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Cellular Computing
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Cellular Computing
Cellular Computing

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Scientists turn mammalian cells into complex biocomputers
http://bit.ly/2nHqXfk

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Large-scale design of robust genetic circuits with multiple inputs and outputs for mammalian cells
http://go.nature.com/2orN3jc
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DNA could store all of the world's data in one room
by
Robert Service
"Humanity has a data storage problem: More data were created in the past 2 years than in all of preceding history. And that torrent of information may soon outstrip the ability of hard drives to capture it. Now, researchers report that they’ve come up with a new way to encode digital data in DNA to create the highest-density large-scale data storage scheme ever invented. Capable of storing 215 petabytes (215 million gigabytes) in a single gram of DNA, the system could, in principle, store every bit of datum ever recorded by humans in a container about the size and weight of a couple of pickup trucks. But whether the technology takes off may depend on its cost.

DNA has many advantages for storing digital data. It’s ultracompact, and it can last hundreds of thousands of years if kept in a cool, dry place. And as long as human societies are reading and writing DNA, they will be able to decode it. “DNA won’t degrade over time like cassette tapes and CDs, and it won’t become obsolete,” says Yaniv Erlich, a computer scientist at Columbia University. And unlike other high-density approaches, such as manipulating individual atoms on a surface, new technologies can write and read large amounts of DNA at a time, allowing it to be scaled up.

Scientists have been storing digital data in DNA since 2012. That was when Harvard University geneticists George Church, Sri Kosuri, and colleagues encoded a 52,000-word book in thousands of snippets of DNA, using strands of DNA’s four-letter alphabet of A, G, T, and C to encode the 0s and 1s of the digitized file. Their particular encoding scheme was relatively inefficient, however, and could store only 1.28 petabytes per gram of DNA. Other approaches have done better. But none has been able to store more than half of what researchers think DNA can actually handle, about 1.8 bits of data per nucleotide of DNA. (The number isn’t 2 bits because of rare, but inevitable, DNA writing and reading errors.)

Erlich thought he could get closer to that limit. So he and Dina Zielinski, an associate scientist at the New York Genome Center, looked at the algorithms that were being used to encode and decode the data. They started with six files, including a full computer operating system, a computer virus, an 1895 French film called Arrival of a Train at La Ciotat, and a 1948 study by information theorist Claude Shannon. They first converted the files into binary strings of 1s and 0s, compressed them into one master file, and then split the data into short strings of binary code. They devised an algorithm called a DNA fountain, which randomly packaged the strings into so-called droplets, to which they added extra tags to help reassemble them in the proper order later. In all, the researchers generated a digital list of 72,000 DNA strands, each 200 bases long.

They sent these as text files to Twist Bioscience, a San Francisco, California–based startup, which then synthesized the DNA strands. Two weeks later, Erlich and Zielinski received in the mail a vial with a speck of DNA encoding their files. To decode them, the pair used modern DNA sequencing technology. The sequences were fed into a computer, which translated the genetic code back into binary and used the tags to reassemble the six original files. The approach worked so well that the new files contained no errors, they report today in Science. They were also able to make a virtually unlimited number of error-free copies of their files through polymerase chain reaction, a standard DNA copying technique. What’s more, Erlich says, they were able to encode 1.6 bits of data per nucleotide, 60% better than any group had done before and 85% the theoretical limit.

“I love the work,” says Kosuri, who is now a biochemist at the University of California, Los Angeles. “I think this is essentially the definitive study that shows you can [store data in DNA] at scale.”

However, Kosuri and Erlich note the new approach isn’t ready for large-scale use yet. It cost $7000 to synthesize the 2 megabytes of data in the files, and another $2000 to read it. The cost is likely to come down over time, but it still has a long ways to go, Erlich says. And compared with other forms of data storage, writing and reading to DNA is relatively slow. So the new approach isn’t likely to fly if data are needed instantly, but it would be better suited for archival applications. Then again, who knows? Perhaps those giant Facebook and Amazon data centers will one day be replaced by a couple of pickup trucks of DNA."
http://bit.ly/2llwYP2
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Deconstruction of complex protein signaling switches: a roadmap toward engineering higher-order gene regulators
http://bit.ly/2lGBVQN

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Gate-controlled conductance switching in DNA
http://go.nature.com/2lISHiY

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Synchronous long-term oscillations in a synthetic gene circuit
by
Laurent Potvin-Trottier, Nathan D. Lord, Glenn Vinnicombe & Johan Paulsson
"Synthetically engineered genetic circuits can perform a wide variety of tasks but are generally less accurate than natural systems. Here we revisit the first synthetic genetic oscillator, the repressilator1, and modify it using principles from stochastic chemistry in single cells. Specifically, we sought to reduce error propagation and information losses, not by adding control loops, but by simply removing existing features. We show that this modification created highly regular and robust oscillations. Furthermore, some streamlined circuits kept 14 generation periods over a range of growth conditions and kept phase for hundreds of generations in single cells, allowing cells in flasks and colonies to oscillate synchronously without any coupling between them. Our results suggest that even the simplest synthetic genetic networks can achieve a precision that rivals natural systems, and emphasize the importance of noise analyses for circuit design in synthetic biology."
http://go.nature.com/2ejXuoR
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Twister ribozymes as highly versatile expression platforms for artificial riboswitches

by
Felletti M, Stifel J, Wurmthaler LA, Geiger S, Hartig JS

"The utilization of ribozyme-based synthetic switches in biotechnology has many advantages such as an increased robustness due to in cis regulation, small coding space and a high degree of modularity. The report of small endonucleolytic twister ribozymes provides new opportunities for the development of advanced tools for engineering synthetic genetic switches. Here we show that the twister ribozyme is distinguished as an outstandingly flexible expression platform, which in conjugation with three different aptamer domains, enables the construction of many different one- and two-input regulators of gene expression in both bacteria and yeast. Besides important implications in biotechnology and synthetic biology, the observed versatility in artificial genetic control set-ups hints at possible natural roles of this widespread ribozyme class."
http://go.nature.com/2cKFwsN.
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Microsoft's "Biological Computing" Lab Aims To Fight Diseases By Reprogramming Cells
http://bit.ly/2deKFIl

