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"Biological systems Computer simulation Periodicals"
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Artificial intelligence in peer review: How can evolutionary computation support journal editors?
With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors' workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.
Journal Article
Predator-prey dynamics with refuge, alternate food, and harvesting strategies in a patchy habitat
A predator-prey dynamic reaction model is investigated in a two-layered water body where only the prey is subjected to harvesting. The surface layer (Layer-1) provides food for both species, while the prey migrates to deeper layer (Layer-2) as a refuge from predation. Although the prey is the preferred food for the predator, the predator can also consume alternative food resources that are abundantly available. The availability of alternative food resources plays a crucial role in species' coexistence by mitigating the risk of extinction. The main objective of the work was to explore the effect of different harvesting strategies (nonlinear and linear harvesting) on a predator-prey model with effort dynamics in a heterogeneous habitat. The analysis incorporates a dual timescale approach: the prey species migrate between the layers on a fast timescale, whereas the growth of resource biomass, prey-predator interactions, and harvesting dynamics evolve on a slow timescale. The complete model involving both slow and fast timescales has been investigated by using aggregated model. The reduced aggregated model is analyzed analytically as well as numerically. Moreover, it is demonstrated that the reduced system exhibits the bifurcations (transcritical and Hopf point) by setting the additional food parameter as a bifurcation parameter. A comparative study using different harvesting strategies found that there is chaos in the system when using linear harvesting in the predator-prey model. However, nonlinear harvesting gives only stable or periodic solutions. This concludes that nonlinear harvesting can control the chaos in the system. Additionally, a one-dimensional parametric bifurcation, phase portraits, and time series plots are also explored in the numerical simulation.
Journal Article
Identifying the fundamental units of bacterial diversity: A paradigm shift to incorporate ecology into bacterial systematics
The central questions of bacterial ecology and evolution require a method to consistently demarcate, from the vast and diverse set of bacterial cells within a natural community, the groups playing ecologically distinct roles (ecotypes). Because of a lack of theory-based guidelines, current methods in bacterial systematics fail to divide the bacterial domain of life into meaningful units of ecology and evolution. We introduce a sequence-based approach (\"ecotype simulation\") to model the evolutionary dynamics of bacterial populations and to identify ecotypes within a natural community, focusing here on two Bacillus clades surveyed from the \"Evolution Canyons\" of Israel. This approach has identified multiple ecotypes within traditional species, with each predicted to be an ecologically distinct lineage; many such ecotypes were confirmed to be ecologically distinct, with specialization to different canyon slopes with different solar exposures. Ecotype simulation provides a long-needed natural foundation for microbial ecology and systematics.
Journal Article
Modelling of a seasonally perturbed competitive three species impulsive system
The population of biological species in the ecosystem is known sensitive to the periodic fluctuations of seasonal change, food resources and climatic conditions. Research in the ecological management discipline conventionally models the behavior of such dynamic systems through specific impulsive response functions, but the results of such research are applicable only when the environments conform exactly to the conditions as defined by the specific response functions that have been implemented for specific scenarios. This means that the application of previous work may be somewhat limited. Moreover, the intra and inter competitions among species have been seldom studied for modelling the prey-predator ecosystem. To fill in the gaps this paper models the delicate balance of two-prey and one-predator system by addressing three main areas of: ⅰ) instead of using the specific impulse response this work models the ecosystem through a more general response function; ⅱ) to include the effects due to the competition between species and ⅲ) the system is subjected to the influences of seasonal factors. The seasonal factor has been implemented here in terms of periodic functions to represent the growth rates of predators. The sufficient condition for the local and global asymptotic stability of the prey-free periodic solution and the permanence of the system have been subsequently obtained by using the Comparison techniques and the Floquet theorems. Finally, the correctness of developed theories is verified by numerical simulation, and the corresponding biological explanation is given.
Journal Article
A demographic approach to study effects of climate change in desert plants
Desert species respond strongly to infrequent, intense pulses of precipitation. Consequently, indigenous flora has developed a rich repertoire of life-history strategies to deal with fluctuations in resource availability. Examinations of how future climate change will affect the biota often forecast negative impacts, but these—usually correlative—approaches overlook precipitation variation because they are based on averages. Here, we provide an overview of how variable precipitation affects perennial and annual desert plants, and then implement an innovative, mechanistic approach to examine the effects of precipitation on populations of two desert plant species. This approach couples robust climatic projections, including variable precipitation, with stochastic, stage-structured models constructed from long-term demographic datasets of the short-lived Cryptantha flava in the Colorado Plateau Desert (USA) and the annual Carrichtera annua in the Negev Desert (Israel). Our results highlight these populations' potential to buffer future stochastic precipitation. Population growth rates in both species increased under future conditions: wetter, longer growing seasons for Cryptantha and drier years for Carrichtera. We determined that such changes are primarily due to survival and size changes for Cryptantha and the role of seed bank for Carrichtera. Our work suggests that desert plants, and thus the resources they provide, might be more resilient to climate change than previously thought.
