RNA and proteins. But the idea of evolution really comes down to some very basic ideas of heredity, replication and competition. The philosopher of science Daniel Dennett has emphasized that we see evolution as this instantiation, this form of biological life, but the principles of it are much more general than that.

I would say that my latest directions of research have been primarily by way of talking with super-smart colleagues and serving on committees of graduate students who are using these systems. I’m less involved in designing experiments or formulating specific hypotheses, because that field has been moving extremely quickly. I feel I was very lucky to pick off some of the low-hanging fruit, but now I feel like I’m in there as a biologist, maybe criticizing hypotheses, suggesting controls that might be done in some experiments.

So your interest in digital organisms was one reason you considered shutting down the long-term experiment. What was the other?
At that point, the other thing that was a little frustrating about the long-term lines was that the rate at which the bacteria were changing was slowing down. The way I thought about it, it was almost as though evolution had stopped. I thought that this was just too simple an environment, and there wasn’t that much more for them to do.

So those two different things made me think about stopping the experiment. And I spoke to a few colleagues and they basically told me: You can’t do that. You shouldn’t do that. I talked with my wife, Madeleine, by the way, when I was getting very interested in these digital organisms — we were actually on sabbatical in France at that time — and I said, “Maybe I should call home and shut down the lab.” And she said, “I don’t think you should do that.

Why did your wife and your colleagues have that reaction?
The experiment had already been quite profitable in a scientific sense, providing very rich data about the dynamics of evolutionary change. It was more or less unique in the timescales it was probing. So I think it was very good advice they gave me. I don’t know whether I could have ever quite pulled the plug myself. I certainly was a bit frustrated and thinking about it — but anyhow, people said no!

Logan Zillmer for Quanta Magazine
Video: Lenski discusses how he has been surprised by evolution.

Did you get past the plateau where you said you felt like the organisms weren’t evolving that much?
That actually has been one of the really cool findings from the experiment. When I started the long-term experiment, I thought that the bacteria would quickly reach some sort of limit to their growth. It was only a few years ago that we began to realize that the bacteria would always be able to beat anything we had inferred in the past about what their hard limit might be. I realized that we’re just fundamentally not thinking about this the right way. Even in the simplest environment, there’s always the potential for organisms to do any step in their metabolism, or any step in their biochemistry, a little bit better. And natural selection, although it won’t get it right on any given step, will over the long term always be favoring these subtle improvements.

One line of bacteria evolved the ability to use citrate as a food source. Did that happen before or after you were thinking of shutting down the experiment?
That was one of the things that made me realize we wouldn’t shut down the experiment. In 2003, one lineage evolved the ability to use citrate. That became a game changer: realizing that even in this super simple environment, there were some major things for the bacteria to evolve and figure out.

I like to say that the bacteria would eat dinner every night without realizing there was this nice, lemony dessert right around the corner. And so far, even after 65,000 generations, only one of the 12 populations has figured out how to consume that citrate.

You also mentioned that certain populations within your experiment have developed mutations at a greater rate. What does that look like?
After over 60,000 generations, six out of the 12 populations have evolved to be hypermutable. They’ve evolved changes in their DNA repair and DNA metabolic processes that causes them to have new mutations somewhere on the order of 100 times the rate at which the ancestor [at the start of the experiment] did.
Logan Zillmer for Quanta Magazine. Lenski’s laboratory at Michigan State University.
It’s a very interesting process, because it’s both good and bad from the bacteria’s perspective. It’s bad because most mutations are harmful or at best neutral. Only the rare nugget in the mine is a beneficial mutation. The bacteria that have the higher mutation rate are a little bit more likely to discover one of those nuggets. But on the other hand, they’re also more likely to produce children and grandchildren with deleterious mutations.

Was the line that was able to consume citrate part of the group that had evolved to be hypermutable?
That’s a great question. The line that evolved the ability to use citrate did not have an elevated mutation rate. Interestingly, it became one of the ones with a higher mutation rate, but only after it evolved the ability to use citrate. It’s consistent with the benefit of the higher mutation rate — the additional capacity for exploration. The bacteria were actually quite poor at using citrate to begin with, so there were a lot of opportunities after they evolved the ability to use citrate to refine that ability.

