When to sleep and consume caffeine to boost alertness.

STUDY OBJECTIVES: Sleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to sub-optimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the day. METHODS: We combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm's recommendations against that achieved by following the U.S. Army caffeine guidelines. RESULTS: Compared to the alertness-impairment levels in the original studies, the algorithm's recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine guidelines. CONCLUSIONS: We provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times.

Can we rely on wearable sleep-tracker devices for fatigue management?

STUDY OBJECTIVES: Wearable sleep-tracker devices are ubiquitously used to measure sleep; however, the estimated sleep parameters often differ from the gold-standard polysomnography (PSG). It is unclear to what extent we can tolerate these errors within the context of a particular clinical or operational application. Here, we sought to develop a method to quantitatively determine whether a sleep tracker yields acceptable sleep-parameter estimates for assessing alertness impairment. METHODS: Using literature data, we characterized sleep-measurement errors of 18 unique sleep-tracker devices with respect to PSG. Then, using predictions based on the unified model of performance, we compared the temporal variation of alertness in terms of the psychomotor vigilance test mean response time for simulations with and without added PSG-device sleep-measurement errors, for nominal schedules of 5, 8, or 9 hours of sleep/night or an irregular sleep schedule each night for 30 consecutive days. Finally, we deemed a device error acceptable when the predicted differences were smaller than the within-subject variability of 30 milliseconds. We also established the capability to estimate the extent to which a specific sleep-tracker device meets this acceptance criterion. RESULTS: On average, the 18 sleep-tracker devices overestimated sleep duration by 19 (standard deviation = 44) minutes. Using these errors for 30 consecutive days, we found that, regardless of sleep schedule, in nearly 80% of the time the resulting predicted alertness differences were smaller than 30 milliseconds. CONCLUSIONS: We provide a method to quantitatively determine whether a sleep-tracker device produces sleep measurements that are operationally acceptable for fatigue management.

Electroencephalographic markers from routine sleep discriminate individuals who are vulnerable or resilient to sleep loss.

Sleep loss impairs cognition; however, individuals differ in their response to sleep loss. Current methods to identify an individual's vulnerability to sleep loss involve time-consuming sleep-loss challenges and neurobehavioural tests. Here, we sought to identify electroencephalographic markers of sleep-loss vulnerability obtained from routine night sleep. We retrospectively analysed four studies in which 50 healthy young adults (21 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects as resilient or vulnerable to sleep loss, we extracted three electroencephalographic features from four channels during the baseline nights, evaluated the discriminatory power of these features using the first two studies (discovery), and assessed reproducibility of the results using the remaining two studies (reproducibility). In the discovery analysis, we found that, compared to resilient subjects, vulnerable subjects exhibited: (1) higher slow-wave activity power in channel O1 (p < 0.0042, corrected for multiple comparisons) and in channels O2 and C3 (p < 0.05, uncorrected); (2) higher slow-wave activity rise rate in channels O1 and O2 (p < 0.05, uncorrected); and (3) lower sleep spindle frequency in channels C3 and C4 (p < 0.05, uncorrected). Our reproducibility analysis confirmed the discovery results on slow-wave activity power and slow-wave activity rise rate, and for these two electroencephalographic features we observed consistent group-difference trends across all four channels in both analyses. The higher slow-wave activity power and slow-wave activity rise rate in vulnerable individuals suggest that they have a persistently higher sleep pressure under normal rested conditions.

2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness.

STUDY OBJECTIVES: If properly consumed, caffeine can safely and effectively mitigate the effects of sleep loss on alertness. However, there are no tools to determine the amount and time to consume caffeine to maximize its effectiveness. Here, we extended the capabilities of the 2B-Alert app, a unique smartphone application that learns an individual's trait-like response to sleep loss, to provide personalized caffeine recommendations to optimize alertness. METHODS: We prospectively validated 2B-Alert's capabilities in a 62-hour total sleep deprivation study in which 21 participants used the app to measure their alertness throughout the study via the psychomotor vigilance test (PVT). Using PVT data collected during the first 36 hours of the sleep challenge, the app learned the participant's sleep-loss response and provided personalized caffeine recommendations so that each participant would sustain alertness at a pre-specified target level (mean response time of 270 milliseconds) during a 6-hour period starting at 44 hours of wakefulness, using the least amount of caffeine possible. Starting at 42 hours, participants consumed 0 to 800 mg of caffeine, per the app recommendation. RESULTS: 2B-Alert recommended no caffeine to five participants, 100-400 mg to 11 participants, and 500-800 mg to five participants. Regardless of the consumed amount, participants sustained the target alertness level ~80% of the time. CONCLUSIONS: 2B-Alert automatically learns an individual's phenotype and provides personalized caffeine recommendations in real time so that individuals achieve a desired alertness level regardless of their sleep-loss susceptibility.

