Signaling pathways are characterized by crosstalk, feedback and feedfonNard mechanisms giving rise to highly complex and cell-context specific signaling networks .

However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions.

Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks .

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.

We combine interaction graph and dynamic modeling with quantitative experimental data to study the hepatocyte growth factor induced signaling network in primary mouse hepatocytes.

The identification and quantitative description of relevant feedback, feedforward and crosstalk regulation of signaling pathways is an important step towards understanding cellular signaling networks and a key prerequisite for the development of successful drug targeting strategies [41—43]

In conclusion, model simulations and experimental verifications suggested that the considered signaling network is less sensitive to single interventions, but can be efficiently targeted by combinatorial treatments.

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].

Thus, due to the high combinatorial complexity, a systematic method is required to facilitate unbiased identification of cell-context specific structure of signaling networks .

Several computational methods have been developed to infer and analyze signaling networks .

First, it has been studied and validated in great detail [26—28] , such that the available signaling network from the KEGG database [22] can be used as a reliable source to compare to.

Again we used uniform priors for 6 and imposed no priors for the signaling networks other than the maXimal out-degree of Z (plus the transitive edges that need to be added).

Nested effects models, a method tailored to reconstruct signaling networks from high-dimensional readouts of gene silencing experiments, have so far been only applied on the cell population level.

Especially, when the underlying true signaling networks are expected to be sparse, NEMix is beneficial.

The two most plausible explanations, of alternative receptor states versus alternative network wiring, may not necessarily be mutually exclusive, since alternative receptor states are likely to represent responses to alternative states of the signaling networks either intracellularly or extracellularly.

Accordingly many workers seek to simply study the combination effects without considering additional information regarding the signaling network .

Our goal was to develop efficient and practical methods to identify combinations of platelet inhibitors that would be robust in inhibiting platelets under multiple conditions, and would provide insights into platelet signaling networks .

We found that two related proteins (FYN and LYN) act like central hubs in the tyrosine kinase signaling network that change intracellular localization and activity in response to activation of different receptors.

Tyrosine kinase signaling networks play a major role in governing cell differentiation, including in neuroblastoma [16].

Src Homology 2 (SH2) domains (and one-fifth of phosphotyrosine-binding or PTB domains) mediate selective protein—protein interactions with proteins phosphorylated on tyrosine residues, and thus mediate assembly of phosphotyrosine signaling networks [19].

Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes.

Studies of other biological signaling networks such as epidermal receptor signaling [21], GPCR signaling [22], signaling in both the innate [23,24] and the adaptive immune system also documented bow-tie organizations [4,25].

hypothesized that the narrow intermediate layer in signaling networks may serve to distinguish between different sets of inputs and assigns the correct set of outputs for each.