An in-silico porcine model of phosgene-induced lung injury predicts clinically relevant benefits from application of continuous positive airway pressure up to 8 h post exposure.

We present the first computational model of the pathophysiological consequences of phosgene-induced lung injury in porcine subjects. Data from experiments previously performed in several cohorts of large healthy juvenile female pigs (111 data points from 37 subjects), including individual arterial blood gas readings, respiratory rate and heart rate, were used to develop the computational model. Close matches are observed between model outputs (PaO2 and PaCO2) and the experimental data, for both terminally anaesthetised and conscious subjects. The model was applied to investigate the effectiveness of continuous positive airway pressure (CPAP) as a pre-hospital treatment method when treatment is initiated at different time points post exposure. The model predicts that clinically relevant benefits are obtained when 10 cmH2O CPAP is initiated within approximately 8 h after exposure. Supplying low-flow oxygen (40%) rather than medical air produced larger clinical benefits than applying higher CPAP pressure levels. This new model can be used as a tool for conducting investigations into ventilation strategies and pharmaceutical treatments for chemical lung injury of diverse aetiology, and for helping to refine and reduce the use of animals in future experimental studies.

Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.

A Theoretical Framework for Implementable Nucleic Acids Feedback Systems.

Chemical reaction networks can be utilised as basic components for nucleic acid feedback control systems' design for Synthetic Biology application. DNA hybridisation and programmed strand-displacement reactions are effective primitives for implementation. However, the experimental validation and scale-up of nucleic acid control systems are still considerably falling behind their theoretical designs. To aid with the progress heading into experimental implementations, we provide here chemical reaction networks that represent two fundamental classes of linear controllers: integral and static negative state feedback. We reduced the complexity of the networks by finding designs with fewer reactions and chemical species, to take account of the limits of current experimental capabilities and mitigate issues pertaining to crosstalk and leakage, along with toehold sequence design. The supplied control circuits are quintessential candidates for the first experimental validations of nucleic acid controllers, since they have a number of parameters, species, and reactions small enough for viable experimentation with current technical capabilities, but still represent challenging feedback control systems. They are also well suited to further theoretical analysis to verify results on the stability, performance, and robustness of this important new class of control systems.

Evaluating current guidelines for cardiopulmonary resuscitation using an integrated computational model of the cardiopulmonary system.

OBJECTIVE: We aimed to use a high-fidelity computational model that captures key interactions between the cardiovascular and pulmonary systems to investigate whether current CPR protocols could potentially be improved. METHODS: We developed and validated the computational model against available human data. We used a global optimisation algorithm to find CPR protocol parameters that optimise the outputs associated with return of spontaneous circulation in a cohort of 10 virtual subjects. RESULTS: Compared with current protocols, myocardial tissue oxygen volume was more than 5 times higher, and cerebral tissue oxygen volume was nearly doubled, during optimised CPR. While the optimal maximal sternal displacement (5.5 cm) and compression ratio (51%) found using our model agreed with the current American Heart Association guidelines, the optimal chest compression rate was lower (67 compressions min-1). Similarly, the optimal ventilation strategy was more conservative than current guidelines, with an optimal minute ventilation of 1500 ml min-1 and inspired fraction of oxygen of 80%. The end compression force was the parameter with the largest impact on CO, followed by PEEP, the compression ratio and the CC rate. CONCLUSIONS: Our results indicate that current CPR protocols could potentially be improved. Excessive ventilation could be detrimental to organ oxygenation during CPR, due to the negative haemodynamic effect of increased pulmonary vascular resistance. Particular attention should be given to the chest compression force to achieve satisfactory CO. Future clinical trials aimed at developing improved CPR protocols should explicitly consider interactions between chest compression and ventilation parameters.

Why Reduced Inspiratory Pressure Could Determine Success of Non-Invasive Ventilation in Acute Hypoxic Respiratory Failure.

