Pub Date : 2024-11-22DOI: 10.1016/j.biosystems.2024.105365
Mostafa Herajy , Fei Liu , Monika Heiner
With the steady advance of in-silico biological experimentation, model construction and simulation becomes a ubiquitous tool to understand and predict the behaviour of many biological systems. However, biological processes may contain components from different types of reaction networks, resulting in models with different (e.g., slow and fast) timescales. Hybrid simulation is one approach which can be employed to efficiently execute multi-timescale models. In this paper, we present a methodology and workflow utilizing (coloured) hybrid Petri nets to construct smaller and more complicated hybrid models. The presented workflow integrates algorithms and ideas from hybrid simulation of biochemical reaction networks as well as Petri nets. We also construct multi-timescale hybrid models and then show how these models can be efficiently executed using three different advanced hybrid simulation algorithms.
随着实验室内生物实验的稳步发展，构建和模拟模型已成为了解和预测许多生物系统行为的普遍工具。然而，生物过程可能包含来自不同类型反应网络的成分，导致模型具有不同（如慢速和快速）的时标。混合模拟是有效执行多时间尺度模型的一种方法。在本文中，我们介绍了一种利用（彩色）混合 Petri 网构建更小、更复杂混合模型的方法和工作流程。所介绍的工作流程整合了生化反应网络混合模拟以及 Petri 网的算法和理念。我们还构建了多时标混合模型，然后展示了如何使用三种不同的高级混合仿真算法高效执行这些模型。
{"title":"A workflow for the hybrid modelling and simulation of multi-timescale biological systems","authors":"Mostafa Herajy , Fei Liu , Monika Heiner","doi":"10.1016/j.biosystems.2024.105365","DOIUrl":"10.1016/j.biosystems.2024.105365","url":null,"abstract":"<div><div>With the steady advance of in-silico biological experimentation, model construction and simulation becomes a ubiquitous tool to understand and predict the behaviour of many biological systems. However, biological processes may contain components from different types of reaction networks, resulting in models with different (e.g., slow and fast) timescales. Hybrid simulation is one approach which can be employed to efficiently execute multi-timescale models. In this paper, we present a methodology and workflow utilizing (coloured) hybrid Petri nets to construct smaller and more complicated hybrid models. The presented workflow integrates algorithms and ideas from hybrid simulation of biochemical reaction networks as well as Petri nets. We also construct multi-timescale hybrid models and then show how these models can be efficiently executed using three different advanced hybrid simulation algorithms.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105365"},"PeriodicalIF":2.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1016/j.biosystems.2024.105375
Massimo Di Giulio
The length of the deepest branches of the tree of life would tend to support the hypothesis that the distance of the branch that separates the sequences of archaea from those of bacteria, i.e. the interdomain one, is longer than the intradomain ones, i.e. those that separate the sequences of archaea and those of bacteria within them. Why should interdomain distance be larger than intradomain distances? The fact that the rate of amino acid substitutions was slowed as the domains of life appeared would seem to imply an evolutionary transition. The slowdown in the speed of evolution that occurred during the formation of the two domains of life would be the consequence of the progenote- > cell evolutionary transition. Indeed, the evolutionary stage of the progenote being characterized by an accelerated tempo and mode of evolution might explain the considerable interdomain distance because the accumulation of many amino acid substitutions on this branch would indicate the progenote stage that is also characterized by a high rate of amino acid substitutions. Furthermore, the fact that intradomain distances are smaller than interdomain distances would corroborate the hypothesis of the achievement of cellularity at the appearance of the main phyletic lineages. Indeed, the cell stage, unlike the progenotic one, definitively establishes the relationship between the genotype and phenotype, lowering the rate of evolution. Therefore, the arguments presented lead to the conclusion that LUCA was a progenote.
