Takashi Shimada, Kazumori Mise, Kai Morino, Shigeto Otsuka
Soil microbial communities are known to be robust against perturbations such as nutrition inputs, which appears as an obstacle for the soil improvement. On the other hand, its adaptable aspect has been also reported. Here we propose simple measures for these seemingly contradicting features of soil microbial communities, robustness and plasticity, based on the distribution of the populations. The first measure is the similarity in the population balance, i.e. the shape of the distribution function, which is found to show resilience against the nutrition inputs. The other is the similarity in the composition of the species measured by the rank order of the population, which shows an adaptable response during the population balance is recovering. These results clearly show that the soil microbial system is robust (or, homeostatic) in its population balance, while the composition of the species is rather plastic and adaptable.
{"title":"Simple measures to capture the robustness and the plasticity of soil microbial communities","authors":"Takashi Shimada, Kazumori Mise, Kai Morino, Shigeto Otsuka","doi":"arxiv-2409.03372","DOIUrl":"https://doi.org/arxiv-2409.03372","url":null,"abstract":"Soil microbial communities are known to be robust against perturbations such\u0000as nutrition inputs, which appears as an obstacle for the soil improvement. On\u0000the other hand, its adaptable aspect has been also reported. Here we propose\u0000simple measures for these seemingly contradicting features of soil microbial\u0000communities, robustness and plasticity, based on the distribution of the\u0000populations. The first measure is the similarity in the population balance,\u0000i.e. the shape of the distribution function, which is found to show resilience\u0000against the nutrition inputs. The other is the similarity in the composition of\u0000the species measured by the rank order of the population, which shows an\u0000adaptable response during the population balance is recovering. These results\u0000clearly show that the soil microbial system is robust (or, homeostatic) in its\u0000population balance, while the composition of the species is rather plastic and\u0000adaptable.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mariana Harris, Pablo Aguirre, Víctor F. Breña-Medina
Quorum sensing orchestrates bacterial communication, which is vital for bacteria's population behaviour. We propose a mathematical model that unveils chaotic dynamics within quorum sensing networks, challenging predictability. The model considers the interaction between autoinducers (molecular signalling) and two subtypes of bacteria. We analyze the different dynamical scenarios to find parameter regimes for long-term steady-state behaviour, periodic oscillations, and even chaos. In the latter case, we find that the complicated dynamics can be explained by the presence of homoclinic Shilnikov bifurcations.
{"title":"Homoclinic Chaos Unveiling Quorum Sensing Dynamics","authors":"Mariana Harris, Pablo Aguirre, Víctor F. Breña-Medina","doi":"arxiv-2409.02764","DOIUrl":"https://doi.org/arxiv-2409.02764","url":null,"abstract":"Quorum sensing orchestrates bacterial communication, which is vital for\u0000bacteria's population behaviour. We propose a mathematical model that unveils\u0000chaotic dynamics within quorum sensing networks, challenging predictability.\u0000The model considers the interaction between autoinducers (molecular signalling)\u0000and two subtypes of bacteria. We analyze the different dynamical scenarios to\u0000find parameter regimes for long-term steady-state behaviour, periodic\u0000oscillations, and even chaos. In the latter case, we find that the complicated\u0000dynamics can be explained by the presence of homoclinic Shilnikov bifurcations.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vicky Chuqiao Yang, Christopher P. Kempes, S. Redner, Geoffrey B. West, Hyejin Youn
Regulatory functions are essential in both socioeconomic and biological systems, from corporate managers to regulatory genes in genomes. Regulatory functions come with substantial costs, but are often taken for granted. Here, we empirically examine regulatory costs across diverse systems -- biological organisms (bacteria and eukaryotic genomes), human organizations (companies, federal agencies, universities), and decentralized entities (Wikipedia, cities) -- using scaling analysis. We guide the empirical analysis with a conceptual model, which anticipates the scaling of regulatory costs to shift with the system's internal interaction structure -- well-mixed or modular. We find diverse systems exhibit consistent scaling patterns -- well-mixed systems exhibit superlinear scaling, while modular ones show sublinear or linear scaling. Further, we find that the socioeconomic systems containing more diverse occupational functions tend to have more regulatory costs than expected from their size, confirming the type of interactions also plays a role in regulatory costs. While many socioeconomic systems exhibit efficiencies of scale, regulatory costs in many social systems have grown disproportionally over time. Our finding suggests that the increasing complexity of functions may contribute to this trend. This cross-system comparison offers a framework for understanding regulatory costs and could guide future efforts to identify and mitigate regulatory inefficiencies.