The magnitude of inspiratory effort relief within the first 2 hours of non-invasive ventilation for hypoxic respiratory failure was shown in a recent exploratory clinical study to be an early and accurate predictor of outcome at 24 hours. We simulated the application of non-invasive ventilation to three patients whose physiological and clinical characteristics match the data in that study. Reductions in inspiratory effort corresponding to reductions of esophageal pressure swing greater than 10 cmH2O more than halved the values of total lung stress, driving pressure, power and transpulmonary pressure swing. In the absence of significant reductions in inspiratory pressure, multiple indicators of lung injury increased after application of non-invasive ventilation. Clinical Relevance- We show using computer simulation that reduced inspiratory pressure after application of noninvasive ventilation translates directly into large reductions in multiple well-established indicators of lung injury, providing a potential physiological explanation for recent clinical findings.

A computational cardiopulmonary physiology simulator accurately predicts individual patient responses to changes in mechanical ventilator settings.

We present new results validating the capability of a high-fidelity computational simulator to accurately predict the responses of individual patients with acute respiratory distress syndrome to changes in mechanical ventilator settings. 26 pairs of data-points comprising arterial blood gasses collected before and after changes in inspiratory pressure, PEEP, FiO2, and I:E ratio from six mechanically ventilated patients were used for this study. Parallelized global optimization algorithms running on a high-performance computing cluster were used to match the simulator to each initial data point. Mean absolute percentage errors between the simulator predicted values of PaO2 and PaCO2 and the patient data after changing ventilator parameters were 10.3% and 12.6%, respectively. Decreasing the complexity of the simulator by reducing the number of independent alveolar compartments reduced the accuracy of its predictions. Clinical Relevance- These results provide further evidence that our computational simulator can accurately reproduce patient responses to mechanical ventilation, highlighting its usefulness as a clinical research tool.

gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs.

MOTIVATION: A widely applicable strategy to create cell factories is to knockout (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesize at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow. RESULTS: To address this multi-objective optimization problem, we developed a software tool named gcFront-using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth-coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run-granting users flexibility in selecting designs to take to the lab. AVAILABILITY AND IMPLEMENTATION: gcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling Mechanical Ventilation In Silico-Potential and Pitfalls.

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.

Optimising respiratory support for early COVID-19 pneumonia: a computational modelling study.

BACKGROUND: Optimal respiratory support in early COVID-19 pneumonia is controversial and remains unclear. Using computational modelling, we examined whether lung injury might be exacerbated in early COVID-19 by assessing the impact of conventional oxygen therapy (COT), high-flow nasal oxygen therapy (HFNOT), continuous positive airway pressure (CPAP), and noninvasive ventilation (NIV). METHODS: Using an established multi-compartmental cardiopulmonary simulator, we first modelled COT at a fixed FiO2 (0.6) with elevated respiratory effort for 30 min in 120 spontaneously breathing patients, before initiating HFNOT, CPAP, or NIV. Respiratory effort was then reduced progressively over 30-min intervals. Oxygenation, respiratory effort, and lung stress/strain were quantified. Lung-protective mechanical ventilation was also simulated in the same cohort. RESULTS: HFNOT, CPAP, and NIV improved oxygenation compared with conventional therapy, but also initially increased total lung stress and strain. Improved oxygenation with CPAP reduced respiratory effort but lung stress/strain remained elevated for CPAP >5 cm H2O. With reduced respiratory effort, HFNOT maintained better oxygenation and reduced total lung stress, with no increase in total lung strain. Compared with 10 cm H2O PEEP, 4 cm H2O PEEP in NIV reduced total lung stress, but high total lung strain persisted even with less respiratory effort. Lung-protective mechanical ventilation improved oxygenation while minimising lung injury. CONCLUSIONS: The failure of noninvasive ventilatory support to reduce respiratory effort may exacerbate pulmonary injury in patients with early COVID-19 pneumonia. HFNOT reduces lung strain and achieves similar oxygenation to CPAP/NIV. Invasive mechanical ventilation may be less injurious than noninvasive support in patients with high respiratory effort.

