Abstract

The dynamics of complex biological networks may be modeled in a Boolean framework, where the state of each system component is either abundant (ON) or scarce/absent (OFF), and each component's dynamic trajectory is determined by a logical update rule involving the state(s) of its regulator(s). It is possible to encode the update rules in the topology of the so-called expanded graph, analysis of which reveals the long-term behavior, or attractors, of the network. Here, we develop an algorithm to perturb the expanded graph (or, equivalently, the logical update rules) to eliminate stable motifs: subgraphs that cause a subset of components to stabilize to one state. Depending on the topology of the expanded graph, these perturbations lead to the modification or loss of the corresponding attractor. While most perturbations of biological regulatory networks in the literature involve the knockout (fixing to OFF) or constitutive activation (fixing to ON) of one or more nodes, we here consider edgetic perturbations, where a node's update rule is modified such that one or more of its regulators is viewed as ON or OFF regardless of its actual state. We apply the methodology to two biological networks. In a network representing T-LGL leukemia, we identify edgetic perturbations that eliminate the cancerous attractor, leaving only the healthy attractor representing cell death. In a network representing drought-induced closure of plant stomata, we identify edgetic perturbations that modify the single attractor such that stomata, instead of being fixed in the closed state, oscillates between the open and closed states.

Highlights

  • The behavior of cells may be viewed as an emergent property of the complex network of interactions among intraand extra-cellular proteins and other biomolecules

  • The dynamical behavior of the system may be expressed in terms of the state transition network (STN), which includes every possible combination of node states, and the transitions between them that are allowed by the update rules of each node

  • A Boolean model for T-cell large granular lymphocyte (T-LGL) leukemia shows both a healthy attractor, corresponding to activation induced cell death and a diseased attractor that corresponds to abnormal survival of activated T cells

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Summary

Introduction

The behavior of cells may be viewed as an emergent property of the complex network of interactions among intraand extra-cellular proteins and other biomolecules. In a Boolean model, interactions are expressed in terms of a logical update rule ( called regulatory function) for each node. This update rule specifies, for example, that the node may become abundant only in the presence of a certain positive regulator and the absence of a second, negative regulator.. The so-called attractors of the STN correspond to stable outcomes for the network, wherein the dynamic state of the network remains fixed at a single state (i.e., at a single combination of node states) or oscillates among a subset of states.. A Boolean model for T-LGL leukemia shows both a healthy attractor, corresponding to activation induced cell death and a diseased attractor that corresponds to abnormal survival of activated T cells.

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