A Boolean Seven-Layer Platelet Decision Model Models Consistently the Logic and Semiquantitatively the Dynamics of...

A platelet decision model integrates available experimental data to consistently model the Boolean logic in line with all available pathophysiological experimental platelet data. Due to insufficient kinetic data on involved protein networks, we semiquantitatively describe the dynamics of platelet activation and inhibition, i.e., the order of events and the relative strength of receptor activation or protein phosphorylation. The Boolean network model (76 nodes, 115 interactions) incorporates 7 layers of platelet signaling and regulatory feedback loops: (a) platelet activation stimuli resulting from vascular injury, (b) the platelet receptor layer (GPVI, GPIb-IX-V, PAR1/4, P2Y₁/P2Y₁₂, P2X1, TP, 5-HT2A, and IP) that receives signals, (c) central kinases (Src, Fyn, Lyn, PKC, PKA, and PKG) that converge diverse signals, (d) the translation of signaling into changes in platelet ion homeostasis, (e) alterations in platelet shape/adhesion, (f) the activation of other platelets, and (g) the formation of a platelet plug. Dynamic simulations based on curated omics, genetics, and functional data from experiments demonstrate network robustness by multiple and redundant positive feedback mechanisms. These allow rapid and irreversible aggregation and make the platelets resilient to even genetic knockouts (e.g., Trpm7). Signaling in chronic inflammatory conditions is modeled, such as in atherosclerosis, inflammatory arthritis, and aging, as well as inherited platelet disorders. We describe how acute decision responses can become maladaptive leading to chronic inflammatory decisions: This implies a risk of thrombo-inflammatory and cardiovascular diseases.