Programmable Manipulation of Cells
by
Isabelle Fol
"A team led by ETH Zurich Professor Yaakov Benenson has developed a Synthetic Biology platform that senses intracellular activities of mammalian transcription factors. This platform opens the way to precise sensing of and responding to diverse cell states and activities.
For millennia humans have been altering the genetic code of plants and animals by selectively breeding individuals with desirable features. As scientists have learned more about reading and manipulating the genetic code, they started transferring genetic information associated with useful features from one organism to another. Recent advances in Synthetic Biology have enabled bio-engineers to design multiple new DNA sequences from scratch. By combining these advances with engineering principles, synthetic biologists are now able to design cells and even organisms with new features.
Biology meets engineering
The interdisciplinary nature of Synthetic Biology makes it a particularly promising discipline, but the application of engineering principles to biological components creates challenges as well. As the engineering perspective is applied at all levels of biological structures - from molecules to cells, tissues and organisms - the redesign and construction of novel artificial biological pathways call for precise understanding of cellular processes. Consequently, one of the goals of Synthetic Biology is to develop programmable artificial gene networks that respond to endogenous molecular cues in order to analyse and understand cell behaviour. Transcription factors are an important class of such molecular cues due to their key role in determining cell identity and function. Yaakov (Kobi) Benenson, ETH Professor of Synthetic Biology at the Department of Biosystems Science and Engineering in Basel, and co-workers have developed a Synthetic Biology platform that is able to analyse and respond to cellular processes using endogenous transcriptional inputs. This programmable platform enables sensing and integrating multiple transcription factors, therefore leading to precise understanding of and response to diverse cell states and behaviors.
Cell machinery
Every cell of a living organism contains an instruction set that determines its identity and function. These instructions are encoded in DNA: Complex molecular "strings" containing the genetic code, the so-called genome. The first step of decoding the genome, namely the transcription of genetic information from DNA to messenger RNA, is controlled by proteins called transcription factors. Numerous types of transcription factors interact to create the complex language of gene expression. Transcription factors perform this function by promoting or blocking the recruitment of RNA polymerase - an enzyme that transcribes genetic information from DNA to RNA - to specific genes. Bartolomeo Angelici, the project leader in the Benenson group, explains: "Transcription factors are proteins that have the ability to bind specific DNA sequences, switching the nearby genes ON or OFF, and determining which genomic instructions are carried out. Active genes are transcribed into mRNA and eventually translated into proteins, while inactive ones lay dormant." These so-called "expressed" genes determine cellular identity and behavior. He affirms: "This is why sensing combinations of active transcription factors is a powerful way to recognise specific cell types."
Sensing transcriptional activity
Recently, Angelici, Benenson and co-workers have established a framework for systematic design of selective and robust sensing, integration and transduction of transcriptional activity in mammalian cells. "The idea is to build synthetic gene circuits that sense specific transcriptional activities and are further wired to various downstream components. In this way, endogenous transcription factors can be rewired to control diverse processes in a programmable fashion", adds Benenson. "In order to sense transcriptional activity, we take advantage of the transcription factors’ ability to bind specific DNA sequences." Each minimal sensor is a DNA molecule containing the response element recognised by a given transcription factor, followed by one or more genes that are further wired to additional engineered components. Importantly, one of these genes is an artificial transcription factor that also serves as a sensor input, in what is known as "positive feedback" mechanism. Thus, after initial sensor induction by an endogenous factor, the artificial regulator is produced, amplifying its own amount and the amount of additional gene products.
Determining cell identity
Precise control of gene expression is a long-standing goal of Biotechnology and Biomedicine. Benenson’s new Synthetic Biology platform not only allows processing signals from multiple transcription factors and sensing molecular cues, but also responding with biologically active outcomes in a controlled fashion. For example, the platform might enable precise targeting of cells with a highly specific transcriptional profile. This is particularly interesting in complex diseases like cancer, because cancer cells are known to harbor abnormal transcriptional activities that distinguish them from healthy cells. In the future, a synthetic logic circuit sensing transcriptional factors could be used to specifically target cancer cells without harming healthy tissues.
Reference
Bartolomeo Angelici, Erik Mailand, Benjamin Haefliger, Yaakov Benenson: Synthetic Biology Platform for Sensing and Integrating Endogenous Transcriptional Inputs in Mammalian Cells. Cell Reports 2016. DOI: http://dx.doi.org/10.1016/j.celrep.2016.07.061"
http://bit.ly/2cCAQ7J

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One-pot synthesis towards sulfur-based organic semiconductors A short and simple synthetic route for thiophene-fused aromatic compounds

by
Nagoya

"Thiophene-fused polycyclic aromatic hydrocarbons (PAHs) are known to be useful as organic semiconductors due to their high charge transport properties. Scientists at Nagoya University have developed a short route to form various thiophene-fused PAHs by simply heating mono-functionalized PAHs with sulfur. This new method is expected to contribute towards the efficient development of novel thiophene-based electronic materials."
http://bit.ly/2cvRANR
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