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Home - SynBio axlr8r

Home - SynBio axlr8r | SynBioFromLeukipposInstitute | Scoop.it
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Accelerate your Synthetic Biology vision!
Design, Build and Launch http://bit.ly/1aCKgHv

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Synthetic biology and microbioreactor platforms for programmable production of biologics at the point-of-care 

Synthetic biology and microbioreactor platforms for programmable production of biologics at the point-of-care  | SynBioFromLeukipposInstitute | Scoop.it
Current biopharmaceutical manufacturing systems are not compatible with portable or distributed production of biologics, as they typically require the development of single biologic-producing cell lines followed by their cultivation at very large scales. Therefore, it remains challenging to treat patients in short time frames, especially in remote locations with limited infrastructure. To overcome these barriers, we developed a platform using genetically engineered Pichia pastoris strains designed to secrete multiple proteins on programmable cues in an integrated, benchtop, millilitre-scale microfluidic device. We use this platform for rapid and switchable production of two biologics from a single yeast strain as specified by the operator. Our results demonstrate selectable and near-single-dose production of these biologics in <24 h with limited infrastructure requirements. We envision that combining this system with analytical, purification and polishing technologies could lead to a small-scale, portable and fully integrated personal biomanufacturing platform that could advance disease treatment at point-of-care.
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Recognizing and engineering digital-like logic gates and switches in gene regulatory networks

A central aim of synthetic biology is to build organisms that can perform useful activities in response to specified conditions. The digital computing paradigm which has proved so successful in electrical engineering is being mapped to synthetic biological systems to allow them to make such decisions. However, stochastic molecular processes have graded input-output functions, thus, bioengineers must select those with desirable characteristics and refine their transfer functions to build logic gates with digital-like switching behaviour. Recent efforts in genome mining and the development of programmable RNA-based switches, especially CRISPRi, have greatly increased the number of parts available to synthetic biologists. Improvements to the digital characteristics of these parts are required to enable robust predictable design of deeply layered logic circuits.
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The DNA Typewriter: Building a modular system to encode text in DNA

The DNA Typewriter: Building a modular system to encode text in DNA | SynBioFromLeukipposInstitute | Scoop.it
Experiment is an online platform for funding and sharing scientific discoveries. Push the boundaries of knowledge in biology, chemistry, medicine, physics, computer science, paleontology, economics, engineering, neuroscience, and more.
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A Parts Database with Consensus Parameter Estimation
for Synthetic Circuit Design

Linh Huynh and Ilias Tagkopoulos
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Synthetic biology: Bacteria synchronized for drug delivery

A synthetic genetic circuit that mimics the quorum-sensing systems used by bacterial populations to coordinate gene expression enables bacteria to deliver drugs to mouse tumours in repeated and synchronized cycles.
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Synthetic transitions: towards a new synthesis

Synthetic transitions: towards a new synthesis | SynBioFromLeukipposInstitute | Scoop.it
The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of non-genetic information, language or even consciousness. Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. In both cases, transitions can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of major transitions, both natural and artificial. Here, we review some advances made at the crossroads between statistical physics, artificial life, synthetic biology and evolutionary robotics.
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Scientists work toward storing digital information in DNA