{"title":"Regulatory Functions from Cells to Society","authors":"Vicky Chuqiao Yang, Christopher P. Kempes, S. Redner, Geoffrey B. West, Hyejin Youn","doi":"arxiv-2409.02884","DOIUrl":"https://doi.org/arxiv-2409.02884","url":null,"abstract":"Regulatory functions are essential in both socioeconomic and biological\u0000systems, from corporate managers to regulatory genes in genomes. Regulatory\u0000functions come with substantial costs, but are often taken for granted. Here,\u0000we empirically examine regulatory costs across diverse systems -- biological\u0000organisms (bacteria and eukaryotic genomes), human organizations (companies,\u0000federal agencies, universities), and decentralized entities (Wikipedia, cities)\u0000-- using scaling analysis. We guide the empirical analysis with a conceptual\u0000model, which anticipates the scaling of regulatory costs to shift with the\u0000system's internal interaction structure -- well-mixed or modular. We find\u0000diverse systems exhibit consistent scaling patterns -- well-mixed systems\u0000exhibit superlinear scaling, while modular ones show sublinear or linear\u0000scaling. Further, we find that the socioeconomic systems containing more\u0000diverse occupational functions tend to have more regulatory costs than expected\u0000from their size, confirming the type of interactions also plays a role in\u0000regulatory costs. While many socioeconomic systems exhibit efficiencies of\u0000scale, regulatory costs in many social systems have grown disproportionally\u0000over time. Our finding suggests that the increasing complexity of functions may\u0000contribute to this trend. This cross-system comparison offers a framework for\u0000understanding regulatory costs and could guide future efforts to identify and\u0000mitigate regulatory inefficiencies.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a numerical analysis of local community assembly through weak migration from a regional species pool. At equilibrium, the local community consists of a subset ("clique") of species from the regional community. Our analysis reveals that the interaction networks of these cliques exhibit nontrivial architectures. Specifically, we demonstrate the pronounced nested structure of the clique interaction matrix in the case of symmetric interactions and the hyperuniform structure seen in asymmetric communities.
{"title":"Numerical Study of Interaction Network Structures in Competitive Ecosystems","authors":"David A. Kessler, Nadav M. Shnerb","doi":"arxiv-2409.01894","DOIUrl":"https://doi.org/arxiv-2409.01894","url":null,"abstract":"We present a numerical analysis of local community assembly through weak\u0000migration from a regional species pool. At equilibrium, the local community\u0000consists of a subset (\"clique\") of species from the regional community. Our\u0000analysis reveals that the interaction networks of these cliques exhibit\u0000nontrivial architectures. Specifically, we demonstrate the pronounced nested\u0000structure of the clique interaction matrix in the case of symmetric\u0000interactions and the hyperuniform structure seen in asymmetric communities.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Małgorzata Fic, Frank Bastian, Jacek Miękisz, Chaitanya S. Gokhale
Real-world processes often exhibit temporal separation between actions and reactions - a characteristic frequently ignored in many modelling frameworks. Adding temporal aspects, like time delays, introduces a higher complexity of problems and leads to models that are challenging to analyse and computationally expensive to solve. In this work, we propose an intermediate solution to resolve the issue in the framework of evolutionary game theory. Our compartment-based model includes time delays while remaining relatively simple and straightforward to analyse. We show that this model yields qualitatively comparable results with models incorporating explicit delays. Particularly, we focus on the case of delays between parents' interaction and an offspring joining the population, with the magnitude of the delay depending on the parents' strategy. We analyse Stag-Hunt, Snowdrift, and the Prisoner's Dilemma game and show that strategy-dependent delays are detrimental to affected strategies. Additionally, we present how including delays may change the effective games played in the population, subsequently emphasising the importance of considering the studied systems' temporal aspects to model them accurately.
