Intracellular signaling processes and cell decisions using stochastic algorithms
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| Description: |
Lopez, C (Vanderbilt University)
Wednesday 6th April 2016 - 11:00 to 11:45 |
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| Created: | 2016-04-08 14:22 |
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| Collection: | Stochastic Dynamical Systems in Biology: Numerical Methods and Applications |
| Publisher: | Isaac Newton Institute |
| Copyright: | Lopez, C |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Cancer cells within a tumor environment exhibit a complex and adaptive nature whereby genetically and epigenetically distinct subpopulations compete for resources. The probabilistic nature of gene expression and intracellular molecular interactions confer a significant amount of stochasticity in cell fate decisions. This cellular heterogeneity is believed to underlie cases of cancer recurrence, acquired drug resistance, and so-called exceptional responders. From a population dynamics perspective, clonal heterogeneity and cell-fate stochasticity are distinct sources of noise, the former arising from genetic mutations and/or epigenetic transitions, extrinsic to the fate decision signaling pathways and the latter being intrinsic to biochemical reaction networks. Here, we present our results and ongoing work of a kinetic modeling study based on experimental time course data for EGFR-addicted non-small cell lung cancer (PC9) cells in both parental and isolated sublines. When PC9 c ells are treated with erlotinib, an EGFR inhibitor, a complex array of division and death cell decisions arise within a given population in response to treatment. Although deterministic (ODE) simulations capture the effects of clonal heterogeneity and describe the overall trends of experimentally treated tumor cell populations, these are not capable of explaining the observed variability of drug response trajectories, including response magnitude and time to rebound. Our stochastic simulations, instead, capture the effects of intrinsically noisy cell fate decisions that cause significant variability in cell population trajectories. These findings indicate that stochastic simulations are necessary to distinguish the contribution of extrinsic (clonal heterogeneity) and intrinsic (cell fate decisions) noise to understand the variability of cancer-cell response treatment. Furthermore, they suggest that, whereas tumors with distinct clon-al structures are expected to behave differently in response. |
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