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Widespread interest in nondestructive biomarkers of aging has led to a multitude of biological ages that each proffers a "true" health-adjusted individual age. Although each measure provides salient information on the aging process, they are each univariate, in contrast to the "hallmark" and "pillar" theories of aging, which are explicitly multidimensional, multicausal, and multiscale. Fortunately, multiple biological ages can be systematically combined into a multidimensional network representation. The interaction network between these biological ages permits analysis of the multidimensional effects of aging, as well as quantification of causal influences during both natural aging and, potentially, after anti-aging intervention. The behavior of the system as a whole can then be explored using dynamical network stability analysis, which identifies new, efficient biomarkers that quantify long-term resilience scores on the timescale between measurements (years). We demonstrate this approach using a set of 8 biological ages from the longitudinal Swedish Adoption/Twin Study of Aging (SATSA). After extracting an interaction network between these biological ages, we observed that physiological age, a proxy for cardiometabolic health, serves as a central node in the network, implicating it as a key vulnerability for slow, age-related decline. We furthermore show that while the system as a whole is stable, there is a weakly stable direction along which recovery is slow—on the timescale of a human lifespan. This slow direction provides an aging biomarker, which correlates strongly with chronological age and predicts longitudinal decline in health—suggesting that it estimates an important driver of age-related changes. [ABSTRACT FROM AUTHOR]

Counting fluorescence photobleaching steps is commonly used to infer the number n0 of monomeric units of individual oligomeric protein complexes or misfolded protein aggregates. We present a principled Bayesian approach for counting that incorporates the statistics of photobleaching. Our physics-based prior leads to a simple and efficient numerical scheme for maximum a posteriori probability (MAP) estimates of the initial fluorophore number n ^ 0 . Our focus here is on using a calibration to precisely estimate n ^ 0 , though our approach can also be used to calibrate the photophysics. Imaging noise increases with n ^ 0 , while bias is often introduced by temporal averaging. We examine the effects of fluorophore number n ^ 0 of the oligomer or aggregate, lifetime photon yield μeff of an individual fluorophore, and exposure time Δt of each image frame in a time-lapse experiment. We find that, in comparison with standard ratiometric approaches or with a "change-point" step-counting algorithm, our MAP approach is both more precise and less biased. [ABSTRACT FROM AUTHOR]

We model the effects of disease and other exogenous damage during human aging. Even when the exogenous damage is repaired at the end of acute disease, propagated secondary damage remains. We consider both short-term mortality effects due to (acute) exogenous damage and long-term mortality effects due to propagated damage within the context of a generic network model (GNM) of individual aging that simulates a U.S. population. Across a wide range of disease durations and severities we find that while excess short-term mortality is highest for the oldest individuals, the long-term years of life lost are highest for the youngest individuals. These appear to be universal effects of human disease. We support this conclusion with a phenomenological model coupling damage and mortality. Our results are consistent with previous lifetime mortality studies of atom bomb survivors and post-recovery health studies of COVID-19. We suggest that short-term health impact studies could complement lifetime mortality studies to better characterize the lifetime impacts of disease on both individuals and populations. [ABSTRACT FROM AUTHOR]