{"title":"Compartment model of strategy-dependent time delays in replicator dynamics","authors":"Małgorzata Fic, Frank Bastian, Jacek Miękisz, Chaitanya S. Gokhale","doi":"arxiv-2409.01116","DOIUrl":"https://doi.org/arxiv-2409.01116","url":null,"abstract":"Real-world processes often exhibit temporal separation between actions and\u0000reactions - a characteristic frequently ignored in many modelling frameworks.\u0000Adding temporal aspects, like time delays, introduces a higher complexity of\u0000problems and leads to models that are challenging to analyse and\u0000computationally expensive to solve. In this work, we propose an intermediate\u0000solution to resolve the issue in the framework of evolutionary game theory. Our\u0000compartment-based model includes time delays while remaining relatively simple\u0000and straightforward to analyse. We show that this model yields qualitatively\u0000comparable results with models incorporating explicit delays. Particularly, we\u0000focus on the case of delays between parents' interaction and an offspring\u0000joining the population, with the magnitude of the delay depending on the\u0000parents' strategy. We analyse Stag-Hunt, Snowdrift, and the Prisoner's Dilemma\u0000game and show that strategy-dependent delays are detrimental to affected\u0000strategies. Additionally, we present how including delays may change the\u0000effective games played in the population, subsequently emphasising the\u0000importance of considering the studied systems' temporal aspects to model them\u0000accurately.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soukaina Sabir, Odelaisy León-Triana, Sergio Serrano, Roberto Barrio, Victor M. Pérez-García
CAR-T cell therapies have demonstrated significant success in treating B-cell leukemia in children and young adults. However, their effectiveness in treating B-cell lymphomas has been limited. Unlike leukemia, lymphoma often manifests as solid masses of cancer cells in lymph nodes, glands, or organs, making these tumors harder to access thus hindering treatment response. In this paper we present a mathematical model that elucidates the dynamics of diffuse large B-cell lymphoma and CAR-T cells in a lymph node. The mathematical model aids in understanding the complex interplay between the cell populations involved and proposes ways to identify potential underlying dynamical causes of treatment failure. We also study the phenomenon of immunosuppression induced by tumor cells and theoretically demonstrate its impact on cell dynamics. Through the examination of various response scenarios, we underscore the significance of product characteristics in treatment outcomes.
CAR-T 细胞疗法在治疗儿童和年轻人的 B 细胞白血病方面取得了巨大成功。然而，它们在治疗 B 细胞淋巴瘤方面的效果有限。与白血病不同，淋巴瘤通常表现为淋巴结、腺体或器官中的癌细胞团块，这使得肿瘤更难进入，从而阻碍了治疗反应。在本文中，我们提出了一个数学模型，该模型阐明了弥漫大B细胞淋巴瘤和CAR-T细胞在淋巴结中的动态变化。该数学模型有助于理解相关细胞群之间复杂的相互作用，并提出了识别治疗失败潜在潜在动态原因的方法。我们还研究了肿瘤细胞诱导的免疫抑制现象，并从理论上证明了它对细胞动力学的影响。通过对各种反应情况的研究，我们强调了产品特性在治疗结果中的重要性。
{"title":"Mathematical model of CAR-T-cell therapy for a B-cell Lymphoma lymph node","authors":"Soukaina Sabir, Odelaisy León-Triana, Sergio Serrano, Roberto Barrio, Victor M. Pérez-García","doi":"arxiv-2409.01164","DOIUrl":"https://doi.org/arxiv-2409.01164","url":null,"abstract":"CAR-T cell therapies have demonstrated significant success in treating B-cell\u0000leukemia in children and young adults. However, their effectiveness in treating\u0000B-cell lymphomas has been limited. Unlike leukemia, lymphoma often manifests as\u0000solid masses of cancer cells in lymph nodes, glands, or organs, making these\u0000tumors harder to access thus hindering treatment response. In this paper we\u0000present a mathematical model that elucidates the dynamics of diffuse large\u0000B-cell lymphoma and CAR-T cells in a lymph node. The mathematical model aids in\u0000understanding the complex interplay between the cell populations involved and\u0000proposes ways to identify potential underlying dynamical causes of treatment\u0000failure. We also study the phenomenon of immunosuppression induced by tumor\u0000cells and theoretically demonstrate its impact on cell dynamics. Through the\u0000examination of various response scenarios, we underscore the significance of\u0000product characteristics in treatment outcomes.