Assessment of the unified model of performance: accuracy of group-average and individualised alertness predictions.

To be effective as a key component of fatigue-management systems, biomathematical models that predict alertness impairment as a function of time of day, sleep history, and caffeine consumption must demonstrate the ability to make accurate predictions across a range of sleep-loss and caffeine schedules. Here, we assessed the ability of the previously reported unified model of performance (UMP) to predict alertness impairment at the group-average and individualised levels in a comprehensive set of 12 studies, including 22 sleep and caffeine conditions, for a total of 301 unique subjects. Given sleep and caffeine schedules, the UMP predicted alertness impairment based on the psychomotor vigilance test (PVT) for the duration of the schedule. To quantify prediction performance, we computed the root mean square error (RMSE) between model predictions and PVT data, and the fraction of measured PVTs that fell within the models' prediction intervals (PIs). For the group-average model predictions, the overall RMSE was 43 ms (range 15-74 ms) and the fraction of PVTs within the PIs was 80% (range 41%-100%). At the individualised level, the UMP could predict alertness for 81% of the subjects, with an overall average RMSE of 64 ms (range 32-147 ms) and fraction of PVTs within the PIs conservatively estimated as 71% (range 41%-100%). Altogether, these results suggest that, for the group-average model and 81% of the individualised models, in three out of four PVT measurements we cannot distinguish between study data and model predictions.

Search for a Shared Genetic or Biochemical Basis for Biofilm Tolerance to Antibiotics across Bacterial Species.

Is there a universal genetically programmed defense providing tolerance to antibiotics when bacteria grow as biofilms? A comparison between biofilms of three different bacterial species by transcriptomic and metabolomic approaches uncovered no evidence of one. Single-species biofilms of three bacterial species (Pseudomonas aeruginosa, Staphylococcus aureus, and Acinetobacter baumannii) were grown in vitro for 3 days and then challenged with respective antibiotics (ciprofloxacin, daptomycin, and tigecycline) for an additional 24 h. All three microorganisms displayed reduced susceptibility in biofilms compared to planktonic cultures. Global transcriptomic profiling of gene expression comparing biofilm to planktonic and antibiotic-treated biofilm to untreated biofilm was performed. Extracellular metabolites were measured to characterize the utilization of carbon sources between biofilms, treated biofilms, and planktonic cells. While all three bacteria exhibited a species-specific signature of stationary phase, no conserved gene, gene set, or common functional pathway could be identified that changed consistently across the three microorganisms. Across the three species, glucose consumption was increased in biofilms compared to planktonic cells, and alanine and aspartic acid utilization were decreased in biofilms compared to planktonic cells. The reasons for these changes were not readily apparent in the transcriptomes. No common shift in the utilization pattern of carbon sources was discerned when comparing untreated to antibiotic-exposed biofilms. Overall, our measurements do not support the existence of a common genetic or biochemical basis for biofilm tolerance against antibiotics. Rather, there are likely myriad genes, proteins, and metabolic pathways that influence the physiological state of individual microorganisms in biofilms and contribute to antibiotic tolerance.

2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study.

