Boolean network simulations for life scientists

Albert, István; Thakar, Juilee; Li, Song; Zhang, Ranran; Albert, Réka

doi:10.1186/1751-0473-3-16

Cited by 265 publications

(264 citation statements)

References 36 publications

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“…Together, they reveal a number of valuable insights related to regulating the phases of the cell cycle. The first is that many of the motifs involve nodes [5,8,9,10 in budding yeast (16)(17)(18)(19)(20) and 2, 3, 4, 6 in fission (21)(22)(23)(24)(25)] known to be master regulators. This is not surprising, but it is a confirmation that the type of analysis presented here correlates with what is known by biologists.…”

Section: Resultsmentioning

confidence: 99%

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Process-based network decomposition reveals backbone motif structure

Wang

Chen

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated). Prior work on network decomposition-understanding a network's components-has focused on two types of analysis. The first, which we will call motif occurrence analysis, examines all possible small motifs with two, three, or four nodes and by searching for these motifs in known networks, identifies those motifs that occur most frequently across all known networks (7-9). The assumption is that frequently occurring motifs then form a useful building block or module that confers some functionality or property. The second type of work, which we will call motif function analysis, focuses more closely on network function or dynamics. This approach starts with a given network and its known dynamic behavior (the function of the network) and, by removing the edges in a small motif, tries to characterize the effect of the motif. The thinking here is if the removal of a motif results in a loss of function, the motif can be said to contribute to the function. Note that, because any subset of connected edges can be a plausible motif, the number of trials needed for a systematic search of all motifs grows exponentially large, a limitation that also afflicts the motif-occurrence approach. These approaches leave open the question: Do networks contain large motifs that are a primary determining factor in achieving a network's function?In this paper, we present a unique approach to decomposition that addresses the above large-motif question in the affirmative. This approach, which we call process-based analysis, starts by characterizing the space of all possible networks that provide the desired function (process) and then identifies, among these, the minimal networks (with the fewest edges). These minimal networks, it turns out, are few in number and capture the primary functionality-the removal of any single edge from a minimal network destroys the network's function. Thus, suc...

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Section: Resultsmentioning

confidence: 99%

“…Furthermore, interactions are modeled as either stimulatory or inhibitory. We note that such assumptions are standard in the Boolean model (1,10,11), which is often used in place of models based on differential equations to elicit higher-level network properties.…”

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confidence: 99%

Process-based network decomposition reveals backbone motif structure

Wang

Chen

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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“…18, 31-35, 40, 61, and 62). As quantitative information becomes more available, more specific network models, such as asynchronous Boolean and continuous/ Boolean hybrid models, may offer additional insight (60)(61)(62)(63)(64)(65). We anticipate that this unique approach to modeling mutualistic communities will also be applied to a range of other ecological questions.…”

Section: Discussionmentioning

confidence: 99%

A network model for plant–pollinator community assembly

Campbell¹,

Yang

Albert

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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105

124

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Community assembly models, usually constructed for food webs, are an important component of our understanding of how ecological communities are formed. However, models for mutualistic community assembly are still needed, especially because these communities are experiencing significant anthropogenic disturbances that affect their biodiversity. Here, we present a unique network model that simulates the colonization and extinction process of mutualistic community assembly. We generate regional source pools of species interaction networks on the basis of statistical properties reported in the literature. We develop a dynamic synchronous Boolean framework to simulate, with few free parameters, the dynamics of new mutualistic community formation from the regional source pool. This approach allows us to deterministically map out every possible trajectory of community formation. This level of detail is rarely observed in other analytic approaches and allows for thorough analysis of the dynamical properties of community formation. As for food web assembly, we find that the number of stable communities is quite low, and the composition of the source pool influences the abundance and nature of community outcomes. However, in contrast to food web assembly, stable mutualistic communities form rapidly. Small communities with minor fluctuations in species presence/absence (selfsimilar limit cycles) are the most common community outcome. The unique application of this Boolean network approach to the study of mutualistic community assembly offers a great opportunity to improve our understanding of these critical communities.mutualism | transition graph | bipartite

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“…Given the scarcity of kinetic and quantitative characterization of the immune processes involved in the allergic asthma response, we employed a discrete dynamic modeling approach to simulate the development of allergic asthma (29). In this approach, the network's nodes were assumed to have two qualitative states: 0 (off) and 1 (on), corresponding to a baseline (subthreshold) and high (above-threshold) concentration or activity, respectively.…”

Section: Computational Modelmentioning

confidence: 99%

Computational and Experimental Analysis Reveals a Requirement for Eosinophil-Derived IL-13 for the Development of Allergic Airway Responses in C57BL/6 Mice

Walsh

Thakar

Stokes

et al. 2011

The Journal of Immunology

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Eosinophils are found in the lungs of humans with allergic asthma, as well as in the lungs of animals in models of this disease. Increasing evidence suggests that these cells are integral to the development of allergic asthma in C57BL/6 mice. However, the specific function of eosinophils that is required for this event is not known. In this study, we experimentally validate a dynamic computational model and perform follow-up experimental observations to determine the mechanism of eosinophil modulation of T cell recruitment to the lung during development of allergic asthma. We find that eosinophils deficient in IL-13 were unable to rescue airway hyperresponsiveness, T cell recruitment to the lungs, and Th2 cytokine/chemokine production in ΔdblGATA eosinophil-deficient mice, even if Th2 cells were present. However, eosinophil-derived IL-13 alone was unable to rescue allergic asthma responses in the absence of competence of other IL-13–producing cells. We further computationally investigate the role of other cell types in the production of IL-13, which led to the various predictions including early and late pulses of IL-13 during airway hyperresponsiveness. These experiments suggest that eosinophils and T cells have an interdependent relationship, centered on IL-13, which regulates T cell recruitment to the lung and development of allergic asthma.

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Boolean network simulations for life scientists

Cited by 265 publications

References 36 publications

Process-based network decomposition reveals backbone motif structure

Process-based network decomposition reveals backbone motif structure

A network model for plant–pollinator community assembly

Computational and Experimental Analysis Reveals a Requirement for Eosinophil-Derived IL-13 for the Development of Allergic Airway Responses in C57BL/6 Mice

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