Validation of at-the-bedside formulae for estimating ventilator driving pressure during airway pressure release ventilation using computer simulation.

BACKGROUND: Airway pressure release ventilation (APRV) is widely available on mechanical ventilators and has been proposed as an early intervention to prevent lung injury or as a rescue therapy in the management of refractory hypoxemia. Driving pressure ([Formula: see text]) has been identified in numerous studies as a key indicator of ventilator-induced-lung-injury that needs to be carefully controlled. [Formula: see text] delivered by the ventilator in APRV is not directly measurable in dynamic conditions, and there is no "gold standard" method for its estimation. METHODS: We used a computational simulator matched to data from 90 patients with acute respiratory distress syndrome (ARDS) to evaluate the accuracy of three "at-the-bedside" methods for estimating ventilator [Formula: see text] during APRV. RESULTS: Levels of [Formula: see text] delivered by the ventilator in APRV were generally within safe limits, but in some cases exceeded levels specified by protective ventilation strategies. A formula based on estimating the intrinsic positive end expiratory pressure present at the end of the APRV release provided the most accurate estimates of [Formula: see text]. A second formula based on assuming that expiratory flow, volume and pressure decay mono-exponentially, and a third method that requires temporarily switching to volume-controlled ventilation, also provided accurate estimates of true [Formula: see text]. CONCLUSIONS: Levels of [Formula: see text] delivered by the ventilator during APRV can potentially exceed levels specified by standard protective ventilation strategies, highlighting the need for careful monitoring. Our results show that [Formula: see text] delivered by the ventilator during APRV can be accurately estimated at the bedside using simple formulae that are based on readily available measurements.

Restoring circadian gene profiles in clock networks using synthetic feedback control.

The circadian system-an organism's built-in biological clock-is responsible for orchestrating biological processes to adapt to diurnal and seasonal variations. Perturbations to the circadian system (e.g., pathogen attack, sudden environmental change) often result in pathophysiological responses (e.g., jetlag in humans, stunted growth in plants, etc.) In view of this, synthetic biologists are progressively adapting the idea of employing synthetic feedback control circuits to alleviate the effects of perturbations on circadian systems. To facilitate the design of such controllers, suitable models are required. Here, we extend our recently developed model for the plant circadian clock-termed the extended S-System model-to model circadian systems across different kingdoms of life. We then use this modeling strategy to develop a design framework, based on an antithetic integral feedback (AIF) controller, to restore a gene's circadian profile when it is subject to loss-of-function due to external perturbations. The use of the AIF controller is motivated by its recent successful experimental implementation. Our findings provide circadian biologists with a systematic and general modeling and design approach for implementing synthetic feedback control of circadian systems.

Pre-hospital continuous positive airway pressure after blast lung injury and hypovolaemic shock: a modelling study.

BACKGROUND: In non-traumatic respiratory failure, pre-hospital application of CPAP reduces the need for intubation. Primary blast lung injury (PBLI) accompanied by haemorrhagic shock is common after mass casualty incidents. We hypothesised that pre-hospital CPAP is also beneficial after PBLI accompanied by haemorrhagic shock. METHODS: We performed a computer-based simulation of the cardiopulmonary response to PBLI followed by haemorrhage, calibrated from published controlled porcine experiments exploring blast injury and haemorrhagic shock. The effect of different CPAP levels was simulated in three in silico patients who had sustained mild, moderate, or severe PBLI (10%, 25%, 50% contusion of the total lung) plus haemorrhagic shock. The primary outcome was arterial partial pressure of oxygen (Pao2) at the end of each simulation. RESULTS: In mild blast lung injury, 5 cm H2O ambient-air CPAP increased Pao2 from 10.6 to 12.6 kPa. Higher CPAP did not further improve Pao2. In moderate blast lung injury, 10 cm H2O CPAP produced a larger increase in Pao2 (from 8.5 to 11.1 kPa), but 15 cm H2O CPAP produced no further benefit. In severe blast lung injury, 5 cm H2O CPAP inceased Pao2 from 4.06 to 8.39 kPa. Further increasing CPAP to 10-15 cm H2O reduced Pao2 (7.99 and 7.90 kPa, respectively) as a result of haemodynamic impairment resulting from increased intrathoracic pressures. CONCLUSIONS: Our modelling study suggests that ambient air 5 cm H2O CPAP may benefit casualties suffering from blast lung injury, even with severe haemorrhagic shock. However, higher CPAP levels beyond 10 cm H2O after severe lung injury reduced oxygen delivery as a result of haemodynamic impairment.

