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A Poisson process follows these postulates: $\lim\limits_{h\to0+}\frac{P(N_h=1)}h=\lambda$ i.e. the probability of occurrence one event in a very small interval of time is equal to the macroscopic rate or intensity ($\lambda\,$). $P(N_h\geqslant2)=o(h)$ i.e. the probability of occurrence of more than one events in an infinitesimal interval is essentially ...


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Is the standard Lotka-Volterra (LV) model an exact fit for insulin-glucose (IG) dynamics? No. Can a similar model built on the same principles capture most of the essential features of the IG dynamics? Absolutely. How to capture most of the insulin-glucose dynamics using a slightly modified Lotka-Volterra model We can figure out how to change the LV ...


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No, the Lotka-Volterra model is a description of predator-prey dynamics. Although in some respects over-simplifying it is well suited for educating population dynamics and basic research. The physiology of carbohydrate homeostasis is different. Although, similar to population count in the Lotka-Volterra model, the elimination of both glucose and insulin ...


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There is an important difference in the dynamics of the 2 situations. The Lotka-Volterra model undergoes repeated oscillations in predator and prey levels. Changes in one population affect the changes in the other population, and vice versa. In the glucose-control model, what you have plotted are the changes in glucose and insulin levels following a meal ...


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Interpreting your question as "would the Lotka-Volterra predator-prey model be a good model for the glucose-insulin system?" my answer is "no". The predator-prey equations capture assumptions about how prey and predator interact with each other, and how they would fare on their own. These assumptions are not equivalent to any reasonable assumptions about the ...


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we included a script to calculate this in supplemental material http://onlinelibrary.wiley.com/doi/10.1111/mec.13034/full ....single segregating site per locus or up to a maximum of four SNPs, as is expected for short-read genomic data (see attached R script for estimation).


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Reiterating the above comments. Have a look at Tajima's D. It provides an estimate for the number of segregation sites for a population under a neutral mutation model. The general form of the estimation for a diploid population is $E[S]=4N\mu\sum_{i=0}^{n-1} \frac{1}{i}$. Here the mutation rate of is per-genome not per-site, so $\mu=L * 10^{-9}$ where $L$ ...



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