SynBioFromLeukipposInstitute

When it comes to genome size, a rare Japanese flower, called Paris japonica, is the current heavyweight champ, with 50 times more DNA than humans. At the other end of the scale, there's now a new lightweight record-holder growing in petri dishes in southern California. This week in Science, researchers led by genome sequencing pioneer Craig Venter report engineering a bacterium to have the smallest genome—and the fewest genes—of any freely living organism, smaller than the flower's by a factor of 282,000. Known as Syn 3.0, the new organism has a genome whittled down to the bare essentials needed to survive and reproduce, just 473 genes. The microbe's streamlined genetic structure excites evolutionary biologists and biotechnologists, who anticipate adding genes back to it one by one to study their effects. "It's a tour de force," says George Church, a synthetic biologist at Harvard University.

Synthetic biology is today harnessing the design of novel and greener biosynthesis routes for the production of added-value chemicals and natural products. The design of novel pathways often requires a detailed selection of enzyme sequences to import into the chassis at each of the reaction steps. To address such design requirements in an automated way, we present here a tool for exploring the space of enzymatic reactions. Given a reaction and an enzyme the tool provides a probability estimate that the enzyme catalyses the reaction. Our tool first considers the similarity of a reaction to known biochemical reactions with respect to signatures around their reaction centers. Signatures are defined based on chemical transformation rules by using extended connectivity fingerprint descriptors. A semi-supervised Gaussian process model associated with the similar known reactions then provides the probability estimate. The Gaussian process model uses information about both the reaction and the enzyme in providing the estimate. These estimates were validated experimentally by the application of the Gaussian process model to a newly identified metabolite in E. coli in order to search for the enzymes catalyzing its associated reactions. Furthermore, we show with several pathway design examples how such ability to assign probability estimates to enzymatic reactions provides the potential to assist in bioengineering applications, providing experimental validation to our proposed approach. To the best of our knowledge, the proposed approach is the first application of Gaussian Processes dealing with biological sequences and chemicals, the use of a semi-supervised Gaussian Process framework is also novel in the context of machine learning applied to bioinformatics. However, the ability of an enzyme to catalyse a reaction depends on the affinity between the substrates of the reaction and the enzyme. This affinity is generally quantified by the Michaelis Constant KM. Therefore, we also demonstrate using Gaussian Process regression to predict KM given a substrate-enzyme pair.

In recent years, the marine environment has been the subject of increasing attention from biotechnological and pharmaceutical industries as a valuable and promising source of novel bioactive compounds. Marine biodiscovery programmes have begun to reveal the extent of novel compounds encoded within the enormous bacterial richness and diversity of the marine ecosystem. A combination of unique physicochemical properties and spatial niche-specific substrates, in wide-ranging and extreme habitats, underscores the potential of the marine environment to deliver on functionally novel biocatalytic activities. With the growing need for green alternatives to industrial processes, and the unique transformations which nature is capable of performing, marine biocatalysts have the potential to markedly improve current industrial pipelines. Furthermore, biocatalysts are known to possess chiral selectivity and specificity, a key focus of pharmaceutical drug design. In this review, we discuss how the explosion in genomics based sequence analysis, allied with parallel developments in synthetic and molecular biology, have the potential to fast-track the discovery and subsequent improvement of a new generation of marine biocatalysts.

Expanding the range of amino acids polymerizable by ribosomes could enable new functionalities to be added to polypeptides. Now, the genetic code has been reprogrammed using a reconstituted in vitro translation system to enable synthesis of unnatural peptides with unmatched flexibility.

Speeding up design-build-test (DBT) cycles is a fundamental challenge facing biochemical engineering. To address this challenge, we report a new cell-free protein synthesis driven metabolic engineering (CFPS-ME) framework for rapid biosynthetic pathway prototyping. In our framework, cell-free cocktails for synthesizing target small molecules are assembled in a mix-and-match fashion from crude cell lysates either containing selectively enriched pathway enzymes from heterologous overexpression or directly producing pathway enzymes in lysates by CFPS. As a model, we apply our approach to n-butanol biosynthesis showing that Escherichia coli lysates support a highly active 17-step CoA-dependent n-butanol pathway in vitro. The elevated degree of flexibility in the cell-free environment allows us to manipulate physiochemical conditions, access enzymatic nodes, discover new enzymes, and prototype enzyme sets with linear DNA templates to study pathway performance. We anticipate that CFPS-ME will facilitate efforts to define, manipulate, and understand metabolic pathways for accelerated DBT cycles without the need to reengineer organisms.

Nanoscale objects of increasing complexity can be constructed from DNA or RNA. However, the scope of potential applications could be enhanced by expanding beyond the moderate chemical diversity of natural nucleic acids. Here, we explore construction of nano-objects made entirely from alternative building blocks: synthetic genetic polymers not found in nature (also called xeno nucleic acids, XNAs). Specifically, we describe assembly of 70 kDa tetrahedra elaborated in four different XNA chemistries (2'F-RNA, FANA, HNA and CeNA) as well as mixed designs, and of a ~600 kDa all-FANA octahedron visualized by electron microscopy. Our results extend the chemical scope for programmable nanostructure assembly, with implications for the design of nano-objects and materials with an expanded range of structural and physicochemical properties, including enhanced biostability.

A report on the second meeting on 'Genome Engineering and Synthetic Biology: Tools and Technologies', held 28-29 January 2016 in Ghent, Belgium.

