By Richard Durrett
This quantity develops effects on non-stop time branching strategies and applies them to check expense of tumor progress, extending vintage paintings at the Luria-Delbruck distribution. thus, the writer calculate the chance that mutations that confer resistance to remedy are current at detection and quantify the level of tumor heterogeneity. As purposes, the writer assessment ovarian melanoma screening innovations and provides rigorous proofs for result of Heano and Michor referring to tumor metastasis. those notes might be obtainable to scholars who're accustomed to Poisson procedures and non-stop time Markov chains.
Richard Durrett is a arithmetic professor at Duke collage, united states. he's the writer of eight books, over two hundred magazine articles, and has supervised greater than forty Ph.D scholars. such a lot of his present learn matters the functions of likelihood to biology: ecology, genetics and so much lately cancer.
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Branching Process Models of Cancer
This quantity develops effects on non-stop time branching strategies and applies them to review fee of tumor development, extending vintage paintings at the Luria-Delbruck distribution. to that end, the writer calculate the chance that mutations that confer resistance to therapy are current at detection and quantify the level of tumor heterogeneity.
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Extra resources for Branching Process Models of Cancer
Example text
This difference is due to the fact that the formula they use ((1) on the cited page) employs the approximation 1=2 1. Turning to the later waves, we note that: (i) the first “main” term in (86) corresponds to the answer in (87). 1 ˛k / D ˛k = sin. ˛k / > 1, so the “correction” term not present in (87) is < 0, which is consistent with the fact that the heuristic leading to (87) considers only the first successful mutation. Branching Process Models of Cancer 43 Table 1 Comparison of expected waiting times from (86) and (87).
However, as we will now show U1 has a power law tail, a result that [24] demonstrated by simulation. 1= 1 / log y, then it is likely that fU1 > yg. 1= 1 / log y e 1 0s 1 ds D Mu1 e . 1= 0 / y ˛ : 0 As in Section 9 one can prove this rigorously by looking at the asymptotics for the Laplace transform as  ! 0. b1 a1 e Â/ dx dx as  ! 0. Branching Process Models of Cancer 37 The probability of having a type 1 at time TM is, by (37),  P D1 exp Mu1 F 1 b0 =a0 à where F is given in (36). 1 b0 =a0 / xD1 x where using notation from Section 6, ˛N D 1 = 0 D 1=˛.
E /. e @ 1/; (69) which agrees P with (50) in Zheng [37] and pages 125–129 in Bailey [4]. Inserting K. t/ C e ˇ1 t : The formula for the mean is the same as in (66). e ˇ1 t 1/ ˇ1 2ˇ2 te ˇ2 t / te ˇ1 t ˇ1 D 2ˇ2 ; ˇ1 D ˇ2 : In the special case ˇ1 D ˇ2 these go back to Bailey. Zheng claims credit for the general formulas, see his (52) and (53). Using this approach we could derive exact formulas for the variance of our process rather than simply the bounds on the second moment given in Section 10. 34 R.