Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study.

BACKGROUND: Many DNA methylation-based indicators have been developed as summary measures of epigenetic aging. We examine the associations between 13 epigenetic clocks, including 4 second generation clocks, as well as the links of the clocks to social, demographic, and behavioral factors known to be related to health outcomes: sex, race/ethnicity, socioeconomic status, obesity, and lifetime smoking pack-years. METHODS: The Health and Retirement Study is the data source which is a nationally representative sample of Americans over age 50. Assessment of DNA methylation was based on the EPIC chip and epigenetic clocks were developed based on existing literature. RESULTS: The clocks vary in the strength of their relationships with age, with each other and with independent variables. Second generation clocks trained on health-related characteristics tend to relate more strongly to the sociodemographic and health behaviors known to be associated with health outcomes in this age group. CONCLUSIONS: Users of this publicly available data set should be aware that epigenetic clocks vary in their relationships to age and to variables known to be related to the process of health change with age.

Living System Adapts Harmonics of Peristaltic Wave for Cost-Efficient Optimization of Pumping Performance.

Wavelike patterns driving transport are ubiquitous in life. Peristaltic pumps are a paradigm of efficient mass transport by contraction driven flows-often limited by energetic constraints. We show that a cost-efficient increase in pumping performance can be achieved by modulating the phase difference between harmonics to increase occlusion. In experiments we find a phase difference shift in the living peristalsis model P. polycephalum as dynamic response to forced mass transport. Our findings provide a novel metric for wavelike patterns and demonstrate the crucial role of nonlinearities in life.

Robust oscillations in multi-cyclic Markov state models of biochemical clocks.

Organisms often use cyclic changes in the concentrations of chemical species to precisely time biological functions. Underlying these biochemical clocks are chemical reactions and transport processes, which are inherently stochastic. Understanding the physical basis for robust biochemical oscillations in the presence of fluctuations has thus emerged as an important problem. In a previous paper [C. del Junco and S. Vaikuntanathan, Phys. Rev. E 101, 012410 (2020)], we explored this question using the non-equilibrium statistical mechanics of single-ring Markov state models of biochemical networks that support oscillations. Our finding was that they can exploit non-equilibrium driving to robustly maintain the period and coherence of oscillations in the presence of randomness in the rates. Here, we extend our work to Markov state models consisting of a large cycle decorated with multiple small cycles. These additional cycles are intended to represent alternate pathways that the oscillator may take as it fluctuates about its average path. Combining a mapping to single-cycle networks based on first passage time distributions with our previously developed theory, we are able to make analytical predictions for the period and coherence of oscillations in these networks. One implication of our predictions is that a high energy budget can make different network topologies and arrangements of rates degenerate as far as the period and coherence of oscillations are concerned. Excellent agreement between analytical and numerical results confirms that this is the case. Our results suggest that biochemical oscillators can be more robust to fluctuations in the path of the oscillator when they have a high energy budget.

The Impact of Time of Day on Energy Expenditure: Implications for Long-Term Energy Balance.

There is evidence to indicate that the central biological clock (i.e., our endogenous circadian system) plays a role in physiological processes in the body that impact energy regulation and metabolism. Cross-sectional data suggest that energy consumption later in the day and during the night is associated with weight gain. These findings have led to speculation that when, as well as what, we eat may be important for maintaining energy balance. Emerging literature suggests that prioritising energy intake to earlier during the day may help with body weight maintenance. Evidence from tightly controlled acute experimental studies indicates a disparity in the body's ability to utilise (expend) energy equally across the day and night. Energy expenditure both at rest (resting metabolic rate) and after eating (thermic effect of food) is typically more efficient earlier during the day. In this review, we discuss the key evidence for a circadian pattern in energy utilisation and balance, which depends on meal timing. Whilst there is limited evidence that simply prioritising energy intake to earlier in the day is an effective strategy for weight loss, we highlight the potential benefits of considering the role of meal timing for improving metabolic health and energy balance. This review demonstrates that to advance our understanding of the contribution of the endogenous circadian system toward energy balance, targeted studies that utilise appropriate methodologies are required that focus on meal timing and frequency.

