A Decomposition-based Approach towards the Control of Boolean Networks

Paul, Soumya; Su, Cui; Pang, Jun; Mizera, Andrzej

doi:10.1145/3233547.3233550

Cited by 25 publications

(58 citation statements)

References 25 publications

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“…P i , i ∈ [0, m − 1] stores the direct predecessors of W i+1 at layer i and W i , i ∈ [0, m − 1] is the basin of P i at layer i. The basin is computed using the fixed point computation of strong basins in [16]. In this way, any state s, s ∈ P i can reach a state s , s ∈ W i+1 with one perturbation and any state s, s ∈ (W i \ P i ) can reach a state s , s ∈ P i spontaneously.…”

Section: Methods For the Computation Of The Temporary Sequential Repromentioning

confidence: 99%

“…For each pair of the source and target attractors, we compute the minimal number of perturbations and all inevitable reprogramming paths for the sequential control. Table 1, 2 and 3 give the minimal number of perturbations for the initial reprogramming [16] and the sequential control on three networks for all combinations of the source and the target attractors. It is obvious that the sequential control always outperforms the initial reprogramming in terms of the number of perturbations.…”

Section: Case Studiesmentioning

confidence: 99%

“…It is obvious that the sequential control always outperforms the initial reprogramming in terms of the number of perturbations. This is due to the fact that the initial reprogramming performs all the perturbations at once [16], while our sequential control takes advantage of the spontaneous evolutions of the networks, which helps to drive the network towards the desired target.…”

Section: Case Studiesmentioning

confidence: 99%

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Algorithms for the Sequential Reprogramming of Boolean Networks

Mandon

Pang

et al. 2019

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Cellular reprogramming, a technique that opens huge opportunities in modern and regenerative medicine, heavily relies on identifying key genes to perturb. Most of the existing computational methods for controlling which attractor (steady state) the cell will reach focus on finding mutations to apply to the initial state. However, it has been shown, and is proved in this article, that waiting between perturbations so that the update dynamics of the system prepares the ground, allows for new reprogramming strategies. To identify such sequential perturbations, we consider a qualitative model of regulatory networks, and rely on Binary Decision Diagrams to model their dynamics and the putative perturbations. Our method establishes a set identification of sequential perturbations, whether permanent (mutations) or only temporary, to achieve the existential or inevitable reachability of an arbitrary state of the system. We apply an implementation for temporary perturbations on models from the literature, illustrating that we are able to derive sequential perturbations to achieve trans-differentiation. !

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Section: Methods For the Computation Of The Temporary Sequential Repromentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

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Algorithms for the Sequential Reprogramming of Boolean Networks

Mandon

Pang

et al. 2019

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Paul et al (Paul et al 2018) provide a strong heuristic method for control of asynchronous Boolean networks. The idea underlying the decomposition based approach is to use the strongly connected components to divide the network into blocks which then form a directed acyclic graph.…”

Section: Heuristic and Sub-optimal Control Techniquesmentioning

confidence: 99%

Learning versus optimal intervention in random Boolean networks

Karlsen

Moschoyiannis

Georgiev³

2019

Appl Netw Sci

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Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system (LCS) -an eXtended Classifier System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state (attractor). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and 'natural' steps. We then compare the performance of this optimal rule calculator algorithm (ORC) and the XCS variant of learning classifier systems. We find that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Sequential Reprogramming of Boolean Networks Made Practical

Mandon

Haar

et al. 2019

Computational Methods in Systems Biology

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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.

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A Decomposition-based Approach towards the Control of Boolean Networks

Cited by 25 publications

References 25 publications

Algorithms for the Sequential Reprogramming of Boolean Networks

Algorithms for the Sequential Reprogramming of Boolean Networks

Learning versus optimal intervention in random Boolean networks

Sequential Reprogramming of Boolean Networks Made Practical

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