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Loriaux P.M.,University of California at San Diego | Loriaux P.M.,The San Diego Center for Systems Biology | Hoffmann A.,University of California at San Diego | Hoffmann A.,The San Diego Center for Systems Biology
PLoS Computational Biology | Year: 2013

Stimulus-induced perturbations from the steady state are a hallmark of signal transduction. In some signaling modules, the steady state is characterized by rapid synthesis and degradation of signaling proteins. Conspicuous among these are the p53 tumor suppressor, its negative regulator Mdm2, and the negative feedback regulator of NFκB, IκBα. We investigated the physiological importance of this turnover, or flux, using a computational method that allows flux to be systematically altered independently of the steady state protein abundances. Applying our method to a prototypical signaling module, we show that flux can precisely control the dynamic response to perturbation. Next, we applied our method to experimentally validated models of p53 and NFκB signaling. We find that high p53 flux is required for oscillations in response to a saturating dose of ionizing radiation (IR). In contrast, high flux of Mdm2 is not required for oscillations but preserves p53 sensitivity to sub-saturating doses of IR. In the NFκB system, degradation of NFκB-bound IκB by the IκB kinase (IKK) is required for activation in response to TNF, while high IKK-independent degradation prevents spurious activation in response to metabolic stress or low doses of TNF. Our work identifies flux pairs with opposing functional effects as a signaling motif that controls the stimulus-sensitivity of the p53 and NFκB stress-response pathways, and may constitute a general design principle in signaling pathways. © 2013 Loriaux and Hoffmann.

Loriaux P.M.,University of California at San Diego | Loriaux P.M.,The San Diego Center for Systems Biology | Tesler G.,University of California at San Diego | Hoffmann A.,University of California at San Diego | Hoffmann A.,The San Diego Center for Systems Biology
PLoS Computational Biology | Year: 2013

The steady states of cells affect their response to perturbation. Indeed, diagnostic markers for predicting the response to therapeutic perturbation are often based on steady state measurements. In spite of this, no method exists to systematically characterize the relationship between steady state and response. Mathematical models are established tools for studying cellular responses, but characterizing their relationship to the steady state requires that it have a parametric, or analytical, expression. For some models, this expression can be derived by the King-Altman method. However, King-Altman requires that no substrate act as an enzyme, and is therefore not applicable to most models of signal transduction. For this reason we developed py-substitution, a simple but general method for deriving analytical expressions for the steady states of mass action models. Where the King-Altman method is applicable, we show that py-substitution yields an equivalent expression, and at comparable efficiency. We use py-substitution to study the relationship between steady state and sensitivity to the anti-cancer drug candidate, dulanermin (recombinant human TRAIL). First, we use py-substitution to derive an analytical expression for the steady state of a published model of TRAIL-induced apoptosis. Next, we show that the amount of TRAIL required for cell death is sensitive to the steady state concentrations of procaspase 8 and its negative regulator, Bar, but not the other procaspase molecules. This suggests that activation of caspase 8 is a critical point in the death decision process. Finally, we show that changes in the threshold at which TRAIL results in cell death is not always equivalent to changes in the time of death, as is commonly assumed. Our work demonstrates that an analytical expression is a powerful tool for identifying steady state determinants of the cellular response to perturbation. All code is available at http://signalingsystems.ucsd.edu/pg/pysub or as supplementary material accompanying this paper. © 2013 Loriaux et al.

Till A.,University of California at San Diego | Till A.,The San Diego Center for Systems Biology | Till A.,Life and Brain GmbH | Saito R.,The San Diego Center for Systems Biology | And 15 more authors.
Autophagy | Year: 2015

All eukaryotic cells utilize autophagy for protein and organelle turnover, thus assuring subcellular quality control, homeostasis, and survival. In order to address recent advances in identification of human autophagy associated genes, and to describe autophagy on a system-wide level, we established an autophagy-centered gene interaction network by merging various primary data sets and by retrieving respective interaction data. The resulting network (‘AXAN’) was analyzed with respect to subnetworks, e.g. the prime gene subnetwork (including the core machinery, signaling pathways and autophagy receptors) and the transcription subnetwork. To describe aspects of evolution within this network, we assessed the presence of protein orthologs across 99 eukaryotic model organisms. We visualized evolutionary trends for prime gene categories and evolutionary tracks for selected AXAN genes. This analysis confirms the eukaryotic origin of autophagy core genes while it points to a diverse evolutionary history of autophagy receptors. Next, we used module identification to describe the functional anatomy of the network at the level of pathway modules. In addition to obvious pathways (e.g., lysosomal degradation, insulin signaling) our data unveil the existence of context-related modules such as Rho GTPase signaling. Last, we used a tripartite, image-based RNAi – screen to test candidate genes predicted to play a role in regulation of autophagy. We verified the Rho GTPase, CDC42, as a novel regulator of autophagy-related signaling. This study emphasizes the applicability of system-wide approaches to gain novel insights into a complex biological process and to describe the human autophagy pathway at a hitherto unprecedented level of detail. © 2015 Taylor and Francis Group, LLC.

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