{"title":"The existence of the two domains of life, Bacteria and Archaea, would in itself imply that LUCA and the ancestors of these domains were progenotes","authors":"Massimo Di Giulio","doi":"10.1016/j.biosystems.2024.105375","DOIUrl":"10.1016/j.biosystems.2024.105375","url":null,"abstract":"<div><div>The length of the deepest branches of the tree of life would tend to support the hypothesis that the distance of the branch that separates the sequences of archaea from those of bacteria, i.e. the interdomain one, is longer than the intradomain ones, i.e. those that separate the sequences of archaea and those of bacteria within them. Why should interdomain distance be larger than intradomain distances? The fact that the rate of amino acid substitutions was slowed as the domains of life appeared would seem to imply an evolutionary transition. The slowdown in the speed of evolution that occurred during the formation of the two domains of life would be the consequence of the progenote- > cell evolutionary transition. Indeed, the evolutionary stage of the progenote being characterized by an accelerated tempo and mode of evolution might explain the considerable interdomain distance because the accumulation of many amino acid substitutions on this branch would indicate the progenote stage that is also characterized by a high rate of amino acid substitutions. Furthermore, the fact that intradomain distances are smaller than interdomain distances would corroborate the hypothesis of the achievement of cellularity at the appearance of the main phyletic lineages. Indeed, the cell stage, unlike the progenotic one, definitively establishes the relationship between the genotype and phenotype, lowering the rate of evolution. Therefore, the arguments presented lead to the conclusion that LUCA was a progenote.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105375"},"PeriodicalIF":2.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding circadian clock mechanisms is fundamental in order to counteract the harmful effects of clock malfunctioning and associated diseases. Biochemical, genetic and systems biology approaches have provided invaluable information on the mechanisms of the circadian clock, from which many mathematical models have been developed to understand the dynamics and quantitative properties of the circadian oscillator. To better analyze and compare quantitatively all these circadian cycles, we propose a method based on a previously proposed circadian cycle segmentation into stages. We notably identify a sequence of eight stages that characterize the progress of the circadian cycle. Next, we apply our approach to an experimental dataset and to five different models, all built with ordinary differential equations. Our method permits to assess the agreement of mathematical model cycles with biological properties or to detect some inconsistencies. As another application of our method, we provide insights on how this segmentation into stages can help to analyze the effect of a clock gene loss of function on the dynamic of a genetic oscillator. The strength of our method is to provide a benchmark for characterization, comparison and improvement of new mathematical models of circadian oscillators in a wide variety of model systems.
{"title":"Benchmark for quantitative characterization of circadian clock cycles","authors":"Odile Burckard , Michèle Teboul , Franck Delaunay , Madalena Chaves","doi":"10.1016/j.biosystems.2024.105363","DOIUrl":"10.1016/j.biosystems.2024.105363","url":null,"abstract":"<div><div>Understanding circadian clock mechanisms is fundamental in order to counteract the harmful effects of clock malfunctioning and associated diseases. Biochemical, genetic and systems biology approaches have provided invaluable information on the mechanisms of the circadian clock, from which many mathematical models have been developed to understand the dynamics and quantitative properties of the circadian oscillator. To better analyze and compare quantitatively all these circadian cycles, we propose a method based on a previously proposed circadian cycle segmentation into stages. We notably identify a sequence of eight stages that characterize the progress of the circadian cycle. Next, we apply our approach to an experimental dataset and to five different models, all built with ordinary differential equations. Our method permits to assess the agreement of mathematical model cycles with biological properties or to detect some inconsistencies. As another application of our method, we provide insights on how this segmentation into stages can help to analyze the effect of a clock gene loss of function on the dynamic of a genetic oscillator. The strength of our method is to provide a benchmark for characterization, comparison and improvement of new mathematical models of circadian oscillators in a wide variety of model systems.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105363"},"PeriodicalIF":2.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-17DOI: 10.1016/j.biosystems.2024.105374
F. Baluška , W.B. Miller , P. Slijepcevic , A.S. Reber
Cells represent the basic units of life, not only as structural building blocks, but also as cognitive agents endowed with subjective cellular feelings, sentience (consciousness), and cognitive infocomputatioal competence. Living cells act as ‘Kantian Wholes’: All of its parts exist for and by means of the whole system, allowing cells to use sentient agency for solving existential problems and evolve as living self-organizing units. Cell sentience is based on its excitable plasma membrane generating bioelectromagnetic fields that link to a whole-cell sensory architecture. This cellular sensory apparatus, termed its senome, represents the totality of cellular self-referential information obtained by cells via their sensory systems, including the subjective cellular inside and the cell’s self-referential appraisal of its external environment. The plasma membrane was ‘invented’ by the very first cells and has been uninterruptedly inherited by cells for billions of years through successive cell divisions.