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Infectious diseases pose significant human and economic burdens. Accurately forecasting disease incidence can enable public health agencies to respond effectively to existing or emerging diseases. Despite progress in the field, developing accurate forecasting models remains a significant challenge. This thesis proposes two methodological frameworks using neural networks (NNs) with associated uncertainty estimates - a critical component limiting the application of NNs to epidemic forecasting thus far. We develop our frameworks by forecasting influenza-like illness (ILI) in the United States. Our first proposed method uses Web search activity data in conjunction with historical ILI rates as observations for training NN architectures. Our models incorporate Bayesian layers to produce uncertainty intervals, positioning themselves as legitimate alternatives to more conventional approaches. The best performing architecture: iterative recurrent neural network (IRNN), reduces mean absolute error by 10.3% and improves Skill by 17.1% on average in forecasting tasks across four flu seasons compared to the state-of-the-art. We build on this method by introducing IRNNs, an architecture which changes the sampling procedure in the IRNN to improve the uncertainty estimation. Our second framework uses neural ordinary differential equations to bridge the gap between mechanistic compartmental models and NNs; benefiting from the physical constraints that compartmental models provide. We evaluate eight neural ODE models utilising a mixture of ILI rates and Web search activity data to provide forecasts. These are compared with the IRNN and IRNN0 - the IRNN using only ILI rates. Models trained without Web search activity data outperform the IRNN0 by 16% in terms of Skill. Future work should focus on more effectively using neural ODEs with Web search data to compete with the best performing IRNN.
传染病给人类和经济造成了巨大负担。准确预测疾病的发病率可以使公共卫生机构有效应对现有的或新出现的疾病。尽管该领域取得了进展，但开发准确的预测模型仍是一项重大挑战。本论文提出了两个使用神经网络（NN）的方法框架以及相关的不确定性估计，这是迄今为止限制神经网络应用于流行病预测的一个关键因素。我们通过预测美国的流感样疾病（ILI）来开发我们的框架。我们提出的第一种方法将网络搜索活动数据与历史 ILI 发病率结合起来，作为训练 NN 架构的观测数据。我们的模型结合贝叶斯层来产生不确定性区间，将自己定位为传统方法的合法替代品。在四个流感季节的预测任务中，表现最好的架构：迭代递归神经网络（IRNN）与最先进的架构相比，平均绝对误差减少了 10.3%，技能提高了 17.1%。我们在这一方法的基础上引入了 IRNN，这一架构改变了 IRNN 中的采样过程，从而改进了不确定性估计。我们的第二个框架使用神经常微分方程来弥合机理分区模型和神经网络之间的差距，并从分区模型提供的物理约束中获益。我们利用 ILI 率和网络搜索活动数据的混合物来提供预测，并对八个神经 ODE 模型进行了评估。这些模型与 IRNN 和 IRNN0（IRNN 仅使用 ILI 率）进行了比较。不使用网络搜索活动数据训练的模型在技能方面比 IRNN0 高出 16%。未来的工作重点应该是更有效地利用网络搜索数据来使用神经 ODE，从而与表现最好的 IRNN 竞争。
{"title":"Forecasting infectious disease prevalence with associated uncertainty using neural networks","authors":"Michael Morris","doi":"arxiv-2409.01154","DOIUrl":"https://doi.org/arxiv-2409.01154","url":null,"abstract":"Infectious diseases pose significant human and economic burdens. Accurately\u0000forecasting disease incidence can enable public health agencies to respond\u0000effectively to existing or emerging diseases. Despite progress in the field,\u0000developing accurate forecasting models remains a significant challenge. This\u0000thesis proposes two methodological frameworks using neural networks (NNs) with\u0000associated uncertainty estimates - a critical component limiting the\u0000application of NNs to epidemic forecasting thus far. We develop our frameworks\u0000by forecasting influenza-like illness (ILI) in the United States. Our first\u0000proposed method uses Web search activity data in conjunction with historical\u0000ILI rates as observations for training NN architectures. Our models incorporate\u0000Bayesian layers to produce uncertainty intervals, positioning themselves as\u0000legitimate alternatives to more conventional approaches. The best performing\u0000architecture: iterative recurrent neural network (IRNN), reduces mean absolute\u0000error by 10.3% and improves Skill by 17.1% on average in forecasting tasks\u0000across four flu seasons compared to the state-of-the-art. We build on this\u0000method by introducing IRNNs, an architecture which changes the sampling\u0000procedure in the IRNN to improve the uncertainty estimation. Our second\u0000framework uses neural ordinary differential equations to bridge the gap between\u0000mechanistic compartmental models and NNs; benefiting from the physical\u0000constraints that compartmental models provide. We evaluate eight neural ODE\u0000models utilising a mixture of ILI rates and Web search activity data to provide\u0000forecasts. These are compared with the IRNN and IRNN0 - the IRNN using only ILI\u0000rates. Models trained without Web search activity data outperform the IRNN0 by\u000016% in terms of Skill. Future work should focus on more effectively using\u0000neural ODEs with Web search data to compete with the best performing IRNN.