Biomolecular Composition and Revenue Explained by Interactions between Extrinsic Factors and Endogenous Rhythms of Saccharina latissima.

This review provides a systematic overview of the spatial and temporal variations in the content of biomolecular constituents of Saccharina latissima on the basis of 34 currently-available scientific studies containing primary measurements. We demonstrate the potential revenue of seaweed production and biorefinery systems by compiling a product portfolio of high-value extract products. An investigation into the endogenous rhythms and extrinsic factors that impact the biomolecular composition of S. latissima is presented, and key performance factors for optimizing seaweed production are identified. Besides the provisioning ecosystem service, we highlight the contribution of green-engineered seaweed production systems to the mitigation of the ongoing and historical anthropogenic disturbances of the climate balance and nutrient flows. We conclude that there are risks of mismanagement, and we stress the importance and necessity of creating an adaptive ecosystem-based management framework within a triple-helix partnership for balancing the utilization of ecosystem services and long-term resilience of aquatic environment.

Quantifying fluctuations in reversible enzymatic cycles and clocks.

Biochemical reactions are fundamentally noisy at a molecular scale. This limits the precision of reaction networks, but it also allows fluctuation measurements that may reveal the structure and dynamics of the underlying biochemical network. Here, we study nonequilibrium reaction cycles, such as the mechanochemical cycle of molecular motors, the phosphorylation cycle of circadian clock proteins, or the transition state cycle of enzymes. Fluctuations in such cycles may be measured using either of two classical definitions of the randomness parameter, which we show to be equivalent in general microscopically reversible cycles. We define a stochastic period for reversible cycles and present analytical solutions for its moments. Furthermore, we associate the two forms of the randomness parameter with the thermodynamic uncertainty relation, which sets limits on the timing precision of the cycle in terms of thermodynamic quantities. Our results should prove useful also for the study of temporal fluctuations in more general networks.

Ensemble methods for stochastic networks with special reference to the biological clock of Neurospora crassa.

A major challenge in systems biology is to infer the parameters of regulatory networks that operate in a noisy environment, such as in a single cell. In a stochastic regime it is hard to distinguish noise from the real signal and to infer the noise contribution to the dynamical behavior. When the genetic network displays oscillatory dynamics, it is even harder to infer the parameters that produce the oscillations. To address this issue we introduce a new estimation method built on a combination of stochastic simulations, mass action kinetics and ensemble network simulations in which we match the average periodogram and phase of the model to that of the data. The method is relatively fast (compared to Metropolis-Hastings Monte Carlo Methods), easy to parallelize, applicable to large oscillatory networks and large (~2000 cells) single cell expression data sets, and it quantifies the noise impact on the observed dynamics. Standard errors of estimated rate coefficients are typically two orders of magnitude smaller than the mean from single cell experiments with on the order of ~1000 cells. We also provide a method to assess the goodness of fit of the stochastic network using the Hilbert phase of single cells. An analysis of phase departures from the null model with no communication between cells is consistent with a hypothesis of Stochastic Resonance describing single cell oscillators. Stochastic Resonance provides a physical mechanism whereby intracellular noise plays a positive role in establishing oscillatory behavior, but may require model parameters, such as rate coefficients, that differ substantially from those extracted at the macroscopic level from measurements on populations of millions of communicating, synchronized cells.

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes.

Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. In addition to slow chromatin and/or DNA looping dynamics, one source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analysing the properties of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying chemical master equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Last, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site.

Circadian Rhythms: The Price of Electric Light.

A new study demonstrates that modern electric lighting has caused the near-24-hour biological clock to be set to a later time and that humans physiologically respond to seasonal changes in day length under conditions of natural light exposure.

Co-emergence of multi-scale cortical activities of irregular firing, oscillations and avalanches achieves cost-efficient information capacity.

