Temporal Reprogramming of Boolean Networks

Abstract

Cellular reprogramming, a technique that opens huge opportunities in modern and regenerative medicine, heavily relies on identifying key genes to perturb. Most of computational methods focus on finding mutations to apply to the initial state in order to control which attractor the cell will reach. However, it has been shown, and is proved in this article, that waiting between the perturbations and using the transient dynamics of the system allow new reprogramming strategies. To identify these temporal perturbations, we consider a qualitative model of regulatory networks, and rely on Petri nets to model their dynamics and the putative perturbations. Our method establishes a complete characterization of temporal perturbations, whether permanent (mutations) or only temporary, to achieve the existential or inevitable reachability of an arbitrary state of the system. We apply a prototype implementation on small models from the literature and show that we are able to derive temporal perturbations to achieve trans-differentiation.

A Decomposition-based Approach towards the Control of Boolean Networks

“…Nevertheless, they are only applicable to systems with linear time-invariant dynamics. The control methods proposed in [2,11,12,16,17,23,24] are designed for networks governed by non-linear dynamics. Among these methods, the ones based on the computation of the feedback vertex set (FVS) [2,16,24] and the 'stable motifs' of the network [17] drive the network towards a target state by regulating a component of the network with some constraints (feedback vertex sets and stable motifs).…”

“…a minimal subset of nodes of the BN are controlled or the control is applied only for a minimal number of time steps. Under such constraints, it is known that the problem of driving the BN from a source to a target attractor (the control problem) is computationally difficult [11,12] and does not scale well to large networks. Thus a simple global approach (see Section 3.4 for a description) treating the entire network in one-go is usually highly inefficient.…”

Algorithms for the Sequential Reprogramming of Boolean Networks

“…This article extends the conference article [12] by generalizing the algorithm, and making experimentations on bigger models thanks to a new implementation.…”

Sequential Reprogramming of Boolean Networks Made Practical

“…Existing works focus on one-step reprogramming [5,7,10,16,18], or in rare instances, on sequential reprogramming, e.g., [12]. One-step reprogramming allows applying perturbations only once as shown in Fig.…”

“…This leads the network dynamics to a state in the strong basin of the target attractor, from which the network always eventually reaches the target attractor. By taking advantage of the natural dynamics of the network, sequential reprogramming can provide alternative predictions to one-step reprogramming, notably requiring considerably less perturbations [12]. However, in order to apply the perturbations at the correct time, sequential reprogramming requires complete observability of the network (i.e., the state of the network is known at any discrete time), which is rarely feasible in practice.…”