High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational modelling study.

BACKGROUND: There is on-going controversy regarding the potential for increased respiratory effort to generate patient self-inflicted lung injury (P-SILI) in spontaneously breathing patients with COVID-19 acute hypoxaemic respiratory failure. However, direct clinical evidence linking increased inspiratory effort to lung injury is scarce. We adapted a computational simulator of cardiopulmonary pathophysiology to quantify the mechanical forces that could lead to P-SILI at different levels of respiratory effort. In accordance with recent data, the simulator parameters were manually adjusted to generate a population of 10 patients that recapitulate clinical features exhibited by certain COVID-19 patients, i.e., severe hypoxaemia combined with relatively well-preserved lung mechanics, being treated with supplemental oxygen. RESULTS: Simulations were conducted at tidal volumes (VT) and respiratory rates (RR) of 7 ml/kg and 14 breaths/min (representing normal respiratory effort) and at VT/RR of 7/20, 7/30, 10/14, 10/20 and 10/30 ml/kg / breaths/min. While oxygenation improved with higher respiratory efforts, significant increases in multiple indicators of the potential for lung injury were observed at all higher VT/RR combinations tested. Pleural pressure swing increased from 12.0 ± 0.3 cmH2O at baseline to 33.8 ± 0.4 cmH2O at VT/RR of 7 ml/kg/30 breaths/min and to 46.2 ± 0.5 cmH2O at 10 ml/kg/30 breaths/min. Transpulmonary pressure swing increased from 4.7 ± 0.1 cmH2O at baseline to 17.9 ± 0.3 cmH2O at VT/RR of 7 ml/kg/30 breaths/min and to 24.2 ± 0.3 cmH2O at 10 ml/kg/30 breaths/min. Total lung strain increased from 0.29 ± 0.006 at baseline to 0.65 ± 0.016 at 10 ml/kg/30 breaths/min. Mechanical power increased from 1.6 ± 0.1 J/min at baseline to 12.9 ± 0.2 J/min at VT/RR of 7 ml/kg/30 breaths/min, and to 24.9 ± 0.3 J/min at 10 ml/kg/30 breaths/min. Driving pressure increased from 7.7 ± 0.2 cmH2O at baseline to 19.6 ± 0.2 cmH2O at VT/RR of 7 ml/kg/30 breaths/min, and to 26.9 ± 0.3 cmH2O at 10 ml/kg/30 breaths/min. CONCLUSIONS: Our results suggest that the forces generated by increased inspiratory effort commonly seen in COVID-19 acute hypoxaemic respiratory failure are comparable with those that have been associated with ventilator-induced lung injury during mechanical ventilation. Respiratory efforts in these patients should be carefully monitored and controlled to minimise the risk of lung injury.

Designing an irreversible metabolic switch for scalable induction of microbial chemical production.

Bacteria can be harnessed to synthesise high-value chemicals. A promising strategy for increasing productivity uses inducible control systems to switch metabolism from growth to chemical synthesis once a large population of cell factories are generated. However, use of expensive chemical inducers limits scalability of this approach for biotechnological applications. Switching using cheap nutrients is an appealing alternative, but their tightly regulated uptake and consumption again limits scalability. Here, using mathematical models of fatty acid uptake in E. coli as an exemplary case study, we unravel how the cell's native regulation and program of induction can be engineered to minimise inducer usage. We show that integrating positive feedback loops into the circuitry creates an irreversible metabolic switch, which, requiring only temporary induction, drastically reduces inducer usage. Our proposed switch should be widely applicable, irrespective of the product of interest, and brings closer the realization of scalable and sustainable microbial chemical production.

Ventilation strategies for front of neck airway rescue: an in silico study.

BACKGROUND: During induction of general anaesthesia a 'cannot intubate, cannot oxygenate' (CICO) situation can arise, leading to severe hypoxaemia. Evidence is scarce to guide ventilation strategies for small-bore emergency front of neck airways that ensure effective oxygenation without risking lung damage and cardiovascular depression. METHODS: Fifty virtual subjects were configured using a high-fidelity computational model of the cardiovascular and pulmonary systems. Each subject breathed 100% oxygen for 3 min and then became apnoeic, with an obstructed upper airway. When arterial haemoglobin oxygen saturation reached 40%, front of neck airway access was simulated with various configurations. We examined the effect of several ventilation strategies on re-oxygenation, pulmonary pressures, cardiovascular function, and oxygen delivery. RESULTS: Re-oxygenation was achieved in all ventilation strategies. Smaller airway configurations led to dynamic hyperinflation for a wide range of ventilation strategies. This effect was absent in airways with larger internal diameter (≥3 mm). Intrapulmonary pressures increased quickly to supra-physiological values with the smallest airways, resulting in pronounced cardio-circulatory depression (cardiac output <3 L min-1 and mean arterial pressure <60 mm Hg), impeding oxygen delivery (<600 ml min-1). Limiting tidal volume (≤200 ml) and ventilatory frequency (≤8 bpm) for smaller diameter cannulas reduced dynamic hyperinflation and gas trapping, preventing cardiovascular depression. CONCLUSIONS: Dynamic hyperinflation can be demonstrated for a wide range of front of neck airway cannulae when the upper airway is obstructed. When using small-bore cannulae in a CICO situation, ventilation strategies should be chosen that prevent gas trapping to prevent severe adverse events including cardio-circulatory depression.

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.

In Silico Modeling of Coronavirus Disease 2019 Acute Respiratory Distress Syndrome: Pathophysiologic Insights and Potential Management Implications.

OBJECTIVES: Patients with coronavirus disease 2019 acute respiratory distress syndrome appear to present with at least two distinct phenotypes: severe hypoxemia with relatively well-preserved lung compliance and lung gas volumes (type 1) and a more conventional acute respiratory distress syndrome phenotype, displaying the typical characteristics of the "baby lung" (type 2). We aimed to test plausible hypotheses regarding the pathophysiologic mechanisms underlying coronavirus disease 2019 acute respiratory distress syndrome and to evaluate the resulting implications for ventilatory management. DESIGN: We adapted a high-fidelity computational simulator, previously validated in several studies of acute respiratory distress syndrome, to: 1) develop quantitative insights into the key pathophysiologic differences between the coronavirus disease 2019 acute respiratory distress syndrome and the conventional acute respiratory distress syndrome and 2) assess the impact of different positive end-expiratory pressure, Fio2, and tidal volume settings. SETTING: Interdisciplinary Collaboration in Systems Medicine Research Network. SUBJECTS: The simulator was calibrated to represent coronavirus disease 2019 acute respiratory distress syndrome patients with both normal and elevated body mass indices undergoing invasive mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: An acute respiratory distress syndrome model implementing disruption of hypoxic pulmonary vasoconstriction and vasodilation leading to hyperperfusion of collapsed lung regions failed to replicate clinical data on type 1 coronavirus disease 2019 acute respiratory distress syndrome patients. Adding mechanisms to reflect disruption of alveolar gas-exchange due to the effects of pneumonitis and heightened vascular resistance due to the emergence of microthrombi produced levels of ventilation perfusion mismatch and hypoxemia consistent with data from type 1 coronavirus disease 2019 acute respiratory distress syndrome patients, while preserving close-to-normal lung compliance and gas volumes. Atypical responses to positive end-expiratory pressure increments between 5 and 15 cm H2O were observed for this type 1 coronavirus disease 2019 acute respiratory distress syndrome model across a range of measures: increasing positive end-expiratory pressure resulted in reduced lung compliance and no improvement in oxygenation, whereas mechanical power, driving pressure, and plateau pressure all increased. Fio2 settings based on acute respiratory distress syndrome network protocols at different positive end-expiratory pressure levels were insufficient to achieve adequate oxygenation. Incrementing tidal volumes from 5 to 10 mL/kg produced similar increases in multiple indicators of ventilator-induced lung injury in the type 1 coronavirus disease 2019 acute respiratory distress syndrome model to those seen in a conventional acute respiratory distress syndrome model. CONCLUSIONS: Our model suggests that use of standard positive end-expiratory pressure/Fio2 tables, higher positive end-expiratory pressure strategies, and higher tidal volumes may all be potentially deleterious in type 1 coronavirus disease 2019 acute respiratory distress syndrome patients, and that a highly personalized approach to treatment is advisable.

Architectures for Combined Transcriptional and Translational Resource Allocation Controllers.

Recent work on engineering synthetic cellular circuitry has shown that non-regulatory interactions mediated by competition for gene expression resources can result in degraded performance or even failure. Transcriptional and translational resource allocation controllers based on orthogonal circuit-specific gene expression machinery have separately been shown to improve modularity and circuit performance. Here, we investigate the potential advantages, challenges, and design trade-offs involved in combining transcriptional and translational controllers into a "dual resource allocation control system." We show that separately functional, translational, and transcriptional controllers cannot generally be combined without extensive redesign. We analyze candidate architectures for direct design of dual resource allocation controllers and propose modifications to improve their performance (in terms of decoupling and expression level) and robustness. We show that dual controllers can be built that are composed only of orthogonal gene expression resources and demonstrate that such designs offer both superior performance and robustness characteristics.

Management of primary blast lung injury: a comparison of airway pressure release versus low tidal volume ventilation.

BACKGROUND: Primary blast lung injury (PBLI) presents as a syndrome of respiratory distress and haemoptysis resulting from explosive shock wave exposure and is a frequent cause of mortality and morbidity in both military conflicts and terrorist attacks. The optimal mode of mechanical ventilation for managing PBLI is not currently known, and clinical trials in humans are impossible due to the sporadic and violent nature of the disease. METHODS: A high-fidelity multi-organ computational simulator of PBLI pathophysiology was configured to replicate data from 14 PBLI casualties from the conflict in Afghanistan. Adaptive and responsive ventilatory protocols implementing low tidal volume (LTV) ventilation and airway pressure release ventilation (APRV) were applied to each simulated patient for 24 h, allowing direct quantitative comparison of their effects on gas exchange, ventilatory parameters, haemodynamics, extravascular lung water and indices of ventilator-induced lung injury. RESULTS: The simulated patients responded well to both ventilation strategies. Post 24-h investigation period, the APRV arm had similar PF ratios (137 mmHg vs 157 mmHg), lower sub-injury threshold levels of mechanical power (11.9 J/min vs 20.7 J/min) and lower levels of extravascular lung water (501 ml vs 600 ml) compared to conventional LTV. Driving pressure was higher in the APRV group (11.9 cmH2O vs 8.6 cmH2O), but still significantly less than levels associated with increased mortality. CONCLUSIONS: Appropriate use of APRV may offer casualties with PBLI important mortality-related benefits and should be considered for management of this challenging patient group.