The brain is highly energy consuming, therefore is under strong selective pressure to achieve cost-efficiency in both cortical connectivities and activities. However, cost-efficiency as a design principle for cortical activities has been rarely studied. Especially it is not clear how cost-efficiency is related to ubiquitously observed multi-scale properties: irregular firing, oscillations and neuronal avalanches. Here we demonstrate that these prominent properties can be simultaneously observed in a generic, biologically plausible neural circuit model that captures excitation-inhibition balance and realistic dynamics of synaptic conductance. Their co-emergence achieves minimal energy cost as well as maximal energy efficiency on information capacity, when neuronal firing are coordinated and shaped by moderate synchrony to reduce otherwise redundant spikes, and the dynamical clusterings are maintained in the form of neuronal avalanches. Such cost-efficient neural dynamics can be employed as a foundation for further efficient information processing under energy constraint.

Continuously growing rodent molars result from a predictable quantitative evolutionary change over 50 million years.

The fossil record is widely informative about evolution, but fossils are not systematically used to study the evolution of stem-cell-driven renewal. Here, we examined evolution of the continuous growth (hypselodonty) of rodent molar teeth, which is fuelled by the presence of dental stem cells. We studied occurrences of 3,500 North American rodent fossils, ranging from 50 million years ago (mya) to 2 mya. We examined changes in molar height to determine whether evolution of hypselodonty shows distinct patterns in the fossil record, and we found that hypselodont taxa emerged through intermediate forms of increasing crown height. Next, we designed a Markov simulation model, which replicated molar height increases throughout the Cenozoic and, moreover, evolution of hypselodonty. Thus, by extension, the retention of the adult stem cell niche appears to be a predictable quantitative rather than a stochastic qualitative process. Our analyses predict that hypselodonty will eventually become the dominant phenotype.

The impact of circadian phenotype and time since awakening on diurnal performance in athletes.

Circadian rhythms, among other factors, have been shown to regulate key physiological processes involved in athletic performance. Personal best performance of athletes in the evening was confirmed across different sports. Contrary to this view, we identified peak performance times in athletes to be different between human "larks" and "owls" (also called "morningness/eveningness types" or "chronotypes" and referred to as circadian phenotypes in this paper), i.e., individuals with well-documented genetic and physiological differences that result in disparities between their biological clocks and how they entrain to exogenous cues, such as the environmental light/dark cycle and social factors. We found time since entrained awakening to be the major predictor of peak performance times, rather than time of day, as well as significant individual performance variations as large as 26% in the course of a day. Our novel approach combining the use of an athlete-specific chronometric test, longitudinal circadian analysis, and physical performance tests to characterize relevant sleep/wake and performance parameters in athletes allows a comprehensive analysis of the link between the circadian system and diurnal performance variation. We establish that the evaluation of an athlete's personal best performance requires consideration of circadian phenotype, performance evaluation at different times of day, and analysis of performance as a function of time since entrained awakening.

Assessment of robustness against dissolved oxygen/substrate oscillations for C. glutamicum DM1933 in two-compartment bioreactor.

Corynebacterium glutamicum is an important organism for industrial biotechnology; particularly, in amino acid production (e.g. L-lysine). Production scales often reach reactor working volumes of several hundred cubic meters, which triggers inhomogeneous distribution of substrates and dissolved gasses due to increasing mixing times. Individual cells which follow the flow profile through the reactor are experiencing oscillating microenvironments. Oscillations can have an influence on the process performance, which is a subject of scale-down experiments. In this work, L-lysine-producing C. glutamicum DM1933 was assessed for its robustness against continuous dissolved oxygen and substrate supply oscillation in two-compartment scale-down bioreactors. Aerobic, substrate-limited stirred tank and non-aerated, substrate-excess plug flow compartments were applied for oscillation. Inhomogeneity of substrate and oxygen supply was observed to cause rapid side product turnover, redistribution of oxygen uptake from oxygen limited into fully aerobic zones, and intermediate medium acidification. However, process inhomogeneity did not impair productivity or growth at plug flow residence times of several minutes. In a focused analysis of proteome, metabolome, transcriptome, and other physiological parameters, no changes were identified in response to process inhomogeneity. In conclusion, fed-batch processes with C. glutamicum DM1933 possess remarkable robustness against oxygen and substrate supply oscillation, which is a unique property in the field of published scale-down studies. Microbial physiology of C. glutamicum appears to be ideally adapted to both homogeneous and inhomogeneous conditions. This ensures exceptional suitability for cultivation at increased mixing times, which is suggested to constitute an important basis for the long-lasting success in large scale bioprocess application.