BACKGROUND: One-third of the US population experiences sleep loss, with the potential to impair physical and cognitive performance, reduce productivity, and imperil safety during work and daily activities. Computer-based fatigue-management systems with the ability to predict the effects of sleep schedules on alertness and identify safe and effective caffeine interventions that maximize its stimulating benefits could help mitigate cognitive impairment due to limited sleep. To provide these capabilities to broad communities, we previously released 2B-Alert Web, a publicly available tool for predicting the average alertness level of a group of individuals as a function of time of day, sleep history, and caffeine consumption. OBJECTIVE: In this study, we aim to enhance the capability of the 2B-Alert Web tool by providing the means for it to automatically recommend safe and effective caffeine interventions (time and dose) that lead to optimal alertness levels at user-specified times under any sleep-loss condition. METHODS: We incorporated a recently developed caffeine-optimization algorithm into the predictive models of the original 2B-Alert Web tool, allowing the system to search for and identify viable caffeine interventions that result in user-specified alertness levels at desired times of the day. To assess the potential benefits of this new capability, we simulated four sleep-deprivation conditions (sustained operations, restricted sleep with morning or evening shift, and night shift with daytime sleep) and compared the alertness levels resulting from the algorithm's recommendations with those based on the US Army caffeine-countermeasure guidelines. In addition, we enhanced the usability of the tool by adopting a drag-and-drop graphical interface for the creation of sleep and caffeine schedules. RESULTS: For the 4 simulated conditions, the 2B-Alert Web-proposed interventions increased mean alertness by 36% to 94% and decreased peak alertness impairment by 31% to 71% while using equivalent or smaller doses of caffeine as the corresponding US Army guidelines. CONCLUSIONS: The enhanced capability of this evidence-based, publicly available tool increases the efficiency by which diverse communities of users can identify safe and effective caffeine interventions to mitigate the effects of sleep loss in the design of research studies and work and rest schedules.

Optimal sleep and work schedules to maximize alertness.

STUDY OBJECTIVES: Working outside the conventional "9-to-5" shift may lead to reduced sleep and alertness impairment. Here, we developed an optimization algorithm to identify sleep and work schedules that minimize alertness impairment during work hours, while reducing impairment during non-work hours. METHODS: The optimization algorithm searches among a large number of possible sleep and work schedules and estimates their effectiveness in mitigating alertness impairment using the Unified Model of Performance (UMP). To this end, the UMP, and its extensions to estimate sleep latency and sleep duration, predicts the time course of alertness of each potential schedule and their physiological feasibility. We assessed the algorithm by simulating four experimental studies, where we compared alertness levels during work periods for sleep schedules proposed by the algorithm against those used in the studies. In addition, in one of the studies we assessed the algorithm's ability to simultaneously optimize sleep and work schedules. RESULTS: Using the same amount of sleep as in the studies but distributing it optimally, the sleep schedules proposed by the optimization algorithm reduced alertness impairment during work periods by an average of 29%. Similarly, simultaneously optimized sleep and work schedules, for a recovery period following a chronic sleep restriction challenge, accelerated the return to baseline levels by two days when compared to the conventional 9-to-5 work schedule. CONCLUSIONS: Our work provides the first quantitative tool to optimize sleep and work schedules and extends the capabilities of existing fatigue-management tools.

Models for predicting sleep latency and sleep duration.

STUDY OBJECTIVES: Planning effective sleep-wake schedules for civilian and military settings depends on the ability to predict the extent to which restorative sleep is likely for a specified sleep period. Here, we developed and validated two mathematical models, one for predicting sleep latency and a second for predicting sleep duration, as decision aids to predict efficacious sleep periods. METHODS: We extended the Unified Model of Performance (UMP), a well-validated mathematical model of neurobehavioral performance, to predict sleep latency and sleep duration, which vary nonlinearly as a function of the homeostatic sleep pressure and the circadian rhythm. To this end, we used the UMP to predict the time course of neurobehavioral performance under different conditions. We developed and validated the models using experimental data from 317 unique subjects from 24 different studies, which included sleep conditions spanning the entire circadian cycle. RESULTS: The sleep-latency and sleep-duration models accounted for 42% and 84% of the variance in the data, respectively, and yielded acceptable average prediction errors for planning sleep schedules (4.0 min for sleep latency and 0.8 h for sleep duration). Importantly, we identified conditions under which small shifts in sleep onset timing result in disproportionately large differences in sleep duration-knowledge that may be applied to improve performance, safety, and sustainability in civilian and military operations. CONCLUSIONS: These models extend the capabilities of existing predictive fatigue-management tools, allowing users to anticipate the most opportune times to schedule sleep periods.

Conceptual Model of Biofilm Antibiotic Tolerance That Integrates Phenomena of Diffusion, Metabolism, Gene Expression, and Physiology.