{"title":"Sensing, feeling and sentience in unicellular organisms and living cells","authors":"F. Baluška , W.B. Miller , P. Slijepcevic , A.S. Reber","doi":"10.1016/j.biosystems.2024.105374","DOIUrl":"10.1016/j.biosystems.2024.105374","url":null,"abstract":"<div><div>Cells represent the basic units of life, not only as structural building blocks, but also as cognitive agents endowed with subjective cellular feelings, sentience (consciousness), and cognitive infocomputatioal competence. Living cells act as ‘Kantian Wholes’: All of its parts exist for and by means of the whole system, allowing cells to use sentient agency for solving existential problems and evolve as living self-organizing units. Cell sentience is based on its excitable plasma membrane generating bioelectromagnetic fields that link to a whole-cell sensory architecture. This cellular sensory apparatus, termed its senome, represents the totality of cellular self-referential information obtained by cells via their sensory systems, including the subjective cellular inside and the cell’s self-referential appraisal of its external environment. The plasma membrane was ‘invented’ by the very first cells and has been uninterruptedly inherited by cells for billions of years through successive cell divisions.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105374"},"PeriodicalIF":2.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1016/j.biosystems.2024.105364
André C.R. Martins
The question of why we age is a fundamental one. It is about who we are, and it also might have critical practical aspects as we try to find ways to age slower. Or to not age at all. Different reasons point at distinct strategies for the research of anti-aging drugs. While the main reason why biological systems work as they do is evolution, for quite a while, it was believed that aging required another explanation. Aging seems to harm individuals so much that even if it has group benefits, those benefits were unlikely to be enough. That has led many scientists to propose non-evolutionary explanations as to why we age. But those theories seem to fail at explaining all the data on how species age. Here, I will show that the insistence of finding the one idea that explains it all might be at the root of the difficulty of getting a full picture. By exploring an evolutionary model of aging where locality and temporal changes are fundamental aspects of the problem, I will show that environmental change causes the barrier for group advantages to become much weaker. That weakening might help small group advantages to add up to the point they could make an adaptive difference. To answer why we age, we might have to abandon asking which models are correct. The full answer might come from considering how much each hypothesis behind each existing model, evolutionary and non-evolutionary ones, contributes to the real world’s solution.
{"title":"Senescence, change, and competition: When the desire to pick one model harms our understanding","authors":"André C.R. Martins","doi":"10.1016/j.biosystems.2024.105364","DOIUrl":"10.1016/j.biosystems.2024.105364","url":null,"abstract":"<div><div>The question of why we age is a fundamental one. It is about who we are, and it also might have critical practical aspects as we try to find ways to age slower. Or to not age at all. Different reasons point at distinct strategies for the research of anti-aging drugs. While the main reason why biological systems work as they do is evolution, for quite a while, it was believed that aging required another explanation. Aging seems to harm individuals so much that even if it has group benefits, those benefits were unlikely to be enough. That has led many scientists to propose non-evolutionary explanations as to why we age. But those theories seem to fail at explaining all the data on how species age. Here, I will show that the insistence of finding the one idea that explains it all might be at the root of the difficulty of getting a full picture. By exploring an evolutionary model of aging where locality and temporal changes are fundamental aspects of the problem, I will show that environmental change causes the barrier for group advantages to become much weaker. That weakening might help small group advantages to add up to the point they could make an adaptive difference. To answer why we age, we might have to abandon asking which models are correct. The full answer might come from considering how much each hypothesis behind each existing model, evolutionary and non-evolutionary ones, contributes to the real world’s solution.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105364"},"PeriodicalIF":2.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1016/j.biosystems.2024.105361
Luis Fernando Ontiveros-Araiza
Since the early attempts to understand the brain made by Greek philosophers more than 2000 years ago, one of the main questions in neuroscience has been how the brain perceives all the stimuli in the environment and uses this information to implement a response. Recent hypotheses of the neural code rely on the existence of an ideal observer, whether on specific areas of the cerebral cortex or distributed network composed of cortical and subcortical elements. The Neurobehavioral State hypothesis stipulates that neurons are in a quasi-stable state due to the dynamic interaction of their molecular components. This increases their computational capabilities and electrophysiological behavior further than a binary active/inactive state. Together, neuronal populations across the brain learn to identify and associate internal and external stimuli with actions and emotions. Furthermore, such associations can be stored through the regulation of neuronal components as new quasi-stable states. Using this framework, behavior arises as the result of the dynamic interaction between internal and external stimuli together with previously established quasi-stable states that delineate the behavioral response. Finally, the Neurobehavioral State hypothesis is firmly grounded on present evidence of the complex dynamics within the brain, from the molecular to the network level, and avoids the need for a central observer by proposing the brain configures itself through experience-driven associations.