Highly efficient noise-assisted energy transport in classical oscillator systems.

Photosynthesis is a biological process that involves the highly efficient transport of energy captured from the Sun to a reaction center, where conversion into useful biochemical energy takes place. Using a quantum description, Rebentrost et al. [New J. Phys. 11, 033003 (2009)] and Plenio and Huelga [New J. Phys. 10, 113019 (2008)] have explained this high efficiency as the result of the interplay between the quantum coherent evolution of the photosynthetic system and noise introduced by its surrounding environment. Even though one can always use a quantum perspective to describe any physical process, since everything follows the laws of quantum mechanics, is the use of quantum theory imperative to explain this high efficiency? Recently, it has been shown by Eisfeld and Briggs [Phys. Rev. E 85, 046118 (2012)] that a purely classical model can be used to explain main aspects of the energy transfer in photosynthetic systems. Using this approach, we demonstrate explicitly here that highly efficient noise-assisted energy transport can be found as well in purely classical systems. The wider scope of applicability of the enhancement of energy transfer assisted by noise might open new ways for developing new technologies aimed at enhancing the efficiency of a myriad of energy transfer systems, from information channels in microelectronic circuits to long-distance high-voltage electrical lines.

Synchronizing with auditory and visual rhythms: an fMRI assessment of modality differences and modality appropriateness.

Synchronizing movements with auditory beats, compared to visual flashes, yields divergent activation in timing-related brain areas as well as more stable tapping synchronization. The differences in timing-related brain activation could reflect differences in tapping synchronization stability, rather than differences between modality (i.e., audio-motor vs. visuo-motor integration). In the current fMRI study, participants synchronized their finger taps with four types of visual and auditory pacing sequences: flashes and a moving bar, as well as beeps and a frequency-modulated 'siren'. Behavioral tapping results showed that visuo-motor synchronization improved with moving targets, whereas audio-motor synchronization degraded with frequency-modulated sirens. Consequently, a modality difference in synchronization occurred between the discrete beeps and flashes, but not between the novel continuous siren and moving bar. Imaging results showed that activation in the putamen, a key timing area, paralleled the behavioral results: putamen activation was highest for beeps, intermediate for the continuous siren and moving bar, and was lowest for the flashes. Putamen activation differed between modalities for beeps and flashes, but not for the novel moving bar and siren. By dissociating synchronization performance from modality, we show that activation in the basal ganglia is associated with sensorimotor synchronization stability rather than modality-specificity in this task. Synchronization stability is apparently contingent upon the modality's processing affinity: discrete auditory and moving visual signals are modality appropriate, and can be encoded reliably for integration with the motor system.

Approximate probabilistic analysis of biopathway dynamics.

MOTIVATION: Biopathways are often modeled as systems of ordinary differential equations (ODEs). Such systems will usually have many unknown parameters and hence will be difficult to calibrate. Since the data available for calibration will have limited precision, an approximate representation of the ODEs dynamics should suffice. One must, however, be able to efficiently construct such approximations for large models and perform model calibration and subsequent analysis. RESULTS: We present a graphical processing unit (GPU) based scheme by which a system of ODEs is approximated as a dynamic Bayesian network (DBN). We then construct a model checking procedure for DBNs based on a simple probabilistic linear time temporal logic. The GPU implementation considerably extends the reach of our previous PC-cluster-based implementation (Liu et al., 2011b). Further, the key components of our algorithm can serve as the GPU kernel for other Monte Carlo simulations-based analysis of biopathway dynamics. Similarly, our model checking framework is a generic one and can be applied in other systems biology settings. We have tested our methods on three ODE models of bio-pathways: the epidermal growth factor-nerve growth factor pathway, the segmentation clock network and the MLC-phosphorylation pathway models. The GPU implementation shows significant gains in performance and scalability whereas the model checking framework turns out to be convenient and efficient for specifying and verifying interesting pathways properties. AVAILABILITY: The source code is freely available at http://www.comp.nus.edu.sg/~rpsysbio/pada-gpu/

An assessment of swinger techniques for the playground swing oscillatory motion.

