Computational assessment of lipid production in Rhodosporidium toruloides in two-stage and one-stage batch bioprocesses.

Oleaginous yeasts are promising platforms for microbial lipids production as a renewable and sustainable alternative to vegetable oils in biodiesel production. In this paper, a thorough in silico assessment of lipid production in batch cultivation by Rhodosporidium toruloides was developed. By means of dynamic flux balance analysis, the traditional two-stage bioprocess (TSB) performed by the native strain was contrasted with one-stage bioprocess (OSB) using four designed strains obtained by gene knockout strategies. Lipid titer, yield, content, and productivity were analyzed at different initial C/N ratios as relevant performance indicators used in bioprocesses. By weighting these indicators, a global lipid efficiency metric (GLEM) was defined to consider different scenarios. Under simulated conditions, designed strains for lipid overproduction in OSB outperformed the TSB in terms of lipid title (up to threefold), lipid yield (up to 2.4-fold), lipid content (up to 2.8-fold, with a maximum of 76%), and productivity (up to 1.3-fold), depending on C/N ratios. Using these efficiency parameters and the proposed GLEM, the process of selecting the most suitable candidates for lipid production could be carried out before experimental assays. This methodology holds the potential to be extended to other oleaginous microorganisms and diverse strain design techniques.

First Outpatient Clinical Trial of a Full Closed-Loop Artificial Pancreas System in South America.

BACKGROUND: The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented. METHOD: Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165. RESULTS: The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL. CONCLUSION: The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.

A Dual-Hormone Multicontroller for Artificial Pancreas Systems.

Artificial pancreas (AP) algorithms can be divided into single-hormone (SH) and dual-hormone (DH). SH algorithms regulate glycemia using insulin as their control input. On the other hand, DH algorithms also use glucagon to counteract insulin. While SH-AP systems are already commercially available, DH-AP systems are still in an earlier research phase. DH-AP systems have been questioned since the added complexity of glucagon infusion does not always guarantee hypoglycemia prevention and might significantly raise insulin delivery. In this work, a DH multicontroller is proposed based on a SH linear quadratic gaussian (LQG) algorithm with an additional LQG controller to deliver glucagon. This strategy has a switched structure that allows activating one of the following three controllers when necessary: a conservative insulin LQG controller to modulate basal delivery ( K1), an aggressive insulin LQG controller to counteract meals ( K2), or a glucagon LQG controller to avoid imminent hypoglycemia ( K3). Here, an in silico study of the benefits of incorporating controller K3 is carried out. Intra-patient variability and mixed meals are considered. Results indicate that the proposed switched, dual-hormone strategy yields to a reduction in hypoglycemia without increasing hyperglycemia, with no significant rise in insulin delivery.

Reporte de altos requerimientos de insulina en pacientes críticos pediátricos con COVID-19. Experiencia con monitoreo remoto continuo de glucosa / Report of high insulin requirements in pediatric critically ill patients with COVID-19. Experience with continuous remote glucose monitoring

Los pacientes en estado crítico con COVID-19 sufren hiperglucemias sostenidas de difícil manejo. A esto se suma el desafío de minimizar la exposición al contagio. En el presente artículo analizamos la evolución metabólica de dos pacientes pediátricos con COVID-19 admitidos en unidad de cuidados intensivos (UCI) para pacientes COVID-19 del Hospital "Prof. Dr. Juan P. Garrahan" de la Ciudad Autónoma de Buenos Aires, Argentina, que requirieron tratamiento con insulina endovenosa y cuya glucemia fue monitoreada de manera remota con la plataforma InsuMate® desarrollada en la Universidad Nacional de La Plata. Los pacientes requirieron tasas de infusión de insulina en dosis marcadamente mayores que las de otros pacientes críticos que impresionaron relacionadas con los valores de marcadores de inflamación. La infusión pudo ajustarse con cuatro monitoreos diarios de glucosa y las métricas obtenidas con el monitor de glucosa. El uso del sistema de monitoreo remoto continuo de glucosa permitió disminuir la frecuencia de monitoreo glucémico durante el tratamiento.