Journal Article
Recent advances in ODEs modeling of tumor-immune responses: a focus on delay effects
This review examines recent developments in modeling the interaction between tumor cells and the immune system, with a specific focus on the application of delay differential equations (DDEs). The models serve as crucial tools to simulate and predict the immune response to tumor proliferation, thus facilitating a more effective evaluation of clinical and therapeutic strategies before their implementation. This approach enables the hypothetical testing of various interventions, thus resulting in significant time and resource savings. The central theme is the integration of DDEs to represent biologically realistic time delays. These delays-inherent in biological processes such as the activation and migration of immune cells to the tumor site-are essential for a more accurate and dynamic representation of the system. Furthermore, this document acknowledges the inherent limitations of these mathematical models, which are simplified representations of complex biological phenomena by nature. The precision and practical utility of these models depend on the use of biologically plausible delay formulations, the validation of parameters with empirical data, and the alignment of model predictions with clinical outcomes. Ultimately, this work underscores the considerable potential and significant challenges of employing mathematical models as a bridge between theoretical understanding and applied oncology.
Journal Article
On the global stability of the discrete-time epidemic models: A new approach
We developed a unified analytical framework for the global dynamics of discrete-time susceptible infectious susceptible (SIS) epidemic models with nonlinear recruitment. Emphasis was placed on demographic feedback through Beverton-Holt and Ricker-type recruitment, which regulates host population size and thereby shapes transmission and long-term persistence (Persistence allows population densities to approach zero asymptotically, wheras uniform persistence requires them to remain bounded away from zero). Under minimal assumptions, we reduced non-autonomous systems to appropriately defined autonomous limiting systems and used this reduction to obtain a complete global threshold characterization: When the basic reproduction number $ R_{0} > 1 $, the endemic equilibrium existed and was globally asymptotically stable; when $ R_{0}\\le 1 $, solutions converged to the disease-free state. The approach extended to periodically forced SIS models, which showed that the threshold and stability conclusions persisted in the periodic non-autonomous setting. The results unified and strengthened prior work and clarify how recruitment dynamics govern persistence in discrete-time epidemic systems.
Journal Article
Behavior-induced oscillations in epidemic outbreaks with distributed memory: Beyond the linear chain trick using numerical methods
We considered a model for an infectious disease outbreak, when the depletion of susceptible individuals is negligible, and assumed that individuals adapt their behavior according to the information they receive about new cases. In line with the information index approach, we supposed that individuals react to past information according to a memory kernel that is continuously distributed in the past. We analyzed equilibria and their stability, with analytical results for selected cases. Thanks to the recently developed pseudospectral approximation of delay equations, we studied numerically the long-term dynamics of the model for memory kernels defined by gamma distributions with a general non-integer shape parameter, extending the analysis beyond what is allowed by the linear chain trick. In agreement with previous studies, we showed that behavior adaptation alone can cause sustained waves of infections even in an outbreak scenario, and notably in the absence of other processes like demographic turnover, seasonality, or waning immunity. Our analysis gives a more general insight into how the period and peak of epidemic waves depend on the shape of the memory kernel and how the level of minimal contact impacts the stability of the behavior-induced positive equilibrium.
Journal Article
Eco-evolutionary dynamics of structured populations in periodically fluctuating environments: a G function approach
Understanding the ecological and evolutionary dynamics of populations is critical for both basic and applied purposes in a variety of biological contexts. Although several modeling frameworks have been developed to simulate eco-evolutionary dynamics, many fewer address how to model structured populations. In a prior paper, we put forth the first modeling approach to simulate eco-evolutionary dynamics in structured populations under the
G
function modeling framework. However, this approach does not allow for accurate simulation under fluctuating environmental conditions. To address this limitation, we draw on the study of periodic differential equations to propose a modified approach that uses a different definition of fitness more suitable for fluctuating environments. We illustrate this method with a simple toy model of life history trade-offs. The generality of this approach allows it to be used in a variety of biological contexts.
Journal Article