How does the long-term experiment help you understand the evolution of life on a larger scale?
For me, one of the lessons of the long-term experiment has been how rich and interesting life can be, even in the dullest, simplest environment. The fact that evolution can generate this diversity, and discover doors left slightly ajar that it can push through, speaks to the awesome inventiveness of evolution. And if it can be so inventive and creative on this minuscule spatial and temporal scale, and in such a dull environment, it just creates more awe in me, when I think of how much more remarkable it is out in nature.

What most surprised you about this project?
That it’s still going on after all these years. One of my goals in life is to make sure that the experiment continues. I would like to raise an endowment to keep the experiment going on in perpetuity.

What’s your hope for the long-term experiment in the future?
My hope for the project is that it will yield many more surprises. For instance, two lineages have coexisted for 60,000 generations in one of the populations, where one of them is feeding off of the product that the other one is generating. I think it’s fascinating to wonder if, at some point, that might turn into something more like a predator-prey interaction. It’s certainly not outside the realm of possibilities. Whether it would ever happen, I don’t know.

It has also been a tremendous joy to work with students, postdocs and collaborators, and to see them grow and develop. That’s really the biggest joy for me of being a scientist. I like to tell people that I’m a bigamist. I have two families: 

  • I have my lab family and 
  • my biological family, and 
they both are incredibly wonderful.

Correction: This post was revised on November 3 to reflect that the citrate-using bacteria appeared in 2003, not 2008. The paper describing that change was published in 2008.

ORIGINAL: Quanta Magazine
By Stephanie Bucklin
November 3, 2016

jueves, 3 de noviembre de 2016

Kate Rubins’ Space Station Science Scrapbook

As a child, Kate Rubins dreamed of being an astronaut and a scientist. During the past four months aboard the International Space Station, that dream came full circle. She became the first person to sequence DNA in space, among other research during her recent mission, adding to her already impressive experience. She holds a doctorate in molecular biology, and previously led a lab of 14 researchers studying viruses, including Ebola.
Here’s a look back at Rubins in her element, conducting research aboard your orbiting laboratory.

Kate inside Destiny, the U.S. Laboratory Module

Destiny houses the Microgravity Science Glovebox (MSG), in which Kate worked on the Heart Cells experiment.
The U.S. national laboratory, called Destiny, is the primary research laboratory for U.S. payloads, supporting a wide range of experiments and studies contributing to health, safety, and quality of life for people all over the world. 

Swabbing for Surface Samples
Microbes that can cause illness could present problems for current and future long duration space missions. 
Understanding what microbe communities thrive in space habitats could help researchers design antimicrobial technology. Here, Kate is sampling various surfaces of the Kibo module for the Microbe-IV investigation.

Culturing Beating Heart Cells in Space
The Heart Cells investigation uses human skin cells that are induced to become stem cells, which can then differentiate into any type of cell.
Researchers forced the stem cells to grow into human heart cells, which Rubins cultured aboard the space station for one month.

Rubins described seeing the heart cells beat for the first time as “pretty amazing. First of all, there’s a few things that have made me gasp out loud up on board the [space] station. Seeing the planet was one of them, but I gotta say, getting these cells in focus and watching heart cells actually beat has been another pretty big one.”

Innovative Applied Research Experiment from Eli Lilly
The Hard to Wet Surfaces investigation from Eli Lilly, and sponsored by the Center for the Advancement of Science in Space (CASIS), looks at liquid-solid interactions and how certain pharmaceuticals dissolve, which may lead to more potent and effective medicines in space and on Earth. 
Rubins set up vials into which she injected buffer solutions and then set up photography to track how tablets dissolved in the solution in microgravity.

Capturing Dragon
Rubins assisted in the capture of the SpaceX Dragon cargo spacecraft in July. The ninth SpaceX resupply mission delivered more than two thousand pounds of science to the space station. 
Biological samples and additional research were returned on the Dragon spacecraft more than a month later. 

Sliding Science Outside the Station
Science doesn’t just happen inside the space station. External Earth and space science hardware platforms are located at various places along the outside of the orbiting laboratory. 

