We use evolutionary game theory to study the evolution of harming behavior (spite) in settings involving both tags and (anti-) correlated interaction. Our results show an interesting interaction between these mechanisms. The presence of tags shows that pure spite is less likely to evolve when theoretically expected, but that conditional spite is more likely than expected. Moreover, we identify a novel tag-based equilibrium where spite occurs within but not between groups. We discuss implications for the evolution of spite, punishment, and the methodological approach of using exogenous parameters to represent (anti-) correlated interactions.

Men and women exhibit notable differences not only in the reproductive system and reproductive behaviors, but in many other organ and physiological systems as well. Notable examples include; the stress and immune systems, the anatomy of the brain, and the metabolic and cardiovascular functions. Furthermore, female physiology is affected by the menstrual cycle and by pregnancy and lactation. If we are to successfully develop effective sex-based therapies, we must attain a comprehensive understanding of the effects of sex hormones, the menstrual cycle, and pregnancy on physiological function. By analyzing experimental findings, mathematical modelling can play a major role in facilitating and contributing to advancing the understanding of sex differences in physiology and pathophysiology. In this review, we present a survey of existing sex-specific modelling studies of physiological systems, describe the impact of the menstrual cycle and pregnancy, and discuss future modelling opportunities.

We explored a computational model of astrocytic energy metabolism and demonstrated the theoretical plausibility that this type of pathway might be capable of coding information about stimuli in addition to its known functions in cellular energy and carbon budgets. Simulation results indicate that glycogenolytic glycolysis triggered by activation of adrenergic receptors can capture the intensity and duration features of a neuromodulator waveform and can respond in a dose-dependent manner, including non-linear state changes that are analogous to action potentials. We show how this metabolic pathway can translate information about external stimuli to production profiles of energy-carrying molecules such as lactate with a precision beyond simple signal transduction or non-linear amplification. The results suggest the operation of a metabolic state-machine from the spatially discontiguous yet interdependent metabolite elements. Such metabolic pathways might be well-positioned to code an additional level of salient information about a cell's environmental demands to impact its function. Our hypothesis has implications for the computational power and energy efficiency of the brain.

In this study, we extend evolutionary game dynamics with non-uniform interaction rates to the situation with finite population. Our main goal is to show how the fixation probability is influenced by the non-uniform interaction rates under weak selection. Based on the diffusion approximation of the Moran process and assumption of weak selection, the stochastic dynamic properties of a two-phenotype game with non-uniform interaction rates in a finite population are investigated. By the analysis of some cases, we show that the non-uniform interaction rates may result in the potential evolutionary complexity of game dynamics in finite population.

We construct a compact model to mimic the membrane voltage response to the concentration of acetylcholine ([ACh]) which is mediated by the stochastic gating of acetylcholine (ACh) receptors. The patterns of the voltage depolarization against [ACh] as well as the accompanying voltage noises are presented. The mechanism of the voltage fluctuation that caused by the stochastic gating of receptors is explained. We consider that our results explain the frequently observed "end-plate (potential) noise" in physiology and electromyographic literature. These results, together with the requirements of evolution pressure on the motor units, explain reasonably the anatomical structure of the neuromuscular junction.

Update of medRxiv. 2022 Feb 14;:

This paper explores the genotype-phenotype relationship. It outlines conditions under which the dependence of a quantitative trait on the genome might be predictable, based on measurement of a limited subset of genotypes. It uses the theory of real-valued Boolean functions in a systematic way to translate trait data into the Fourier domain. Important trait features, such as the roughness of the trait landscape or the modularity of a trait have a simple Fourier interpretation. Ruggedness at a gene location corresponds to high sensitivity to mutation, while a modular organization of gene activity reduces such sensitivity. Traits where rugged loci are rare will naturally compress gene data in the Fourier domain, leading to a sparse representation of trait data, concentrated in identifiable, low-level coefficients. This Fourier representation of a trait organizes epistasis in a form which is isometric to the trait data. As Fourier matrices are known to be maximally incoherent with the standard basis, this permits employing compressive sensing techniques to work from data sets that are relatively small-sometimes even of polynomial size-compared to the exponentially large sets of possible genomes. This theory provides a theoretical underpinning for systematic use of Boolean function machinery to dissect the dependency of a trait on the genome and environment.

Cells often encounter various external and internal signals in a non-sustained pulsatile manner with varying amplitude, duration and residual value. However, the effect of signal pulse on the regulatory networks is poorly understood. In order gain a quantitative understating of pulse processing by bistable switches, we investigated pulse induced population inversion kinetics in bistable switches generated either by mutual activation or by mutual inhibition motifs. We show that a transient intense pulse or a prolonged weak pulse both can induce population inversion, however by distinct mechanisms. An intense pulse facilitates the population inversion by reducing the inversion time, while a weak prolonged pulse allows more late responders to flip their steady state causing increased average transition time. Although the inversion is controlled by the pulse amplitude and duration, however the fate of the inverted state is dictated by the residual signal that determines the mean residence at the flipped state. Therefore, population inversion and its maintenance require a proper tuning of all three signal parameters. Bistable system of mutual activation motif is more prone to make a transient response to the pulse however it is less susceptible to flip its steady state. While the bistability of mutual inhibition motif does not make a transient response yet it is more prone to switch its steady state. By comparing the pulse parameters and statistical properties of associated times scales, we conclude that a bistable switch originating from mutual activation loop is less susceptible to spurious signals as compared to the mutual inhibition loop.

