Abstract
Binding of platelet activators to their receptors initiates a signal transduction
network, where intracellular signal is filtered, amplified, and transformed. Computational
systems biology methods could be a powerful tool to address and analyze dynamics and
regulation of the crucial steps in this cascade. Here we review these approaches and
show the logic of their use for a relatively simple case of SFLLRN-induced procoagulant
activity. Use of a typical model is employed to track signaling events along the main
axis, from the binding of the peptide to PAR1 receptor down to the mPTP opening. Temporal
dynamics, concentration dependence, formation of calcium oscillations and their deciphering,
and role of stochasticity are quantified for all essential signaling molecules and
their complexes. The initial step-wise activation stimulus is transformed to a peak
at the early stages, then to oscillation calcium spikes, and then back to a peak shape.
The model can show how both amplitude and width of the peak encode the information
about the activation level, and show the principle of decoding calcium oscillations
via integration of the calcium signal by the mitochondria. Use of stochastic algorithms
can reveal that the complexes of Gq, in particular the complex of phospholipase C
with Gq, which are the limiting steps in the cascade with their numbers not exceeding
several molecules per platelet at any given time; it is them that cause stochastic
appearance of the signals downstream. Application of reduction techniques to simplify
the system is demonstrated.
Keywords
signal transduction - mathematical models - computational systems biology - secondary
messengers - reduction
