Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model

Akutsu, Tatsuya; Kuhara, Satoru; Maruyama, Osamu; Miyano, Satoru

doi:10.1016/s0304-3975(02)00425-5

Cited by 98 publications

(95 citation statements)

References 17 publications

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“…Several techniques have been proposed in the past to model the network and the dependency between a given set of genes [Kauffman, 1993;Gardner et al, 2000;Shmulevich et al, 2002;Liang et al, 1998;Akutsu et al, 1998]. Each of these techniques works under certain implicit assumptions for the gene expression data.…”

Section: Discussionmentioning

confidence: 99%

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Modeling genetic networks from clonal analysis

Nagarajan

Aubin

Peterson

2004

Journal of Theoretical Biology

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In this report a systematic approach is used to determine the approximate genetic network and robust dependencies underlying differentiation. The data considered is in the form of a binary matrix and represent the expression of the nine genes across the ninety-nine colonies. The report is divided into two parts: the first part identifies significant pair-wise dependencies from the given binary matrix using linear correlation and mutual information. A new method is proposed to determine statistically significant dependencies estimated using the mutual information measure. In the second, a Bayesian approach is used to obtain an approximate description (equivalence class) of network structures. The robustness of linear correlation, mutual information and the equivalence class of networks is investigated with perturbation and decreasing colony number. Introduc tionBiological processes such as cell differentiation are mediated by specific networks of genes, which interact with one another. Such systems evolved with time and can be aptly characterized as coupled nonlinear dynamical systems. Dynamical systems can be broadly classified into linear and nonlinear systems. While the former can exhibit interesting behavior only in the presence of noise, the latter can give rise to a wide range of behavior even in the absence of noise. Current techniques used in modeling genetic networks rely on the concepts of biochemical networks (Goldbeter, 1996;Jacob and Monod, 1961;Kauffman, 1969;Tyson et al, 2001;Yagil and Yagil, 1971). The interaction between genes is often modeled as the outcome of a deterministic sequence of events (Gardner et al., 2000;Huang and Ferrell, 1996). However, recent reports have indicated that, in spite of the deterministic phenotypic outcome, the underlying dynamics may be noisy (Hasty et al, 2000;McAdams and Arkin, 1997;McAdams and Arkin, 1999). Noise can be categorized broadly into either dynamical or measurement noise.While the former is coupled to the dynamics, the latter occurs externally (Fig. 1). Various factors that contribute to dynamical noise include: fluctuations in protein concentrations, variation in cell-to-cell switching time and micro-environment. The phenotypic outcome is deterministic in the average sense and has been attributed to population transcriptional cooperation, checkpoints, and redundancies in genes and regulatory p athways, (McAdams and Arkin, 1999). In a recent study, it was demonstrated how noise can be used to control switching and regulation of gene expression (Hasty et al., 2000). On the other hand, a purely nonlinear deterministic approach has been used to model gene interactions (Gardner et al., 2000). The gene expression values are determined with a 4 measurement device, which acts as a black box between the true bio logical activity and the output (Fig. 1). Thus, what is externally observed is the output of the measurement device and not the true biological phenomena. It might not be surprising to note that the input and output of the measurement device m...

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Section: Discussionmentioning

confidence: 99%

“…Either of the structures is a possible explanation of the result. However, a prudent choice of gene knockout can be helpful in inferring network structure, Akutsu et al (1998).…”

Section: Bayesian Network Approach To Determine Network Structurementioning

confidence: 99%

Modeling genetic networks from clonal analysis

Nagarajan

Aubin

Peterson

2004

Journal of Theoretical Biology

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“…Besides, behavior can be captured with a simple combination consisting of two components, like in Section 3.2 of [4]. If we output only one model, we find that the complexity falls down to O(c · 16) = O(c).…”

Section: Theorem 2 (Complexitymentioning

confidence: 99%

“…Reverse engineering of gene regulatory networks from expression data have been handled by various approaches [4][5][6][7][8]. Most of them are only static.…”

Section: Introductionmentioning

confidence: 99%

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

et al. 2017

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Abstract:Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems' dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.

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“…A modeling method which is of a particular interest in this paper is the so-called Boolean Network model originally introduced in [16]. This model has been expanded in recent years in several directions, all attempting to identify the structure of gene regulatory networks from expression data [17,1,3,4,2,20]. However, all these Boolean Network models assume that the expression levels of all genes are explicitely available and, moreover, that they can be discretized into just two Boolean values (gene is either ON or OFF).…”

Section: Introductionmentioning

confidence: 99%

Analysing DNA microarray data using Boolean techniques

2010

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Abstract. We address in this manuscript a problem arising in molecular biology, namely a problem of discovering dependencies among gene expression levels. The problem is formulated in mathematical terms as a search for a fully defined three valued function defined on three valued variables which is partially specified by the DNA microarray measurments. This formulation as well as our solution methods are strongly motivated by results in the area of logical analysis of data (LAD) and in the area of partially defined Boolean functions (pdBfs), in particular by procedures for finding fully defined extensions of pdBfs. On one hand we present several algorithms which (under some assumptions) construct the desired three valued functional extension of the input data, and on the other hand we derive several proofs showing that (under different assumptions) finding such an extension is NP-hard.

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Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model

Cited by 98 publications

References 17 publications

Modeling genetic networks from clonal analysis

Modeling genetic networks from clonal analysis

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

Analysing DNA microarray data using Boolean techniques

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