SynBioFromLeukipposInstitute

Synthetic biologists have created software that automates the design of DNA circuits for living cells.

The aim is to help people who are not skilled biologists to quickly design working biological systems, says synthetic biologist Christopher Voigt at the Massachusetts Institute of Technology in Cambridge, who led the work. “This is the first example where we’ve literally created a programming language for cells,” he says.

In the new software — called Cello — a user first specifies the kind of cell they are using and what they want it to do: for example, sense metabolic conditions in the gut and produce a drug in response. They type in commands to explain how these inputs and outputs should be logically connected, using a computing language called Verilog that electrical engineers have long relied on to design silicon circuits. Finally, Cello translates this information to design a DNA sequence that, when put into a cell, will execute the demands.

Voigt says his team is writing user interfaces that would allow biologists to write a single program and be returned different DNA sequences for different organisms. Anyone can access Cello through a Web-based interface, or by downloading its open-source code from the online repository GitHub.

”This paper solves the problem of the automated design, construction and testing of logic circuits in living cells,” says bioengineer Herbert Sauro at the University of Washington in Seattle, who was not involved in the study. The work is published in Science.1

Working together
Creating Cello required a decade of labour, says Voigt. The hard part, he says, was not writing the software itself but working out how to make biological parts — logic gates, by analogy with electronic circuits — that reliably worked together to carry out the functions programmed into the circuit by Verilog. For instance, the team had to develop a combination of genetic components that work together as an 'insulator' — ensuring that each biological part works no matter where in the DNA sequence it is placed.

How to design reliable, complex biological computing systems is a central problem of synthetic biology, Voigt says. Researchers found that DNA-based analogues of electronic switches and transistors would work in simple cases but would often fail when organized into more complex circuits. But inexpensive gene-synthesis technologies and the use of fast, cheap genetic sequencing to look at what goes wrong have enabled huge advances in understanding.


Living factories of the future
“What we’re finding over time is that biology isn’t this kind of mysterious unpredictable substrate; it just felt that way because we didn’t really have the tools to see what was going on,” Voigt says.

Voigt’s team tested 60 designs made using Cello; 45 worked correctly the first time. He estimates that it would take about a week to design 60 biological circuits with Cello; by contrast, it took a postdoc three years to design, test and build one successful biological circuit for a paper published in 2012, he says2.

Synthetic biologist Adam Arkin at the University of California, Berkeley, who was not involved in the work, says that Cello is one of a series of steps that aim to push synthetic biology closer to meeting its founding goals of using the principles of engineering to enable the design of new biological circuits.

“What is wonderful is seeing the original conception of synthetic biology — to build the infrastructure to make the engineering of new biological function vastly more efficient, predictable, transparent and safe — come to fruition in such powerful computational tools and biological reagents,” Arkin says.

Programming circuitry for synthetic biology

As synthetic biology techniques become more powerful, researchers are anticipating a future in which the design of biological circuits will be similar to the design of integrated circuits in electronics. Nielsen et al. describe what is essentially a programming language to design computational circuits in living cells. The circuits generated on plasmids expressed in Escherichia coli required careful insulation from their genetic context, but primarily functioned as specified. The circuits could, for example, regulate cellular functions in response to multiple environmental signals. Such a strategy can facilitate the development of more complex circuits by genetic engineering.

By BENJAMIN BOETTNERMarch 19, 2016
From the tension of contracting muscle fibers to hydrodynamic stresses within flowing blood, molecules within our bodies are subject to a wide variety of mechanical forces that directly influence their form and function.

Analyzing the responses of single molecules under conditions where they experience such forces could lead to a better understanding of many biological processes and potentially the development of more accurately acting drugs.

Until now, the experimental analysis of single-molecule interactions under force has been expensive, tedious and difficult to perform. It requires the use of sophisticated equipment, such as an atomic force microscope or optical tweezers, which permit analysis of only one molecule at a time.

Now, a research team led by Wesley Wong of Harvard’s Wyss Institute, Harvard Medical School and Boston Children’s Hospital has made a major advance. The scientists have developed an inexpensive method that allows analysis of the force responses of thousands of similar molecules simultaneously.

They report in Nature Communications how programmable DNA nanoswitches can be used in combination with a newly designed miniaturized centrifuge force microscope as a highly reliable tool to observe thousands of individual molecules and their responses to mechanical forces in parallel.

“This new combined approach will allow us and others to examine how single-molecule complexes behave when they are thrown out of their equilibrium by the tunable force generated in our newly designed centrifuge force microscope,” said Wong, who is the study’s senior author.  

Wong is an associate faculty member at the Wyss Institute for Biologically Inspired Engineering and an HMS assistant professor of biological chemistry and molecular pharmacology at Boston Children’s. “By basing this instrument on something that most researchers already have and use—the benchtop centrifuge—we hope to make single-molecule force measurements accessible to almost everyone.”