Scientists work toward storing digital information in DNA | SynBioFromLeukipposInstitute | Scoop.it
Her computer, Karin Strauss says, contains her "digital attic"—a place where she stores that published math paper she wrote in high school, and computer science schoolwork from college.
She'd like to preserve the stuff "as long as I live, at least," says Strauss, 37. But computers must be replaced every few years, and each time she must copy the information over, "which is a little bit of a headache."
It would be much better, she says, if she could store it in DNA—the stuff our genes are made of.
Strauss, who works at Microsoft Research in Redmond, Washington, is working to make that sci-fi fantasy a reality.
She and other scientists are not focused in finding ways to stow high school projects or snapshots or other things an average person might accumulate, at least for now. Rather, they aim to help companies and institutions archive huge amounts of data for decades or centuries, at a time when the world is generating digital data faster than it can store it.
To understand her quest, it helps to know how companies, governments and other institutions store data now: For long-term storage it's typically disks or a specialized kind of tape, wound up in cartridges about three inches on a side and less than an inch thick. A single cartridge containing about half a mile of tape can hold the equivalent of about 46 million books of 200 pages apiece, and three times that much if the data lends itself to being compressed.
A tape cartridge can store data for about 30 years under ideal conditions, says Matt Starr, chief technology officer of Spectra Logic, which sells data-storage devices. But a more practical limit is 10 to 15 years, he says.
It's not that the data will disappear from the tape. A bigger problem is familiar to anybody who has come across an old eight-track tape or floppy disk and realized he no longer has a machine to play it. Technology moves on, and data can't be retrieved if the means to read it is no longer available, Starr says.
So for that and other reasons, long-term archiving requires repeatedly copying the data to new technologies.
Into this world comes the notion of DNA storage. DNA is by its essence an information-storing molecule; the genes we pass from generation to generation transmit the blueprints for creating the human body. That information is stored in strings of what's often called the four-letter DNA code. That really refers to sequences of four building blocks—abbreviated as A, C, T and G—found in the DNA molecule. Specific sequences give the body directions for creating particular proteins.
Digital devices, on the other hand, store information in a two-letter code that produces strings of ones and zeroes. A capital "A," for example, is 01000001.
Converting digital information to DNA involves translating between the two codes. In one lab, for example, a capital A can become ATATG. The idea is once that transformation is made, strings of DNA can be custom-made to carry the new code, and hence the information that code contains.
One selling point is durability. Scientists can recover and read DNA sequences from fossils of Neanderthals and even older life forms. So as a storage medium, "it could last thousands and thousands of years," says Luis Ceze of the University of Washington, who works with Microsoft on DNA data storage.
Advocates also stress that DNA crams information into very little space. Almost every cell of your body carries about six feet of it; that adds up to billions of miles in a single person. In terms of information storage, that compactness could mean storing all the publicly accessible data on the internet in a space the size of a shoebox, Ceze says.
In fact, all the digital information in the world might be stored in a load of whitish, powdery DNA that fits in space the size of a large van, says Nick Goldman of the European Bioinformatics Institute in Hinxton, England.
What's more, advocates say, DNA storage would avoid the problem of having to repeatedly copy stored information into new formats as the technology for reading it becomes outmoded.
"There's always going to be someone in the business of making a DNA reader because of the health care applications," Goldman says. "It's always something we're going to want to do quickly and inexpensively."
Getting the information into DNA takes some doing. Once scientists have converted the digital code into the 4-letter DNA code, they have to custom-make DNA. For some recent research Strauss and Ceze worked on, that involved creating about 10 million short strings of DNA.
Twist Bioscience of San Francisco used a machine to create the strings letter by letter, like snapping together Lego pieces to build a tower. The machine can build up to 1.6 million strings at a time.
Each string carried just a fragment of information from a digital file, plus a chemical tag to indicate what file the information came from.
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gro

gro | SynBioFromLeukipposInstitute | Scoop.it
gro is a language for programming, modeling, specifying and simulating the behavior of cells in growing microcolonies of microorganisms. The simulator models cell growth, cell division, intrinsic and extrinsic noise, diffusing molecular signals, microchemostats, chemotaxis, and more. The gro framework is intended to be used in synthetic biology to prototype distributed, multicell behaviors and check that, logically, the local interaction rules you specify produce the desired global result. The language allows behaviors to be specified at whatever level of abstraction makes sense: from high level code, to low level biomolecular interations.
The gro framework has been also been used in the classroom, at UW and elsewhere, to teach synthetic biology to engineers. If you would like to learn more about gro, start by reading the documentation (see the link at the left). The tutorial, in particular, describes many of the main features of gro.
For examples, click on the Gallery link on the left, or visit our youtube channel!
Publications
S.S. Jang, K.T. Oishi, R.G. Egbert, and E. Klavins, "Specification and simulation of multicelled behaviors", ACS Synthetic Biology, July, 2012.
Tutorial Slides
Writing programs, modeling protein expression, working with data. (pptx), (pdf)
Controlling the simulation, chemostats, program composition. (pptx), (pdf)
Signals and reaction/diffusion equations. (pptx), (pdf)
Saving frames and making movies. (pptx), (pdf)
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Soft micromachines with programmable motility and morphology

