| Abstract | It is known that defects in these mechanisms cause neu-roblastoma, but how multiple signaling pathways interact to govern cell behavior is unknown. |
| Abstract | Clusters of proteins in these networks are indicative of functional signaling pathways . |
| Author Summary | The analysis revealed that signaling pathways are functionally and physically compartmentalized into distinct collaborative groups distinguished by phosphorylation patterns and intracellular localization. |
| Introduction | Thus, the complexity of kinase-sub-strate and other protein-protein interactions in tyrosine kinase signaling pathways is important to understand because these pathways govern the choice between differentiation and cancer. |
| Introduction | SH3 domain-containing proteins, which typically bind to proline-rich motifs [26] , are functionally linked to both endocytosis and tyrosine kinase signaling pathways [24]. |
| Introduction | By combining pattern recognition techniques with gene ontology (GO) and protein-protein interaction (PPI) data, we learned that clusters that contain interacting proteins are likely to indicate functional signaling pathways [34—40]. |
| Neuroblastoma Phosphoproteomic Network | To ask whether these data were complete enough for analysis of signaling pathways , we employed graph theory, which describes the properties of networks [35,38]. |
| Neuroblastoma Phosphoproteomic Network | We hypothesize that proteins containing tyrosine kinase, tyrosine phosphatase, SH2 and SH3 domains (PNCPs) will collectively initiate and control phosphotyrosine signaling pathways [19,24]. |
| Neuroblastoma Phosphoproteomic Network | This remarkable diversity in phosphotyrosine signaling pathways likely represents a snapshot of signaling pathways activated in the sympathoadrenal lineage of neural crest that gives rise to neuroblastoma at different stages of development [2—6]. |
| Phosphoproteomics | These cells were fractionated to isolate endosomes and detergent-resistant lipid rafts [32,33], and analyzed under different conditions that changed the state of their signaling pathways . |
| Abstract | The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway . |
| Author Summary | Targeting specific protein signaling pathways offers potentially more potent therapies. |
| Author Summary | One promising potential target is the insulin signaling pathway , which is known to contribute to glioblastoma progression. |
| Author Summary | However, drugs targeting this pathway have shown mixed results in clinical trials, and the detailed mechanisms of how the insulin signaling pathway promotes glioblastoma growth remain to be elucidated. |
| Development of a computational model | Thus, we created for the first time, a computational chemical-kinetic model linking the insulin signaling pathway to glioblastoma growth. |
| Development of a computational model | Fig 1A highlights intracellular insulin signaling pathways present in brain cancer cells. |
| Development of a computational model | Some roles for HIFloc in glioma progression and in the insulin signaling pathway specifically have been identified: HIFloc promotes malignant cell growth, and elevated expression of HIFloc has been strongly correlated to tumor malignancy [58—60]. |
| Insulin signaling interactions in glioblastoma | Based on previous literature on the insulin signaling pathway , we constructed a model comprised of 4 differential equations and 1 mass conservation equation which describe interactions between components in the insulin signaling system (see Fig 1B). |
| Introduction | In order to treat glioma by targeting the insulin signaling pathway, the detailed molecular mechanisms linking this signaling pathway to cancer growth need to be understood. |
| Abstract | Signaling pathways are characterized by crosstalk, feedback and feedfonNard mechanisms giving rise to highly complex and cell-context specific signaling networks. |
| Author Summary | Cellular responses to extracellular stimuli are driven by activation of intracellular signaling pathways . |
| Author Summary | The interconnections between signaling pathways contribute to the high complexity of signaling networks, therefore playing an important role in response to treatment in pathological conditions. |
| Author Summary | Specifically, we analyze the interconnections within and between PI3K and MAPK signaling pathways involved in hepatocytes proliferation. |
| Introduction | Cells receive extracellular signals and process them through intracellular signaling pathways to regulate cellular responses. |
| Introduction | However, signaling pathways involve extensive crosstalk and feedforward as well as feedback loops resulting in complex, nonlinear intracellular signaling networks, whose topologies are often context-specific and altered in diseases [1]. |
| Introduction | Upon binding to its receptor Met, HGF activates the phosphoinositide-(PI)-3-kinase (PI3K) and the mitogen activated protein kinase (MAPK) signaling pathways . |
| Abstract | To address this confounding factor in network inference, we explicitly model the stimulation status of a signaling pathway in individual cells. |
| Discussion | Here, we have identified one confounding factor, namely heterogeneous signaling pathway activation within a cell population, and incorporated it directly into a novel probabilistic model for pathway reconstruction. |
| Discussion | To address the problem of unknown activation of signaling pathways during network inference, we have introduced a general framework, building on NEMs, to handle hidden combinatorial knockdowns in a probabilistic manner. |
| Introduction | In all previous NEM models and applications, the signaling pathway under observation is assumed to be active and the signal flow disrupted by silencing the signaling genes one by one. |
| Introduction | To address the problem of network learning when the activation state of the signaling pathway is unknown we introduce a new model, called NEMix, extending the existing NEM framework in several ways. |
| Network inference under unknown pathway activity | We seek the structure of (I), i.e., the topology of the signaling pathway , by inferring it from the nested structure of observed effects. |
| The NEM framework | Silencing experiments can be noisy for many different reasons and it might be unknown whether the signaling pathway of interest is actually activated during knockdown of a gene. |
| The NEM framework | Let furthermore p0 = P(Zk = 0) be the probability that the signaling pathway has not been activated, and p1 = P(Zk = 1) = 1 — p0 the probability that it is active. |
| The NEM framework | For infected cells, the external input signal from the pathogen reached the cell and the signaling pathway is active (Zk = 1). |
| The early peak signature is enriched for lEGs and signalling pathways | TGF-beta signaling pathway |
| The early peak signature is enriched for lEGs and signalling pathways | Toll receptor signaling pathway |
| The early peak signature is enriched for lEGs and signalling pathways | Cadherin signaling pathway |