{"title":"The Neurobehavioral State hypothesis","authors":"Luis Fernando Ontiveros-Araiza","doi":"10.1016/j.biosystems.2024.105361","DOIUrl":"10.1016/j.biosystems.2024.105361","url":null,"abstract":"<div><div>Since the early attempts to understand the brain made by Greek philosophers more than 2000 years ago, one of the main questions in neuroscience has been how the brain perceives all the stimuli in the environment and uses this information to implement a response. Recent hypotheses of the neural code rely on the existence of an ideal observer, whether on specific areas of the cerebral cortex or distributed network composed of cortical and subcortical elements. The Neurobehavioral State hypothesis stipulates that neurons are in a quasi-stable state due to the dynamic interaction of their molecular components. This increases their computational capabilities and electrophysiological behavior further than a binary active/inactive state. Together, neuronal populations across the brain learn to identify and associate internal and external stimuli with actions and emotions. Furthermore, such associations can be stored through the regulation of neuronal components as new quasi-stable states. Using this framework, behavior arises as the result of the dynamic interaction between internal and external stimuli together with previously established quasi-stable states that delineate the behavioral response. Finally, the Neurobehavioral State hypothesis is firmly grounded on present evidence of the complex dynamics within the brain, from the molecular to the network level, and avoids the need for a central observer by proposing the brain configures itself through experience-driven associations.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105361"},"PeriodicalIF":2.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1016/j.biosystems.2024.105360
Juan A. Garcia, Anass Bouchnita
The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of proteins and their interactions. To gain insights into the spatial effects shaping intracellular regulation, we apply machine learning (ML) to explore a previously developed spatial model of the epidermal growth factor receptor (EGFR) signaling. The model describes the reactions between molecular species inside of cells following the transient activation of EGF receptors. To train our ML models, we conduct 10,000 numerical simulations in parallel where we calculate the cumulative activation of molecules and transcription factors under various conditions such as different diffusion speeds, inactivation rates, and cell structures. We take advantage of the low computational cost of ML algorithms to investigate the effects of cell and nucleus sizes, the diffusion speed of proteins, and the inactivation rate of the Ras molecules on the activation strength of transcription factors. Our results suggest that the predictions by both neural networks and random forests yielded minimal mean square error (MSEs), while linear generalized models displayed a significantly larger MSE. The exploration of the surrogate models has shown that smaller cell and nucleus radii as well, larger diffusion coefficients, and reduced inactivation rates increase the activation of transcription factors. These results are confirmed by numerical simulations. Our ML algorithms can be readily incorporated within multiscale models of tumor growth to embed the spatial effects regulating intracellular pathways, enabling the use of complex cell models within multiscale models while reducing the computational cost.
细胞的命运受其内部发生的生化反应（即细胞内途径）调控。细胞显示出与其形状、结构和所含液体有关的各种特征，这些特征影响着蛋白质的扩散及其相互作用。为了深入了解影响细胞内调控的空间效应，我们应用机器学习（ML）来探索之前开发的表皮生长因子受体（EGFR）信号传导空间模型。该模型描述了表皮生长因子受体瞬时激活后细胞内分子物种之间的反应。为了训练我们的 ML 模型，我们并行进行了 10,000 次数值模拟，计算在不同扩散速度、失活率和细胞结构等条件下分子和转录因子的累积激活。我们利用 ML 算法计算成本低的优势，研究了细胞和细胞核大小、蛋白质扩散速度和 Ras 分子失活率对转录因子激活强度的影响。我们的研究结果表明，神经网络和随机森林的预测均方误差（MSE）最小，而线性广义模型的 MSE 明显较大。对代用模型的探索表明，较小的细胞和细胞核半径、较大的扩散系数和较低的失活率都会增加转录因子的活化。数值模拟证实了这些结果。我们的 ML 算法可以很容易地融入肿瘤生长的多尺度模型中，以嵌入调节细胞内通路的空间效应，从而在多尺度模型中使用复杂的细胞模型，同时降低计算成本。
{"title":"Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models","authors":"Juan A. Garcia, Anass Bouchnita","doi":"10.1016/j.biosystems.2024.105360","DOIUrl":"10.1016/j.biosystems.2024.105360","url":null,"abstract":"<div><div>The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of proteins and their interactions. To gain insights into the spatial effects shaping intracellular regulation, we apply machine learning (ML) to explore a previously developed spatial model of the epidermal growth factor receptor (EGFR) signaling. The model describes the reactions between molecular species inside of cells following the transient activation of EGF receptors. To train our ML models, we conduct 10,000 numerical simulations in parallel where we calculate the cumulative activation of molecules and transcription factors under various conditions such as different diffusion speeds, inactivation rates, and cell structures. We take advantage of the low computational cost of ML algorithms to investigate the effects of cell and nucleus sizes, the diffusion speed of proteins, and the inactivation rate of the Ras molecules on the activation strength of transcription factors. Our results suggest that the predictions by both neural networks and random forests yielded minimal mean square error (MSEs), while linear generalized models displayed a significantly larger MSE. The exploration of the surrogate models has shown that smaller cell and nucleus radii as well, larger diffusion coefficients, and reduced inactivation rates increase the activation of transcription factors. These results are confirmed by numerical simulations. Our ML algorithms can be readily incorporated within multiscale models of tumor growth to embed the spatial effects regulating intracellular pathways, enabling the use of complex cell models within multiscale models while reducing the computational cost.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"247 ","pages":"Article 105360"},"PeriodicalIF":2.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.biosystems.2024.105362
Shounan Lu , Yang Wang
Understanding and explaining cooperative behavior in human society has become an open question. In this paper, we propose a dynamic adjustment of pair relationships in a spatial prisoner's dilemma game. Unlike previous studies that individuals dynamically adjust the intensity of interaction with their opponents at each step, this work consider tolerance, in which the intensity of interaction is adjusted when the time of successive defections by an individual exceeds a tolerance threshold T. We find that although the proposed mechanism can significantly improve cooperation compared to traditional versions, a higher tolerance for continuous defection behavior is not conducive to the evolution of cooperation. Furthermore, an environmental adaptor that dynamically adjusts the paired relationship with the opponent at all times is beneficial for the evolution of cooperation. And the higher the degree of adjustment in the paired relationship, the lower the probability of continuous exploitation by defector. We hope that our work can provide some insights into explaining the existence and maintenance of cooperation.
