Quantitative modeling in biology can be difficult due to the scarcity of parameter values . An alternative is qualitative modeling since it requires few to no parameters. This article presents a qualitative modeling derived from boolean networks where fuzzy logic is used and where edges can be tuned. Fuzzy logic being continuous, its variables can be finely valued while remaining qualitative. To consider that some interactions are slower or weaker than other ones, edge states are computed to modulate in speed and strength the signal they convey. The proposed formalism is illustrated through its implementation on an example network. The simulations show that continuous results are produced, thus allowing a fine analysis, and that modulating the signal conveyed by the edges allows their tuning according to knowledge about the interaction they model. The present work is expected to bring enhancements in the ability of qualitative models to simulate biological networks.
Quantitative modeling in biology can be difficult due to parameter value scarcity . An alternative is qualitative modeling since it requires few to no parameters. This article presents a qualitative modeling derived from boolean networks where fuzzy logic is used and where edges can be tuned. Fuzzy logic being continuous, its variables can be finely valued while remaining qualitative. To consider that some interactions are slower or weaker than other ones, edge states are computed to modulate in speed and strength the signal they convey. The proposed formalism is illustrated through its implementation on an example network. Simulations show that continuous results are produced, thus allowing fine analysis, and that modulating the signal conveyed by the edges allows their tuning according to knowledge about the interaction they model. The present work is expected to bring enhancements in the ability of qualitative models to simulate biological networks.
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the scarcity of parameter values
parameter value scarcity
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The simulations
Simulations
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