A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits.

Boolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision. After calibrating the model on experimental data from two circuits, we demonstrate close agreement between model outputs and data on the other 111 circuits that have so far been experimentally constructed using the 2-input BLADE platform. Model simulations of the remaining 143 circuits that have yet to be tested experimentally predict excellent performance of the 2-input BLADE platform across the range of possible circuits. Circuits from both the tested and untested subsets that perform less well consist of a disproportionally high number of STOP sequences. Model predictions suggested that circuit performance declines with a decrease in recombinase expression and new experimental data was generated that confirms this relationship.

C-reactive protein-guided use of procalcitonin in COVID-19.

BACKGROUND: A low procalcitonin (PCT) concentration facilitates exclusion of bacterial co-infections in COVID-19, but high costs associated with PCT measurements preclude universal adoption. Changes in inflammatory markers, including C-reactive protein (CRP), can be concordant, and predicting low PCT concentrations may avoid costs of redundant tests and support more cost-effective deployment of this diagnostic biomarker. OBJECTIVES: To explore whether, in COVID-19, low PCT values could be predicted by the presence of low CRP concentrations. METHODS: Unselected cohort of 224 COVID-19 patients admitted to hospital that underwent daily PCT and CRP measurements as standard care. Both 0.25 ng/mL and 0.5 ng/mL were used as cut-offs for positive PCT test results. Geometric mean was used to define high and low CRP values at each timepoint assessed. RESULTS: Admission PCT was <0.25 ng/mL in 160/224 (71.4%), 0.25-0.5 ng/mL in 27 (12.0%) and >0.5 ng/mL in 37 (16.5%). Elevated PCT was associated with increased risk of death (P = 0.0004) and was more commonly associated with microbiological evidence of bacterial co-infection (P < 0.0001). For high CRP values, significant heterogeneity in PCT measurements was observed, with maximal positive predictive value of 50% even for a PCT cut-off of 0.25 ng/mL. In contrast, low CRP was strongly predictive of low PCT concentrations, particularly <0.5 ng/mL, with a negative predictive value of 97.6% at time of hospital admission and 100% 48 hours into hospital stay. CONCLUSIONS: CRP-guided PCT testing algorithms can reduce unnecessary PCT measurement and costs, supporting antimicrobial stewardship strategies in COVID-19.

Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit.

Inducible genetic switches based on tyrosine recombinase-based DNA excision are a promising platform for the regulation and control of chimeric antigen receptor (CAR) T cell activity in cancer immunotherapy. These switches exploit the increased stability of DNA excision in tyrosine recombinases through an inversion-excision circuit design. Here, the authors develop the first mechanistic mathematical model of switching dynamics in tyrosine recombinases and validate it against experimental data through both global optimisation and statistical approximation approaches. Analysis of this model provides guidelines regarding which system parameters are best suited to experimental tuning in order to establish optimal switch performance in vivo. In particular, they find that the switching response can be made significantly faster by increasing the concentration of the inducer drug 4-OHT and/or by using promoters generating higher expression levels of the FlpO recombinase.

Modeling the architecture of the regulatory system controlling methylenomycin production in Streptomyces coelicolor.

BACKGROUND: The antibiotic methylenomycin A is produced naturally by Streptomyces coelicolor A3(2), a model organism for streptomycetes. This compound is of particular interest to synthetic biologists because all of the associated biosynthetic, regulatory and resistance genes are located on a single cluster on the SCP1 plasmid, making the entire module easily transferable between different bacterial strains. Understanding further the regulation and biosynthesis of the methylenomycin producing gene cluster could assist in the identification of motifs that can be exploited in synthetic regulatory systems for the rational engineering of novel natural products and antibiotics. RESULTS: We identify and validate a plausible architecture for the regulatory system controlling methylenomycin production in S. coelicolor using mathematical modeling approaches. Model selection via an approximate Bayesian computation (ABC) approach identifies three candidate model architectures that are most likely to produce the available experimental data, from a set of 48 possible candidates. Subsequent global optimization of the parameters of these model architectures identifies a single model that most accurately reproduces the dynamical response of the system, as captured by time series data on methylenomycin production. Further analyses of variants of this model architecture that capture the effects of gene knockouts also reproduce qualitative experimental results observed in mutant S. coelicolor strains. CONCLUSIONS: The mechanistic mathematical model developed in this study recapitulates current biological knowledge of the regulation and biosynthesis of the methylenomycin producing gene cluster, and can be used in future studies to make testable predictions and formulate experiments to further improve our understanding of this complex regulatory system.

Mechanistic Modeling of a Rewritable Recombinase Addressable Data Module.

Many of the most important applications predicted to arise from Synthetic Biology will require engineered cellular memory with the capability to store data in a rewritable and reversible manner upon induction by transient stimuli. DNA recombination provides an ideal platform for cellular data storage and has allowed the development of a rewritable recombinase addressable data (RAD) module, capable of efficient data storage within a chromosome. Here, we develop the first detailed mechanistic model of DNA recombination, and validate it against a new set of in vitro data on recombination efficiencies across a range of different concentrations of integrase and gp3. Investigation of in vivo recombination dynamics using our model reveals the importance of fully accounting for all mechanistic features of DNA recombination in order to accurately predict the effect of different switching strategies on RAD module performance, and highlights its usefulness as a design tool for building future synthetic circuitry.

Development and experimental validation of a mechanistic model of in vitro DNA recombination.

Engineering cellular memory is a key area of research in which Synthetic Biology has already begun to make significant impacts. Recent work elucidating transcriptional memory devices has paved the way for the creation of bistable genetic switches based on DNA recombination. Attempts to experimentally design and build synthetic systems using recombinases have thus far been hindered by a lack of validated computational models that capture the mechanistic basis of DNA recombination. The predictive capabilities of such models could be exploited by Synthetic Biologists to reduce the number of iterative cycles required to align experimental results with design performance requirements. Here, we develop and validate the first detailed mechanistic model of DNA recombination, with a focus on how efficiently recombination can occur, and the model features required to replicate and predict experimental data.