Decompositions of large-scale biological systems based on dynamical properties

Soranzo, Nicola; Ramezani, Fahimeh; Iacono, Giovanni; Altafini, Claudio

doi:10.1093/bioinformatics/btr620

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…A social network is an intuitive parallel of this, in that unfriendly relationships (negative signs) increase social tension and decrease social monotonicity (Facchetti et al, 2011). We considered it interesting to assess whether our scRNA-seq-derived regulatory networks preserve the near-to-monotone behavior previously found in manually curated networks Soranzo et al, 2012). The T2D regulatory network possessed more negative edges than the healthy counterpart, suggesting that diabetes causes an increase in chaotic signaling in the pancreas.…”

Section: Monotone Behavior Of Healthy and Diseased Pancreatic Tissuementioning

confidence: 99%

Single-Cell Transcriptomics Unveils Gene Regulatory Network Plasticity

Iacono

Massoni-Badosa

Heyn

2018

Preprint

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Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies. We have devised a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We developed correlation metrics that are specifically tailored to single-cell data, and then generated, validated and interpreted single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using advanced tools from graph theory, we computed an unbiased quantification of a gene's biological relevance, and accurately pinpointed key players in organ function and drivers of diseases. Our approach detected multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis. In summary, we have established the feasibility and value of regulatory network analysis using scRNA-seq datasets, which significantly broadens the biological insights that can be obtained with this leading technology.

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Section: Monotone Behavior Of Healthy and Diseased Pancreatic Tissuementioning

confidence: 99%

Single-Cell Transcriptomics Unveils Gene Regulatory Network Plasticity

Iacono

Massoni-Badosa

Heyn

2018

Preprint

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“…Such approaches have been employed for the analysis of complex networks in systems biology in order to produce a hierarchical layout and trace nodes functioning as regulators and targets (Ispolatov and Maslov 2008). Other recent applications include the analysis of large-scale biological systems, that is, gene regulatory networks (Soranzo et al 2012). Similar concepts and the appropriate network metrics have been employed for unraveling PFC regions that are anatomically embedded in such way to predominantly influence rather than get influenced by other regions (Kötter et al 2001).…”

Section: Problem Formulationmentioning

confidence: 99%

Mapping the Hierarchical Layout of the Structural Network of the Macaque Prefrontal Cortex

2012

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A consensus on the prefrontal cortex (PFC) holds that it is pivotal for flexible behavior and the integration of the cognitive, affective, and motivational domains. Certain models have been put forth and a dominant model postulates a hierarchical anterior-posterior gradient. The structural connectivity principles of this model dictate that increasingly anterior PFC regions exhibit more efferent connections toward posterior ones than vice versa. Such hierarchical asymmetry principles are thought to pertain to the macaque PFC. Additionally, the laminar patterns of the connectivity of PFC regions can be used for defining hierarchies. In the current study, we formally tested the asymmetry-based hierarchical principles of the anterior-posterior model by employing an exhaustive dataset on macaque PFC connectivity and tools from network science. On the one hand, the asymmetry-based principles and predictions of the hierarchical anterior-posterior model were not confirmed. The wiring of the macaque PFC does not fully correspond to the principles of the model, and its asymmetry-based hierarchical layout does not follow a strict anterior-posterior gradient. On the other hand, our results suggest that the laminar-based hierarchy seems a more tenable working hypothesis for models advocating an anterior-posterior gradient. Our results can inform models of the human PFC.

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“…The notion comes from a variety of studies such as social psychology and biological systems 1 2 3 4 5 6 7 8 9 10 11 . Generally, the positive/negative ties in signed networks can be used to characterize the friendly/hostile or cooperative/competitive relationships in social networks 1 2 3 4 5 , and are also suitable for representation of the activating/inhibiting interactions in biochemical networks 6 7 8 9 10 11 . In recent years, increasing interests have arisen to infer the dynamical behaviors of a social or biological network through analysis of the corresponding signed network 12 13 14 15 16 17 .…”

mentioning

confidence: 99%

“…In social networks, structural balance, also called social balance, is formulated to understand the stability or tensions in population systems 18 19 20 21 . In biochemical networks, structural balance, equivalent to the monotonicity property, equips the dynamical system with useful properties as diverse as convergence, high predictability, and robustness 6 7 8 9 . Formally, a signed network is structurally balanced if and only if all its cycles have an even number of negative edges.…”

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confidence: 99%

An evolutionary game approach for determination of the structural conflicts in signed networks

Tan

Lü

2016

Sci Rep

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Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks.

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Decompositions of large-scale biological systems based on dynamical properties

Abstract: altafini@sissa.it

Cited by 9 publications

References 28 publications

Single-Cell Transcriptomics Unveils Gene Regulatory Network Plasticity

Single-Cell Transcriptomics Unveils Gene Regulatory Network Plasticity

Mapping the Hierarchical Layout of the Structural Network of the Macaque Prefrontal Cortex

An evolutionary game approach for determination of the structural conflicts in signed networks

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