Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming

Abstract

BackgroundNumerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour.MethodsIn the context of non-deterministic automata networks, we propose a static identification of so-called bifurcations, i.e., transitions after which a given goal is no longer reachable. Such transitions are naturally good candidates for controlling the occurrence of the goal, notably by modulating their propensity. Our method combines Answer-Set Programming with static analysis of reachability properties to provide an under-approximation of all the existing bifurcations.ResultsWe illustrate our discrete bifurcation analysis on several models of biological systems, for which we identify transitions which impact the reachability of given long-term behaviour. In particular, we apply our implementation on a regulatory network among hundreds of biological species, supporting the scalability of our approach.ConclusionsOur method allows a formal and scalable identification of transitions which are responsible for the lost of capability to reach a given state. It can be applied to any asynchronous automata networks, which encompass Boolean and multi-valued models. An implementation is provided as part of the Pint software, available at http://loicpauleve.name/pint.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-017-0110-3) contains supplementary material, which is available to authorized users.

Static Analysis of Dengue Biological Regulatory Network’s

“…These parameters change the type of stability in the dynamic system. The implementation analysis result shows a condition [21,22]. One of the genes is one of the solution's parameters.…”

“…Reachability considers the result of a Boolean operation as true or false from the source to the destination during the transition. This static analysis was discovered using the Pint tool software [21]…”

“…When TLR is -1, activate IFNAB. There are two main goals: One bifurcation in the DENGUE route, one pathogenic (IFNAB = 1), and one homeostatic (TLR = 1), which prevents them from reaching their desired states [21,22]. While SOCS = 1 denotes a sick state, DENV = 1 denotes a healthy state.…”

Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface

“…Identification of bifurcation transitions -Pint implements static analysis for identifying so-called bifurcation transitions [8] after which the systems loses the capability to reach a given goal. Bifurcation transitions correspond to local transitions of the automata network which turn out to be important decision steps during differentiation processes.…”