A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches

Cantone, Irene; Marucci, Lucia; Iorio, Francesco; Ricci, Maria Aurelia; Belcastro, Vincenzo; Bansal, Mukesh; Santini, Stefania; Bernardo, Mario di; Bernardo, Diego di; Cosma, María Pía

doi:10.1016/j.cell.2009.01.055

Cited by 355 publications

(429 citation statements)

References 44 publications

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“…Meanwhile, parallel developments in synthetic biology (23) have endowed researchers with new tools that allow precise emulation of naturally occurring topologies (21,22). Networks orthogonal to the cellular milieu can serve as a biomolecular topological "ground truth" (20,24). Data gathered from benchmark synthetic circuits can complement and inform algorithms, and offer a unique opportunity to correlate topological properties to system identification.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, engineered synthetic gene circuits are orthogonal to the endogenous pathways yet operate within the natural cellular context using the available resources. Thus, synthetic networks are a versatile platform for investigating specific connectivities and topological properties and can ultimately guide us to deriving fundamental insights about biological systems and pathways (20)(21)(22)(23).…”

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confidence: 99%

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Discriminating direct and indirect connectivities in biological networks

Kang

Moore

et al. 2015

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

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Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks. A focal point of systems biology is the reverse engineering of gene regulatory networks (1-5). The methods have shifted from intuitive inference of local connectivities to comprehensive analysis of large networks, involving heterogeneous data sets from high-throughput experiments and complex theoretical tools (6-10). Despite significant advances, a fundamental reverse engineering bottleneck is the ability to discriminate between direct and indirect connections. In a simple case, assuming three nodes in a cascade formulation, where an input node is activating an intermediary node which in turn is activating an output node, a reverse engineering algorithm may infer an activating edge from the input node to the output, even though there is no direct biological interaction.Unfortunately, the limitation in correctly distinguishing the effects stemming from indirect connectivities is pervasive (11-13) and justifies the urgent need for new and reliable methods to eliminate spurious edges. Importantly, remedies to address this problem should not further muddle the interpretation by removing true network edges (14). A number of theoretical approaches have been proposed to overcome this hurdle (4, 15-18), but the ability to experimentally verify the conclusions drawn by reverse engineering tools remains paramount.The majority of efforts to address the verification issue adopt in silico benchmark suites that are based on biological pathway approximations (19). Although these models do include a number of commonly observed topologies and have provided significant insights, they do not fully capture the complexity of the biological realm and the associated heterogeneity and intrinsic variability. On the other hand, engineered synthetic gene circuits are orthogonal to the endogenous pathways yet operate within the natural cellular context using the available resources. Thus, sy...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Discriminating direct and indirect connectivities in biological networks

Kang

Moore

et al. 2015

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

View full text Add to dashboard Cite

show abstract

“…While the relative merits of these competing approaches have previously been discussed in the context of in silico [11,13,15,16] and in vivo networks [8,12,13,15], there remains disagreement as to the best approach. Previous work by Bansal et al [11], published in 2007, suggests that while 'further improvements are needed', network inference algorithms have become 'practically useful'.…”

Section: Introductionmentioning

confidence: 99%

“…To address the various challenges associated with inferring GRNs from transcriptional time series, a number of theoretical approaches have been applied, including ordinary and stochastic differential equations (ODEs/SDEs) [10][11][12], Bayesian and dynamic Bayesian networks (BNs/DBNs) [7,8,[11][12][13] and information theoretic or correlation-based methods [11,14]. While the relative merits of these competing approaches have previously been discussed in the context of in silico [11,13,15,16] and in vivo networks [8,12,13,15], there remains disagreement as to the best approach.…”

Section: Introductionmentioning

confidence: 99%

How to infer gene networks from expression profiles, revisited

2011

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Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance of these competing approaches have previously been assessed using a variety of in silico and in vivo datasets. Here, we revisit this work by assessing the performance of more recent network inference algorithms, including a novel non-parametric learning approach based upon nonlinear dynamical systems. For larger GRNs, containing hundreds of genes, these non-parametric approaches more accurately infer network structures than do traditional approaches, but at significant computational cost. For smaller systems, DBNs are competitive with the non-parametric approaches with respect to computational time and accuracy, and both of these approaches appear to be more accurate than Granger causality-based methods and those using simple ODEs models.

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“…Data simulated with mathematical models that are designed to be as biologically plausible as possible can be used, as simulated data assure a systematic, rigorous assessment 16 . But the use of simulated data does not ensure that the challenge is necessarily realistic 17 . Many different methods, including regression, mutual information, correlation, Bayesian networks and others 18 , have been used to address this challenge.…”

Section: Limitations Of Peer Review For Validationmentioning

confidence: 99%