Eusociality and the Evolution of Aging in Superorganisms.

AbstractEusocial insects-ants, bees, wasps, and termites-are being recognized as model organisms to unravel the evolutionary paradox of aging for two reasons: (1) queens (and kings, in termites) of social insects outlive similarly sized solitary insects by up to several orders of magnitude and (2) all eusocial taxa show a divergence of long queen and shorter worker life spans, despite their shared genomes and even under risk-free laboratory environments. Traditionally, these observations have been explained by invoking the classical evolutionary aging theory: well-protected inside their nests, queens are much less exposed to external hazards than foraging workers, and this provides natural selection the opportunity to favor queens that perform well at advanced ages. Although quite plausible, these verbal arguments have not been backed up by mathematical analysis. Here, for the first time, we provide quantitative models for the evolution of caste-specific aging patterns. We show that caste-specific mortality risks are in general neither sufficient nor necessary to explain the evolutionary divergence in life span between queens and workers and the extraordinary queen life spans. Reproductive monopolization and the delayed production of sexual offspring in highly social colonies lead natural selection to inherently favor queens that live much longer than workers, even when exposed to the same external hazards. Factors that reduce a colony's reproductive skew, such as polygyny and worker reproduction, tend to reduce the evolutionary divergence in life span between queens and workers. Caste-specific extrinsic hazards also affect life span divergence, but to a much smaller extent than reproductive monopolization.

Exit time as a measure of ecological resilience.

Ecological resilience is the magnitude of the largest perturbation from which a system can still recover to its original state. However, a transition into another state may often be invoked by a series of minor synergistic perturbations rather than a single big one. We show how resilience can be estimated in terms of average life expectancy, accounting for this natural regime of variability. We use time series to fit a model that captures the stochastic as well as the deterministic components. The model is then used to estimate the mean exit time from the basin of attraction. This approach offers a fresh angle to anticipating the chance of a critical transition at a time when high-resolution time series are becoming increasingly available.

Stability estimation of autoregulated genes under Michaelis-Menten-type kinetics.

Feedback loops are typical motifs appearing in gene regulatory networks. In some well-studied model organisms, including Escherichia coli, autoregulated genes, i.e., genes that activate or repress themselves through their protein products, are the only feedback interactions. For these types of interactions, the Michaelis-Menten (MM) formulation is a suitable and widely used approach, which always leads to stable steady-state solutions representative of homeostatic regulation. However, in many other biological phenomena, such as cell differentiation, cancer progression, and catastrophes in ecosystems, one might expect to observe bistable switchlike dynamics in the case of strong positive autoregulation. To capture this complex behavior we use the generalized family of MM kinetic models. We give a full analysis regarding the stability of autoregulated genes. We show that the autoregulation mechanism has the capability to exhibit diverse cellular dynamics including hysteresis, a typical characteristic of bistable systems, as well as irreversible transitions between bistable states. We also introduce a statistical framework to estimate the kinetics parameters and probability of different stability regimes given observational data. Empirical data for the autoregulated gene SCO3217 in the SOS system in Streptomyces coelicolor are analyzed. The coupling of a statistical framework and the mathematical model can give further insight into understanding the evolutionary mechanisms toward different cell fates in various systems.

What Do You Mean, 'Tipping Point'?

Over the past 10 years the use of the term 'tipping point' in the scientific literature has exploded. It was originally used loosely as a metaphor for the phenomenon that, beyond a certain threshold, runaway change propels a system to a new state. Although several specific mathematical definitions have since been proposed, we argue that these are too narrow and that it is better to retain the original definition.