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 .
Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale . An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic when modeling biological processes. Consequently, this work proposes a modeling approach derived from Boolean networks where fuzzy operators are used and where edges are tuned. Fuzzy operators allow variables to be continuous and then to be more finely valued than with discrete modeling approaches, such as Boolean networks, while remaining qualitative. Moreover, to consider that in a given biological network some interactions are slower and/or weaker relative to other ones, edge states are computed in order to modulate in speed and strength the signal they convey. The proposed formalism is illustrated through its implementation on a tiny sample of the epidermal growth factor receptor signaling pathway. The obtained simulations show that continuous results are produced, thus allowing finer analysis, and that modulating the signal conveyed by the edges allows their tuning according to knowledge about the modeled interactions, thus incorporating more knowledge. The proposed modeling approach is expected to bring enhancements in the ability of qualitative models to simulate the dynamics of biological networks while not requiring quantitative information .
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parameter value scarcity
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parameters. This article presents a qualitative modeling derived from boolean
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logic being continuous, its variables can be finely valued
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interaction they model. The present work
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biological networks
the dynamics of biological networks while not requiring quantitative information