Amplified Ras-MAPK signal states correlate with accelerated EGFR internalization, cytostasis and delayed HER2 tumor onset in Fer-deficient model systems.

The non-receptor tyrosine kinase Fer belongs to a distinct subfamily of F-BAR domain containing kinases implicated in vesicular trafficking and signaling downstream of adhesion and growth factor receptors. Targeted inactivation of the fer gene in a transgenic mouse model of HER2(+), breast cancer was associated with delayed tumor onset and reduced proliferative rates in tumor cells. Fer deficiency was associated with increased rates of epidermal growth factor (EGF)-induced epidermal growth factor receptor (EGFR) internalization and amplified Ras-Raf-Mek-Erk (Ras-MAPK) signaling in primary mammary tumor epithelial cells, as well as increased cytotoxic and anti-proliferative sensitivity to the dual EGFR/HER2 inhibitor Lapatinib (LPN). These observations suggest a model in which accelerated ligand-induced EGFR internalization in Fer-deficient cells hypersensitizes the Ras-MAPK pathway to EGF, resulting in MAPK signal amplification to levels that induce cytostasis, rather than proliferation. Thus, Ras-MAPK cytostatic signaling delays HER2 tumor initiation and increases LPN cytotoxicity in Fer-deficient model systems. Taken together, these data suggest that targeting Fer alone, or in combination with LPN, may be of therapeutic benefit in HER2(+) breast cancer.

Systems level modeling of the cell cycle using budding yeast.

Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism.

Autonomously oscillating biochemical systems: parametric sensitivity of extrema and period.

This work addresses sensitivity analysis of autonomously oscillating biochemical systems. Building on results from the engineering literature, a general analysis is presented which addresses key features of oscillatory trajectories, namely the period and local maximum or minimum values of species concentrations and reaction rates. A discussion of sensitivity invariants generalises results from steady-state sensitivity analysis to this context. The results are illustrated by the application to a model of a circadian oscillator.