  Due to the scarcity of quantitative details about biological phenomena,
quantitative modeling in systems biology can be compromised, especially at the
subcellular scale. One way to get around this is qualitative modeling because
it requires few to no quantitative information. One of the most popular
qualitative modeling approaches is the Boolean network formalism. However,
Boolean models allow variables to take only two values, which can be too
simplistic in some cases. The present work proposes a modeling approach derived
from Boolean networks where continuous logical operators are used and where
edges can be tuned. Using continuous logical operators allows variables to be
more finely valued while remaining qualitative. To consider that some
biological interactions can be slower or weaker than other ones, edge states
are also computed in order to modulate in speed and strength the signal they
convey. The proposed formalism is illustrated on a toy network coming from the
epidermal growth factor receptor signaling pathway. The obtained simulations
show that continuous results are produced, thus allowing finer analysis. The
simulations also show that modulating the signal conveyed by the edges allows
to incorporate knowledge about the interactions they model. The goal is to
provide enhancements in the ability of qualitative models to simulate the
dynamics of biological networks while limiting the need of quantitative
information.