The population dynamics of human health and mortality can be jointly captured by complex network models using scale-free network topology. To validate and understand the choice of scale-free networks, we investigate which network topologies maximize either lifespan or health span. Using the Generic Network Model (GNM) of organismal aging, we find that both health span and lifespan are maximized with a "star" motif. Furthermore, these optimized topologies exhibit maximal lifespans that are not far above the maximal observed human lifespan. To approximate the complexity requirements of the underlying physiological function, we then constrain network entropies. Using non-parametric stochastic optimization of network structure, we find that disassortative scale-free networks exhibit the best of both lifespan and health span. Parametric optimization of scale-free networks behaves similarly. We further find that higher maximum connectivity and lower minimum connectivity networks enhance both maximal lifespans and health spans by allowing for more disassortative networks. Our results validate the scale-free network assumption of the GNM and indicate the importance of disassortativity in preserving health and longevity in the face of damage propagation during aging. Our results highlight the advantages provided by disassortative scale-free networks in biological organisms and subsystems. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ydbio.2007.09.045 Byline: Jun F. Allard (a), Alison L. Hill (b), Andrew D. Rutenberg (a) Keywords: Filamentous cyanobacteria; Anabaena; Heterocyst commitment; Lateral inhibition; Fixed nitrogen; Diazotrophic; Differentiation; Computational modeling; Pattern formation; Growth and division Abstract: We have quantitatively modeled heterocyst differentiation after fixed nitrogen step-down in the filamentous cyanobacterium Anabaena sp. PCC 7120 without lateral inhibition due to the patterning proteins PatS or HetN. We use cell growth and division together with fixed-nitrogen dynamics and allow heterocysts to differentiate upon the local exhaustion of available fixed nitrogen. Slow transport of fixed nitrogen along a shared periplasmic space allows for fast growing cells to differentiate ahead of their neighbors. Cell-to-cell variability in growth rate determines the initial heterocyst pattern. Early release of fixed nitrogen from committed heterocysts allows a significant fraction of vegetative cells to be retained at later times. We recover the experimental heterocyst spacing distributions and cluster size distributions of Khudyakov and Golden [Khudyakov, I.Y., Golden, J.W., 2004. Different functions of HetR, a master regulator of heterocyst differentiation in Anabaena sp PCC 7120, can be separated by mutation. Proc. Natl. Acad. Sci. U. S. A. 101, 16040-16045]. Author Affiliation: (a) Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1Z9 (b) Department of Physics, Queen's University, Kingston Ontario, Canada K7L 3N6 Article History: Received 25 April 2006; Revised 23 September 2007; Accepted 24 September 2007

Phage [lambda] lyses the host Escherichia coli at a precisely scheduled time after induction. Lysis timing is determined by the action of phage holins, which are small proteins that induce hole formation in the bacterium's cytoplasmic membrane. We present a two-stage nucleation model of lysis timing, with the nucleation of condensed holin rafts on the inner membrane followed by the nucleation of a hole within those rafts. The nucleation of holin rafts accounts for most of the delay of lysis after induction. Our simulations of this model recover the accurate lysis timing seen experimentally and show that the timing accuracy is optimal. An enhanced holin-holin interaction is needed in our model to recover experimental lysis delays after the application of membrane poison, and such early triggering of lysis is possible only after the inner membrane is supersaturated with holin. Antiholin reduces the delay between membrane depolarization and lysis and leads to an earlier time after which triggered lysis is possible.

We observed that the oscillation period of MinD within rod-like and filamentous cells of Escherichia coli varied by a factor of 4 in the temperature range from 20[degrees]C to 40[degrees]C. The detailed dependence was Arrhenius, with a slope similar to the overall temperature-dependent growth curve of E. coli. The detailed pattern of oscillation, including the characteristic wavelength in filamentous cells, remained independent of temperature. A quantitative model of MinDE oscillation exhibited similar behavior, with an activated temperature dependence of the MinE-stimulated MinD-ATPase rate.

Randomly rotating particles that have been isotropically labeled with rigidly linked fluorophores will undergo non-isotropic (patchy) photobleaching under illumination due to the dipole coupling of fluorophores with light. For a rotational diffusion rate D of the particle and a photobleaching time scale τ of the fluorophores, the dynamics of this process are characterized by the dimensionless combination Dτ. We find significant interparticle fluctuations at intermediate Dτ. These fluctuations vanish at both large and small Dτ or at small or large elapsed times t. Associated with these fluctuations between particles, we also observe transient non-monotonicities of the brightness of individual particles. These non-monotonicities can be as much as 20% of the original brightness. We show that these novel photobleach-fluctuations dominate over variability of single-fluorophore orientation when there are at least 10³ fluorophores on individual particles. [ABSTRACT FROM AUTHOR]

