Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

Martin, O. C.; Krzywicki, A.; Zagórski, Marcin

doi:10.1016/j.plrev.2016.06.002

Cited by 11 publications

(7 citation statements)

References 205 publications

(304 reference statements)

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“…11(B)]. Specifically, a FFL motif can provide control over the temporal integration of environmental signals, buffering noise or sharpening the response to signals [254]. The statistical significance of a motif is described as the Z score [247],…”

Section: Motifsmentioning

confidence: 99%

Computational network biology: Data, models, and applications

et al. 2020

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Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. Benefiting from the advances of network science and high-throughput biomedical technologies, studying the biological systems from network biology has attracted much attention in recent years, and networks have long been central to our understanding of biological systems, in the form of linkage maps among genotypes, phenotypes, and the corresponding environmental factors. In this review, we summarize the recent developments of computational network biology, first introducing various types of biological networks and network structural properties. We then review the network-based approaches, ranging from some network metrics to the complicated machine-learning methods, and emphasize how to use these algorithms to gain new biological insights. Furthermore, we highlight the application in neuroscience, human disease, and drug developments from the perspectives of network science, and we discuss some major challenges and future directions. We hope that this review will draw increasing

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Section: Motifsmentioning

confidence: 99%

Computational network biology: Data, models, and applications

et al. 2020

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“…Other considerations include the network dynamical properties [55] and protein production requirements [6]. Above all, evolution is a random process and certain network designs become fixed and continue propagating [56][57][58][59]. For example, new transcription factors often evolve by duplication of an existing TF followed by sub-or neofunctionalization, thereby preserving the form of regulation of the ancestral TF [60].…”

Section: Discussionmentioning

confidence: 99%

The relation between crosstalk and gene regulation form revisited

Grah

Friedlander

2020

PLoS Comput Biol

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Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called "crosstalk", could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite 'idle' design, where the default unregulated state of genes is their frequently required activity state. We found, that 'idle' design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

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“…These network motifs can exhibit specific dynamic characteristics (Figure 1). Depending on the network motif, delayed, transient, or increased activation of target genes can occur (Figure 1; Martin et al, 2016). Thus, as a result of their dynamic behavior, network motifs contribute to GRN dynamics and complexity (Figure 1).…”

Section: Biological Properties Of Gene Regulatory Network and Approamentioning

confidence: 99%

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

et al. 2020

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Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.

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Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

Cited by 11 publications

References 205 publications

Computational network biology: Data, models, and applications

Computational network biology: Data, models, and applications

The relation between crosstalk and gene regulation form revisited

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

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