Automated insulin delivery systems are becoming increasingly available in the treatment of type 1 diabetes. They can improve glycemic outcomes while reducing patient burden, but good glycemic control ...during and after exercise remains challenging. Exercise causes substantial and prolonged changes in insulin sensitivity that consequently affect insulin requirements, and can lead to late-onset hypoglycemia if not accounted for.
Here, we present a model predictive control algorithm that adjusts insulin delivery during the recovery period to improve glycemic outcomes after aerobic exercise. The algorithm continuously estimates insulin sensitivity from glucose measurements via an unscented Kalman filter. It integrates the estimate by continuously updating the target insulin input as well as the process model to account for changing insulin demands. The proposed approach is generic and transferable to other control formulations.
We evaluate our new control strategy in-silico using a validated diabetes patient model with aerobic exercise. We consider a virtual patient population in full-day simulations including a wide variety of exercise scenarios covering moderate to high intensities and different timing and duration of the exercise. We demonstrate improved glycemic outcomes over night following a day with exercise for all scenarios and show robustness of our approach to common disturbances.
•Exercise elevates insulin sensitivity and changes insulin demands in type 1 diabetes.•We integrate insulin sensitivity estimates in a model predictive control algorithm.•Exercise-adjusted insulin delivery improves glucose outcomes.
Saccharomyces cerevisiae cells grown in a small volume of chemically defined media neither reach the desired cell density nor grow at a fast enough rate to scale down the volume and increase the ...sample number of classical biochemical assays, as the detection limit of the readout often requires a high number of cells as an input. To ameliorate this problem, we developed and optimised a new high cell density (HCD) medium for S. cerevisiae. Starting from a widely used synthetic medium composition, we systematically varied the concentrations of all components without the addition of other compounds. We used response surface methodology to develop and optimise the five components of the medium: glucose, yeast nitrogen base, amino acids, monosodium glutamate, and inositol. We monitored growth, cell number, and cell size to ensure that the optimisation was towards a greater density of cells rather than just towards an increase in biomass (i.e., larger cells). Cells grown in the final medium, HCD, exhibit growth more similar to the complex medium yeast extract peptone dextrose (YPD) than to the synthetic defined (SD) medium. Whereas the final cell density of HCD prior to the diauxic shift is increased compared with YPD and SD about threefold and tenfold, respectively. We found normal cell‐cycle behaviour throughout the growth phases by monitoring DNA content and protein expression using fluorescent reporters. We also ensured that HCD media could be used with a variety of strains and that they allow selection for all common yeast auxotrophic markers.
Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. ...Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability.
Here, we present a computational framework and an efficient algorithm to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a Non-linear Mixed-Effects (NLME) framework into a Markov Chain Monte-Carlo (MCMC) algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability.
We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.
Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, ...leading to inaccurate predictions of an organism's potential or actual metabolic operations.
We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of Escherichia coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E.coli's metabolic capabilities.
Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta.
Supplementary data are available at Bioinformatics online.
The molecular mechanisms governing the transition from hematopoietic stem cells (HSCs) to lineage-committed progenitors remain poorly understood. Transcription factors (TFs) are powerful cell ...intrinsic regulators of differentiation and lineage commitment, while cytokine signaling has been shown to instruct the fate of progenitor cells. However, the direct regulation of differentiation-inducing hematopoietic TFs by cell extrinsic signals remains surprisingly difficult to establish. PU.1 is a master regulator of hematopoiesis and promotes myeloid differentiation. Here we report that tumor necrosis factor (TNF) can directly and rapidly upregulate PU.1 protein in HSCs in vitro and in vivo. We demonstrate that in vivo, niche-derived TNF is the principal PU.1 inducing signal in HSCs and is both sufficient and required to relay signals from inflammatory challenges to HSCs.
•TNF integrates inflammatory signals to directly regulate the lineage instructing transcription factor PU.1 in HSCs.
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Despite increasing survival rates of pediatric leukemia patients over the past decades, the outcome of some leukemia subtypes has remained dismal. Drug sensitivity and resistance testing on ...patient‐derived leukemia samples provide important information to tailor treatments for high‐risk patients. However, currently used well‐based drug screening platforms have limitations in predicting the effects of prodrugs, a class of therapeutics that require metabolic activation to become effective. To address this issue, a microphysiological drug‐testing platform is developed that enables co‐culturing of patient‐derived leukemia cells, human bone marrow mesenchymal stromal cells, and human liver microtissues within the same microfluidic platform. This platform also enables to control the physical interaction between the diverse cell types. Herein, it is made possible to recapitulate hepatic prodrug activation of ifosfamide in their platform, which is very difficult in traditional well‐based assays. By testing the susceptibility of primary patient‐derived leukemia samples to the prodrug ifosfamide, sample‐specific sensitivities to ifosfamide in primary leukemia samples are identified. The microfluidic platform is found to enable the recapitulation of physiologically relevant conditions and the testing of prodrugs including short‐lived and unstable metabolites. The platform holds great potential for clinical translation and precision chemotherapy selection.
