Wisdom of crowds for robust gene network inference

Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicole M.; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R; Kellis, M; Collins, James J.; Stolovitzky, Gustavo

doi:10.1038/nmeth.2016

Cited by 1,581 publications

(2,162 citation statements)

References 51 publications

Supporting

Mentioning

2,075

Contrasting

Unclassified

Order By: Relevance

“…The inability to disentangle these interactions hampers reverse engineering progress. Recent advancements in high-throughput approaches, combined with algorithm and methodological advances through a host of community-wide efforts (12,14,19,35) have examined these aspects. In fact, attempts to fundamentally address the issue by recognizing and filtering out the effects of indirect interactions at a global scale have begun to surface (11).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

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...

show abstract

Section: Discussionmentioning

confidence: 99%

“…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)(16)(17)(18), but the ability to experimentally verify the conclusions drawn by reverse engineering tools remains paramount.…”

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

“…A drawback of experimentally derived PPI or GRN is that such methods detect up to 50% false positives while many true interactions are missed (Huang & Bader, 2009; Marbach et al ., 2012). Even more importantly, those reference networks completely ignore the tempo‐spatial properties of the interactions.…”

Section: From Omics To Systems Biologymentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

View full text Add to dashboard Cite

SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

show abstract

“…gene expression and ChiP‐seq data – 65, 66, 67, and therefore, they give little insights into differences in regulatory interactions that underlie cellular heterogeneity in the pluripotent state. Indeed, the application of differential network analysis to study cellular differentiation is fundamental, since regulatory interactions inferred using population studies might not actually occur in individual cells, in which cell‐fate decisions take place 36, 68.…”

Section: Subpopulation‐specific Gene Regulatory Network Can Be Infermentioning

confidence: 99%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

View full text Add to dashboard Cite

Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

show abstract

Wisdom of crowds for robust gene network inference

Cited by 1,581 publications

References 51 publications

Discriminating direct and indirect connectivities in biological networks

Discriminating direct and indirect connectivities in biological networks

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Contact Info

Product

Resources

About