Phage-mediated intercellular CRISPRi for biocomputation in bacterial consortia.

Coordinated actions of cells in microbial communities and multicellular organisms enable them to perform complex tasks otherwise difficult for single cells. This has inspired biological engineers to build cellular consortia for larger circuits with improved functionalities while implementing communication systems for coordination among cells. Here, we investigate the signalling dynamics of a phage-mediated synthetic DNA messaging system and couple it with CRISPR interference to build distributed circuits that perform logic gate operations in multicellular bacterial consortia.

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.

Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost.

A versatile active learning workflow for optimization of genetic and metabolic networks.

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude.

CRISPR interference and its applications.

Sequence-specific control of gene expression is a powerful tool for identifying and studying gene functions and cellular processes. CRISPR interference (CRISPRi) is an RNA-based method for highly specific silencing of the transcription in prokaryotic or eukaryotic cells. The typical CRISPRi system is a type II CRISPR (clustered regularly interspaced palindromic repeats) machinery of Streptococcus pyogenes.

Harnessing the central dogma for stringent multi-level control of gene expression.

Strictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided, but without affecting the achievable range of expression. Here, we demonstrate how the central dogma offers a simple solution to this challenge.

Biosensor-based enzyme engineering approach applied to psicose biosynthesis.

Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts.

Large scale active-learning-guided exploration for in vitro protein production optimization.

Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free buffer compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.

Metabolic perceptrons for neural computing in biological systems.

Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems.

Optimizing Cell-Free Biosensors to Monitor Enzymatic Production.

Cell-free systems are promising platforms for rapid and high-throughput prototyping of biological parts in metabolic engineering and synthetic biology. One main limitation of cell-free system applications is the low fold repression of transcriptional repressors. Hence, prokaryotic biosensor development, which mostly relies on repressors, is limited.

Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors.

Cell-free transcription-translation systems have great potential for biosensing, yet the range of detectable chemicals is limited. Here we provide a workflow to expand the range of molecules detectable by cell-free biosensors through combining synthetic metabolic cascades with transcription factor-based networks. These hybrid cell-free biosensors have a fast response time, strong signal response, and a high dynamic range.

Custom-made transcriptional biosensors for metabolic engineering.

Transcriptional biosensors allow screening, selection, or dynamic regulation of metabolic pathways, and are, therefore, an enabling technology for faster prototyping of metabolic engineering and sustainable chemistry. Recent advances have been made, allowing for routine use of heterologous transcription factors, and new strategies such as chimeric protein design allow engineers to tap into the reservoir of metabolite-binding proteins. However, extending the sensing scope of biosensors is only the first step, and computational models can help in fine-tuning properties of biosensors for custom-made behavior.

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.