Progress in signal transduction research has been restricted by the use of differential equation modeling which produces high complexity for the analysis and comparison even of small-size systems. Other approaches are usually not directly compatible with mass-action kinetic reaction models. We suggest to analyze concentration changes as input-dependent response functions and separately calculate delays until steady states are reached. This allows to generate pre-computed matrices as transfer functions for any range of inputs ('protein signaling functions' (psfs)). They can be used to perform discrete dynamical simulations, but most importantly, these matrices offer an unprecedented range of possibilities to analyze seemingly complex systems in a simple manner. We show for instance how we can predict active vs. inactive signal transmission links, so that we know which units in the network will respond to input. The results correspond closely with biological knowledge .
We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation .
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Progress in signal transduction research has been restricted by the use of differential equation modeling which produces high complexity for the analysis and comparison even of small-size systems. Other approaches are usually not directly compatible with
We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to
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kinetic reaction models. We suggest to analyze concentration changes as
kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing
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response functions and separately calculate delays until steady states are reached. This allows to generate pre-computed matrices as transfer functions for any range of inputs ('protein signaling functions' (psfs)). They can be used to perform discrete dynamical simulations, but most importantly, these matrices offer an unprecedented range of possibilities to analyze seemingly complex systems in a simple manner. We show for instance how we can predict active vs. inactive signal transmission links, so that we know which units in the network will respond to input. The results correspond closely with biological knowledge
signal transmission inactivation