Research shows that gene duplication followed by either repurposing or removal of duplicated genes is an important contributor to evolution of gene and protein interaction networks. We aim to identify which characteristics of a network can arise through this process, and which must have been produced in a different way. To model the network evolution, we postulate vertex duplication and edge deletion as evolutionary operations on graphs. Using the novel concept of an ancestrally distinguished subgraph, we show how features of present-day networks require certain features of their ancestors. In particular, ancestrally distinguished subgraphs cannot be introduced by vertex duplication. Additionally, if vertex duplication and edge deletion are the only evolutionary mechanisms, then a graph's ancestrally distinguished subgraphs must be contained in all of the graph's ancestors. We analyze two experimentally derived genetic networks and show that our results accurately predict lack of large ancestrally distinguished subgraphs, despite this feature being statistically improbable in associated random networks. This observation is consistent with the hypothesis that these networks evolved primarily via vertex duplication. The tools we provide open the door for analyzing ancestral networks using current networks. Our results apply to edge-labeled (e.g. signed) graphs which are either undirected or directed.

Cognitive biases like underinference, the hard-easy effect, and recurrently non-monotonic confidence are evolutionarily puzzling when viewed as persistent flaws in how people learn from environmental feedback. To explain these empirically robust cognitive biases from an evolutionary perspective, we propose a model of ancestral human learning based on the cultural-evolutionary-theoretic hypothesis that the primary selection pressure acting on ancestral human cognition pertained not to learning individually from environmental feedback, but to socially learning task-specific knowledge. In our model-which is inspired by classical Bayesian models-an ancestral human learner (the student) attempts to learn task-specific knowledge from a role model, with the option of switching between different tasks and role models. Suppose that the student's method of learning from their role model is a priori uncertain-in that it can either be successful imitation learning or de facto innovation learning-and the ecological fitness costs of meaningfully retaining environmental feedback are high. Then, the student's fitness-maximizing strategy does not retain their environmental feedback and-depending on the choice of model parameters-can be characterized by all of the aforementioned cognitive biases. Specifically, in order for the evolutionarily optimal estimate of confidence in this learning environment to be recurrently non-monotonic, it is necessary (as long as the environment's marginal payoff function satisfies a plausible quantitative condition) that a positive proportion of ancestral humans' attempted imitation learning was unknowingly implemented as de facto innovation learning. Moreover, an ecologically rational strategy of selective social learning can plausibly cause the evolutionarily optimal estimate of confidence to be recurrently non-monotonic in the empirically documented way: general increase with an intermediate period of decrease.

We consider a specific form of domain invasion that is an abstraction of pancreatic tissue eliminating precancerous mutant cells through juxtacrine signalling. The model is explored discretely, continuously, stochastically and deterministically, highlighting unforeseen nonlinear dependencies on the dimension of the solution domain. Specifically, stochastically simulated populations invade with a dimension dependent wave speed that can be over twice as fast as their deterministic analogues. Although the wave speed can be analytically derived in the cases of small domains, the probabilistic state space grows exponentially and, thus, we use numeric simulation and curve fitting to predict limiting dynamics.

Based on reported trends in relapse incidence among patients with relapsing-remitting multiple sclerosis, an original model for the response to disease modifying therapies is proposed. With a population approach and separate states for patients accounting for their risk of relapses, a system of nonlinear equations is formulated, similarly to established epidemiological models. Different parameters describe the effect of drugs and treatment switch in reducing the frequency of relapses. The model allows for a good fit to previously published data for experiments where different drugs are used. It also shows that different treatments maintain a high degree of similarity, with analogous dynamical features: a pre-treatment increment in relapse frequency leading to a distinct peak, a rapid drop after treatment switch and a plateau corresponding to a new base relapse activity, which seems dependant on the treatment chosen. A sensitivity analysis shows that the uncertainty in the initial proportions of different populations and the frequency of relapses can modify the overall dynamics of the response to treatment. Drugs are observed to induce effects that depend on patient sample's intrinsic characteristics, producing two clearly distinct and independent dynamics of relapse response. This confirms the clinical observation that certain drugs may be overall more successful in lowering the rate of relapses more significantly than others, notwithstanding the fact that patients behave differently across experiments.

Many corals form close associations with a diverse assortment of coral-dwelling fishes and other fauna. As coral reefs around the world are increasingly threatened by mass bleaching events, it is important to understand how these biotic interactions influence corals' susceptibility to bleaching. We used dynamic energy budget modeling to explore how nitrogen excreted by coral-dwelling fish affects the physiological performance of host corals. In our model, fish presence influenced the functioning of the coral-Symbiodiniaceae symbiosis by altering nitrogen availability, and the magnitude and sign of these effects depended on environmental conditions. Although our model predicted that fish-derived nitrogen can promote coral growth, the relationship between fish presence and coral tolerance of photo-oxidative stress was non-linear. Fish excretions supported denser symbiont populations that provided protection from incident light through self-shading. However, these symbionts also used more of their photosynthetic products for their own growth, rather than sharing with the coral host, putting the coral holobiont at a higher risk of becoming carbon-limited and bleaching. The balance between the benefits of increased symbiont shading and costs of reduced carbon sharing depended on environmental conditions. Thus, while there were some scenarios under which fish presence increased corals' tolerance of light stress, fish could also exacerbate bleaching and slow or prevent subsequent recovery. We discuss how the contrast between the potentially harmful effects of fish predicted by our model and results of empirical studies may relate to key model assumptions that warrant further investigation. Overall, this study provides a foundation for future work on how coral-associated fauna influence the bioenergetics of their host corals, which in turn has implications for how these corals respond to bleaching-inducing stressors.