How can we kill cancer cells: insights from the computational models of apoptosis

Cancer cells are widely known to be protected from apoptosis, which is a major hurdle to successful anti-cancer therapy. Over-expression of several anti-apoptotic proteins, or mutations in pro-apoptotic factors, has been recognized to confer such resistance. Development of new experimental strategies, such as in silico modeling of biological pathways, can increase our understanding of how abnormal regulation of apoptotic pathway in cancer cells can lead to tumour chemoresistance. Monte Carlo simulations are in particular well suited to study inherent variability, such as spatial heterogeneity and cell-to-cell variations in signaling reactions. Using this approach, often in combination with experimental validation of the computational model, we observed that large cell-to-cell variability could explain the kinetics of apoptosis, which depends on the type of pathway and the strength of stress stimuli. Most importantly, Monte Carlo simulations of apoptotic signaling provides unexpected insights into the mechanisms of fractional cell killing induced by apoptosis-inducing agents, showing that not only variation in protein levels, but also inherent stochastic variability in signaling reactions, can lead to survival of a fraction of treated cancer cells.Comment: 10 pages; will appear in World Journal of Clinical Oncology (2010

Maximal Height Scaling of Kinetically Growing Surfaces

The scaling properties of the maximal height of a growing self-affine surface with a lateral extent $L$ are considered. In the late-time regime its value measured relative to the evolving average height scales like the roughness: $h^{*}_{L} \sim L^{\alpha}$. For large values its distribution obeys $\log{P(h^{*}_{L})} \sim -A({h^{*}_{L}}/L^{\alpha})^{a}$, charaterized by the exponential-tail exponent $a$. In the early-time regime where the roughness grows as $t^{\beta}$, we find $h^{*}_{L} \sim t^{\beta}[\ln{L}-({\beta\over \alpha})\ln{t} + C]^{1/b}$ where either $b=a$ or $b$ is the corresponding exponent of the velocity distribution. These properties are derived from scaling and extreme-values arguments. They are corroborated by numerical simulations and supported by exact results for surfaces in 1D with the asymptotic behavior of a Brownian path.Comment: One reference added. Minor stylistic changes in the abstarct and the paper. 4 pages, 3 figure

Movies, measurement, and modeling: the three Ms of mechanistic immunology

Immunological phenomena that were once deduced from genetic, biochemical, and in situ approaches are now being witnessed in living color, in three dimensions, and in real time. The information in time-lapse imaging can provide valuable mechanistic insight into a host of processes, from cell migration to signal transduction. What we need now are methods to quantitate these new visual data and to exploit computational resources and statistical mechanical methods to develop mechanistic models