Layered feedback control overcomes performance trade-off in synthetic biomolecular networks.

Layered feedback is an optimization strategy in feedback control designs widely used in engineering. Control theory suggests that layering multiple feedbacks could overcome the robustness-speed performance trade-off limit. In natural biological networks, genes are often regulated in layers to adapt to environmental perturbations. It is hypothesized layering architecture could also overcome the robustness-speed performance trade-off in genetic networks. In this work, we validate this hypothesis with a synthetic biomolecular network in living E. coli cells. We start with system dynamics analysis using models of various complexities to guide the design of a layered control architecture in living cells. Experimentally, we interrogate system dynamics under three groups of perturbations. We consistently observe that the layered control improves system performance in the robustness-speed domain. This work confirms that layered control could be adopted in synthetic biomolecular networks for performance optimization. It also provides insights into understanding genetic feedback control architectures in nature.

"Can virtue be taught?": a content analysis of medical students' opinions of the professional and ethical challenges to their professional identity formation.

BACKGROUND: Efforts have begun to characterize the ethical and professional issues encountered by medical students in their clinical years. By applying previously identified taxonomies to a national sample of medical students, this study seeks to develop generalizable insights that can inform professional identity formation across various clerkships and medical institutions. METHODS: In a national survey of medical students, participants answered an open-ended survey item that asked them to describe a clinical experience involving an ethical or professional issue. We conducted a content analysis with these responses using the Kaldjian taxonomy of ethical and professionalism themes in medical education through an iterative, consensus-building process. Noting the emerging virtues-based approach to ethics and professionalism, we also reexamined the data using a taxonomy of virtues. RESULTS: The response rate to this survey item was 144 out of 499 eligible respondents (28.9%). All 144 responses were successfully coded under one or more themes in the original taxonomy of ethical and professional issues, resulting in a total of 173 coded responses. Professional duties was the most frequently coded theme (29.2%), followed by Communication (26.4%), Quality of care (18.8%), Student-specific issues of moral distress (16.7%), Decisions regarding treatment (16.0%), and Justice (13.2%). In the virtues taxonomy, 180 total responses were coded from the 144 original responses, and the most frequent virtue coded was Wisdom (23.6%), followed by Respectfulness (20.1%) and Compassion or Empathy (13.9%). CONCLUSIONS: Originally developed from students' clinical experiences in one institution, the Kaldjian taxonomy appears to serve as a useful analytical framework for categorizing a variety of clinical experiences faced by a national sample of medical students. This study also supports the development of virtue-based programs that focus on cultivating the virtue of wisdom in the practice of medicine.

More random motor activity fluctuations predict incident frailty, disability, and mortality.

Mobile healthcare increasingly relies on analytical tools that can extract meaningful information from ambulatory physiological recordings. We tested whether a nonlinear tool of fractal physiology could predict long-term health consequences in a large, elderly cohort. Fractal physiology is an emerging field that aims to study how fractal temporal structures in physiological fluctuations generated by complex physiological networks can provide important information about system adaptability. We assessed fractal temporal correlations in the spontaneous fluctuations of ambulatory motor activity of 1275 older participants at baseline, with a follow-up period of up to 13 years. We found that people with reduced temporal correlations (more random activity fluctuations) at baseline had increased risk of frailty, disability, and all-cause death during follow-up. Specifically, for 1-SD decrease in the temporal activity correlations of this studied cohort, the risk of frailty increased by 31%, the risk of disability increased by 15 to 25%, and the risk of death increased by 26%. These incidences occurred on average 4.7 years (frailty), 3 to 4.2 years (disability), and 5.8 years (death) after baseline. These observations were independent of age, sex, education, chronic health conditions, depressive symptoms, cognition, motor function, and total daily activity. The temporal structures in daily motor activity fluctuations may contain unique prognostic information regarding wellness and health in the elderly population.

Interaction between the progression of Alzheimer's disease and fractal degradation.

Many outputs from healthy neurophysiological systems including motor activity display nonrandom fluctuations with fractal scaling behavior as characterized by similar temporal fluctuation patterns across a range of time scales. Degraded fractal regulation predicts adverse consequences including Alzheimer's dementia. We examined longitudinal changes in the scaling behavior of motor activity fluctuations during the progression of Alzheimer's disease (AD) in 1068 participants in the Rush Memory and Aging Project. Motor activity of up to 10 days was recorded annually for up to 13 years. Cognitive assessments and clinical diagnoses were administered annually in the same participants. We found that fractal regulation gradually degraded over time (p < 0.0001) even during the stage with no cognitive impairment. The degradation rate was more than doubled after the diagnosis of mild cognitive impairment and more than doubled further after the diagnosis of Alzheimer's dementia (p's ≤ 0.0005). Besides, the longitudinal degradation of fractal regulation significantly correlated with the decline in cognitive performance throughout the progression from no cognitive impairment to mild cognitive impairment, and to AD (p < 0.001). All effects remained the same in subsequent sensitivity analyses that included only 255 decedents with autopsy-confirmed Alzheimer's pathology. These results indicate that the progression of AD accelerates fractal degradation and that fractal degradation may be an integral part of the process of AD.