We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our dynamic joint interpretable network (DJIN) model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections between health variables as well as clusters of strongly connected health variables. We use English Longitudinal Study of Aging (ELSA) data to train our model and show that it performs better than multiple dedicated linear models for health outcomes and survival. We compare our model with flexible lower-dimensional latent-space models to explore the dimensionality required to accurately model aging health outcomes. Our DJIN model can be used to generate synthetic individuals that age realistically, to impute missing data, and to simulate future aging outcomes given arbitrary initial health states. Author summary: Aging is the process of age-dependent functional decline of biological organisms. This process is high-dimensional, involving changes in all aspects of organism functioning. The progression of aging is often simplified with low-dimensional summary measures to describe the overall health state. While these summary measures of aging can be used predict mortality and are correlated with adverse health outcomes, we demonstrate that the prediction of individual aging health outcomes cannot be done accurately with these low-dimensional measures, and requires a high-dimensional model. This work presents a machine learning approach to model high-dimensional aging health trajectories and mortality. This approach is made interpretable by inferring a network of pairwise interactions between the health variables, describing the interactions used by the model to make predictions and suggesting plausible biological mechanisms. [ABSTRACT FROM AUTHOR]

To explore the mechanistic relationships between aging, frailty and mortality, we developed a computational model in which possible health attributes are represented by the nodes of a complex network, with the connections showing a scale-free distribution. Each node can be either damaged (i.e. a deficit) or undamaged. Damage of connected nodes facilitates local damage and makes local recovery more difficult. Our model demonstrates the known patterns of frailty and mortality without any assumption of programmed aging. It helps us to understand how the observed maximum of the frailty index (FI) might arise. The model facilitates an initial understanding of how local damage caused by random perturbations propagates through a dynamic network of interconnected nodes. Very large model populations (here, 10 million individuals followed continuously) allow us to exploit new analytic tools, including information theory, showing, for example that highly connected nodes are more informative than less connected nodes. This model permits a better understanding of factors that influence the health trajectories of individuals. [ABSTRACT FROM AUTHOR]

COMPUTATIONAL INSIGHTS INTO FOLDED PROTEIN TRANSPORT BY DYNAMIC CHANNEL FORMATION IN BACTERIA. CHITRA NAYAK, DEPARTMENT OF PHYSICS, TUSKEGEE UNIVERSITY, AL 36832. AIDAN I BROWN, DALHOUSIE UNIVERSITY, NOVASCOTIA, CANADA, B3H3J5. ANDREW [...]

The endoplasmic reticulum (ER) is required for the de novo biogenesis of peroxisomes in mammalian cells. However, its role in peroxisome maintenance is unclear. To explore ER involvement in the maintenance of peroxisomes, we redirect a peroxisomal membrane protein (PMP), PEX3, to directly target to the ER using the N-terminal ER signal sequence from preprolactin. Using biochemical techniques and fluorescent imaging, we find that ER-targeting PEX3 (ssPEX3) is continuously imported into pre-existing peroxisomes. This suggests that the ER constitutively provides membrane proteins and associated lipids to pre-existing peroxisomes. Using quantitative time-lapse live-cell fluorescence microscopy applied to cells that were either depleted of or exogenously expressing PEX16, we find that PEX16 mediates the peroxisomal trafficking of two distinct peroxisomal membrane proteins, PEX3 and PMP34, via the ER. These results not only provide insight into peroxisome maintenance and PMP trafficking in mammalian cells but also highlight important similarities and differences in the mechanisms of PMP import between the mammalian and yeast systems. [ABSTRACT FROM AUTHOR]

Peroxisomes are membrane-bound organelles within eukaryotic cells that post-translationally import folded proteins into their matrix. Matrix protein import requires a shuttle receptor protein, usually PEX5, that cycles through docking with the peroxisomal membrane, ubiquitination, and export back into the cytosol followed by deubiquitination. Matrix proteins associate with PEX5 in the cytosol and are translocated into the peroxisome lumen during the PEX5 cycle. This cargo translocation step is not well understood, and its energetics remain controversial. We use stochastic computational models to explore different ways the AAA ATPase driven removal of PEX5 may couple with cargo translocation in peroxisomal importers of mammalian cells. The first model considered is uncoupled, in which translocation is spontaneous, and does not immediately depend on PEX5 removal. The second is directly coupled, in which cargo translocation only occurs when its PEX5 is removed from the peroxisomal membrane. The third, novel, model is cooperatively coupled and requires two PEX5 on a given importomer for cargo translocation — one PEX5 with associated cargo and one with ubiquitin. We measure both the PEX5 and the ubiquitin levels on the peroxisomes as we vary the matrix protein cargo addition rate into the cytosol. We find that both uncoupled and directly coupled translocation behave identically with respect to PEX5 and ubiquitin, and the peroxisomal ubiquitin signal increases as the matrix protein traffic increases. In contrast, cooperatively coupled translocation behaves dramatically differently, with a ubiquitin signal that decreases with increasing matrix protein traffic. Recent work has shown that ubiquitin on mammalian peroxisome membranes can lead to selective degradation by autophagy, or ‘pexophagy.’ Therefore, the high ubiquitin level for low matrix cargo traffic with cooperatively coupled protein translocation could be used as a disuse signal to mediate pexophagy. This mechanism may be one way that cells could regulate peroxisome numbers. [ABSTRACT FROM AUTHOR]