Critically ill patients with COVID-19 suffer from sustained hyperglycemia that is difficult to manage. Added to this is the challenge of minimizing exposure to contagion. In this article we analyze the metabolic evolution of two pediatric patients with COVID-19 admitted to the intensive care unit (ICU) for COVID-19 patients at the Hospital "Prof. Dr. Juan P. Garrahan "from the Autonomous City of Buenos Aires, Argentina, who required treatment with intravenous insulin and whose blood glucose was remotely monitored with the InsuMate® platform developed at the National University of La Plata. The patients required insulin infusion rates in doses markedly higher than those of other critically ill patients, who were impressively related to the values of inflammation markers. The infusion could be adjusted with four daily glucose monitors and the metrics obtained with the glucose monitor. The use of the continuous remote glucose monitoring system made it possible to decrease the frequency of glycemic monitoring during treatment.

Potentials of constrained sliding mode control as an intervention guide to manage COVID19 spread.

This work evaluates the potential of using sliding mode reference conditioning (SMRC) techniques as a guide for non-pharmaceutical intervention (NPI) to control the COVID-19 pandemic. In particular, for the epidemiological problem addressed here, it is used to compute the contact rate reduction requirement in order to limit the infectious population to a given threshold. The SMRC controller allows the desired output variable limit and its approaching rate to be tuned explicitly. Implementation issues are taken into account and a periodically update of the NPI is proposed for the real life application. The strategy is evaluated under different scenarios where its distinctive features are exhibited.

Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study.

Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, an algorithm based on switched control and time-varying IOB constraints is presented. The proposed control strategy is evaluated in silico using the FDA-approved UVA/ Padova simulator and its performance contrasted with the previously introduced Automatic Regulation of Glucose (ARG) algorithm in the pediatric population. The effect of unannounced meals is also explored. Results indicate that the proposed strategy achieves lower hypo- and hyperglycemia than the ARG for both announced and unannounced meals. Graphical Abstract Block diagram and illustrative example of insulin and glucose evolution over time for the proposed algorithm (ARGAE).

Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement.

BACKGROUND: Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses-the so-called automatic regulation of glucose (ARG)-was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. METHOD: An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. RESULTS: The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). CONCLUSION: In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.

Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses.

BACKGROUND: Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial. METHODS: A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller. RESULTS: For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported. CONCLUSIONS: The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.

Comprehensive analysis of a metabolic model for lipid production in Rhodosporidium toruloides.

The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with flux balance analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism.

Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps.

BACKGROUND AND OBJECTIVE: Although there has been significant progress towards closed-loop type 1 diabetes mellitus (T1DM) treatments, most diabetic patients still treat this metabolic disorder in an open-loop manner, based on insulin pump therapy (basal and bolus insulin infusion). This paper presents a method for automatic insulin bolus shaping based on insulin-on-board (IOB) as an alternative to conventional bolus dosing. METHODS: The methodology presented allows the pump to generate the so-called super-bolus (SB) employing a two-compartment IOB dynamic model. The extra amount of insulin to boost the bolus and the basal cutoff time are computed using the duration of insulin action (DIA). In this way, the pump automatically re-establishes basal insulin when IOB reaches its basal level. Thus, detrimental transients caused by manual or a-priori computations are avoided. RESULTS: The potential of this method is illustrated via in-silico trials over a 30 patients cohort in single meal and single day scenarios. In the first ones, improvements were found (standard treatment vs. automatic SB) both in percentage time in euglycemia (75g meal: 81.9 ±â€¯15.59 vs. 89.51 ±â€¯11.95, ρ ≃ 0; 100g meal: 75.12 ±â€¯18.23 vs. 85.46 ±â€¯14.96, ρ ≃ 0) and time in hypoglecymia (75g meal: 5.92 ±â€¯14.48 vs. 0.97 ±â€¯4.15, ρ=0.008; 100g meal: 9.5 ±â€¯17.02 vs. 1.85 ±â€¯7.05, ρ=0.014). In a single day scenario, considering intra-patient variability, the time in hypoglycemia was reduced (9.57 ±â€¯14.48 vs. 4.21 ±â€¯6.18, ρ=0.028) and improved the time in euglycemia (79.46 ±â€¯17.46 vs. 86.29 ±â€¯11.73, ρ=0.007). CONCLUSIONS: The automatic IOB-based SB has the potential of a better performance in comparison with the standard treatment, particularly for high glycemic index meals with high carbohydrate content. Both glucose excursion and time spent in hypoglycemia were reduced.

