A biological circuit is a neural or biochemical cascade, taking inputs and producing outputs. How have biological circuits learned to solve environmental challenges over the history of life? The answer certainly follows Dobzhansky's famous quote that ``nothing in biology makes sense except in the light of evolution.'' But that quote leaves out the mechanistic basis by which natural selection's trial-and-error learning happens, which is exactly what we have to understand. How does the learning process that designs biological circuits actually work? How much insight can we gain about the form and function of biological circuits by studying the processes that have made those circuits? Because life's circuits must often solve the same problems as those faced by machine learning, such as environmental tracking, homeostatic control, dimensional reduction, or classification, we can begin by considering how machine learning designs computational circuits to solve problems. We can then ask: How much insight do those computational circuits provide about the design of biological circuits? How much does biology differ from computers in the particular circuit designs that it uses to solve problems? This article steps through two classic machine learning models to set the foundation for analyzing broad questions about the design of biological circuits. One insight is the surprising power of randomly connected networks. Another is the central role of internal models of the environment embedded within biological circuits, illustrated by a model of dimensional reduction and trend prediction. Overall, many challenges in biology have machine learning analogs, suggesting hypotheses about how biology's circuits are designed.
{"title":"Circuit design in biology and machine learning. I. Random networks and dimensional reduction","authors":"Steven A. Frank","doi":"arxiv-2408.09604","DOIUrl":"https://doi.org/arxiv-2408.09604","url":null,"abstract":"A biological circuit is a neural or biochemical cascade, taking inputs and\u0000producing outputs. How have biological circuits learned to solve environmental\u0000challenges over the history of life? The answer certainly follows Dobzhansky's\u0000famous quote that ``nothing in biology makes sense except in the light of\u0000evolution.'' But that quote leaves out the mechanistic basis by which natural\u0000selection's trial-and-error learning happens, which is exactly what we have to\u0000understand. How does the learning process that designs biological circuits\u0000actually work? How much insight can we gain about the form and function of\u0000biological circuits by studying the processes that have made those circuits?\u0000Because life's circuits must often solve the same problems as those faced by\u0000machine learning, such as environmental tracking, homeostatic control,\u0000dimensional reduction, or classification, we can begin by considering how\u0000machine learning designs computational circuits to solve problems. We can then\u0000ask: How much insight do those computational circuits provide about the design\u0000of biological circuits? How much does biology differ from computers in the\u0000particular circuit designs that it uses to solve problems? This article steps\u0000through two classic machine learning models to set the foundation for analyzing\u0000broad questions about the design of biological circuits. One insight is the\u0000surprising power of randomly connected networks. Another is the central role of\u0000internal models of the environment embedded within biological circuits,\u0000illustrated by a model of dimensional reduction and trend prediction. Overall,\u0000many challenges in biology have machine learning analogs, suggesting hypotheses\u0000about how biology's circuits are designed.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204372","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}
The COVID-19 pandemic is spreading rapidly around the world, causing countries to impose lockdowns and efforts to develop vaccines on a global scale. However, human-to-animal and animal-to-human transmission cannot be ignored, as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread rapidly in farmed and wild animals. This could create a worrying cycle of SARS-CoV-2 spillover from humans to animals and spillback of new strains back into humans, rendering vaccines ineffective. This study provides a key indicator of animals that may be potential susceptible hosts for SARS-CoV-2 and coronavirus infections by analysing the phylogenetic distance between host angiotensin-converting enzyme 2 and the coronavirus spike protein. Crucially, our analysis identifies animals that are at elevated risk from a spillover and spillback incident. One group of animals has been identified as potentially susceptible to SARS-CoV-2 by harbouring a parasitic coronavirus spike protein similar to the SARS-CoV-2 spike protein. These animals may serve as amplification hosts in spillover events from zoonotic reservoirs. Tracing interspecies transmission in multi-host environments based solely on in vitro and in vivo examinations of animal susceptibility or serology is a time-consuming task. This approach allows rapid identification of high-risk animals to prioritize research and assessment of the risk of zoonotic disease transmission in the environment. It is a tool to rapidly identify zoonotic species that may cause outbreaks or participate in expansion cycles of coexistence with their hosts. This prevents the spread of coronavirus infections between species, preventing spillover and spillback incidents from occurring.