INTRODUCTION
In 1984, the simplest cells capable of autonomous growth, the mycoplasmas, were proposed as models for understanding the basic principles of life. In 1995, we reported the first complete cellular genome sequences (Haemophilus influenza, 1815 genes, and Mycoplasma genitalium, 525 genes). Comparison of these sequences revealed a conserved core of about 250 essential genes, much smaller than either genome. In 1999, we introduced the method of global transposon mutagenesis and experimentally demonstrated that M. genitalium contains many genes that are nonessential for growth in the laboratory, even though it has the smallest genome known for an autonomously replicating cell found in nature. This implied that it should be possible to produce a minimal cell that is simpler than any natural one. Whole genomes can now be built from chemically synthesized oligonucleotides and brought to life by installation into a receptive cellular environment. We have applied whole-genome design and synthesis to the problem of minimizing a cellular genome.
RATIONALE
Since the first genome sequences, there has been much work in many bacterial models to identify nonessential genes and define core sets of conserved genetic functions, using the methods of comparative genomics. Often, more than one gene product can perform a particular essential function. In such cases, neither gene will be essential, and neither will necessarily be conserved. Consequently, these approaches cannot, by themselves, identify a set of genes that is sufficient to constitute a viable genome. We set out to define a minimal cellular genome experimentally by designing and building one, then testing it for viability. Our goal is a cell so simple that we can determine the molecular and biological function of every gene.
RESULTS
Whole-genome design and synthesis were used to minimize the 1079–kilobase pair (kbp) synthetic genome of M. mycoides JCVI-syn1.0.  An initial design, based on collective knowledge of molecular biology in combination with limited transposon mutagenesis data, failed to produce a viable cell. Improved transposon mutagenesis methods revealed a class of quasi-essential genes that are needed for robust growth, explaining the failure of our initial design. Three more cycles of design, synthesis, and testing, with retention of quasi-essential genes, produced JCVI-syn3.0 (531 kbp, 473 genes). Its genome is smaller than that of any autonomously replicating cell found in nature. JCVI-syn3.0 has a doubling time of ~180 min, produces colonies that are morphologically similar to those of JCVI-syn1.0, and appears to be polymorphic when examined microscopically.
CONCLUSION
The minimal cell concept appears simple at first glance but becomes more complex upon close inspection. In addition to essential and nonessential genes, there are many quasi-essential genes, which are not absolutely critical for viability but are nevertheless required for robust growth. Consequently, during the process of genome minimization, there is a trade-off between genome size and growth rate. JCVI-syn3.0 is a working approximation of a minimal cellular genome, a compromise between small genome size and a workable growth rate for an experimental organism. It retains almost all the genes that are involved in the synthesis and processing of macromolecules. Unexpectedly, it also contains 149 genes with unknown biological functions, suggesting the presence of undiscovered functions that are essential for life. JCVI-syn3.0 is a versatile platform for investigating the core functions of life and for exploring whole-genome design.

Synthetic Biology is a new open access journal which covers all aspects of synthetic biology; including papers on the mathematical modelling and practical engineering in the field. 

This includes genetic circuit design, computational methods, genetic systems and circuit design, viral engineering, cell design and construction, assembly platforms, DNA synthesis, synthetic metagenomics, pathway construction, gene optimization, protein engineering, metabolic engineering, programmed evolution, cellular manufacturing, mathematical modelling and engineering processes.

The aim of the journal is to provide a forum for original research papers, reviews, commentaries, reports on practical projects, and a venue for in-depth discussion on the topics relevant to synthetic biology.

This paper describes a rigorous methodology for quantification of model errors in fungal growth models. This is essential to choose the model that best describes the data and guide modeling efforts. Mathematical modeling of growth of filamentous fungi is necessary in fungal biology for gaining systems level understanding on hyphal and colony behaviors in different environments. A critical challenge in the development of these mathematical models arises from the indeterminate nature of their colony architecture, which is a result of processing diverse intracellular signals induced in response to a heterogeneous set of physical and nutritional factors. There exists a practical gap in connecting fungal growth models with measurement data. Here, we address this gap by introducing the first unified computational framework based on Bayesian inference that can quantify individual model errors and rank the statistical models based on their descriptive power against data. We show that this Bayesian model comparison is just a natural formalization of Occam's razor. The application of this framework is discussed in comparing three models in the context of synthetic data generated from a known true fungal growth model. This framework of model comparison achieves a trade-off between data fitness and model complexity and the quantified model error not only helps in calibrating and comparing the models, but also in making better predictions and guiding model refinements.

Demands on the industrial and academic yeast strain engineer have increased significantly in the era of synthetic biology. Installing complex biosynthetic pathways and combining point mutations are tedious and time-consuming using traditional methods. With multiplex engineering tools, these tasks can be completed in a single step, typically achieving up to six-fold compression in strain engineering timelines. To capitalize on this potential, a variety of yeast CRISPR-Cas methods have been developed, differing largely in how the guide RNA (gRNA) reagents that direct the Cas9 nuclease are delivered. However, in nearly all reported protocols, the time savings of multiplexing is offset by multiple days of cloning to prepare the required reagents. Here, we discuss the advantages and opportunities of CRISPR-Cas Assisted Multiplexing (CAM), a same-day, cloning-free method for multi-locus engineering in yeast