Soft micromachines with programmable motility and morphology | SynBioFromLeukipposInstitute | Scoop.it
Nature provides a wide range of inspiration for building mobile micromachines that can navigate through confined heterogenous environments and perform minimally invasive environmental and biomedical operations. For example, microstructures fabricated in the form of bacterial or eukaryotic flagella can act as artificial microswimmers. Due to limitations in their design and material properties, these simple micromachines lack multifunctionality, effective addressability and manoeuvrability in complex environments. Here we develop an origami-inspired rapid prototyping process for building self-folding, magnetically powered micromachines with complex body plans, reconfigurable shape and controllable motility. Selective reprogramming of the mechanical design and magnetic anisotropy of body parts dynamically modulates the swimming characteristics of the micromachines. We find that tail and body morphologies together determine swimming efficiency and, unlike for rigid swimmers, the choice of magnetic field can subtly change the motility of soft microswimmers.
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Chinese Scientists Plan First Human Test With Gene Editing Tool

Chinese Scientists Plan First Human Test With Gene Editing Tool | SynBioFromLeukipposInstitute | Scoop.it
Chinese scientists are embarking on what appear to be the first human trials with the Crispr gene editing tool, the latest effort by the country’s researchers to master a technology that might someday be a potent tool in developing therapies worldwide.
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Synthetic recombinase-based state machines in living cells

Finite state machines are logic circuits with a predetermined sequence of actions that are triggered depending on the starting conditions. They are used for a variety of devices and biological systems, from vending machines to neural circuits. Roquet et al. have taken a finite state machine approach to control the expression of integrases, or enzymes that insert or excise phage DNA into or out of bacterial chromosomes. The integrases altered the DNA sequence of a plasmid to record all five possible combinations of two inputs. Such circuits can be used to record the states that the cell experienced over time and can be deployed in state-dependent gene expression programs.
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Heterodimer Autorepression Loop: A Robust and Flexible Pulse-Generating Genetic Module

Heterodimer Autorepression Loop: A Robust and Flexible Pulse-Generating Genetic Module | SynBioFromLeukipposInstitute | Scoop.it
We investigate the dynamics of the heterodimer autorepression loop (HAL), a small genetic module in which a protein A acts as an autorepressor and binds to a second protein B to form an AB dimer. For suitable values of the rate constants, the HAL produces pulses of A alternating with pulses of B. By means of analytical and numerical calculations, we show that the duration of A pulses is extremely robust against variation of the rate constants while the duration of the B pulses can be flexibly adjusted. The HAL is thus a minimal genetic module generating robust pulses with a tunable duration, an interesting property for cellular signaling.
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An implementation-focused bio/algorithmic workflow for synthetic biology

As synthetic biology moves away from trial and error and embraces more formal processes, workflows have emerged that cover the roadmap from conceptualization of a genetic device to its construction and measurement. This latter aspect (i.e. characterization and measurement of synthetic genetic constructs) has received relatively little attention thus far, but it is crucial for their outcome. An end-to-end use case for engineering a simple synthetic device is presented which is supported by information standards and computational methods, and which focuses on such characterization/measurement. This workflow captures the main stages of genetic device design and description and offers standardized tools for both population-based measurement and single-cell analysis. To this end, three separate aspects are addressed. First, the specific vector features. Although device/circuit design has been successfully automated, important structural information is usually overlooked, as is the case of plasmid vectors. The use of the Standard European Vector Architecture (SEVA) is advocated for selecting the optimal carrier of a design and its thorough description, in order to unequivocally correlate digital definitions and molecular devices. A digital version of this plasmid format was developed with the Synthetic Biology Open Language (SBOL) along with a software tool that allows users to embed genetic parts in vector cargoes. This enables annotation of a mathematical model of the device's kinetic reactions formatted with the Systems Biology Markup Language (SBML). From that point onwards the experimental results and their in silico counterparts proceed alongside, with constant feedback to preserve consistency between them. A second aspect involves a framework for the calibration of fluorescence-based measurements. One of the most challenging endeavors in standardization, metrology, is tackled by reinterpreting the experimental output in light of simulation results, allowing us to turn arbitrary fluorescent units into relative measurements. Finally, integration of single-cell methods into a framework for multicellular simulation and measurement is addressed, allowing standardized inspection of the interplay between the carrier chassis and the culture conditions.
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Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits.
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Synthetic Gene Circuits Process Analog and Digital Signals 

Synthetic Gene Circuits Process Analog and Digital Signals  | SynBioFromLeukipposInstitute | Scoop.it
Living cells can perform complex computations on the environmental signals they encounter, such as the analog signals found in human voices or vision and digital computations based on on/off processes such as a cell initiating its own death.
So far, synthetic biological systems have focused on either analog or digital processing. Motivated to expand possible applications, MIT engineers have created a way of processing both analog and digital computations in living cells. The resulting gene circuits could carry out complex computations.