{"title":"Predicting potential SARS-CoV-2 spillover and spillback in animals","authors":"Zi Hian Tan, Kian Yan Yong, Jian-Jun Shu","doi":"arxiv-2408.09555","DOIUrl":"https://doi.org/arxiv-2408.09555","url":null,"abstract":"The COVID-19 pandemic is spreading rapidly around the world, causing\u0000countries to impose lockdowns and efforts to develop vaccines on a global\u0000scale. However, human-to-animal and animal-to-human transmission cannot be\u0000ignored, as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can\u0000spread rapidly in farmed and wild animals. This could create a worrying cycle\u0000of SARS-CoV-2 spillover from humans to animals and spillback of new strains\u0000back into humans, rendering vaccines ineffective. This study provides a key\u0000indicator of animals that may be potential susceptible hosts for SARS-CoV-2 and\u0000coronavirus infections by analysing the phylogenetic distance between host\u0000angiotensin-converting enzyme 2 and the coronavirus spike protein. Crucially,\u0000our analysis identifies animals that are at elevated risk from a spillover and\u0000spillback incident. One group of animals has been identified as potentially\u0000susceptible to SARS-CoV-2 by harbouring a parasitic coronavirus spike protein\u0000similar to the SARS-CoV-2 spike protein. These animals may serve as\u0000amplification hosts in spillover events from zoonotic reservoirs. Tracing\u0000interspecies transmission in multi-host environments based solely on in vitro\u0000and in vivo examinations of animal susceptibility or serology is a\u0000time-consuming task. This approach allows rapid identification of high-risk\u0000animals to prioritize research and assessment of the risk of zoonotic disease\u0000transmission in the environment. It is a tool to rapidly identify zoonotic\u0000species that may cause outbreaks or participate in expansion cycles of\u0000coexistence with their hosts. This prevents the spread of coronavirus\u0000infections between species, preventing spillover and spillback incidents from\u0000occurring.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204371","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}
Amit Kahana, Alasdair MacLeod, Hessam Mehr, Abhishek Sharma, Emma Carrick, Michael Jirasek, Sara Walker, Leroy Cronin
Here we demonstrate the first biochemistry-agnostic approach to map evolutionary relationships at the molecular scale, allowing the construction of phylogenetic models using mass spectrometry (MS) and Assembly Theory (AT) without elucidating molecular identities. AT allows us to estimate the complexity of molecules by deducing the amount of shared information stored within them when . By examining 74 samples from a diverse range of biotic and abiotic sources, we used tandem MS data to detect 24102 analytes (9262 unique) and 59518 molecular fragments (6755 unique). Using this MS dataset, together with AT, we were able to infer the joint assembly spaces (JAS) of samples from molecular analytes. We show how JAS allows agnostic annotation of samples without fingerprinting exact analyte identities, facilitating accurate determination of their biogenicity and taxonomical grouping. Furthermore, we developed an AT-based framework to construct a biochemistry-agnostic phylogenetic tree which is consistent with genome-based models and outperforms other similarity-based algorithms. Finally, we were able to use AT to track colony lineages of a single bacterial species based on phenotypic variation in their molecular composition with high accuracy, which would be challenging to track with genomic data. Our results demonstrate how AT can expand causal molecular inference to non-sequence information without requiring exact molecular identities, thereby opening the possibility to study previously inaccessible biological domains.
