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Delaney, R. M.

Publications and source records attributed to Delaney, R. M..

2 recordsLinked to original sources

in vitro analysis of a competitive inhibition model for T7 RNA polymerase biosensors

T7 RNA polymerase (T7 RNAP) biosensors, in which T7 RNAP transcribes some reporter gene or signal in response to external stimuli, have wide applications in synthetic biology and metabolic engineering. We adapted a biochemical reaction network model and used an in vitro transcription assay to determine network parameters for different T7 RNAP constructs. Under conditions where template DNA is limiting, the EC50 values of native and engineered T7 RNAPs ranged from 33 nM (29 -37 95% c.i.) to 570 nM (258 -714 95% c.i.) (wild-type T7 RNAP). The measured EC50 values were largely insensitive to free magnesium, pH, or other buffer conditions. Many biosensor configurations use a split RNAP construct, where the C-terminal (CT7) and N-terminal T7 (NT7) are fused to proximity induced dimerization modules. We used proteolysis and ion exchange chromatography to prepare a CT7 (80 kDa) product. The impact of free CT7 on T7 RNAP transcriptional activity was well described by a competitive inhibition model, with an inhibitory constant KI = 24 nM (22-26 95% c.i.) of the sensor. These model parameters will be useful for forward modeling and design of T7 RNAP-based genetic circuits. HighlightsO_LIEC50 values for natural and engineered T7 RNAPs measured in vitro C_LIO_LIC-terminal T7 RNAP is a competitive inhibitor of full-length T7 RNAP C_LIO_LIThe inhibition constant of C-terminal T7 RNAP is lower than the EC50 of T7 RNAP C_LI

bioengineering↗

APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohns disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.

systems biology↗