Transcriptomic, metabolomic, physiological, and computational modeling approaches were integrated to gain insight into the mechanisms of antibiotic tolerance in an in vitro biofilm system. Pseudomonas aeruginosa biofilms were grown in drip flow reactors on a medium composed to mimic the exudate from a chronic wound. After 4 days, the biofilm was 114 µm thick with 9.45 log10 CFU cm-2 These biofilms exhibited tolerance, relative to exponential-phase planktonic cells, to subsequent treatment with ciprofloxacin. The specific growth rate of the biofilm was estimated via elemental balances to be approximately 0.37 h-1 and with a reaction-diffusion model to be 0.32 h-1, or one-third of the maximum specific growth rate for planktonic cells. Global analysis of gene expression indicated lower transcription of ribosomal genes and genes for other anabolic functions in biofilms than in exponential-phase planktonic cells and revealed the induction of multiple stress responses in biofilm cells, including those associated with growth arrest, zinc limitation, hypoxia, and acyl-homoserine lactone quorum sensing. Metabolic pathways for phenazine biosynthesis and denitrification were transcriptionally activated in biofilms. A customized reaction-diffusion model predicted that steep oxygen concentration gradients will form when these biofilms are thicker than about 40 µm. Mutant strains that were deficient in Psl polysaccharide synthesis, the stringent response, the stationary-phase response, and the membrane stress response exhibited increased ciprofloxacin susceptibility when cultured in biofilms. These results support a sequence of phenomena leading to biofilm antibiotic tolerance, involving oxygen limitation, electron acceptor starvation and growth arrest, induction of associated stress responses, and differentiation into protected cell states.IMPORTANCE Bacteria in biofilms are protected from killing by antibiotics, and this reduced susceptibility contributes to the persistence of infections such as those in the cystic fibrosis lung and chronic wounds. A generalized conceptual model of biofilm antimicrobial tolerance with the following mechanistic steps is proposed: (i) establishment of concentration gradients in metabolic substrates and products; (ii) active biological responses to these changes in the local chemical microenvironment; (iii) entry of biofilm cells into a spectrum of states involving alternative metabolisms, stress responses, slow growth, cessation of growth, or dormancy (all prior to antibiotic treatment); (iv) adaptive responses to antibiotic exposure; and (v) reduced susceptibility of microbial cells to antimicrobial challenges in some of the physiological states accessed through these changes.

Caffeine dosing strategies to optimize alertness during sleep loss.

Sleep loss, which affects about one-third of the US population, can severely impair physical and neurobehavioural performance. Although caffeine, the most widely used stimulant in the world, can mitigate these effects, currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits. In this work, we provide an optimization algorithm, suited for mobile computing platforms, to determine when and how much caffeine to consume, so as to safely maximize neurobehavioural performance at the desired time of the day, under any sleep-loss condition. The algorithm is based on our previously validated Unified Model of Performance, which predicts the effect of caffeine consumption on a psychomotor vigilance task. We assessed the algorithm by comparing the caffeine-dosing strategies (timing and amount) it identified with the dosing strategies used in four experimental studies, involving total and partial sleep loss. Through computer simulations, we showed that the algorithm yielded caffeine-dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64% while using the same total amount of caffeine as in the original studies. In addition, the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65%. Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep-loss condition.

Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism.

A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm metabolism. Our simulation results showed that plausible, non-intuitive explanations of difficult-to-interpret experimental observations could be generated by integrating genome-scale kinetic models with gene expression profiles.

Bridging the gap between gene expression and metabolic phenotype via kinetic models.

BACKGROUND: Despite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism. RESULTS: We developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate. CONCLUSIONS: Our simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.

A computational procedure for optimal engineering interventions using kinetic models of metabolism.

The identification of optimal intervention strategies is a key step in designing microbial strains with enhanced capabilities. In this paper, we propose a general computational procedure to determine which genes/enzymes should be eliminated, repressed or overexpressed to maximize the flux through a product of interest for general kinetic models. The procedure relies on the generalized linearization of a kinetic description of the investigated metabolic system and the iterative application of mixed-integer linear programming (MILP) optimization to hierarchically identify all engineering interventions allowing for reaction eliminations and/or enzyme level modulations. The effect of the magnitude of the allowed changes in concentrations and enzyme levels is investigated, and a variant of the method to explore high-fold changes in enzyme levels is also analyzed. The proposed framework is demonstrated using a kinetic model modeling part of the central carbon metabolism of E. coli for serine overproduction. The proposed computational procedure is a general approach that can be applied to any metabolic system for which a kinetic description is provided.