Capturing cell‐fate decisions from the molecular signatures of a receptor‐dependent signaling response

Abstract

We examined responses of the B-cell antigen receptor-dependent intracellular signaling network to targeted perturbations induced through siRNA-mediated depletion of select signaling intermediates. The constituent nodes displayed graded sensitivities, which resulted from the differential effects of perturbations on the kinetic and quantitative aspects of phosphorylation at each node. By taking the rate of initial phosphorylation, rate of subsequent dephosphorylation, and the total intensity of phosphorylation at each node as separate signaling parameters, we generated data-driven models that accurately predicted the cellular responses of apoptosis, proliferation, and cytokine secretion. Importantly, the effects of perturbation on the primary target alone did not yield successful models. Rather, it also required incorporation of secondary effects on many other nodes. A significant feature of these models was that the three signaling parameters derived from each node functioned largely as independent entities, making distinctive contributions to the cellular response. Thus, the kinetic and quantitative features of phosphorylation at a node appear to play discrete roles during signal processing.

Models of signalling networks – what cell biologists can gain from them and give to them

“…For signal transduction, these combinations point to 'hidden dimensions' within a network, where multiple signalling proteins may be coordinately regulated to execute a common function (Jensen and Janes, 2012). Such models have proved to be remarkably versatile for signalling networks, capturing adaptors, effectors, cell-fate control and cytokine-release profiles in different settings (Beyer and MacBeath, 2012;Cosgrove et al, 2010;Gordus et al, 2009;Janes et al, 2005;Janes et al, 2008;Kemp et al, 2007;Kumar et al, 2007a;Kumar et al, 2007b;Lau et al, 2011;Lee et al, 2012;Miller-Jensen et al, 2007;Tentner et al, 2012). Therefore, the question is no longer whether these model-based simplifications of signalling networks are effective but, rather, why they work so well as often as they do.…”

From Network Structure to Dynamics and Back Again: Relating Dynamical Stability and Connection Topology in Biological Complex Systems

“…This process of phosphorylation is mirrored by the reverse process of deactivation by phosphatases through dephosphorylation. Such reaction cascades are activated by second messengers (e.g., cyclic AMP or calcium ions) and may last for a few minutes, with the number of kinase proteins and other molecules involved in the process increasing with every [12]. The kinases are represented by squares, while other molecules (such as, second messengers and adapters) are depicted as circles.…”

“…As the breakdown of communication in this network can lead to disease (a fact that may be utilised by infectious agents for proliferation), it is of obvious importance to understand the mechanisms by which the network allows the cell response to be sensitive to different stimuli and yet robust in the presence of intra-cellular noise. With this in mind, the time evolution of the activity (i.e., phosphorylation) of about 20 signalling molecules in this network were recorded in a recent experiment by Kumar et al [12]. Apart from observing the activation profiles under normal conditions, the network was also subjected to a series of perturbations, by serially blocking each of these molecules from activating any of the other molecules in the network.…”

Fusing Tissue Engineering and Systems Biology Toward Fulfilling Their Promise

“…To generate a model capable of accurate a priori predictions, the conditions used to train a PLSR model need to strongly and differently activate the breadth of measured cell signaling activities and behaviors. 14,47 PLSR models have been generated using cell signaling and response data from a number of the aforementioned measurement techniques, and have been successful at interpreting and predicting cell signaling-response relationships in varied contexts such as ECM-regulated embryonic stem cell self-renewal and differentiation, 55 cytokine-and pathogen-induced epithelial cell apoptosis-survival, 14,27,47 receptor agonist-induced T-cell and B-cell cytokine release, 34,43 and growth factor-induced mammary epithelial cell proliferation and migration. 44 …”

“…7,53 For instance, successful a priori PLSR model prediction of cell phenotypic changes due to perturbation by one or more small molecular inhibitor(s) has been verified for compounds with specified kinase targets that lie upstream of protein signal(s) contained in the model and implemented by 'computationally inhibiting' only the measured signals in a subset of the training data and then comparing the predicted results to new experiment observations. 34,47 Perturbations that affect signaling networks more globally (therefore less predictably) such as growth factor receptor overexpression, 43 disruption of autocrine ligand signaling, 27 and RNA interference 43 have been evaluated using complete re-collection of signaling data rather than the straightforward estimation methods successful for small molecular inhibitors. When measuring multiple diverse kinds of cell phenotypic behaviors, as is desired for understanding complex tissue physiology, constructing separate submodels for the various behaviors might allow for easier interpretation of the various respective signaling-response relationships.…”