The Japanese Experiment Module airlock can be used to access the JEM Exposed Facility. Rubins installed the JEM ORU Transfer Interface (JOTI) on the JEM airlock sliding table used to install investigations on the exterior of the orbiting laboratory.

Installing Optical Diagnostic Instrument in the MSG
Rubins installed an optical diagnostic instrument in the Microgravity Science Glovebox (MSG) as part of the Selective Optical Diagnostics Instrument (SODI-DCMIX) investigation. Molecules in fluids and gases constantly move and collide. 

When temperature differences cause that movement, called the Soret effect, scientists can track it by measuring changes in the temperature and movement of mass in the absence of gravity. Because the Soret effect occurs in underground oil reservoirs, the results of this investigation could help us better understand such reservoirs.

The Sequencing of DNA in Space
When Rubins’ expedition began, DNA had never been sequenced in space. Within just a few weeks, she and the Biomolecule Sequencer team had sequenced their one billionth “base” – the unit of DNA - aboard the orbiting laboratory. 

The Biomolecule Sequencer investigation seeks to demonstrate that DNA sequencing in microgravity is possible, and adds to the suite of genomics capabilities aboard the space station.

The MinION™ DNA sequencer from Oxford Nanopore Technologies fits in the palm of a hand.
Credits: Oxford Nanopore Technologies

Studying Fluidic Dynamics with SPHERES
The SPHERES-Slosh investigation examines the way liquids move inside containers in a microgravity environment. The phenomena and mechanics associated with such liquid movement are still not well understood and are very different than our common experiences with a cup of coffee on Earth.

Rockets deliver satellites to space using liquid fuels as a power source, and this investigation plans to improve our understanding of how propellants within rockets behave in order to increase the safety and efficiency of future vehicle designs. Rubins conducted a series of SPHERES-Slosh runs during her mission.

Retrieving Science Samples for Their Return to Earth
Precious science samples like blood, urine and saliva are collected from crew members throughout their missions aboard the orbiting laboratory. 

They are stored in the Minus Eighty-Degree Laboratory Freezer for ISS (MELFI) until they are ready to return to Earth aboard a Soyuz or SpaceX Dragon vehicle.

Measuring Gene Expression of Biological Specimens in Space

Rubins ran several WetLab-2 RNA SmartCycler sessions during her mission.
Our WetLab-2 hardware system is bringing to the space station the technology to measure gene expression of biological specimens in space, and to transmit the results to researchers on Earth at the speed of light. 

Studying the First Expandable Habitat Module on the Space Station
The Bigelow Expandable Activity Module (BEAM) is the first expandable habitat to be installed on the space station. It was expanded on May 28, 2016. 

Expandable habitats are designed to take up less room on a spacecraft, but provide greater volume for living and working in space once expanded. Rubins conducted several evaluations inside BEAM, including air and surface sampling.

Better Breathing in Space and Back on Earth
Airway Monitoring, an investigation from ESA (the European Space Agency), uses the U.S. airlock as a hypobaric facility for performing science. Utilizing the U.S. airlock allows unique opportunities for the study of gravity, ambient pressure interactions, and their effect on the human body. 

This investigation studies the occurrence and indicators of airway inflammation in crew members, using ultra-sensitive gas analyzers to evaluate exhaled air. This could not only help in spaceflight diagnostics, but that also hold applications on earth within diagnostics of similar conditions, for example monitoring of asthma.

Hot Science with Cool Flames
Fire behaves differently in space, where buoyant forces are removed. Studying combustion in microgravity can increase scientists’ fundamental understanding of the process, which could lead to improvement of fire detection and suppression systems in space and on Earth. 

Many combustion experiments are performed in the Combustion Integration Rack (CIR) aboard the space station. Rubins replaced two Multi-user Droplet Combustion Apparatus (MDCA) Igniter Tips as part of the CIR igniter replacement operations.

Though Rubins is back on Earth, science aboard the space station continues, and innovative investigations that seek to benefit humans on Earth and further our exploration of the solar system are ongoing. Follow @ISS_Research to keep up with the science happening aboard your orbiting laboratory. 

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