在这里，我们展示了第一种在分子尺度上绘制进化关系图的生化无关方法，这种方法允许在不阐明分子特征的情况下利用质谱法（MS）和组装理论（AT）构建系统发育模型。组装理论允许我们通过推断分子中存储的共享信息量来估计分子的复杂性。通过研究来自不同生物和非生物来源的 74 个样本，我们使用串联质谱数据检测到了 24102 个分析物（9262 个唯一）和 59518 个分子片段（6755 个唯一）。利用该 MS 数据集和 AT，我们能够从分子分析物推断出样本的联合组装空间（JAS）。我们展示了联合组装空间如何在不对分析物的确切身份进行指纹识别的情况下对样品进行不可知的注释，从而有助于准确确定样品的生物属性和分类分组。此外，我们还开发了一个基于 AT 的框架，用于构建生化不可知论的系统发生树，该树与基于基因组的模型一致，并优于其他基于相似性的算法。最后，我们能够利用 AT 根据细菌分子组成的表型变化，高精度地追踪单个细菌物种的菌落谱系，而利用基因组数据追踪菌落谱系则具有挑战性。我们的研究结果表明了 AT 如何在不要求精确分子特征的情况下将因果分子推断扩展到非序列信息，从而为研究以前难以触及的生物领域提供了可能性。
{"title":"Constructing the Molecular Tree of Life using Assembly Theory and Mass Spectrometry","authors":"Amit Kahana, Alasdair MacLeod, Hessam Mehr, Abhishek Sharma, Emma Carrick, Michael Jirasek, Sara Walker, Leroy Cronin","doi":"arxiv-2408.09305","DOIUrl":"https://doi.org/arxiv-2408.09305","url":null,"abstract":"Here we demonstrate the first biochemistry-agnostic approach to map\u0000evolutionary relationships at the molecular scale, allowing the construction of\u0000phylogenetic models using mass spectrometry (MS) and Assembly Theory (AT)\u0000without elucidating molecular identities. AT allows us to estimate the\u0000complexity of molecules by deducing the amount of shared information stored\u0000within them when . By examining 74 samples from a diverse range of biotic and\u0000abiotic sources, we used tandem MS data to detect 24102 analytes (9262 unique)\u0000and 59518 molecular fragments (6755 unique). Using this MS dataset, together\u0000with AT, we were able to infer the joint assembly spaces (JAS) of samples from\u0000molecular analytes. We show how JAS allows agnostic annotation of samples\u0000without fingerprinting exact analyte identities, facilitating accurate\u0000determination of their biogenicity and taxonomical grouping. Furthermore, we\u0000developed an AT-based framework to construct a biochemistry-agnostic\u0000phylogenetic tree which is consistent with genome-based models and outperforms\u0000other similarity-based algorithms. Finally, we were able to use AT to track\u0000colony lineages of a single bacterial species based on phenotypic variation in\u0000their molecular composition with high accuracy, which would be challenging to\u0000track with genomic data. Our results demonstrate how AT can expand causal\u0000molecular inference to non-sequence information without requiring exact\u0000molecular identities, thereby opening the possibility to study previously\u0000inaccessible biological domains.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"66 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204370","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}
Synergy between evolutionary dynamics of cooperation and fluctuating state of shared resource being consumed by the cooperators is essential for averting the tragedy of the commons. Not only in humans, but also in the cognitively-limited organisms, this interplay between the resource and the cooperation is ubiquitously witnessed. The strategically interacting players engaged in such game-environment feedback scenarios naturally pick strategies based on their perception of the environmental state. Such perception invariably happens through some sensory information channels that the players are endowed with. The unfortunate reality is that any sensory channel must be noisy due to various factors; consequently, the perception of the environmental state becomes faulty rendering the players incapable of adopting the strategy that they otherwise would. Intriguingly, situation is not as bad as it sounds. Here we introduce the hitherto neglected information channel between players and the environment into the paradigm of stochastic evolutionary games with a view to bringing forward the counterintuitive possibility of emergence and sustenance of cooperation on account of the noise in the channel. Our primary study is in the simplest non-trivial setting of two-state stochastically fluctuating resource harnessed by a large unstructured population of cooperators and defectors adopting either memory-1 strategies or reactive strategies while engaged in repeated two-player interactions. The effect of noisy information channel in enhancing the cooperation in reactive-strategied population is unprecedented. We find that the propensity of cooperation in the population is inversely related to the mutual information (normalized by the channel capacity) of the corresponding information channel.
{"title":"Noisy information channel mediated prevention of the tragedy of the commons","authors":"Samrat Sohel Mondal, Sagar Chakraborty","doi":"arxiv-2408.08744","DOIUrl":"https://doi.org/arxiv-2408.08744","url":null,"abstract":"Synergy between evolutionary dynamics of cooperation and fluctuating state of\u0000shared resource being consumed by the cooperators is essential for averting the\u0000tragedy of the commons. Not only in humans, but also in the cognitively-limited\u0000organisms, this interplay between the resource and the cooperation is\u0000ubiquitously witnessed. The strategically interacting players engaged in such\u0000game-environment feedback scenarios naturally pick strategies based on their\u0000perception of the environmental state. Such perception invariably happens\u0000through some sensory information channels that the players are endowed with.\u0000The unfortunate reality is that any sensory channel must be noisy due to\u0000various factors; consequently, the perception of the environmental state\u0000becomes faulty rendering the players incapable of adopting the strategy that\u0000they otherwise would. Intriguingly, situation is not as bad as it sounds. Here\u0000we introduce the hitherto neglected information channel between players and the\u0000environment into the paradigm of stochastic evolutionary games with a view to\u0000bringing forward the counterintuitive possibility of emergence and sustenance\u0000of cooperation on account of the noise in the channel. Our primary study is in\u0000the simplest non-trivial setting of two-state stochastically fluctuating\u0000resource harnessed by a large unstructured population of cooperators and\u0000defectors adopting either memory-1 strategies or reactive strategies while\u0000engaged in repeated two-player interactions. The effect of noisy information\u0000channel in enhancing the cooperation in reactive-strategied population is\u0000unprecedented. We find that the propensity of cooperation in the population is\u0000inversely related to the mutual information (normalized by the channel\u0000capacity) of the corresponding information channel.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204216","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}
Based on the SIRD-model a new model including time-delay is proposed for a description of the outbreak of the novel coronavirus Sars-CoV-2 pandemic. All data were analysed by representing all quantities as a function of the susceptible population, as opposed to the usual dependence on time. The total number of deaths could be predicted for the first, second and third wave of the pandemic in Germany with an accuracy of about 10%, shortly after the maximum of infectious people was reached. By using the presentation in phase space, it could be shown that a classical SEIRD- and SIRD-model with constant parameters will not be able to describe the first wave of the pandemic accurately.
{"title":"Analysing pandemics in phase-space","authors":"Olivier Merlo","doi":"arxiv-2408.08036","DOIUrl":"https://doi.org/arxiv-2408.08036","url":null,"abstract":"Based on the SIRD-model a new model including time-delay is proposed for a\u0000description of the outbreak of the novel coronavirus Sars-CoV-2 pandemic. All\u0000data were analysed by representing all quantities as a function of the\u0000susceptible population, as opposed to the usual dependence on time. The total\u0000number of deaths could be predicted for the first, second and third wave of the\u0000pandemic in Germany with an accuracy of about 10%, shortly after the maximum\u0000of infectious people was reached. By using the presentation in phase space, it\u0000could be shown that a classical SEIRD- and SIRD-model with constant parameters\u0000will not be able to describe the first wave of the pandemic accurately.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204421","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}
Laurent Hébert-Dufresne, Matthew M. Kling, Samuel F. Rosenblatt, Stephanie N. Miller, P. Alexander Burnham, Nicholas W. Landry, Nicholas J. Gotelli, Brian J. McGill
Stochastic diffusion is the noisy and uncertain process through which dynamics like epidemics, or agents like animal species, disperse over a larger area. Understanding these processes is becoming increasingly important as we attempt to better prepare for potential pandemics and as species ranges shift in response to climate change. Unfortunately, modeling of stochastic diffusion is mostly done through inaccurate deterministic tools that fail to capture the random nature of dispersal or else through expensive computational simulations. In particular, standard tools fail to fully capture the heterogeneity of the area over which this diffusion occurs. Rural areas with low population density require different epidemic models than urban areas; likewise, the edges of a species range require us to explicitly track low integer numbers of individuals rather than vague averages. In this work, we introduce a series of new tools called "mean-FLAME" models that track stochastic dispersion using approximate master equations that explicitly follow the probability distribution of an area of interest over all of its possible states, up to states that are active enough to be approximated using a mean-field model. In one limit, this approach is locally exact if we explicitly track enough states, and in the other limit collapses back to traditional deterministic models if we track no state explicitly. Applying this approach, we show how deterministic tools fail to capture the uncertainty around the speed of nonlinear dynamical processes. This is especially true for marginal areas that are close to unsuitable for diffusion, like the edge of a species range or epidemics in small populations. Capturing the uncertainty in such areas is key to producing accurate forecasts and guiding potential interventions.
{"title":"Stochastic diffusion using mean-field limits to approximate master equations","authors":"Laurent Hébert-Dufresne, Matthew M. Kling, Samuel F. Rosenblatt, Stephanie N. Miller, P. Alexander Burnham, Nicholas W. Landry, Nicholas J. Gotelli, Brian J. McGill","doi":"arxiv-2408.07755","DOIUrl":"https://doi.org/arxiv-2408.07755","url":null,"abstract":"Stochastic diffusion is the noisy and uncertain process through which\u0000dynamics like epidemics, or agents like animal species, disperse over a larger\u0000area. Understanding these processes is becoming increasingly important as we\u0000attempt to better prepare for potential pandemics and as species ranges shift\u0000in response to climate change. Unfortunately, modeling of stochastic diffusion\u0000is mostly done through inaccurate deterministic tools that fail to capture the\u0000random nature of dispersal or else through expensive computational simulations.\u0000In particular, standard tools fail to fully capture the heterogeneity of the\u0000area over which this diffusion occurs. Rural areas with low population density\u0000require different epidemic models than urban areas; likewise, the edges of a\u0000species range require us to explicitly track low integer numbers of individuals\u0000rather than vague averages. In this work, we introduce a series of new tools\u0000called \"mean-FLAME\" models that track stochastic dispersion using approximate\u0000master equations that explicitly follow the probability distribution of an area\u0000of interest over all of its possible states, up to states that are active\u0000enough to be approximated using a mean-field model. In one limit, this approach\u0000is locally exact if we explicitly track enough states, and in the other limit\u0000collapses back to traditional deterministic models if we track no state\u0000explicitly. Applying this approach, we show how deterministic tools fail to\u0000capture the uncertainty around the speed of nonlinear dynamical processes. This\u0000is especially true for marginal areas that are close to unsuitable for\u0000diffusion, like the edge of a species range or epidemics in small populations.\u0000Capturing the uncertainty in such areas is key to producing accurate forecasts\u0000and guiding potential interventions.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204420","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}
The striking recent putative detection of "dark oxygen" (dark O$_2$) sources on the abyssal ocean floor in the Pacific at $sim 4$ km depth raises the intriguing scenario that complex (i.e., animal-like) life could exist in underwater environments sans oxygenic photosynthesis. In this work, we thus explore the possible (astro)biological implications of this discovery. From the available data, we roughly estimate the concentration of dissolved O$_2$ and the corresponding O$_2$ partial pressure, as well as the flux of O$_2$ production, associated with dark oxygen sources. Based on these values, we infer that organisms limited by internal diffusion may reach maximal sizes of $sim 0.1-1$ mm in habitats with dark O$_2$, while those with circulatory systems might achieve sizes of $sim 0.1-10$ cm. Optimistically, the estimated dark oxygen flux can potentially support biomass densities up to $sim 3-30$ g m$^{-2}$, perhaps surpassing typical reported densities at similar depths in global deep-sea surveys. Finally, we outline how oceanic settings with dark O$_2$ may facilitate the origin(s) of life via the emergence of electrotrophy. Our findings indicate that complex life fueled by dark oxygen is plausibly capable of inhabiting submarine environments devoid of photosynthesis on Earth, conceivably extending likewise to extraterrestrial locations such as icy worlds with subsurface oceans (e.g., Enceladus and Europa), which are likely common throughout the Universe.
{"title":"Dwellers in the Deep: Biological Consequences of Dark Oxygen","authors":"Manasvi Lingam, Amedeo Balbi, Madhur Tiwari","doi":"arxiv-2408.06841","DOIUrl":"https://doi.org/arxiv-2408.06841","url":null,"abstract":"The striking recent putative detection of \"dark oxygen\" (dark O$_2$) sources\u0000on the abyssal ocean floor in the Pacific at $sim 4$ km depth raises the\u0000intriguing scenario that complex (i.e., animal-like) life could exist in\u0000underwater environments sans oxygenic photosynthesis. In this work, we thus\u0000explore the possible (astro)biological implications of this discovery. From the\u0000available data, we roughly estimate the concentration of dissolved O$_2$ and\u0000the corresponding O$_2$ partial pressure, as well as the flux of O$_2$\u0000production, associated with dark oxygen sources. Based on these values, we\u0000infer that organisms limited by internal diffusion may reach maximal sizes of\u0000$sim 0.1-1$ mm in habitats with dark O$_2$, while those with circulatory\u0000systems might achieve sizes of $sim 0.1-10$ cm. Optimistically, the estimated\u0000dark oxygen flux can potentially support biomass densities up to $sim 3-30$ g\u0000m$^{-2}$, perhaps surpassing typical reported densities at similar depths in\u0000global deep-sea surveys. Finally, we outline how oceanic settings with dark\u0000O$_2$ may facilitate the origin(s) of life via the emergence of electrotrophy.\u0000Our findings indicate that complex life fueled by dark oxygen is plausibly\u0000capable of inhabiting submarine environments devoid of photosynthesis on Earth,\u0000conceivably extending likewise to extraterrestrial locations such as icy worlds\u0000with subsurface oceans (e.g., Enceladus and Europa), which are likely common\u0000throughout the Universe.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204424","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}