This Exhibit Helps 8-Year-Olds (And Anyone) Learn The Basics Of Synthetic Biology

Nanorobot Factory Name: Bioworks 1 & 2
With an earlier head start than Zymergen, Ginkgo Bioworks has built the world’s first organism engineering foundry called Bioworks 1. This platform integrates advanced software, robots, and biology to rapidly design, build, and test organisms until they meet certain specifications. The foundry has been built to work with many types of cells, and is currently working on contracts for more than 20 organisms from customers looking for a yeast strain for animal feed, a new sweetener for beverages, and an organic pesticide. Ginkgo Bioworks recently ordered a minimum of 100 million base pairs of synthesized DNA from Twist Bioscience an amount equal to 10% of the total DNA synthesis market in 2015. The Company will use these DNA snippets to create made-to-order industrial microbes for their customers.

Synthetic biology is more prevalent and helpful to people than many give it credit for, and affects your life in more ways than you probably realize.

 J. Craig Venter and his colleagues at Synthetic Genomics Inc update their efforts to create a “hypothetical minimal genome” in this week’s Science.

“JCVI-syn3.0,” or syn3.0 for short, is about 531,000 base pairs organized into 473 genes, serially transplanted into cells of the tiny and fast-replicating Mycoplasma mycoides and M. capricolum. The first iteration of the smallest synthetic genome, JCVI-syn1.0, has just over a million base pairs, and JCVI-syn2.0 has 576,000. DNA Science covered them here.

Creation of syn3.0 will inspire future synthetic biology efforts, but reminds me of two of my all-time favorite experiments, from more than half a century ago.

Could we soon HACK the human body? Scientists use computer code to change how living cells behave

Cytochrome P450 enzymes (P450s) are a superfamily of monooxygenase enzymes with enormous potential for synthetic biology applications. Across Nature, their substrate range is vast and exceeds that of other enzymes. The range of different chemical transformations performed by P450s is also substantial, and continues to expand through interrogation of the properties of novel P450s and by protein engineering studies. The ability of P450s to introduce oxygen atoms at specific positions on complex molecules makes these enzymes particularly valuable for applications in synthetic biology. This review focuses on the enzymatic properties and reaction mechanisms of P450 enzymes, and on recent studies that highlight their broad applications in the production of oxychemicals. For selected soluble bacterial P450s (notably the high-activity P450-cytochrome P450 reductase enzyme P450 BM3), variants with a multitude of diverse substrate selectivities have been generated both rationally and by random mutagenesis/directed evolution approaches. This highlights the robustness and malleability of the P450 fold, and the capacity of these biocatalysts to oxidise a wide range of chemical scaffolds. This article reviews recent research on the application of wild-type and engineered P450s in the production of important chemicals, including pharmaceuticals and drug metabolites, steroids and antibiotics. In addition, the properties of unusual members of the P450 superfamily that do not follow the canonical P450 catalytic pathway are described.

Compartmentalization of enzymes into organelles is a promising strategy for limiting metabolic crosstalk and improving pathway efficiency, but improved tools and design rules are needed to make this strategy available to more engineered pathways. Here we focus on the Saccharomyces cerevisiae peroxisome and develop a sensitive high-throughput assay for peroxisomal cargo import. We identify an enhanced peroxisomal targeting signal type 1 (PTS1) for rapidly sequestering non-native cargo proteins. Additionally, we perform the first systematic in vivo measurements of nonspecific metabolite permeability across the peroxisomal membrane using a polymer exclusion assay. Finally, we apply these new insights to compartmentalize a two-enzyme pathway in the peroxisome and characterize the expression regimes where compartmentalization leads to improved product titre. This work builds a foundation for using the peroxisome as a synthetic organelle, highlighting both promise and future challenges on the way to realizing this goal.

Magic Microbes: The Navy's Next Defense?

Synthetic Biology is a field born from the premise that design and engineering principles can be applied to biological entities. Principles such as encapsulation, modularity and compositional hierarchy pervade much of the field’s literature and are seen as its core tenets (e.g. [1], [2]). Yet when you ask practising synthetic biologists what they do and how they do it, a more complicated picture emerges.

The context-dependent nature of biological entities can lead to the emergence of unexpected systemic properties, which means design(manipulating the system) and science (understanding the system) often go hand in hand. Sometimes this also means taking a black box approach and applying computational techniques which explore the solution space without a complete model of how the system works. Furthermore, the Synthetic Biology community is now recognising the need to not only standardise the characterisation of biological parts but to also standardise the characterisation of the practices leading to the characterisation of these parts, e.g. the different conditions in which parts exhibit particular behaviours. Indeed, more experienced practitioners in the field now see standards themselves as “evolving” entities that need to be adapted to the environments in which they are applied.