The coarsening instability typically disrupts steady-state cluster-size distributions. We show that degradation coupled to the cluster size, such as arising from biological proteolysis, leads to a fixed-point cluster size. Stochastic evaporative and condensative fluxes determine the width of the fixed-point size distribution. At the fixed point, we show how the peak size and width depend on number, interactions, and proteolytic rate. This proteolytic size-control mechanism is consistent with the phenomenology of pseudopilus length control in the general secretion pathway of bacteria. [ABSTRACT FROM AUTHOR]

Background: In Escherichia coli, MinD-GFP fusion proteins show rapid pole to pole oscillations. The objective was to investigate the effects of extracellular cations on the subcellular oscillation of cytoplasmic MinD within Escherichia coli. Methodology/Principal Findings: We exposed bacteria to the extracellular cations Ca++, Mg++, the cationic antimicrobial peptide (CAP) protamine, and the cationic aminoglycoside gentamicin. We found rapid and substantial increases in the average MinD oscillation periods in the presence of any of these polyvalent cations. For Ca++ and Mg++ the increases in period were transient, even with a constant extracellular concentration, while increases in period for protamine or gentamicin were apparently irreversible. We also found striking interdependence in the action of the small cations with protamine or gentamicin, distorted oscillations under the action of intermediate levels of gentamicin and Ca++, and reversible freezing of the Min oscillation at high cationic concentrations. Conclusions/Significance: Intracellular Min oscillations provide a fast single-cell reporter of bacterial response to extracellular polycations, which can be explained by the penetration of polycations into cells. [ABSTRACT FROM AUTHOR]

Collagen fibrils are the main structural component of load-bearing tissues such as tendons, ligaments, skin, the cornea of the eye, and the heart. The D-band of collagen fibrils is an axial periodic density modulation that can be easily characterized by tissue-level X-ray scattering. During mechanical testing, D-band strain is often used as a proxy for fibril strain. However, this approach ignores the coupling between strain and molecular tilt. We examine the validity of this approximation using an elastomeric collagen fibril model that includes both the D-band and a molecular tilt field. In the low strain regime, we show that the D-band strain substantially underestimates fibril strain for strongly twisted collagen fibrils — such as fibrils from skin or corneal tissue. [ABSTRACT FROM AUTHOR]

We computationally study the effects of binding kinetics to the channel wall, leading to transient immobility, on the diffusive transport of particles within narrow channels, that exhibit single-file diffusion (SFD). We find that slow binding kinetics leads to an anomalously slow diffusive transport. Remarkably, the scaled diffusivity D characterizing transport exhibits scaling collapse with respect to the occupation fraction p of sites along the channel. We present a simple "cage-physics" picture that captures the characteristic occupation fraction pscale and the asymptotic 1/p² behavior for p/pscale ≳ 1. We confirm that subdiffusive behavior of tracer particles is controlled by the same D as particle transport. [ABSTRACT FROM AUTHOR]

We have modeled stress concentration around small gaps in anisotropic elastic sheets, corresponding to the peptidoglycan sacculus of bacterial cells, under loading corresponding to the effects of turgor pressure in rod-shaped bacteria. We find that under normal conditions the stress concentration is insufficient to mechanically rupture bacteria, even for gaps up to a micron in length. We then explored the effects of stress-dependent smart autolysins, as hypothesized by A. L. Koch [Adv. Microb. Physiol. 24, 301 (1983); Res. Microbiol. 141, 529 (1990)]. We show that the measured anisotropic elasticity of the peptidoglycan (PG) sacculus can lead to stable circumferential propagation of small gaps in the sacculus. This is consistent with the recent observation of circumferential propagation of PG-associated MreB patches in rod-shaped bacteria. We also find abistable regime of both circumferential and axial gap propagation, which agrees with behavior reported in cytoskeletal mutants of B. subtilis. We conclude that the elastic anisotropics of a bacterial sacculus, as characterized experimentally, may be relevant for maintaining rod-shaped bacterial growth. [ABSTRACT FROM AUTHOR]

We confront a network model of human aging and mortality in which nodes represent health attributes that interact within a scale-free network topology, with observational data that use both clinical and laboratory (preclinical) health deficits as network nodes. We find that individual health attributes exhibit a wide range of mutual information with mortality and that, with a reconstruction of their relative connectivity, higher-ranked nodes are more informative. Surprisingly, we find a broad and overlapping range of mutual information of laboratory measures as compared with clinical measures. We confirm similar behavior between most-connected and least-connected model nodes, controlled by the nearest-neighbor connectivity. Furthermore, in both model and observational data, we find that the least-connected (laboratory) nodes are damaged earlier than the most-connected (clinical) deficits. A mean-field theory of our network model captures and explains this phenomenon, which results from the connectivity of nodes and of their connected neighbors. We find that other network topologies, including random, small-world, and assortative scale-free networks, exhibit qualitatively different behavior. Our disassortative scale-free network model behaves consistently with our expanded phenomenology observed in human aging and so is a useful tool to explore mechanisms of and to develop predictive measures for human aging and mortality. [ABSTRACT FROM AUTHOR]

Aging is associated with the accumulation of damage throughout a persons life. Individual health can be assessed by the Frailty Index (FI). The FI is calculated simply as the proportion ƒ of accumulated age-related deficits relative to the total, leading to a theoretical maximum of ƒ≤1. Observational studies have generally reported a much more stringent bound, with ƒ≤ƒmax<1. The value of ƒmax in observational studies appears to be nonuniversal, but ƒmax≈0.7 is often reported. A previously developed network model of individual aging was unable to recover ƒmax<1 while retaining the other observed phenomenology of increasing ƒ and mortality rates with age. We have developed a computationally accelerated network model that also allows us to tune the scale-free network exponent α. The network exponent α significantly affects the growth of mortality rates with age. However, we are only able to recover ƒmax by also introducing a deficit sensitivity parameter 1-q, which is equivalent to a false-negative rate q. Our value of q=0.3 is comparable to finite sensitivities of age-related deficits with respect to mortality that are often reported in the literature. In light of nonzero q, we use mutual information I to provide a nonparametric measure of the predictive value of the FI with respect to individual mortality. We find that I is only modestly degraded by q<1, and this degradation is mitigated when increasing number of deficits are included in the FI. We also find that the information spectrum, i.e., the mutual information of individual deficits versus connectivity, has an approximately power-law dependence that depends on the network exponent α. Mutual information I is therefore a useful tool for characterizing the network topology of aging populations. [ABSTRACT FROM AUTHOR]

How long people live depends on their health, and how it changes with age. Individual health can be tracked by the accumulation of age-related health deficits. The fraction of age-related deficits is a simple quantitative measure of human aging. This quantitative frailty index (F) is as good as chronological age in predicting mortality. In this paper, we use a dynamical network model of deficits to explore the effects of interactions between deficits, deficit damage and repair processes, and the connection between the F and mortality. With our model, we qualitatively reproduce Gompertz's law of increasing human mortality with age, the broadening of the F distribution with age, the characteristic nonlinear increase of the F with age, and the increased mortality of high-frailty individuals. No explicit time-dependence in damage or repair rates is needed in our model. Instead, implicit time-dependence arises through deficit interactions--so that the average deficit damage rates increase, and deficit repair rates decrease, with age. We use a simple mortality criterion, where mortality occurs when the most connected node is damaged. [ABSTRACT FROM AUTHOR]