Attractor computation using interconnected Boolean networks: Testing growth rate models in E. Coli

Chaves, Madalena; Carta, Alfonso

doi:10.1016/j.tcs.2014.06.021

Cited by 6 publications

(9 citation statements)

References 31 publications

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“…Once the attractors of all the transition graphs are known, a new object will be constructed, called the asymptotic graph, G a * , a graph where the vertices are basically all products of attractors of the form A i u × B j v and the edges are computed based on the transition graphs G A,u The main results in this paper establish that there is a correspondence between A(G a * ) and A(G I * ) which depends on the updating strategy. The asymptotic graph for asynchronous updates was first proposed in [8] and then further studied in [40,6]. This Section briefly reviews the main results.…”

Section: The Asynchronous Asymptotic Graph Methods For Attractor Compumentioning

confidence: 99%

“…A 1 11 × B 1 11 = {0000000000} × {0000000000000}, A 4 17 × {0011 * * * 00 * 10 * }, A 5 15 × {0011 * * * * * * 10 * }, A 6 22 × {1100 * * * * * * 0 * * }, A 7 22 × {1100 * * * * * * 0 * * },…”

Section: T Helper Cells Regulatory Networkmentioning

confidence: 99%

“…This makes sense, as all gene expression is arrested. Outside mitosis (case v = 1), there are four different oscillatory cycles (B 1 11 ∪B 2 12 , B 3 11 ∪B 4 12 , B 5 11 ∪B 6 12 , and B 7 11 ∪B 8 12 ) where only the first two correspond to circadian oscillations.…”

mentioning

confidence: 99%

“…Attractor Q a,2 represents a joint oscillatory solution taking specific pairs (a, b) with a ∈ A 2 11 ∪ A 3 12 or a ∈ A 5 21 ∪ A 6 22 and b ∈ B 5 11 ∪ B 6 12 .…”

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confidence: 99%

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Boolean dynamics revisited through feedback interconnections

2018

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Boolean models of physical or biological systems describe the global dynamics of the system and their attractors typically represent asymptotic behaviors. In the case of large networks composed of several modules, it may be difficult to identify all the attractors. To explore Boolean dynamics from a novel viewpoint, we will analyse the dynamics emerging from the composition of two known Boolean modules. The state transition graphs and attractors for each of the modules can be combined to construct a new asymptotic graph which will (1) provide a reliable method for attractor computation with partial information; (2) illustrate the differences in dynamical behavior induced by the updating strategy (asynchronous, synchronous, or mixed); and (3) show the inherited organization/structure of the original network's state transition graph.

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Section: The Asynchronous Asymptotic Graph Methods For Attractor Compumentioning

confidence: 99%

Section: T Helper Cells Regulatory Networkmentioning

confidence: 99%

mentioning

confidence: 99%

“…Attractor Q a,2 represents a joint oscillatory solution taking specific pairs (a, b) with a ∈ A 2 11 ∪ A 3 12 or a ∈ A 5 21 ∪ A 6 22 and b ∈ B 5 11 ∪ B 6 12 .…”

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confidence: 99%

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Boolean dynamics revisited through feedback interconnections

2018

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“…According to the principle of truth functionality, the values of logical functions in such models are calculated by the values of their arguments in parallel or serial manner [10]. Classical models are widely used in biology and medicine [11][12][13][14], geosciences [15][16][17][18], and social sciences [19][20][21][22]. In [9,[23][24][25][26][27][28], BNs are generalized using conventional, truth functional fuzzy logics.…”

Section: Introductionmentioning

confidence: 99%

Interpolative Boolean Networks

et al. 2017

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Boolean networks are used for modeling and analysis of complex systems of interacting entities. Classical Boolean networks are binary and they are relevant for modeling systems with complex switch-like causal interactions. More descriptive power can be provided by the introduction of gradation in this model. If this is accomplished by using conventional fuzzy logics, the generalized model cannot secure the Boolean frame. Consequently, the validity of the model’s dynamics is not secured. The aim of this paper is to present the Boolean consistent generalization of Boolean networks, interpolative Boolean networks. The generalization is based on interpolative Boolean algebra, the [0,1]-valued realization of Boolean algebra. The proposed model is adaptive with respect to the nature of input variables and it offers greater descriptive power as compared with traditional models. For illustrative purposes, IBN is compared to the models based on existing real-valued approaches. Due to the complexity of the most systems to be analyzed and the characteristics of interpolative Boolean algebra, the software support is developed to provide graphical and numerical tools for complex system modeling and analysis.

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Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits

Chaves

Jong

2021

Methods in Molecular Biology

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Attractor computation using interconnected Boolean networks: Testing growth rate models in E. Coli

Cited by 6 publications

References 31 publications

Boolean dynamics revisited through feedback interconnections

Boolean dynamics revisited through feedback interconnections

Interpolative Boolean Networks

Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits

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