Trap spaces of multi-valued networks: definition, computation, and applications.

Motivation: Boolean networks are simple but efficient mathematical formalism for modelling complex biological systems. However, having only two levels of activation is sometimes not enough to fully capture the dynamics of real-world biological systems. Hence, the need for multi-valued networks (MVNs), a generalization of Boolean networks.

Boolean network sketches: a unifying framework for logical model inference.

Motivation: The problem of model inference is of fundamental importance to systems biology. Logical models (e.g.

Rule-based modelling of biological systems using regulated rewriting.

In systems biology, models play a crucial role in understanding studied systems. There are many modelling approaches, among which rewriting systems provide a framework for describing systems on a mechanistic level. Describing biochemical processes often requires incorporating knowledge on an abstract level to simplify the system description or substitute the missing details.

Temporary and permanent control of partially specified Boolean networks.

Boolean networks (BNs) are a well-accepted modelling formalism in computational systems biology. Nevertheless, modellers often cannot identify only a single BN that matches the biological reality. The typical reasons for this is insufficient knowledge or a lack of experimental data.

AEON.py: Python library for attractor analysis in asynchronous Boolean networks.

Summary: AEON.py is a Python library for the analysis of the long-term behaviour in very large asynchronous Boolean networks. It provides significant computational improvements over the state-of-the-art methods for attractor detection.

Exploring attractor bifurcations in Boolean networks.

Background: Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors-subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes.