{"title":"通过离散采样预测浓度变化","authors":"Age J. Tjalma, Pieter Rein ten Wolde","doi":"arxiv-2402.05825","DOIUrl":null,"url":null,"abstract":"To successfully navigate chemical gradients, microorganisms need to predict\nhow the ligand concentration changes in space. Due to their limited size, they\ndo not take a spatial derivative over their body length but rather a temporal\nderivative, comparing the current signal with that in the recent past, over the\nso-called adaptation time. This strategy is pervasive in biology, but it\nremains unclear what determines the accuracy of such measurements. Using a\ngeneralized version of the previously established sampling framework, we\ninvestigate how resource limitations and the statistics of the input signal set\nthe optimal design of a well-characterized network that measures temporal\nconcentration changes: the Escherichia coli chemotaxis network. Our results\nshow how an optimal adaptation time arises from the trade-off between the\nsampling error, caused by the stochastic nature of the network, and the\ndynamical error, caused by uninformative fluctuations in the input. A larger\nresource availability reduces the sampling error, which allows for a smaller\nadaptation time, thereby simultaneously decreasing the dynamical error.\nSimilarly, we find that the optimal adaptation time scales inversely with the\ngradient steepness, because steeper gradients lift the signal above the noise\nand reduce the sampling error. These findings shed light on the principles that\ngovern the optimal design of the E. coli chemotaxis network specifically, and\nany system measuring temporal changes more broadly.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"21 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting concentration changes via discrete sampling\",\"authors\":\"Age J. Tjalma, Pieter Rein ten Wolde\",\"doi\":\"arxiv-2402.05825\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To successfully navigate chemical gradients, microorganisms need to predict\\nhow the ligand concentration changes in space. Due to their limited size, they\\ndo not take a spatial derivative over their body length but rather a temporal\\nderivative, comparing the current signal with that in the recent past, over the\\nso-called adaptation time. This strategy is pervasive in biology, but it\\nremains unclear what determines the accuracy of such measurements. Using a\\ngeneralized version of the previously established sampling framework, we\\ninvestigate how resource limitations and the statistics of the input signal set\\nthe optimal design of a well-characterized network that measures temporal\\nconcentration changes: the Escherichia coli chemotaxis network. Our results\\nshow how an optimal adaptation time arises from the trade-off between the\\nsampling error, caused by the stochastic nature of the network, and the\\ndynamical error, caused by uninformative fluctuations in the input. A larger\\nresource availability reduces the sampling error, which allows for a smaller\\nadaptation time, thereby simultaneously decreasing the dynamical error.\\nSimilarly, we find that the optimal adaptation time scales inversely with the\\ngradient steepness, because steeper gradients lift the signal above the noise\\nand reduce the sampling error. These findings shed light on the principles that\\ngovern the optimal design of the E. coli chemotaxis network specifically, and\\nany system measuring temporal changes more broadly.\",\"PeriodicalId\":501325,\"journal\":{\"name\":\"arXiv - QuanBio - Molecular Networks\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Molecular Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2402.05825\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Molecular Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2402.05825","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting concentration changes via discrete sampling
To successfully navigate chemical gradients, microorganisms need to predict
how the ligand concentration changes in space. Due to their limited size, they
do not take a spatial derivative over their body length but rather a temporal
derivative, comparing the current signal with that in the recent past, over the
so-called adaptation time. This strategy is pervasive in biology, but it
remains unclear what determines the accuracy of such measurements. Using a
generalized version of the previously established sampling framework, we
investigate how resource limitations and the statistics of the input signal set
the optimal design of a well-characterized network that measures temporal
concentration changes: the Escherichia coli chemotaxis network. Our results
show how an optimal adaptation time arises from the trade-off between the
sampling error, caused by the stochastic nature of the network, and the
dynamical error, caused by uninformative fluctuations in the input. A larger
resource availability reduces the sampling error, which allows for a smaller
adaptation time, thereby simultaneously decreasing the dynamical error.
Similarly, we find that the optimal adaptation time scales inversely with the
gradient steepness, because steeper gradients lift the signal above the noise
and reduce the sampling error. These findings shed light on the principles that
govern the optimal design of the E. coli chemotaxis network specifically, and
any system measuring temporal changes more broadly.