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Rescooped by Sebastian Miranda from Plant Biology Teaching Resources (Higher Education)
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Plant Phys: A modern ampelography: a genetic basis for leaf shape and venation patterning in Vitis vinifera

Plant Phys: A modern ampelography: a genetic basis for leaf shape and venation patterning in Vitis vinifera | Biology | Scoop.it

I had to look up "Ampelography" - here's the definition I found, "the field of botany concerned with the identification and classification of grapevines".

 

Anyhow, it's a lovely paper looking at a combination of morphometric analysis of grape leaves from >1200 accessions, combined with mathematical analysis of shape and venation patterns, and finally with GWAS to look at the genetic basis of leaf shape. Fascinating work, and, of course, very applied (to preserve the interaction between genotype, environment and culture in grape and wine production).


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Rescooped by Sebastian Miranda from Plant Biology Teaching Resources (Higher Education)
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Controversial Seralini GMO-rats paper to be retracted

Controversial Seralini GMO-rats paper to be retracted | Biology | Scoop.it

"A heavily criticized study of the effects of genetically modified maize and the Roundup herbicide on rats is being retracted -- one way or another."

 

Thanks to Retraction Watch for the tip!

http://retractionwatch.com/2013/11/28/controversial-seralini-gmo-rats-paper-to-be-retracted/


Via Mary Williams
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Mary Williams's curator insight, November 28, 2013 9:26 AM

We discussed the problems associated with this paper in Teaching Tools in Plant Biology 16: Genetic Improvements in Agriculture http://www.plantcell.org/site/teachingtools/TTPB16.xhtml

Rescooped by Sebastian Miranda from TAL effector science
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Regulation of Endogenous Human Gene Expression by Ligand-Inducible TALE Transcription Factors - ACS Synthetic Biology

Regulation of Endogenous Human Gene Expression by Ligand-Inducible TALE Transcription Factors - ACS Synthetic Biology | Biology | Scoop.it

(via T. Lahaye, thx)

Mercer et al, 2013

The construction of increasingly sophisticated synthetic biological circuits is dependent on the development of extensible tools capable of providing specific control of gene expression in eukaryotic cells. Here, we describe a new class of synthetic transcription factors that activate gene expression in response to extracellular chemical stimuli. These inducible activators consist of customizable transcription activator-like effector (TALE) proteins combined with steroid hormone receptor ligand-binding domains. We demonstrate that these ligand-responsive TALE transcription factors allow for tunable and conditional control of gene activation and can be used to regulate the expression of endogenous genes in human cells. Since TALEs can be designed to recognize any contiguous DNA sequence, the conditional gene regulatory system described herein will enable the design of advanced synthetic gene networks.


Via dromius
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Rescooped by Sebastian Miranda from SynBioFromLeukipposInstitute
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Trends in Biotechnology - Bio-design automation: software + biology + robots

Trends in Biotechnology - Bio-design automation: software + biology + robots | Biology | Scoop.it

Via Gerd Moe-Behrens
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Gerd Moe-Behrens's curator insight, November 27, 2013 2:22 PM

by
+Douglas Densmore and  +Swapnil Bhatia 

"Synthetic biology promises to usher in a new era of scientific innovation and discovery [1]. Applications of this technology are broad and diverse [2]. Although applications frequently dominate the headlines, of equal importance are the engineering design principles. If these principles are developed rigorously, they will lay the foundation for the field and enable long-term growth and accessibility. A crucial engineering principle is design automation. Design automation is the process of applying tools (software, hardware, and wetware) to remove manual processes. Often, design automation transforms a high-level system objective ‘input’ (e.g., optimize this metabolic pathway) into a physically realized artifact ‘output’ (e.g., DNA, microbial strain, or protein). In order for design automation to be broadly applied it requires solutions to be based on sound definitions, tractable algorithms, and standardized data formats. Design automation promises to lower costs, increase design reuse, improve design reproducibility, reduce design error, and enable complex designs.

