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Figure 1 | Theoretical Biology and Medical Modelling

Figure 1

From: Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

Figure 1

Biochemical reaction space, and integrated information processing of inputs of diverse nature. Signal transduction networks inhabit multidimensional biochemical reaction spaces encompassing repositories of kinetic information, which are integrated along with extracellular stimuli. Such heterogenous sources of information turn out to be simultaneously processed while being integrated, and a signaling ouput, which may determine a particular cellular state, must be robustly calculated according to the set of reaction rules and regulatory schemes encoded in the topology of the network. For simplicity purposes, in this schematic representation a 3D projection drawn from the multidimensional biochemical reaction space is illustrated. Each axis (P i , P j , P k ) in this lower dimensional 3D space represents a reaction kinetic parameter (i.e. an enzyme catalitic rate), and collectively define a surface of inputs which are integrated with extracellulr stimuli, and processed in parallel by the signaling network, from which a given output is computed. Multiple points distributed across the 3D surface of kinetic inputs are sampled by the signaling network, which may represent distinctive reaction conditions stemming from thermal fluctuations in the cell environment, or mutational perturbations in the genetic encoding of the network. Ideally, however, several points distributed across a hypersurface embedded in the N-Dimensional reaction space are systematically sampled by a signal transduction network. In this study, while keeping a given extracellular stimuli constant, the biochemical reaction space is systematically explored around reference operative points via global and local perturbation strategies. In this way, an unbiassed statistical assessment of the robust properties and information processing capabilities of a canonical reaction network underlying TLR4 signaling events was performed.

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