Synthetic Biological Circuits
When an analog input is measured, such as a particular chemical relevant to a disease, the synthetic circuit turns on an output such as the drug to treat the disease if the level is within the correct range. Comparators are electronic devices that have two analog inputs; they then output a digital signal indicating the larger analog signal. Similar to the comparator, the analog input signals of the synthetic devices are converted into a digital output.


Analog signal: a continuous sine waves of varying length and amplitude.
According to lead researcher Timothy Lu, an associate professor of electrical engineering and computer science and of biological engineering at MIT, “Most of the work in synthetic biology has focused on the digital approach, because [digital systems] are much easier to program.”

Digital signal based on a binary system.
“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,” said Lu. “However 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.”
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Engineering an NADPH/NADP+ Redox Biosensor in Yeast

Engineering an NADPH/NADP+ Redox Biosensor in Yeast (ACS Synthetic Biology) https://t.co/HV8y3SCQnF https://t.co/GzhDhr9m8a
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Application of synthetic biology approaches for understanding encounters between cells and their microenvironment

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Electronic Circuit Analog of Synthetic Genetic Networks: Revisited

Electronic circuits are useful tools for studying potential dynamical behaviors of synthetic genetic networks. The circuit models are complementary to numerical simulations of the networks, especially providing a framework for verification of dynamical behaviors in the presence of intrinsic and extrinsic noise of the electrical systems. Here we present an improved version of our previous design of an electronic analog of genetic networks that includes the 3-gene Repressilator and we show conversions between model parameters and real circuit component values to mimic the numerical results in experiments. Important features of the circuit design include the incorporation of chemical kinetics representing Hill function inhibition, quorum sensing coupling, and additive noise. Especially, we make a circuit design for a systematic change of initial conditions in experiment, which is critically important for studies of dynamical systems' behavior, particularly, when it shows multistability. This improved electronic analog of the synthetic genetic network allows us to extend our investigations from an isolated Repressilator to coupled Repressilators and to reveal the dynamical behavior's complexity.
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Experimental
Bioengineered Self-assembled Skin as an Alternative to Skin Grafts

For patients with extensive burns or donor site scarring, the limited availability of autologous and
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Scientists program cells to remember and respond to series of stimuli
New approach to biological circuit design enables scientists to track cell histories

Scientists program cells to remember and respond to series of stimuli<br/>New approach to biological circuit design enables scientists to track cell histories | SynBioFromLeukipposInstitute | Scoop.it
MIT engineers have programmed cells to remember and respond to events. This approach to circuit design enables scientists to create complex cellular state machines and track cell histories.
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Hacking the Cell’s Vending Machine Logic | GEN News Highlights

Computational systems as diverse as vending machines, computers, and cells have something in common: They are all finite-state machines. That is, they may have an initial state, accept inputs, pass to different states according to the inputs received, and even enter states that lead to specific outputs. This kind of activity has been exploited in cells, but not with the degree of control we take for granted with the humble vending machine, which can dispense the right snacks depending on whether we press, say, B4 or C2. The vending machine can even refuse to dispense anything until a certain threshold has been surpassed (the right combination of coins).

In hopes of installing this kind of control into cells, MIT scientists have developed a way to program cells to respond to up to three different inputs. Crucially, the programming is capable of recognizing the order of inputs and responding accordingly. Although three inputs permit just 16 states, the MIT researchers point out that their method is scalable. It could accommodate additional inputs and thereby enable many additional states, enough to permit complex processes such as disease progression and cell differentiation to be tracked. The new synthetic biology approach could even make it possible to intervene in these processes, leading to cancer therapies or guiding the outcome of stem cell development.

Details of the new approach appeared July 22 in the journal Science, in an article entitled, “Synthetic Recombinase-Based State Machines in Living Cells.” The article describes how MIT scientists led by Timothy Lu, Ph.D., followed up on earlier work in which cell circuits were designed that could perform a logic function and then store a memory of the event by encoding it in DNA.

