Fitness response relation of a multitype age-structured population dynamics

Sughiyama, Yuki; Nakashima, So; Kobayashi, Tetsuya

doi:10.1103/physreve.99.012413

Cited by 7 publications

(12 citation statements)

References 28 publications

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“…The copyright holder for this preprint (which was this version posted November 29, 2019. ; https://doi.org/10.1101/488981 doi: bioRxiv preprint a continuous semi-Markov process if one of the daughter cells is ignored (Fig. 3) (37). See also Supplementary Information (Section S2 and S3).…”

Section: Statistical Modeling Of State-switching and Divisionmentioning

confidence: 99%

“…Thereby, πemp and Temp converge to π F and T F , respectively, for a large sample size. We specifically call this type of sampling the chronological sampling and the dynamics generated by π F and T F the forward process (18,20,37). We can also effectively obtain a chronologically sampled lineage from the tree by using the weighting technique proposed in (35).…”

Section: Correction Of the Survivorship Bias In Estimation Of State-switching And Division Dynamicsmentioning

confidence: 99%

“…where λ is the population growth rate of the cells, and Z(x) is a normalization factor (37). See Supplementary Information for the proof (Section S4).…”

Section: Correction Of the Survivorship Bias In Estimation Of State-switching And Division Dynamicsmentioning

confidence: 99%

“…See (37) for the derivation. This is also an extension of our previous work (38) in which the division time was not considered.…”

Section: Correction Of the Survivorship Bias In Estimation Of State-switching And Division Dynamicsmentioning

confidence: 99%

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Lineage EM algorithm for inferring latent states from cellular lineage trees

Nakashima¹,

Sughiyama

Kobayashi

2020

Bioinformatics

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Summary Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivorship bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation–maximization (EM) algorithm, which we call lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data. Availability and implementation An implementation of LEM is available at https://github.com/so-nakashima/Lineage-EM-algorithm. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Statistical Modeling Of State-switching and Divisionmentioning

confidence: 99%

Section: Correction Of the Survivorship Bias In Estimation Of State-switching And Division Dynamicsmentioning

confidence: 99%

“…where λ is the population growth rate of the cells, and Z(x) is a normalization factor (37). See Supplementary Information for the proof (Section S4).…”

Section: Correction Of the Survivorship Bias In Estimation Of State-switching And Division Dynamicsmentioning

confidence: 99%

“…See (37) for the derivation. This is also an extension of our previous work (38) in which the division time was not considered.…”

Section: Correction Of the Survivorship Bias In Estimation Of State-switching And Division Dynamicsmentioning

confidence: 99%

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Lineage EM algorithm for inferring latent states from cellular lineage trees

Nakashima¹,

Sughiyama

Kobayashi

2020

Bioinformatics

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“…1 (c)) (44), whereas they also constitute a continuous semi-Markov process if one of the daughter cells is ignored (Fig. 1 (a)) (45). See Supplimentary Informaion (Section 1 and 2).…”

Section: Statistical Modeling Of State-switching and Divisionmentioning

confidence: 99%

Lineage EM Algorithm for Inferring Latent States from Cellular Lineage Trees

Nakashima

Sughiyama

Kobayashi

2018

Preprint

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Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its inheritance. Although recent advancements in microfluidic technology offer us useful lineage data, they are insufficient to directly identify the phenotypes of the cells. An alternative approach is to infer the phenotype from the lineage data by latent-variable estimation. To this end, however, we must resolve the bias problem in the inference from lineage called survivorship bias. In this work, we clarify how the survivor bias distorts statistical estimations. We then propose a latent-variable estimation algorithm without the survivorship bias from lineage trees based on an expectation-maximization (EM) algorithm, which we call Lineage EM algorithm (LEM). LEM provides a statistical method to identify the traits of the cells applicable to various kinds of lineage data. 1 2 3 4 5 6 7 8 2generations (1-3). Such phenotypic variety leads to behavioral individuality of each cell, which, in turn, generates complicated 3 population phenomena. One example is bacterial persistence in which a fraction of cells in a population survives when the 4 population experiences an antibiotic exposure even though the other fraction dies out (4-8). Persistence is also recently 5 recognized relevant to the drug-resistance of cancers (9, 10). While the survivors, which are also called persisters, were originally 6 conjectured as dormant and thereby drug-insensitive cells in a population, recent bioimaging analysis revealed that persistence 7 is a more intricate phenomenon, which involves resistant but still growing cells (5, 6). Because drug-resistance is tightly related 8 to the manner how the drug is incorporated into a cell and interferes with the self-replicating process, quantitative analysis 9 of persistence requires a characterization of growth states of individual cells and their competition in a population. More 10 generally, the heterogeneities in the growth speed and the death rate as well as their inheritance from a mother to daughter 11 cells constitute Darwinian natural selection among the cells. Such natural selection at the cellular level is also highly relevant 12 to drug-resistances of pathogens and cancers, immunological memories, cell competitions in tissues, and induction of iPS cells 13 (9-13). Moreover, the population dynamics under selection is integratively determined by the growth states of cells, their 14 statistical property, and their inheritance dynamics over generations. Therefore, identification of growth states of cells from 15 data is crucial for predicting and controlling those selection-driven phenomena (14, 15). 16To determine the growth states and their dynamics, we can take advantage of recent bioimaging and microfluidic technology, 17 which provide abundant but incomplete data of the popul...

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Generalized uncertainty relations for semi-Markov processes

Hasegawa

2020

J. Phys.: Conf. Ser.

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The thermodynamic and kinetic uncertainty relations provide finite-time bounds on the observable fluctuation in Markov processes. Herein, we generalize these bounds for semi-Markov processes. Specifically, we prove that, unlike in the Markovian case, the fluctuation of time-antisymmetric observables is bounded not only by entropy production but also by a memory term. For generic observables, we analogously show that the fluctuation is bounded by both dynamical activity and a memory term. Our results indicate that memory plays an important role in the bounds. Interestingly, with a proper form of the waiting-time distribution, the memory can decrease the observable fluctuation. When the waiting-time distribution is Poissonian (i.e., the process becomes Markov), the memory terms vanish, and the derived bounds reduce to the conventional bounds.

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Fitness response relation of a multitype age-structured population dynamics

Cited by 7 publications

References 28 publications

Lineage EM algorithm for inferring latent states from cellular lineage trees

Lineage EM algorithm for inferring latent states from cellular lineage trees

Lineage EM Algorithm for Inferring Latent States from Cellular Lineage Trees

Generalized uncertainty relations for semi-Markov processes

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