Phylogenetic trees play a key role in the reconstruction of evolutionary relationships. Typically, they are derived from aligned sequence data (like DNA, RNA, or proteins) by using optimization criteria like, e.g., maximum parsimony (MP). It is believed that the latter is able to reconstruct the enquote{true} tree, i.e., the tree that generated the data, whenever the number of substitutions required to explain the data with that tree is relatively small compared to the size of the tree (measured in the number $n$ of leaves of the tree, which represent the species under investigation). However, reconstructing the correct tree from any alignment first and foremost requires the given alignment to perform differently on the enquote{correct} tree than on others. A special type of alignments, namely so-called $A_k$-alignments, has gained considerable interest in recent literature. These alignments consist of all binary characters (enquote{sites}) which require precisely $k$ substitutions on a given tree. It has been found that whenever $k$ is small enough (in comparison to $n$), $A_k$-alignments uniquely characterize the trees that generated them. However, recent literature has left a significant gap between $kleq 2k+2$ -- namely the cases in which no such characterization is possible -- and $kgeq 4k$ -- namely the cases in which this characterization works. It is the main aim of the present manuscript to close this gap, i.e., to present a full characterization of all pairs of trees that share the same $A_k$-alignment. In particular, we show that indeed every binary phylogenetic tree with $n$ leaves is uniquely defined by its $A_k$-alignments if $ngeq 2k+3$. By closing said gap, we also ensure that our result is optimal.
{"title":"A complete characterization of pairs of binary phylogenetic trees with identical $A_k$-alignments","authors":"Mirko Wilde, Mareike Fischer","doi":"arxiv-2408.07011","DOIUrl":"https://doi.org/arxiv-2408.07011","url":null,"abstract":"Phylogenetic trees play a key role in the reconstruction of evolutionary\u0000relationships. Typically, they are derived from aligned sequence data (like\u0000DNA, RNA, or proteins) by using optimization criteria like, e.g., maximum\u0000parsimony (MP). It is believed that the latter is able to reconstruct the\u0000enquote{true} tree, i.e., the tree that generated the data, whenever the\u0000number of substitutions required to explain the data with that tree is\u0000relatively small compared to the size of the tree (measured in the number $n$\u0000of leaves of the tree, which represent the species under investigation).\u0000However, reconstructing the correct tree from any alignment first and foremost\u0000requires the given alignment to perform differently on the enquote{correct}\u0000tree than on others. A special type of alignments, namely so-called $A_k$-alignments, has gained\u0000considerable interest in recent literature. These alignments consist of all\u0000binary characters (enquote{sites}) which require precisely $k$ substitutions\u0000on a given tree. It has been found that whenever $k$ is small enough (in\u0000comparison to $n$), $A_k$-alignments uniquely characterize the trees that\u0000generated them. However, recent literature has left a significant gap between\u0000$kleq 2k+2$ -- namely the cases in which no such characterization is possible\u0000-- and $kgeq 4k$ -- namely the cases in which this characterization works. It\u0000is the main aim of the present manuscript to close this gap, i.e., to present a\u0000full characterization of all pairs of trees that share the same\u0000$A_k$-alignment. In particular, we show that indeed every binary phylogenetic\u0000tree with $n$ leaves is uniquely defined by its $A_k$-alignments if $ngeq\u00002k+3$. By closing said gap, we also ensure that our result is optimal.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204419","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}
J. P. Arcede, B. Doungsavanhb, L. A. Estaño, J. C. Jumawan, J. H. Jumawan, Y. Mammeri
Schistosomiasis remains a persistent challenge in tropical freshwater ecosystems, necessitating the development of refined control strategies. Bovines, especially water buffaloes, are commonly used in traditional farming practices across rural areas of the Philippines. Bovines, however, are the biggest reservoir hosts for schistosome eggs, which contribute to the active transmission cycle of schistosomiasis in rice fields. We propose a mathematical model to analyze schistosomiasis dynamics in rice fields near the Lake Mainit in the Philippines, an area known for endemic transmission of schistosomiasis, focusing on human, bovine, and snail populations. Rodents, although considered, were not directly included in the control strategies. Grounded in field data, the model, built on a system of nonlinear ordinary differential equations, enabled us to derive the basic reproduction number and assess various intervention strategies. The simulation of optimal control scenarios, incorporating chemotherapy, mollusciciding, and mechanical methods, provides a comparative analysis of their efficacies. The results indicated that the integrated control strategies markedly reduced the prevalence of schistosomiasis. This study provides insights into optimal control strategies that are vital for policymakers to design effective, sustainable schistosomiasis control programs, underscored by the necessity to include bovine populations in treatment regimens.
