Download Branching Process Models of Cancer by Richard Durrett PDF

By Richard Durrett

This quantity develops effects on non-stop time branching procedures and applies them to review fee of tumor progress, extending vintage paintings at the Luria-Delbruck distribution. therefore, the writer calculate the chance that mutations that confer resistance to therapy are current at detection and quantify the level of tumor heterogeneity. As functions, the writer assessment ovarian melanoma screening thoughts and provides rigorous proofs for result of Heano and Michor touching on tumor metastasis. those notes can be obtainable to scholars who're acquainted with Poisson methods and non-stop time Markov chains.

Richard Durrett is a arithmetic professor at Duke college, united states. he's the writer of eight books, over two hundred magazine articles, and has supervised greater than forty Ph.D scholars. so much of his present examine matters the functions of chance 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 approaches and applies them to check cost 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 remedy are current at detection and quantify the level of tumor heterogeneity.

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X ˇ1 =ˇ2 1/ for x Ä 1. This is the Poisson process for our stable law with the points > 1 removed. t/, but now each mutant starts a Yule process having birth rate ˇ2 . t / . Since jumps from k ! z 1/G. z @z 1/G: (68) For our purposes it is more convenient to use the cumulant generating function K. 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.

Exp. 1 à 0 To explain the assumption that Mu1 ! 0; 1/, note that this implies that resistance is neither certain nor impossible. More concretely, it is estimated that in chronic myeloid leukemia [31] that M D 2:5 105 while u D 4 10 7 so Mu 0:1. b 1 a1 e Â/ the Laplace transform of U1 is not pretty. 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  !

In words, the mutation that confers the ability to migrate does not change the growth rate of the cancer cells. In this case, c1;2 D 1= so (95) becomes Ä P. 2 > TM0 / exp u1 a Z 0 M dx : 1 C . 1 C . x=M //ˇˇ ; 0 so we have  P. 1 C =u2 / : a (96) Since =u2 is large we can drop the 1C inside the logarithm. If the quantity inside the exponent is small, then we have P. 2 Ä TM0 / Mu1 u2 log. =u2 /: a (97) 48 R. Durrett Using ui D a i , and then dropping the =a from inside the logarithm as is done in [21]) the above becomes P.

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