理解和解释人类社会中的合作行为已成为一个悬而未决的问题。在本文中，我们提出了一种在空间囚徒困境博弈中动态调整配对关系的方法。与以往研究中个体在每一步都动态调整与对手互动强度不同，这项工作考虑了容忍度，即当个体连续叛逃的时间超过容忍阈值 T 时，互动强度就会被调整。我们发现，虽然与传统版本相比，所提出的机制能显著提高合作性，但对连续叛逃行为的更高容忍度并不利于合作的演化。此外，随时动态调整与对手配对关系的环境适应器也有利于合作的进化。而且，配对关系的调整程度越高，叛逃者持续利用的概率就越低。我们希望我们的研究能为解释合作的存在和维持提供一些启示。
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Pub Date : 2024-10-30DOI: 10.1016/j.biosystems.2024.105359
Abir U. Igamberdiev
The concept of centers of origin of cultivated plants (crop biodiversity hotspots) developed by Nikolai Vavilov (1887–1943) is essential for understanding the origin and evolution of human civilization. Vavilov formulated the principles of the Neolithic agricultural revolution and substantiated the basic patterns for the emergence of agricultural civilizations. He established that the center of speciation of the plants that have a potential for cultivation determines the origin of primary civilization. Humans actively performed the selection of plants with valuable properties, which led to the formation of new cultivated species and varieties, while the starting point for such unconsciously human-directed evolution was the presence of potentially useful traits due to the increased genetic diversity in the center of origin. The spreading of agriculturally important cultivars from the center of their origin led to the propagation of beneficial farming technologies over large areas. The establishment of human civilization resulted from the dynamic quasi-symbiotic relationship between humans and domesticated plants and animals, which human-driven evolution became an essential factor for the transformation and dynamics of human societies. In the addendum, we present archive materials on the cooperation of Nikolai Vavilov with the historians and ethnologists from the editorial board of the journal “Novy Vostok” (“Nouvel Orient”). These materials include his letters to Professor Ilya Borozdin.
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Pub Date : 2024-10-22DOI: 10.1016/j.biosystems.2024.105358
Guodong Huang, Shu Zhou, Rui Zhu, Yunhai Wang, Yuan Chai
Chaotic sequences are widely used in secure communication due to their high randomness. Chaotic resonance (CR) refers to the resonant response of a system to weak signals induced by chaotic activity, but its practical application remains limited. This study designs a simplified FitzHugh-Nagumo (FHN) auditory neuron model by simulating the physiological activities of auditory neurons and considering the combined stimulation of chaotic activity and sound signals. It is found that the neuron dynamics depend on both external sound stimuli and chaotic current intensity. Chaotic currents induce spikes in the neuron output sequence through CR, and the spikes become more frequent with increasing current intensity, eventually leading to a chaotic state regardless of the initial state. However, the sensitivity of the initial value of this chaotic sequence shifts to the chaotic current excitation system. The injection of chaotic currents can reduce the system's average Hamiltonian energy under certain conditions. By measuring the complexity of the generated sequences, we find that the addition of chaotic currents can enhance the complexity of the original sequences, and the enhancement ability increases with the intensity. This provides a new approach to enhance the complexity of original chaotic sequences. Moreover, different chaotic currents can induce different chaotic sequences with varying abilities to enhance the complexity of the original sequences. We hope our work can contribute to secure communication.
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