SynBioFromLeukipposInstitute

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Zhong Jin, Wei Sun, Yonggang Ke, Chih-Jen Shih, Geraldine L.C. Paulus,Qing Hua Wang, Bin Mu, Peng Yin & Michael S. StranoAffiliationsContributions

"The vision for graphene and other two-dimensional electronics is the direct production of nanoelectronic circuits and barrier materials from a single precursor sheet. DNA origami and single-stranded tiles are powerful methods to encode complex shapes within a DNA sequence, but their translation to patterning other nanomaterials has been limited. Here we develop a metallized DNA nanolithography that allows transfer of spatial information to pattern two-dimensional nanomaterials capable of plasma etching. Width, orientation and curvature can be programmed by specific sequence design and transferred, as we demonstrate for graphene. Spatial resolution is limited by distortion of the DNA template upon Au metallization and subsequent etching. The metallized DNA mask allows for plasmonic enhanced Raman spectroscopy of the underlying graphene, providing information on defects, doping and lattice symmetry. This DNA nanolithography enables wafer-scale patterning of two-dimensional electronic materials to create diverse circuit elements, including nanorings, three- and four-membered nanojunctions, and extended nanoribbons."

http://bit.ly/11PPyMJ


comment:

*Patterning graphene with DNA*

by
Anne Trafton

"DNA’s unique structure is ideal for carrying genetic information, but scientists have recently found ways to exploit this versatile molecule for other purposes: By controlling DNA sequences, they can manipulate the molecule to form many different nanoscale shapes.

Chemical and molecular engineers at MIT and Harvard University have now expanded this approach by using folded DNA to control the nanostructure of inorganic materials. After building DNA nanostructures of various shapes, they used the molecules as templates to create nanoscale patterns on sheets of graphene. This could be an important step toward large-scale production of electronic chips made of graphene, a one-atom-thick sheet of carbon with unique electronic properties.“This gives us a chemical tool to program shapes and patterns at the nanometer scale, forming electronic circuits, for example,” says Michael Strano, a professor of chemical engineering at MIT and a senior author of a paper describing the technique in the April 9 issue of Nature Communications ("Metallized DNA nanolithography for encoding and transferring spatial information for graphene patterning")."

http://bit.ly/YbgJND 

by
Meng H, Wang J, Xiong Z, Xu F, Zhao G, Wang Y.

"Accurate and controllable regulatory elements such as promoters and ribosome binding sites (RBSs) are indispensable tools to quantitatively regulate gene expression for rational pathway engineering. Therefore, de novo designing regulatory elements is brought back to the forefront of synthetic biology research. Here we developed a quantitative design method for regulatory elements based on strength prediction using artificial neural network (ANN). One hundred mutated Trc promoter & RBS sequences, which were finely characterized with a strength distribution from 0 to 3.559 (relative to the strength of the original sequence which was defined as 1), were used for model training and test. A precise strength prediction model, NET90_19_576, was finally constructed with high regression correlation coefficients of 0.98 for both model training and test. Sixteen artificial elements were in silico designed using this model. All of them were proved to have good consistency between the measured strength and our desired strength. The functional reliability of the designed elements was validated in two different genetic contexts. The designed parts were successfully utilized to improve the expression of BmK1 peptide toxin and fine-tune deoxy-xylulose phosphate pathway in Escherichia coli. Our results demonstrate that the methodology based on ANN model can de novo and quantitatively design regulatory elements with desired strengths, which are of great importance for synthetic biology applications."

http://bit.ly/151fIPU

by Ron Leuty

"Jay Keasling wants to build an even better mosquito trap.
As French drug maker Sanofi ramps up production of semisynthetic artemisinin — initially developed in Keasling’s University of California, Berkeley, lab — Keasling and others are moving forward with a new nonprofit to explore how to cheaply get synthetic biology-created antimalarial drugs to the world's neediest populations.
“The idea is to get the drug out to everyone who needs it,” said Keasling, who I interviewed for this week's print edition story on the synthetic biology process used to invent a potentially cheaper and steadier source of the antimalarial drug artemisinin."

http://bit.ly/10J89ao

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"Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology."

http://bit.ly/10lPH9m

By Alan Boyle

"Conservationists say it's high time to consider whether synthetic biology will solve some of the huge problems that beset endangered species, or bring new problems. It just might do both.

http://nbcnews.to/Z2Kh08

"Scientists at Oxford Genetics have developed a range of synthetic DNA products that they believe will revolutionize the genetic research sector. They say the new technology, called SnapFast™, provides a fully interchangeable module-based genetic engineering platform that offers research scientists improved efficiency and flexibility.

http://bit.ly/YIsMWx

by
Waldherr S, Streif S, Allgöwer F.

"A biomolecular network is called adaptive if its output returns to the original value after a transient response even under a persisting stimulus. The conditions for adaptation have been investigated thoroughly with systems theory approaches in the literature and it is easy to check whether they are satisfied in the linear approximation. In contrast, it is in general not easy to modify a non-adaptive network model such that it gains adaptive behaviour, especially for medium- and large-scale networks. The authors present a systematic approach based on the notion of kinetic perturbations to construct adaptive biomolecular network models from non-adaptive ones. An advantage of kinetic perturbations in this application is that neither the stoichiometry nor the steady state of the system is changed. Furthermore, the method covers both parameter and network structure modifications and can be applied to any reaction rate formalism and even to medium-scale or partially unknown models. The approach is exemplified at a small- and a medium-sized biomolecular network, illustrating its potential to systematically evaluate the different network modifications for adaptation. The proposed method will be useful either in iterative model building to construct mathematical models of adaptive biomolecular networks, or in synthetic biology where it can be applied to design or modify synthetic networks for adaptation."

http://bit.ly/Ya5ftR

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http://bit.ly/16LOtVH

by
Bowsher CG, Voliotis M, Swain PS.

"Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments."

 http://bit.ly/YYwzOW

by
Matsumoto K, Taguchi S.

"The development of synthetic biology has transformed microbes into useful factories for producing valuable polymers and/or their precursors from renewable biomass. Recent progress at the interface of chemistry and biology has enabled the production of a variety of new biopolymers with properties that substantially differ from their petroleum-derived counterparts. This review touches on recent trials and achievements in the field of biopolymer synthesis, including chemo-enzymatically synthesized aliphatic polyesters, wholly biosynthesized lactate-based polyesters, polyhydroxyalkanoates and other unusual bacterially synthesized polyesters. The expanding diversities in structure and the material properties of biopolymers are key for exploring practical applications. The enzyme and metabolic engineering approaches toward this goal are discussed by shedding light on the successful case studies."
http://bit.ly/10aQ4n