Modeling and estimation of production rate for the production phase of non-growth-associated high cell density processes.

This paper addresses the estimation of the specific production rate of intracellular products and the modeling of the bioreactor volume dynamics in high cell density fed-batch reactors. In particular, a new model for the bioreactor volume is proposed, suitable to be used in high cell density cultures where large amounts of intracellular products are stored. Based on the proposed volume model, two forms of a high-order sliding mode observer are proposed. Each form corresponds to the cases with residual biomass concentration or volume measurement, respectively. The observers achieve finite time convergence and robustness to process uncertainties as the kinetic model is not required. Stability proofs for the proposed observer are given. The observer algorithm is assessed numerically and experimentally.

Model-based scale-up methodology for aerobic fed-batch bioprocesses: application to polyhydroxybutyrate (PHB) production.

This work presents a general model-based methodology to scale-up fed-batch bioprocesses. The idea behind this approach is to establish a dynamics hierarchy, based on a model of the process, that allows the designer to determine the proper scale factors as well as at which point of the fed-batch the process should be scaled up. Here, concepts and tools of linear control theory, such as the singular value decomposition of the Hankel matrix, are exploited in the context of process design. The proposed scale-up methodology is first described in a bioprocesses general framework highlighting its main features, key variables and parameters. Then, it is applied to a polyhydroxybutyrate (PHB) fed-batch bioreactor and compared with three empirical criteria, that are traditionally employed to determine the scale factors of these processes, showing the usefulness and distinctive features of this proposal. Moreover, this methodology provides theoretical support to a frequently used empirical rule: scale-up aerobic bioreactors at constant volumetric oxygen transfer coefficient. Finally, similar process dynamic behavior and PHB production set at the laboratory scale are predicted at the new operating scale, while it is also determined that is rarely possible to reproduce similar dynamic behavior of the bioreactor using empirical scale-up criteria.

Specific growth rate observer for the growing phase of a Polyhydroxybutyrate production process.

This paper focuses on the specific growth rate estimation problem in a Polyhydroxybutyrate bioplastic production process by industrial fermentation. The kinetics of the process are unknown and there are uncertainties in the model parameters and inputs. During the first hours of the growth phase of the process, biomass concentration can be measured online by an optical density sensor, but as cell density increases this method becomes ineffective and biomass measurement is lost. An asymptotic observer is developed to estimate the growth rate for the case without biomass measurement based on corrections made by a pH control loop. Furthermore, an exponential observer based on the biomass measurement is developed to estimate the growth rate during the first hours, which gives the initial condition to the asymptotic observer. Error bounds and robustness to uncertainties in the models and in the inputs are found. The estimation is independent of the kinetic models of the microorganism. The characteristic features of the observer are illustrated by numerical simulations and validated by experimental results.

Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms.

This paper deals with the estimation of unknown signals in bioreactors using sliding observers. Particular attention is drawn to estimate the specific growth rate of microorganisms from measurement of biomass concentration. In a recent article, notions of high-order sliding modes have been used to derive a growth rate observer for batch processes. In this paper we generalize and refine these preliminary results. We develop a new observer with a different error structure to cope with other types of processes. Furthermore, we show that these observers are equivalent, under coordinate transformations and time scaling, to the classical super-twisting differentiator algorithm, thus inheriting all its distinctive features. The new observers' family achieves convergence to time-varying unknown signals in finite time, and presents the best attainable estimation error order in the presence of noise. In addition, the observers are robust to modeling and parameter uncertainties since they are based on minimal assumptions on bioprocess dynamics. In addition, they have interesting applications in fault detection and monitoring. The observers performance in batch, fed-batch and continuous bioreactors is assessed by experimental data obtained from the fermentation of Saccharomyces Cerevisiae on glucose.