The authors present a microphysiological platform to recapitulate the metabolic activation of prodrugs in vitro for personalized drug screening against leukemia. The platform enables the co‐culture of primary leukemia cells, stromal cells, and liver microtissues in a microfluidic network. Sample‐specific sensitivities to short‐lived ifosfamide metabolites are identified in primary leukemia samples, which cannot be easily assessed in existing screening assays.
Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration ...and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for aerobic moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs.
Abstract
Background
Influenza vaccination efficacy is reduced after hematopoietic stem cell transplantation (HSCT) and patient factors determining vaccination outcomes are still poorly understood.
...Methods
We investigated the antibody response to seasonal influenza vaccination in 135 HSCT patients and 69 healthy volunteers (HVs) in a prospective observational multicenter cohort study. We identified patient factors associated with hemagglutination inhibition titers against A/California/2009/H1N1, A/Texas/2012/H3N2, and B/Massachusetts/2012 by multivariable regression on the observed titer levels and on seroconversion/seroprotection categories for comparison.
Results
Both regression approaches yielded consistent results but regression on titers estimated associations with higher precision. HSCT patients required 2 vaccine doses to achieve average responses comparable to a single dose in HVs. Prevaccination titers were positively associated with time after transplantation, confirming that HSCT patients can elicit potent antibody responses. However, an unrelated donor, absolute lymphocyte counts below the normal range, and treatment with calcineurin inhibitors lowered the odds of responding.
Conclusions
HSCT patients show a highly heterogeneous vaccine response but, overall, patients benefited from the booster shot and can acquire seroprotective antibodies over the years after transplantation. Several common patient factors lower the odds of responding, urging identification of additional preventive strategies in the poorly responding groups.
Clinical Trials Registration
NCT03467074.
Patients after hematopoietic stem cell transplantation show a highly heterogeneous antibody response to seasonal influenza vaccination that can be partially explained by easily accessible clinical data such as the type of immunosuppressive treatment, absolute lymphocyte count, and donor relationship.
Regular exercise is beneficial and recommended for people with type 1 diabetes, but increased glucose demand and changes in insulin sensitivity require treatment adjustments to prevent ...exercise-induced hypoglycemia. Several different adjustment strategies based on insulin bolus reductions and additional carbohydrate intake have been proposed, but large inter- and intraindividual variability and studies using different exercise duration, intensity, and timing impede a direct comparison of their effects. In this study, we use a mathematical model of the glucoregulatory system and implement published guidelines and strategies
to provide a direct comparison on a single 'typical' person on a standard day with three meals. We augment this day by a broad range of exercise scenarios combining different intensity and duration of the exercise session, and different timing with respect to adjacent meals. We compare the resulting blood glucose trajectories and use summary measures to evaluate the time-in-range and risk scores for hypo- and hyperglycemic events for each simulation scenario, and to determine factors that impede prevention of hypoglycemia events. Our simulations suggest that the considered strategies and guidelines successfully minimize the risk for acute hypoglycemia. At the same time, all adjustments substantially increase the risk of late-onset hypoglycemia compared to no adjustment in many cases. We also find that timing between exercise and meals and additional carbohydrate intake during exercise can lead to non-intuitive behavior due to superposition of meal- and exercise-related glucose dynamics. Increased insulin sensitivity appears as a major driver of non-acute hypoglycemic events. Overall, our results indicate that further treatment adjustment might be required both immediately following exercise and up to several hours later, but that the intricate interplay between different dynamics makes it difficult to provide generic recommendations. However, our simulation scenarios extend substantially beyond the original scope of each model component and proper model validation is warranted before applying our
results in a clinical setting.
Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, ...single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.
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•Dynamic single-cell data pose opportunities and challenges for mechanistic models•Non-linear mixed-effects models disentangle sources of cellular heterogeneity•Methods for flexible, scalable parameter inference and for sub-process identification•Several sub-processes contribute dynamically to heterogeneity in yeast endocytosis
Longitudinal single-cell data allow studying cell-to-cell heterogeneity but also pose new challenges for mechanistic modeling. Augmenting “traditional” mechanistic models by allowing cell-specific values for parameters and initial conditions allows disentangling sources of variation and their propagation through the network. We present a flexible and scalable method for parameter inference in such non-linear mixed-effects models. We quantify how sub-processes contribute to observed cellular heterogeneity using correlations and variability of estimated parameters and demonstrate applicability of the method on a medium-sized mechanistic model of yeast endocytosis.
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