Very often, a synthetic biology project or design problem requires a multi-disciplinary team to employ a diverse set of methods, techniques and tools. These range from those drawn from the rational design tradition (e.g. pathway design), to those adapted from Physics or Complexity Science (e.g. mathematical modelling), to freshly developed machine learning techniques (e.g. reinforcement learning). This “kludging” [3] approach reflects the resourceful and adaptive spirit with which the field has come to tackle its complex design problems. These are design problems that involve entities that are inherently difficult to predict (both at the element level and at the system level) and design objectives that are poorly articulated or in flux. Furthermore, many Synthetic Biology endeavours have far-reaching social, economic and ethical implications, thus transforming the social environment in which the field operates as well as responding to it [4].

The Bahen Centre for Information Technology at U of T was overrun with a crowd of over 150 students on March 12. Standing in line, the students were anxiously awaiting participant wristbands for the annual U of T Biohacks competition held by U of T’s iGEM team.

iGEM is an international undergraduate competition for synthetic biology. Last fall, the U of T team placed silver at the iGem Boston jamboree. As the annual synthetic biology competition at U of T, Biohacks attracts students from universities across Ontario. This year’s event attracted a total of 42 teams of up to four students each.

The event began with a keynote from Hui Yuan Xiong, a researcher and cofounder at Deep Genomics, a company that uses computational machine learning to work with biological data for medical applications. Before the competition, a number of coding workshops were held in order to refresh contestants on the basics of computational biology.

Computational biology, which involves developing and using tools to analyze and model biological data and systems, is an immensely broad field. As such, there was an enormous variety of subtopics within which teams could pick a problem to solve.

While U of T’s iGEM provided a list of suggested topics on the biohacks website as a springboard to work off, virtually anything within the realm of computational biology was considered fair game. 

Humans have some 20,000 genes. The most well-studied microbe, E. coli, has some 4,000 genes. And the record for a free-living species is held by Mycoplasma genitalium, with 525 genes.
Now, the pioneering scientist Craig Venter and his colleagues have engineered a bacterium to have only 473 genes. “It is a profound result,” Adam Arkin of Berkeley Synthetic Biology Institute told Quartz.
This is a huge leap toward the ultimate goal of synthetic biology: designing life from scratch, where we know what each gene does. When we get there, it will open up infinite possibilities. In theory at least, we could eventually have bacteria making personalized medicines and algae producing biofuels.
To build Syn 3.0, Venter and his colleagues started with a bacterium they had designed in 2010, Syn 1.0, which had some 900 genes. They made Syn 1.0 by removing the naturally occurring genome of bacteria ​Mycoplasma mycoides and replacing it with a lightly tinkered version that they made in a lab. This was a unique achievement because Syn 1.0 lived with the synthetic genome.
For Syn 3.0, they began knocking out non-essential genes from Syn 1.0. Their first target was to remove genes that occurred in pairs. In many cases the cell kept working with only one copy of the gene. Next they started removing genes whose functions they didn’t know. But then they hit problems. Not knowing the function of a gene did not mean it was not essential for life.
What is “essential” for life depends on the environment that a living cell finds itself in. For instance, the record for the life-form with the fewest genes is held by naturally occurring Tremblaya, which has only 120 genes. But it cannot survive outside its insect host, which is performing many of the critical functions for Tremblaya. That is why Venter’s team wanted “essential” to mean what would be necessary for the organism to live on its own.
Without knowing what all those essential genes are, Venter and his colleagues took a trial-and-error approach. If one of their designed genomes created a living cell, then they would remove a specific gene from it and see if it still lived. If it didn’t, then they would restore that gene and remove another. After doing this for a few years, they got to Syn 2.0 with 516 genes. They kept going until they reached Syn 3.0, with 473 genes.
This is a historic step forward in human knowledge, to be sure, but there remains a certain amount that’s unknowable. Though Syn 3.0 is the free-living cell with the fewest number of genes, Venter and his colleagues still don’t know what 149 of those 473 genes do. If you asked an engineer to build a plane where she doesn’t know what a third of the parts do, she’ll say you are crazy.
So Venter and his colleagues are working on understanding and tinkering with those genes, and they’ve already made progress. They took some 60 genes of Syn 3.0 and reordered them according to their function. The resulting genome, like a defragmented hard drive, was leaner and worked just as well.
This kind of tinkering is of huge significance for synthetic biologists. If they know what each gene does, the hope is that they’ll design life that may be able to do something that, until now, no species can do.

The degeneracy of the genetic code, however, with 61 possible codons specifying 20 different amino acids can trip up the best-designed synthesis schemes—strange DNA syntax in the form of a non-preferred codon within a coding sequence disrupts the dance of translation and can drastically alter overall yields.