Much attention has been devoted to how playground swing amplitudes are built up by swinger techniques, i.e. body actions. However, very little attention has been given to the requirements that such swinger techniques place on the swinger himself. The purpose of this study was to find out whether different swinger techniques yield significantly different maximum torques, endurance and coordinative skills, and also to identify preferable techniques. We modelled the seated swinger as a rigid dumbbell and compared three different techniques. A series of computer simulations were run with each technique, testing the performance with different body rotational speeds, delayed onset of body rotation and different body mass distributions, as swing amplitudes were brought up towards 90°. One technique was found to be extremely sensitive to the timing of body actions, limiting swing amplitudes to 50° and 8° when body action was delayed by 0.03 and 0.3 s, respectively. Two other more robust techniques reached 90° even with the largest of these delays, although more time (and endurance) was needed. However, these two methods also differed with respect to maximum torque and endurance, and none was preferable in both these aspects, being dependent on the swinger goals and abilities.

Efficient characterization of high-dimensional parameter spaces for systems biology.

BACKGROUND: A biological system's robustness to mutations and its evolution are influenced by the structure of its viable space, the region of its space of biochemical parameters where it can exert its function. In systems with a large number of biochemical parameters, viable regions with potentially complex geometries fill a tiny fraction of the whole parameter space. This hampers explorations of the viable space based on "brute force" or Gaussian sampling. RESULTS: We here propose a novel algorithm to characterize viable spaces efficiently. The algorithm combines global and local explorations of a parameter space. The global exploration involves an out-of-equilibrium adaptive Metropolis Monte Carlo method aimed at identifying poorly connected viable regions. The local exploration then samples these regions in detail by a method we call multiple ellipsoid-based sampling. Our algorithm explores efficiently nonconvex and poorly connected viable regions of different test-problems. Most importantly, its computational effort scales linearly with the number of dimensions, in contrast to "brute force" sampling that shows an exponential dependence on the number of dimensions. We also apply this algorithm to a simplified model of a biochemical oscillator with positive and negative feedback loops. A detailed characterization of the model's viable space captures well known structural properties of circadian oscillators. Concretely, we find that model topologies with an essential negative feedback loop and a nonessential positive feedback loop provide the most robust fixed period oscillations. Moreover, the connectedness of the model's viable space suggests that biochemical oscillators with varying topologies can evolve from one another. CONCLUSIONS: Our algorithm permits an efficient analysis of high-dimensional, nonconvex, and poorly connected viable spaces characteristic of complex biological circuitry. It allows a systematic use of robustness as a tool for model discrimination.

Yearning, learning, and conceding: reasons men and women change their childbearing intentions.

People's childbearing intentions change over the course of their reproductive lives. These changes have been conceptualized as occurring in response to the realization that an individual is unlikely to achieve his or her intended fertility, because of constraints such as the "biological clock" or lack of a partner. In this article, we find that changes to child-bearing plans are influenced by a much wider range of factors than this. People change their plans in response to the wishes of their partners, in response to social norms, as the result of repartnering, and as the result of learning about the costs and benefits of parenthood; there are also differences between the factors that influence men's and women's decision-making. In a departure from existing studies in this area, we use a flexible analytical framework that enables us to analyze increases in planned fertility separately from decreases. This allows us to uncover several complexities of the decision-making process that would otherwise be hidden, and leads us to conclude that the determinants of increases in planned fertility are not simply equal and opposite to the determinants of decreases.