Sequential Reprogramming of Boolean Networks Made Practical

Abstract

We address the sequential reprogramming of gene regulatory networks modelled as Boolean networks. We develop an attractor-based sequential reprogramming method to compute all sequential reprogramming paths from a source attractor to a target attractor, where only attractors of the network are used as intermediates. Our method is more practical than existing reprogramming methods as it incorporates several practical constraints: (1) only biologically observable states, viz. attractors, can act as intermediates; (2) certain attractors, such as apoptosis, can be avoided as intermediates; (3) certain nodes can be avoided to perturb as they may be essential for cell survival or difficult to perturb with biomolecular techniques; and (4) given a threshold k, all sequential reprogramming paths with no more than k perturbations are computed. We compare our method with the minimal one-step reprogramming and the minimal sequential reprogramming on a variety of biological networks. The results show that our method can greatly reduce the number of perturbations compared to the one-step reprogramming, while having comparable results with the minimal sequential reprogramming. Moreover, our implementation is scalable for networks of more than 60 nodes.

Controlling Large Boolean Networks with Temporary and Permanent Perturbations

“…Currently, we are working a sequential control method, where other attractors (observable biological phenotypes) can act as intermediates [35]. We want to drive the network from a source state to a target attractor through intermediate attractors by applying a sequence of instantaneous or temporary or permanent perturbations.…”

Drawing the Line: Basin Boundaries in Safe Petri Nets

“…We have developped Petri net-represented structures that allow to identify completely the strong basins of attraction for all attractors present in a finite safe Petri net. Future work will investigate further the different types of boundaries encountered here, and aim at refining and evaluating robustness of attractors and reprogramming strategies [17,16] in the context of concurrency. Finally, in regard to benchmarks in prior work relying on Petri net unfoldings [2,4], the time and space consumption of the proposed algorithms allows to envisage their application to networks with two-digit numbers of nodes.…”

Sequential Temporary and Permanent Control of Boolean Networks

“…We have developed efficient methods to tackle the minimal OI, OT and OP control [21,22,26], as well as ASI control [11,12]. Considering the advantages of sequential control and temporary and permanent perturbations, in this paper, we shall develop methods to solve the AST and ASP control problems based on the methods for the minimal OT and OP control.…”

“…Instantaneous perturbations are applied instantaneously; temporary perturbations are applied for sufficient time steps and then released; permanent perturbations are applied for all the following steps. So far, we have developed methods for the minimal one-step instantaneous, temporary and permanent control (OI, OT and OP) [21,22,26] and the attractor-based sequential instantaneous control (ASI) [11]. In this work, we focus on the attractor-based sequential temporary and permanent control (AST and ASP).…”

“…In this work, we continue to develop efficient methods to solve the AST and ASP control problems. We apply our methods for AST and ASP to several biological networks to show their ability in finding new control paths with fewer perturbations compared to our previous methods [21,22,26,11]. We believe our new methods can provide a better understanding of the mechanismof-action of interventions and improve the efficiency of translating identified reprogramming paths into practical applications.…”