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An understanding of the disease spreading phenomenon based on a mathematical model is extremely needed for the implication of the correct policy measures to contain the disease propagation. Here, we report a new model namely the Ising-SIR model describing contagious disease spreading phenomena including both airborne and direct contact disease transformations. In the airborne case, a susceptible agent can catch the disease either from the environment or its infected neighbors whereas in the second case, the agent can be infected only through close contact with its infected neighbors. We have performed Monte Carlo simulations on a square lattice using periodic boundary conditions to investigate the dynamics of disease spread. The simulations demonstrate that the mechanism of disease spreading plays a significant role in the growth dynamics and leads to different growth exponent. In the direct contact disease spreading mechanism, the growth exponent is nearly equal to two for some model parameters which agrees with earlier empirical observations. In addition, the model predicts various types of spatiotemporal patterns that can be observed in nature.
{"title":"Modeling contagious disease spreading","authors":"Dipak Patra","doi":"arxiv-2409.01103","DOIUrl":"https://doi.org/arxiv-2409.01103","url":null,"abstract":"An understanding of the disease spreading phenomenon based on a mathematical\u0000model is extremely needed for the implication of the correct policy measures to\u0000contain the disease propagation. Here, we report a new model namely the\u0000Ising-SIR model describing contagious disease spreading phenomena including\u0000both airborne and direct contact disease transformations. In the airborne case,\u0000a susceptible agent can catch the disease either from the environment or its\u0000infected neighbors whereas in the second case, the agent can be infected only\u0000through close contact with its infected neighbors. We have performed Monte\u0000Carlo simulations on a square lattice using periodic boundary conditions to\u0000investigate the dynamics of disease spread. The simulations demonstrate that\u0000the mechanism of disease spreading plays a significant role in the growth\u0000dynamics and leads to different growth exponent. In the direct contact disease\u0000spreading mechanism, the growth exponent is nearly equal to two for some model\u0000parameters which agrees with earlier empirical observations. In addition, the\u0000model predicts various types of spatiotemporal patterns that can be observed in\u0000nature.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial swarm systems have been extensively studied and used in computer science, robotics, engineering and other technological fields, primarily as a platform for implementing robust distributed systems to achieve pre-defined objectives. However, such swarm systems, especially heterogeneous ones, can also be utilized as an ideal platform for creating *open-ended evolutionary dynamics* that do not converge toward pre-defined goals but keep exploring diverse possibilities and generating novel outputs indefinitely. In this article, we review Swarm Chemistry and its variants as concrete sample cases to illustrate beneficial characteristics of heterogeneous swarm systems, including the cardinality leap of design spaces, multiscale structures/behaviors and their diversity, and robust self-organization, self-repair and ecological interactions of emergent patterns, all of which serve as the driving forces for open-ended evolutionary processes. Applications to science, engineering, and art/entertainment as well as the directions of further research are also discussed.
