Complete Genome Sequence of Sphingobium xenophagum PH3-15, Isolated from La Roche-Posay Thermal Water Sources.

We report here the complete genome sequence of Sphingobium xenophagum strain PH3-15, which was isolated from La Roche-Posay thermal water sources. The assembled 4.6-Mbp genome consisted of two chromosomes and three plasmids.

Evaluation of Heterologous Biosynthetic Pathways for Methanol-Based 5-Aminovalerate Production by Thermophilic .

The use of methanol as carbon source for biotechnological processes has recently attracted great interest due to its relatively low price, high abundance, high purity, and the fact that it is a non-food raw material. In this study, methanol-based production of 5-aminovalerate (5AVA) was established using recombinant strains. 5AVA is a building block of polyamides and a candidate to become the C5 platform chemical for the production of, among others, δ-valerolactam, 5-hydroxy-valerate, glutarate, and 1,5-pentanediol.

Charting the Metabolic Landscape of the Facultative Methylotroph Bacillus methanolicus.

MGA3 is a thermotolerant and relatively fast-growing methylotroph able to secrete large quantities of glutamate and lysine. These natural characteristics make a good candidate to become a new industrial chassis organism, especially in a methanol-based economy. Intriguingly, the only substrates known to support growth as sole sources of carbon and energy are methanol, mannitol, and, to a lesser extent, glucose and arabitol.

IsoCor: isotope correction for high-resolution MS labeling experiments.

Summary: Mass spectrometry (MS) is widely used for isotopic studies of metabolism and other (bio)chemical processes. Quantitative applications in systems and synthetic biology require to correct the raw MS data for the contribution of naturally occurring isotopes. Several tools are available to correct low-resolution MS data, and recent developments made substantial improvements by introducing resolution-dependent correction methods, hence opening the way to the correction of high-resolution MS (HRMS) data.

A dataset of small molecules triggering transcriptional and translational cellular responses.

The aim of this dataset is to identify and collect compounds that are known for being detectable by a living cell, through the action of a genetically encoded biosensor and is centred on bacterial transcription factors. Such a dataset should open the possibility to consider a wide range of applications in synthetic biology. The reader will find in this dataset the name of the compounds, their InChI (molecular structure), the publication where the detection was reported, the organism in which this was detected or engineered, the type of detection and experiment that was performed as well as the name of the biosensor.

RetroPath2.0: A retrosynthesis workflow for metabolic engineers.

Synthetic biology applied to industrial biotechnology is transforming the way we produce chemicals. However, despite advances in the scale and scope of metabolic engineering, the research and development process still remains costly. In order to expand the chemical repertoire for the production of next generation compounds, a major engineering biology effort is required in the development of novel design tools that target chemical diversity through rapid and predictable protocols.

Extended Metabolic Space Modeling.

Determining the fraction of the chemical space that can be processed in vivo by using natural and synthetic biology devices is crucial for the development of advanced synthetic biology applications. The extended metabolic space is a coding system based on molecular signatures that enables the derivation of reaction rules for metabolic reactions and the enumeration of all possible substrates and products corresponding to the rules. The extended metabolic space expands capabilities for controlling the production, processing, sensing, and the release of specific molecules in chassis organisms.

Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways.

Detection of chemical signals is critical for cells in nature as well as in synthetic biology, where they serve as inputs for designer circuits. Important progress has been made in the design of signal processing circuits triggering complex biological behaviors, but the range of small molecules recognized by sensors as inputs is limited. The ability to detect new molecules will increase the number of synthetic biology applications, but direct engineering of tailor-made sensors takes time.

Sensing new chemicals with bacterial transcription factors.

Bacteria rely on allosteric transcription factors (aTFs) to sense a wide range of chemicals. The variety of effectors has contributed in making aTFs the most used input system in synthetic biological circuits. Considering their enabling role in biotechnology, an important question concerns the size of the chemical space that can potentially be detected by these biosensors.

SensiPath: computer-aided design of sensing-enabling metabolic pathways.

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable.