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Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.

PURPOSE: This study aimed to describe the 24-hour cycle of wearable sensor-obtained heart rate in patients with deterioration-free recovery and to compare it with patients experiencing postoperative deterioration.METHODS: A prospective observational trial was performed in patients following bariatric or major abdominal cancer surgery. A wireless accelerometer patch (Healthdot) continuously measured postoperative heart rate, both in the hospital and after discharge, for a period of 14 days. The circadian pattern, or diurnal rhythm, in the wearable sensor-obtained heart rate was described using peak, nadir and peak-nadir excursions.RESULTS: The study population consisted of 137 bariatric and 100 major abdominal cancer surgery patients. In the latter group, 39 experienced postoperative deterioration. Both surgery types showed disrupted diurnal rhythm on the first postoperative days. Thereafter, the bariatric group had significantly lower peak heart rates (days 4, 7-12, 14), lower nadir heart rates (days 3-14) and larger peak-nadir excursions (days 2, 4-14). In cancer surgery patients, significantly higher nadir (days 2-5) and peak heart rates (days 2-3) were observed prior to deterioration.CONCLUSIONS: The postoperative diurnal rhythm of heart rate is disturbed by different types of surgery. Both groups showed recovery of diurnal rhythm but in patients following cancer surgery, both peak and nadir heart rates were higher than in the bariatric surgery group. Especially nadir heart rate was identified as a potential prognostic marker for deterioration after cancer surgery.

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.

Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from “bottom-up” mechanistic models to “top-down” data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven modeling approach to quantify individuals’ glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.

Background: Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established. Objective: This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients. Methods: The accuracy of the wrist-worn PPG sensor was assessed in 62 post–abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m2). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy. Results: Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis. Conclusions: The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained. Trial Registration: ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127.

Dyslipidemia in obesity results from excessive production and impaired clearance of triglyceride-rich (TG-rich) lipoproteins, which are particularly pronounced in the postprandial state. Here, we investigated the impact of Roux-en-Y gastric bypass (RYGB) surgery on postprandial VLDL1 and VLDL2 apoB and TG kinetics and their relationship with insulin-responsiveness indices. Morbidly obese patients without diabetes who were scheduled for RYGB surgery (n = 24) underwent a lipoprotein kinetics study during a mixed-meal test and a hyperinsulinemic-euglycemic clamp study before the surgery and 1 year later. A physiologically based computational model was developed to investigate the impact of RYGB surgery and plasma insulin on postprandial VLDL kinetics. After the surgery, VLDL1 apoB and TG production rates were significantly decreased, whereas VLDL2 apoB and TG production rates remained unchanged. The TG catabolic rate was increased in both VLDL1 and VLDL2 fractions, but only the VLDL2 apoB catabolic rate tended to increase. Furthermore, postsurgery VLDL1 apoB and TG production rates, but not those of VLDL2, were positively correlated with insulin resistance. Insulin-mediated stimulation of peripheral lipoprotein lipolysis was also improved after the surgery. In summary, RYGB resulted in reduced hepatic VLDL1 production that correlated with reduced insulin resistance, elevated VLDL2 clearance, and improved insulin sensitivity in lipoprotein lipolysis pathways.

Assessing post-operative recovery is a significant component of perioperative care, since this assessment might facilitate detecting complications and determining an appropriate discharge date. However, recovery is difficult to assess and challenging to predict, as no universally accepted definition exists. Current solutions often contain a high level of subjectivity, measure recovery only at one moment in time, and only investigate recovery until the discharge moment. For these reasons, this research aims to create a model that predicts continuous recovery scores in perioperative care in the hospital and at home for objective decision making. This regression model utilized vital signs and activity metrics measured using wearable sensors and the XGBoost algorithm for training. The proposed model described continuous recovery profiles, obtained a high predictive performance, and provided outcomes that are interpretable due to the low number of features in the final model. Moreover, activity features, the circadian rhythm of the heart, and heart rate recovery showed the highest feature importance in the recovery model. Patients could be identified with fast and slow recovery trajectories by comparing patient-specific predicted profiles to the average fast- and slow-recovering populations. This identification may facilitate determining appropriate discharge dates, detecting complications, preventing readmission, and planning physical therapy. Hence, the model can provide an automatic and objective decision support tool.

Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, 13C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.

INTRODUCTION: The shift toward remote patient monitoring methods to detect clinical deterioration requires testing of wearable devices in real-life clinical settings. This study aimed to develop a remote early warning scoring (REWS) system based on continuous measurements using a wearable device, and compare its diagnostic performance for the detection of deterioration to the diagnostic performance of the conventional modified early warning score (MEWS).MATERIALS AND METHODS: The study population of this prospective, single center trial consisted of patients who underwent major abdominal cancer surgery and were monitored using routine in-hospital spotcheck measurements of the vital parameters. Heart and respiratory rates were measured continuously using a wireless accelerometer patch (HealthDot). The prediction by MEWS of deterioration toward a complication graded Clavien-Dindo of 2 or higher was compared to the REWS derived from continuous measurements by the wearable patch.MAIN RESULTS: A total of 103 patients and 1909 spot-check measurements were included in the analysis. Postoperative deterioration was observed in 29 patients. For both EWS systems, the sensitivity (MEWS: 0.20 95% CI: [0.13-0.29], REWS: 0.20 95% CI: [0.13-0.29]) and specificity (MEWS: 0.96 95% CI: [0.95-0.97], REWS: 0.96 95% CI: [0.95-0.97]) were assessed.CONCLUSIONS: The diagnostic value of the REWS method, based on continuous measurements of the heart and respiratory rates, is comparable to that of the MEWS in patients following major abdominal cancer surgery. The wearable patch could detect the same amount of deteriorations, without requiring manual spot check measurements.

Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, 13C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles., BT/Industriele Microbiologie

BACKGROUND: Heart failure (HF) biomarkers have prognostic value. The aim of this study was to combine HF biomarkers into an objective classification system for risk stratification of patients with HF.METHODS: HF biomarkers were analyzed in a population of HF outpatients and expressed relative to their cut-off values (N-terminal pro-B-type natriuretic peptide [NT-proBNP] >1,000 pg/mL, soluble suppression of tumorigenesis-2 [ST2] >35 ng/mL, growth differentiation factor-15 [GDF-15] >2,000 pg/mL, and fibroblast growth factor-23 [FGF-23] >95.4 pg/mL). Biomarkers that remained significant in multivariable analysis were combined to devise the Heartmarker score. The performance of the Heartmarker score was compared to the widely used New York Heart Association (NYHA) classification based on symptoms during ordinary activity.RESULTS: HF biomarkers of 245 patients were analyzed, 45 (18%) of whom experienced the composite endpoint of HF hospitalization, appropriate implantable cardioverter-defibrillator shock, or death. HF biomarkers were elevated more often in patients that reached the composite endpoint than in patients that did not reach the endpoint. NT-proBNP, ST2, and GDF-15 were independent predictors of the composite endpoint and were thus combined as the Heartmarker score. The event-free survival and distance covered in 6 minutes of walking decreased with an increasing Heartmarker score. Compared with the NYHA classification, the Heartmarker score was better at discriminating between different risk classes and had a comparable relationship to functional capacity.CONCLUSIONS: The Heartmarker score is a reproducible and intuitive model for risk stratification of outpatients with HF, using routine biomarker measurements.

Background: Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies. Objectives: We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death. Methods: We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients. Results: The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro–B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients. Conclusions: A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.