Target Control in Logical Models Using the Domain of Influence of Nodes

Abstract

Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system's state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacological inhibition of proteins, and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Boolean network models of biomolecular networks. Specifically, we define the domain of influence (DOI) of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the logical domain of influence (LDOI) of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose DOI can cover the desired target node states. We perform greedy randomized adaptive search in node state space to find such solutions. We apply our strategy to in silico biological network models of real systems to demonstrate its effectiveness.

Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction

“…For 38 nodes, inversion or fixation did not affect predictions in any of the cell lines. Taken together, our results support the hypothesis that a subset of nodes may be most decisive for the state of the model (Gao et al, 2014;Puniya et al, 2016;Campbell et al, 2017;Pentzien et al, 2018;Rozum and Albert, 2018;Yang et al, 2018).…”

“…We next set out to examine the importance of correct node activity assessment, specifically posing the question of whether the correct assessment of activity for some nodes matter more than others. Inspired by previous publications exploring the identification of high-influence nodes ( Gao et al, 2014 ; Puniya et al, 2016 ; Campbell et al, 2017 ; Pentzien et al, 2018 ; Rozum and Albert, 2018 ; Yang et al, 2018 ) as well as by the concept of target control in network biology, assuming that only a subset of nodes control the system ( Gao et al, 2014 ; Yang et al, 2018 ), we pursued the goal to identify such nodes in our network as well as features that identify them. For each node we iteratively fixed its activity both at its stable state activity value and after activity inversion followed by computation of new synergy predictions.…”

“…Previous studies have indicated that a subset of nodes have the ability to affect the behavior of the overall network ( Gao et al, 2014 ; Puniya et al, 2016 ; Campbell et al, 2017 ; Pentzien et al, 2018 ; Rozum and Albert, 2018 ; Yang et al, 2018 ). Translated to our example, this means that calibration of a set of nodes with such characteristics will determine the activity of the remaining nodes.…”

Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

“…We leverage the parity properties of the parity-expanded network to prove new results about driver node sets ( 54 ) and their relation to attractors. Formal statements and proofs are given in text S3.…”

Executable pathway analysis using ensemble discrete-state modeling for large-scale data

“…It has been tested for use with Intel Distribution for Python 2.7. Generation of network representations of rules was performed by modification of previously published code from the Albert Lab [29]. BONITA is designed to be run from the command line by a non-expert user.…”