arXiv subjects

Spencer Farrell

Publications and source records attributed to Spencer Farrell.

5 recordsLinked to original sources

arXiv2021

Non-Fickian single-file pore transport

Single file diffusion (SFD) exhibits anomalously slow collective transport when particles are able to immobilize by binding and unbinding to the one-dimensional channel within which the particles diffuse. We have explored this system for short pore-like channels using a symmetric exclusion process (SEP) with fully stochastic dynamics. We find that for shorter channels, a non-Fickian regime emerges for slow binding kinetics. In this regime the average flux $\langle \Phi \rangle \sim 1/L^3$, where $L$ is the channel length in units of the particle size. We find that a two-state model describes this behavior well for sufficiently slow binding rates, where the binding rates determine the switching time between high-flux bursts of directed transport and low-flux leaky states. Each high-flux burst is Fickian with $\langle \Phi \rangle \sim 1/L$. Longer systems are more often in a low flux state, leading to the non-Fickian behavior.

cond-mat.stat-mech↗

arXiv2021

Interpretable machine learning for high-dimensional trajectories of aging health

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.

q-bio.QM↗

arXiv2020

The potential for complex computational models of aging

The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales -- from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.

q-bio.QM↗

arXiv2017

Unifying ageing and frailty through complex dynamical networks

To explore the mechanistic relationships between ageing, frailty and mortality, we developed a computational model in which possible health attributes are represented by the nodes of a complex network. Each node can be either damaged (i.e. a deficit) or undamaged. Damage of connected nodes facilitates further local damage. Our model recovers the known patterns of frailty and mortality without any programmed ageing. It helps us to understand how the observed maximum of the frailty index (FI) might arise, and allows us to start to understand how health deficits accumulate. Large model populations allow us to exploit new analytic tools, including information theory. This will allow us to systematically characterize the effects of sudden changes in the health trajectories of individuals and serve as a way to evaluate large clinical and population databases.

q-bio.PE↗

arXiv2016

Network model of human aging: frailty limits and information measures

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 $f$ of accumulated age related deficits relative to the total, leading to a theoretical maximum of $f \leq 1$. Observational studies have generally reported a much more stringent bound, with $f \leq f_{max} <1$. The value of $f_{max}$ in observational studies appears to be non-universal, but $f_{max} \approx 0.7$ is often reported. A previously developed network model of individual aging was unable to recover $f_{max}<1$ while retaining the other observed phenomenology of increasing $f$ and mortality rates with age. We have developed a computationally accelerated network model that also allows us to tune the scale-free network exponent $\alpha$. The network exponent $\alpha$ significantly affects the growth of mortality rates with age. However, we are only able to recover $f_{max}$ 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 non-zero $q$, we use mutual information $I$ to provide a non-parametric 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 vs connectivity, has an approximately power-law dependence that depends on the network exponent $\alpha$. Mutual information $I$ is therefore a useful tool for characterizing the network topology of aging populations.

q-bio.PE↗