Enhancing Boolean networks with continuous logical operators and edge tuning

TL;DR

This paper introduces a novel qualitative modeling approach that extends Boolean networks with continuous logical operators and tunable edges, enabling more detailed and flexible simulation of biological networks without requiring extensive quantitative data.

Contribution

It proposes a new formalism combining continuous logical operators and edge tuning to improve qualitative biological network modeling.

Findings

Produces finer simulation results with continuous variables.

Allows modulation of signal speed and strength via edge tuning.

Demonstrates improved modeling of biological interactions.

Abstract

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.