“
“Anti-cancer drugs targeted to specific oncogenic pathways have shown promising therapeutic results in the past few years; however, drug resistance remains an important obstacle for these therapies. Resistance to these drugs can emerge due to a variety of reasons including genetic or epigenetic changes which alter the binding site of the drug target, cellular metabolism or selleck chemicals llc export mechanisms.

Obtaining a better understanding of the evolution of resistant populations during therapy may enable the design of more effective therapeutic regimens which prevent or delay progression of disease due to resistance. In this paper, we use Stochastic mathematical models to study the evolutionary dynamics of resistance under time-varying dosing schedules and pharmacokinetic effects. The Populations of sensitive and resistant cells are modeled as multi-type non-homogeneous birth-death processes in which the drug concentration affects the birth and death Fates of both the sensitive and resistant cell Populations in continuous time. This flexible model allows LIS to consider the effects of generalized treatment strategies as well as detailed pharmacokinetic phenomena such as drug elimination and accumulation over multiple doses. We develop estimates for the probability of developing resistance and moments of the size of PF-02341066 chemical structure the resistant cell population. With these estimates, we optimize treatment schedules over a subspace of tolerated schedules

to minimize the risk of disease progression due to resistance as well as locate ideal schedules for controlling the population size of resistant clones in Situations where Carbohydrate resistance is inevitable. Our methodology can be used to describe dynamics of resistance arising due to a single (epi)genetic alteration in any tumor type. (C) 2009 Elsevier Ltd. All rights reserved.”
“The coordinated development of multicellular organisms is driven by intercellular communication. Differentiation into diverse cell types is usually associated with the existence of distinct attractors of gene regulatory networks, but how these attractors; emerge from cell-cell coupling is

still an open question. In order to understand and characterize the mechanisms through which coexisting attractors arise in multicellular systems, here we systematically investigate the dynamical behavior of a population of synthetic genetic oscillators coupled by chemical means. Using bifurcation analysis and numerical simulations, we identify various attractors and attempt to deduce from these findings a way to predict the organized collective behavior of growing populations. Our results show that dynamical clustering is a generic property of multicellular systems. We argue that Such clustering might provide a basis for functional differentiation and variability in biological systems. (C) 2009 Elsevier Ltd. All rights reserved.”
“High-risk types of human papillomaviruses (HPVs) are the etiological agents in nearly all cases (99.