 Design automation has a rich, 50+ year history in the development of semiconductors, where it is termed electronic design automation (EDA). EDA historically evolved bottom up. Tools were coupled first to the physical fabrication process (1970s), then to design optimization and synthesis (1980s), and finally to design specification and verification (1990s+). This evolution allowed designer focus to shift from semiconductor physics to system optimization and verification. The ability of EDA to allow for this shift was crucial to support the additional computing resources provided by Moore's Law. If carefully developed to respect the unique differences of biology from electronics, aspects of this mature discipline can be applied to biological design [3]. This article outlines how specification (how a system should behave), design (what components should constitute a system), assembly (how to put the system together in the physical world), and data management (how to track system information electronically) serve as four distinct research challenges in the emerging bio-design automation (BDA) discipline. A crucial EDA concept is the separation of concerns [4]. Specifications should separate behavioral requirements (e.g., what functions a system has to perform) from performance requirements (e.g., how the system is measured while performing those functions). In addition, the function behavior (e.g., what the system does) should be separated from the structure (e.g., how the system does it). Developing synthetic biological specification languages with these explicit separation abilities will allow modular, reusable descriptions applicable across host organisms, environmental context, and industrial processes [5,6,7]. These languages should provide: (i) formalized behavior and performance specifications allowing for nontrivial relations among design components and multidimensional objective functions; (ii) formalized constraint mechanisms regarding function, structure, and performance requirements of a design; and (iii) executable semantics allowing for the simulation of system behavior and the capability to produce derivative designs adhering to the system constraints. A BDA framework must have a mechanism for automatically transforming a design specification into a representation that can be physically manufactured. This process needs to explore simultaneously the space of valid designs [8] while optimizing those designs [9]. In particular: (i) libraries of genetic elements (e.g., parts) must be assigned to functional requirements respecting the performance characterization of the elements and their compositional behavior; (ii) algorithms should account for multiple genetic elements that map to the same functionality but differ in performance or cost, and should encode sound rules for making expert design decisions; and (iii) a framework for genetic element interactions must present a model that can capture, distinguish, and predict intended and unintended consequences of genetic composition. The manual manufacturing of synthetic biological designs can lead to human error, wasted reagents and consumables, low design throughput, development of nontransferable laboratory-specific techniques, and selective recording of data. An automated genetic manufacturing process [10,11] will provide: (i) assembly strategies formulated to share design intermediates, reduce overall design stages, allow for manufacturing restarts after failure, account for known biological phenomenon (DNA homology for example), and recognize library elements available to replace de novo synthesis; (ii) translation of assembly strategies into detailed, scheduled protocol execution plans, taking into account physical resources available; and (iii) explicit separation between the operations in a protocol and the commands to manufacturing hardware (e.g., liquid handling robotics or microfluidics). Automation relies on making decisions based on quantitative and empirical data. These data must be accurate and capture biological relations. Its storage must be persistent, allow for various query/retrieval methods, and expand with the community's understanding of biological phenomena. Frameworks that support a flexible data model while providing a programmatic interface to the data can provide a powerful solution [12]. Once curated, such data can allow: (i) machine learning algorithms to create models describing biological relations, requirements, and constraints; (ii) automated redesign of biological systems based on empirical data; and (iii) standardization of design exchange and characterization. Each of these challenge areas can be related in a cyclic design flow, as shown in Figure 1. This ecosystem will allow for a variety of design start points depending on the application and expertise of the designer. Moreover this ecosystem paves the way for a commercialization environment where vertical integration allows for end-to-end solutions and horizontal integration allows for customized solutions within any given design challenge area. The modular nature of the ecosystem with well-defined interfaces will allow for solutions to be developed in isolation and improved upon over time without disrupting the overall design process...."


http://bit.ly/17XNEPX ;

Rescooped by Sebastian Miranda from SynBioFromLeukipposInstitute
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Big Data, Cardiovascular Disease, Public Policy and Synthetic Biology: new Strategic Research Initiatives | University of Cambridge

Big Data, Cardiovascular Disease, Public Policy and Synthetic Biology: new Strategic Research Initiatives | University of Cambridge | Biology | Scoop.it
Big Data, Cardiovascular Disease, Public Policy & Synthetic Biology: new Strategic Research Initiatives at Cambridge: http://t.co/lRCP4mZUu9

Via Gerd Moe-Behrens
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