{"title":"Influence of bovines and rodents in the spread of schistosomiasis across the ricefield-lakescape of Lake Mainit, Philippines: An Optimal Control Study","authors":"J. P. Arcede, B. Doungsavanhb, L. A. Estaño, J. C. Jumawan, J. H. Jumawan, Y. Mammeri","doi":"arxiv-2408.05559","DOIUrl":"https://doi.org/arxiv-2408.05559","url":null,"abstract":"Schistosomiasis remains a persistent challenge in tropical freshwater\u0000ecosystems, necessitating the development of refined control strategies.\u0000Bovines, especially water buffaloes, are commonly used in traditional farming\u0000practices across rural areas of the Philippines. Bovines, however, are the\u0000biggest reservoir hosts for schistosome eggs, which contribute to the active\u0000transmission cycle of schistosomiasis in rice fields. We propose a mathematical\u0000model to analyze schistosomiasis dynamics in rice fields near the Lake Mainit\u0000in the Philippines, an area known for endemic transmission of schistosomiasis,\u0000focusing on human, bovine, and snail populations. Rodents, although considered,\u0000were not directly included in the control strategies. Grounded in field data,\u0000the model, built on a system of nonlinear ordinary differential equations,\u0000enabled us to derive the basic reproduction number and assess various\u0000intervention strategies. The simulation of optimal control scenarios,\u0000incorporating chemotherapy, mollusciciding, and mechanical methods, provides a\u0000comparative analysis of their efficacies. The results indicated that the\u0000integrated control strategies markedly reduced the prevalence of\u0000schistosomiasis. This study provides insights into optimal control strategies\u0000that are vital for policymakers to design effective, sustainable\u0000schistosomiasis control programs, underscored by the necessity to include\u0000bovine populations in treatment regimens.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204423","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}
Mariah C. Boudreau, Jamie A. Cohen, Laurent Hébert-Dufresne
Master equations provide researchers with the ability to track the distribution over possible states of a system. From these equations, we can summarize the temporal dynamics through a method of moments. These distributions and their moments capture the stochastic nature of a system, which is essential to study infectious diseases. In this paper, we define the states of the system to be the number of infected cells of a given type in the epithelium, the hollow organ tissue in the human body. Epithelium found in the cervix provides a location for viral infections to live and persist, such as human papillomavirus (HPV). HPV is a highly transmissible disease which most commonly affects biological females and has the potential to progress into cervical cancer. By defining a master equation model which tracks the infected cell layer dynamics, information on disease extinction, progression, and viral output can be derived from the method of moments. From this methodology and the outcomes we glean from it, we aim to inform differing states of HPV infected cells, and assess the effects of structural information for each outcome.
{"title":"Within-host infection dynamics with master equations and the method of moments: A case study of human papillomavirus in the epithelium","authors":"Mariah C. Boudreau, Jamie A. Cohen, Laurent Hébert-Dufresne","doi":"arxiv-2408.05298","DOIUrl":"https://doi.org/arxiv-2408.05298","url":null,"abstract":"Master equations provide researchers with the ability to track the\u0000distribution over possible states of a system. From these equations, we can\u0000summarize the temporal dynamics through a method of moments. These\u0000distributions and their moments capture the stochastic nature of a system,\u0000which is essential to study infectious diseases. In this paper, we define the\u0000states of the system to be the number of infected cells of a given type in the\u0000epithelium, the hollow organ tissue in the human body. Epithelium found in the\u0000cervix provides a location for viral infections to live and persist, such as\u0000human papillomavirus (HPV). HPV is a highly transmissible disease which most\u0000commonly affects biological females and has the potential to progress into\u0000cervical cancer. By defining a master equation model which tracks the infected\u0000cell layer dynamics, information on disease extinction, progression, and viral\u0000output can be derived from the method of moments. From this methodology and the\u0000outcomes we glean from it, we aim to inform differing states of HPV infected\u0000cells, and assess the effects of structural information for each outcome.","PeriodicalId":501044,"journal":{"name":"arXiv - QuanBio - Populations and Evolution","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204422","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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