Development of SimCells as a novel chassis for functional biosensors

This work serves as a proof-of-concept for bacterially derived SimCells (Simple Cells), which contain the cell machinery from bacteria and designed DNA (or potentially a simplified genome) to instruct the cell to carry out novel, specific tasks. SimCells represent a reprogrammable chassis without a native chromosome, which can host designed DNA to perform defined functions. In this paper, the use of Escherichia coli MC1000 ∆minD minicells as a non-reproducing chassis for SimCells was explored, as demonstrated by their ability to act as sensitive biosensors for small molecules. Highly purified minicells derived from E. coli strains containing gene circuits for biosensing were able to transduce the input signals from several small molecules (glucarate, acrylate and arabinose) into the production of green fluorescent protein (GFP). A mathematical model was developed to fit the experimental data for induction of gene expression in SimCells. The intracellular ATP level was shown to be important for SimCell function. A purification and storage protocol was developed to prepare SimCells which could retain their functions for an extended period of time. This study demonstrates that SimCells are able to perform as 'smart bioparticles' controlled by designed gene circuits

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis

<p>Abstract</p> <p>Background</p> <p>Combining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems.</p> <p>Results</p> <p>We have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in <it>Arabidopsis thaliana</it>. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters.</p> <p>Conclusions</p> <p>Relationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.</p

Versatile cell-free protein synthesis systems based on chinese hamster ovary cells

We present an alternative production platform for the synthesis of complex proteins. Apart from conventionally applied protein production using engineered mammalian cell lines, this protocol describes the preparation and principle of cell-free protein synthesis systems based on CHO cell lysates. The CHO cell-free system contains endogenous microsomes derived from the endoplasmic reticulum, which enables a direct integration of membrane proteins into a nature like milieu and the introduction of posttranslational modifications. Different steps of system development are described including the cultivation of CHO cells, cell harvesting and cell disruption to prepare translationally active CHO cell lysates. The requirements for DNA templates and the generation of linear DNA templates suitable for the CHO cell-free reaction is further depicted to underline the opportunity to produce different protein variants in a short period. This experimental setup provides a basis for hig h-throughput applications. The productivity of the CHO cell-free systems is further increased by using a non-canonical translation initiation due to the attachment of an internal ribosomal entry site of the Cricket paralysis virus (CRPV IRES) to the 5´ UTR of the desired gene. In this way, a direct interaction of the IRES structure with the ribosome facilitates a translation factor independent initiation of translation. Cell-free reactions were performed in fast and efficient batch reactions leading to protein yields up to 40 μg/mL. The reaction format was further adjusted to a continuous exchange CHO cell-free reaction (CHO CECF) to prolong reaction time and thereby increase the productivity of the cell-free systems. Finally, protein yields up to 1 g/L were obtained. The CHO CECF system represents a sophisticated resource to address structural and functional aspects of difficult-to-express proteins in fundamental and applied research