In the new paper, the MIT scientists designed state machine circuits that rely on enzymes called recombinases. When activated by a specific input in the cell, such as a chemical signal, recombinases either delete or invert a particular stretch of DNA, depending on the orientation of two DNA target sequences known as recognition sites. The stretch of DNA between those sites may contain recognition sites for other recombinases that respond to different inputs. Flipping or deleting those sites alters what will happen to the DNA if a second or third recombinase is later activated. Therefore, a cell's history can be determined by sequencing its DNA.

The MIT scientists programmed Escherichia coli cells to respond to substances commonly used in lab experiments, including ATc (an analogue of the antibiotic tetracycline), a sugar called arabinose, and a chemical called DAPG. Such programming could be used with other inputs, such as acidity or the presence of specific transcription factors. In addition, cells could be programmed to not only provide a convenient readout of successive states, but also to enable the complex regulation of gene expression.

“We validated our framework by engineering state machines in Escherichia coli that used one, two, or three chemical inputs to control up to 16 DNA states,” wrote the authors of the Science paper. "These state machines were capable of recording the temporal order of all inputs and performing multi-input, multi-output control of gene expression. We also developed a computational tool for the automated design of gene regulation programs using recombinase-based state machines.”

The researchers tested their approach with three genes that code for different fluorescent proteins—green, red, and blue—constructing a circuit that expressed a different combination of the fluorescent proteins for each identity and order of two inputs. For example, when cells carrying this circuit received input A followed by input B they fluoresced red and green, whereas cells that received B before A fluoresced red and blue.

Dr. Lu's lab now hopes to use this approach to study cellular processes that are controlled by a series of events, such as the appearance of cytokines or other signaling molecules, or the activation of certain genes. "You can build very complex computing systems if you integrate the element of memory together with computation," noted Dr. Lu, who is senior author of the current study.

"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,” added Nathaniel Roquet, an MIT graduate student who is also the paper’s lead author. "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," Roquet says.

For example, scientists could use this technique to follow the trajectory of stem cells or other immature cells into differentiated, mature cell types. They could also follow the progression of diseases such as cancer. A recent study has shown that the order in which cancer-causing mutations are acquired can determine the behavior of the disease, including how cancer cells respond to drugs and develop into tumors. Furthermore, engineers could use the state machine platform developed here to program cell functions and differentiation pathways.
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How Bacteria Will One Day Wire Your Favorite Devices

How Bacteria Will One Day Wire Your Favorite Devices | SynBioFromLeukipposInstitute | Scoop.it
Imagine if one day the electrical wires in your cell phone were made by bacteria — and were even smaller and more conductive than today's... read more
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Scientists Built a Biological Computer Inside a Cell

Scientists Built a Biological Computer Inside a Cell | SynBioFromLeukipposInstitute | Scoop.it
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.
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Injectable biomaterial could be used to manipulate organ behavior 

"Silicon-based invention is tiny, soft, wirelessly functional

In the campy 1966 science fiction movie “Fantastic Voyage,” scientists miniaturize a submarine with themselves inside and travel through the body of a colleague to break up a potentially fatal blood clot. Micro-humans aside, imagine the inflammation that metal sub would cause.

Ideally, injectable or implantable medical devices should not only be small and electrically functional, they should be soft, like the body tissues with which they interact. Scientists from two UChicago labs set out to see if they could design a material with all three of those properties.

The material they came up with, the subject of a study published June 27 in Nature Materials, forms the basis of an ingenious light-activated injectable device that could eventually be used to stimulate nerve cells and manipulate the behavior of muscles and organs.

“Most traditional materials for implants are very rigid and bulky, especially if you want to do electrical stimulation,” said Bozhi Tian, an assistant professor in chemistry whose lab collaborated with that of neuroscientist Francisco Bezanilla, the Lillian Eichelberger Cannon Professor of Biochemistry and Molecular Biology.

The new material, in contrast, is soft and tiny, composed of particles just a few micrometers in diameter—far less than the width of a human hair—that disperse easily in a saline solution so they can be injected. The particles also degrade naturally inside the body after a few months, so no surgery would be needed to remove them."

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