Abstract
Most cellular processes are driven by simple biochemical mechanisms such as protein and lipid phosphorylation, but the sum of all these conversions is exceedingly complex. Hence, intuition alone is not enough to discern the underlying mechanisms in the light of experimental data. Toward this end, mathematical models provide a conceptual and numerical framework to formally evaluate the plausibility of biochemical processes. To illustrate the use of these models, here we built a mechanistic computational model of PI3K (phosphatidylinositol 3-kinase) activity, to determine the kinetics of lipid metabolizing enzymes in single cells. The model is trained to data generated upon perturbation with a reversible small-molecule based chemical dimerization system that allows for the very rapid manipulation of the PIP3 (phosphatidylinositol 3,4,5-trisphosphate) signaling pathway, and monitored with live-cell microscopy. We find that the rapid relaxation system used in this work decreased the uncertainty of estimating kinetic parameters compared to methods based on in vitro assays. We also examined the use of Bayesian parameter inference and how the use of such a probabilistic method gives information on the kinetics of PI3K and PTEN activity.
Highlights
In order to understand signaling networks and their role in the regulation of the cell as well as their deregulation in disease, it is necessary to be able to measure their constituents, such as proteins and their PTMs (Post Translational Modifications) and small molecules involved in signaling, at the single cell level and at multiple time points
We show, using the perturbation data obtained via chemical dimerization in Ref. 13, how Bayesian inference methods can help with model choice and with quantifying the uncertainty of parameter estimates based on such data
The induction of phosphatidylinositol 3-kinase (PI3K) can be investigated by fusing the mRFP-FKBP construct (FKBP bound to a fluorescent tag) with the inter-Src homology 2 domain from p85. p85 is the regulatory subunit of PI3K
Summary
In order to understand signaling networks and their role in the regulation of the cell as well as their deregulation in disease, it is necessary to be able to measure their constituents, such as proteins and their PTMs (Post Translational Modifications) and small molecules involved in signaling, at the single cell level and at multiple time points. There are many experimental methods that are capable of such measurements These include on the one hand fluorescent microscopy techniques such as FRET (Förster Resonance Energy Transfer) and FRAP (Fluorescence Recovery after Photobleaching) for live cells[27] and, on the other hand, more highthroughput techniques such as mass cytometry[8] that destroy the cells during measurement. Populations[23] and the dynamic properties of certain signaling proteins Another powerful approach to explore signaling is to monitor cells not just in their natural resting state, but upon perturbation of the activity of some of their components. A model describing PIP3 homeostasis is introduced and we show that the in vivo kinetics of some of the enzymes controlling PIP3 metabolism (PI3K and PTEN) are much faster than previous estimates from in vitro experiments suggested
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