{"title":"Swarm Systems as a Platform for Open-Ended Evolutionary Dynamics","authors":"Hiroki Sayama","doi":"arxiv-2409.01469","DOIUrl":"https://doi.org/arxiv-2409.01469","url":null,"abstract":"Artificial swarm systems have been extensively studied and used in computer\u0000science, robotics, engineering and other technological fields, primarily as a\u0000platform for implementing robust distributed systems to achieve pre-defined\u0000objectives. However, such swarm systems, especially heterogeneous ones, can\u0000also be utilized as an ideal platform for creating *open-ended evolutionary\u0000dynamics* that do not converge toward pre-defined goals but keep exploring\u0000diverse possibilities and generating novel outputs indefinitely. In this\u0000article, we review Swarm Chemistry and its variants as concrete sample cases to\u0000illustrate beneficial characteristics of heterogeneous swarm systems, including\u0000the cardinality leap of design spaces, multiscale structures/behaviors and\u0000their diversity, and robust self-organization, self-repair and ecological\u0000interactions of emergent patterns, all of which serve as the driving forces for\u0000open-ended evolutionary processes. Applications to science, engineering, and\u0000art/entertainment as well as the directions of further research are also\u0000discussed.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and many even lose their lives. Medical professionals and the government could take accurate measures to protect the people only when the disease dynamics are understood clearly. In this work, we propose a compartmental model to study the dynamics of malaria. We consider the transmission rate dependent on temperature and altitude. We performed the steady state analysis on the proposed model and checked the stability of the disease-free and endemic steady state. An artificial neural network (ANN) is applied to the formulated model to predict the trajectory of all five compartments following the mathematical analysis. Three different neural network architectures namely Artificial neural network (ANN), convolution neural network (CNN), and Recurrent neural network (RNN) are used to estimate these parameters from the trajectory of the data. To understand the severity of a disease, it is essential to calculate the risk associated with the disease. In this work, the risk is calculated using dynamic mode decomposition(DMD) from the trajectory of the infected people.
{"title":"Analysis of a mathematical model for malaria using data-driven approach","authors":"Adithya Rajnarayanan, Manoj Kumar","doi":"arxiv-2409.00795","DOIUrl":"https://doi.org/arxiv-2409.00795","url":null,"abstract":"Malaria is one of the deadliest diseases in the world, every year millions of\u0000people become victims of this disease and many even lose their lives. Medical\u0000professionals and the government could take accurate measures to protect the\u0000people only when the disease dynamics are understood clearly. In this work, we\u0000propose a compartmental model to study the dynamics of malaria. We consider the\u0000transmission rate dependent on temperature and altitude. We performed the\u0000steady state analysis on the proposed model and checked the stability of the\u0000disease-free and endemic steady state. An artificial neural network (ANN) is\u0000applied to the formulated model to predict the trajectory of all five\u0000compartments following the mathematical analysis. Three different neural\u0000network architectures namely Artificial neural network (ANN), convolution\u0000neural network (CNN), and Recurrent neural network (RNN) are used to estimate\u0000these parameters from the trajectory of the data. To understand the severity of\u0000a disease, it is essential to calculate the risk associated with the disease.\u0000In this work, the risk is calculated using dynamic mode decomposition(DMD) from\u0000the trajectory of the infected people.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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