Nocturnal heart rate variability moderates the association between sleep-wake regularity and mood in young adults.

STUDY OBJECTIVES: Sleep-wake regularity (SWR) is often disrupted in college students and mood disorders are rife at this age. Disrupted SWR can cause repetitive and long-term misalignment between environmental and behavioral cycles and the circadian system which may then have psychological and physical health consequences. We tested whether SWR was independently associated with mood and autonomic function in a healthy adult cohort. METHODS: We studied 42 college students over a 3 week period using daily sleep-wake diaries and continuous electrocardiogram recordings. Weekly SWR was quantified by the interdaily stability of sleep-wake times (ISSW) and mood was assessed weekly using the Beck Depression Inventory-II. To assess autonomic function, we quantified the high-frequency (HF) power of heart rate variability (HRV). Linear mixed effects models were used to assess the relationship between repeated weekly measures of mood, SWR, and HF. RESULTS: Low weekly ISSW predicted subsequent poor mood and worsening mood independently of age, sex, race, sleep duration, and physical activity. Although no association was found between ISSW and HF, the ISSW-mood association was significantly moderated by nocturnal HF, i.e. reported mood was lowest after a week with low ISSW and high HF. Prior week mood scores did not significantly predict the subsequent week's ISSW. CONCLUSIONS: Irregular sleep-wake timing appears to precede poor mood in young adults. Further work is needed to understand the implications of high nocturnal HRV in those with low mood and irregular sleep-wake cycles.

Stressing the journey: using life stories to study medical student wellbeing.

While previous studies have considered medical student burnout and resilience at discrete points in students' training, few studies examine how stressors and resilience-building factors can emerge before, and during, medical school. Our study focuses on students' life stories to comprehensively identify factors contributing to student wellbeing. We performed a secondary analysis of life-story interviews with graduating fourth year medical students. These interviews were originally conducted in 2012 as part of the Project on the Good Physician, and then re-analyzed, focusing on student wellbeing. Respondents were encouraged to identify turning points in their life stories. De-identified transcripts were then coded using a consensus-based iterative process. 17 of 21 respondents reported feeling burned out at least once during medical school. Students identified three major stressors: negative role models, difficult rotations, and the United States Medical Licensing Examination Step 1. Two "motivational stressors"-financial concerns and personal life events-emerged as sources of stress that also motivated students to persevere. Finally, students identified four factors-positive role models, support networks, faith and spirituality, and passion-that helped them reframe stressors, making the struggle seem more worthwhile. These findings suggest that a life-story approach can add granularity to current understandings of medical student wellbeing. Initiatives to reduce stress and burnout should extend beyond the immediate medical school context and consider how past challenges might become future sources of resilience. This study also provides an example of secondary analysis of qualitative data, an approach which could be useful to future research in medical education.

Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design.

RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

Generating Effective Models and Parameters for RNA Genetic Circuits.

RNA genetic circuitry is emerging as a powerful tool to control gene expression. However, little work has been done to create a theoretical foundation for RNA circuit design. A prerequisite to this is a quantitative modeling framework that accurately describes the dynamics of RNA circuits. In this work, we develop an ordinary differential equation model of transcriptional RNA genetic circuitry, using an RNA cascade as a test case. We show that parameter sensitivity analysis can be used to design a set of four simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions to determine the 13 parameters of the model. The resulting model accurately recapitulates the dynamic behavior of the cascade, and can be easily extended to predict the function of new cascade variants that utilize new elements with limited additional characterization experiments. Interestingly, we show that inconsistencies between model predictions and experiments led to the model-guided discovery of a previously unknown maturation step required for RNA regulator function. We also determine circuit parameters in two different batches of TX-TL, and show that batch-to-batch variation can be attributed to differences in parameters that are directly related to the concentrations of core gene expression machinery. We anticipate the RNA circuit models developed here will inform the creation of computer aided genetic circuit design tools that can incorporate the growing number of RNA regulators, and that the parametrization method will find use in determining functional parameters of a broad array of natural and synthetic regulatory systems.