Towards a data-integrated cell

Malod-Dognin, Noël; Petschnigg, Julia; Windels, Sam F L; Povh, Janez; Hemingway, Harry; Ketteler, Robin; Pržulj, Nataša

doi:10.1038/s41467-019-08797-8

Cited by 56 publications

(100 citation statements)

References 53 publications

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“…As shown in Figure 6, to avoid repeatedly calculating low-order proximity problems when constructing a high-order affinity network, we need to remove some edges existing in loworder networks from high-order proximity networks. In fact, equation 12is a second-order instance of Tr (1) n in equation (10).…”

Section: Higher-order Proximitymentioning

confidence: 99%

“…W (l) � equation 6; P (l) � equation 7; S (l) � equation (8). (10) end (11) end (12) for l in L do (13) Calculate 3-order proximity by equation (12). (14) Update P (l) according to equation (13).…”

Section: Complexitymentioning

confidence: 99%

“…e abundant information available in mature networks can be quite useful for link prediction and community detection in the emerging networks. (4) In biological multiomic data research studies [10,11], by using the individual's expression in each omic, researchers can construct networks of different omics and then fuse these networks into one comprehensive network to achieve more accurate prediction and analysis. In a word, the information fusion of multiplex networks in different application scenarios is a crucial issue and should be paid more attention.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Structural Fusion for Multiplex Network

et al. 2020

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Many real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fusion of multiplex networks has become a crucial issue. However, most of these existing data fusion methods are inappropriate for researchers to apply to complex network analysis directly. The feature-based fusion methods ignore the sharing and complementarity of interlayer structural information. To tackle this problem, we propose a multiplex network structural fusion (MNSF) model, which can construct a network with comprehensive information. It is composed of two modules: the network feature extraction (NFE) module and the network structural fusion (NSF) module. (1) In NFE, MNSF first extracts a low-dimensional vector representation of a node from each layer. Then, we construct a node similarity network based on embedding matrices and K-D tree algorithm. (2) In NSF, we present a nonlinear enhanced iterative fusion (EIF) strategy. EIF can strengthen high-weight edges presented in one (i.e., complementary information) or more (i.e., shared information) networks and weaken low-weight edges (i.e., redundant information). The retention of low-weight edges shared by all layers depends on the tightness of connections of their K-order proximity. The usage of higher-order proximity in EIF alleviates the dependence on the quality of node embedding. Besides, the fused network can be easily exploited by traditional single-layer network analysis methods. Experiments on real-world networks demonstrate that MNSF outperforms the state-of-the-art methods in tasks link prediction and shared community detection.

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Section: Higher-order Proximitymentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonlinear Structural Fusion for Multiplex Network

et al. 2020

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“…The ever-growing data availability regarding cellular biology has implied in ever more comprehensive computational models of cells 16 , 17 . Having pursued metabolic, signaling, and gene regulation networks toward the limit, we are now interconnecting these data 18 – 21 . Bolder approaches are aiming at complete representations of unicellular organisms 22 – 24 , taking into account all known cellular processes, whether it is network-oriented or not.…”

Section: Introductionmentioning

confidence: 99%

A biochemical network modeling of a whole-cell

Burke

Campos

Costa

et al. 2020

Sci Rep

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All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. in order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the wholecell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. the topological analysis of the network showed structural consistency with biological networks in the literature. in order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. the modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications. Cells are mainly composed of water, proteins, nucleic acids, metabolites, and an enveloping lipid membrane. However, what makes a cell alive are the interactions between these components. Chemical reactions interconnect molecules into intricate biochemical networks in order to perform particular tasks. The whole set of possible chemical interactions is meticulously regulated in order to maintain cellular function, growth, and replication 1. Biochemical interactions with related functions have been traditionally grouped into cellular processes 2,3. Among these processes, metabolism 4,5 , signaling 6-8 , and transcription regulation are those most frequently described and modeled as networks 9-12. Though cellular processes are often described in terms of separate networks 13,14 , they are neither physically nor functionally independent 15. The simple fact that molecular species are shared between them makes their dynamics dependent on each other. For instance, the intracellular concentration of the energetic molecule ATP affects and is affected by, several processes simultaneously. The ever-growing data availability regarding cellular biology has implied in ever more comprehens...

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“…The robustness of multilayer networks has a broad impact on infrastructure networks [35], ecological systems [36], social networks [37], and financial networks [38]. Recently, the growing availability of massive genomic, proteomic, and metabolomics data has stimulated the construction of multilayer biological molecular networks [39][40][41][42][43][44]. For example, Shinde and Jalan [45] proposed a multiplex network composed of six different PPI layers representing different life stages of C. elegans, showing varying degree-degree correlation and spectral properties across the nematode's life cycle.…”

Section: Introductionmentioning

confidence: 99%

Robustness and lethality in multilayer biological molecular networks

Liu

Maiorino

Halu

et al. 2019

Preprint

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Robustness is a prominent feature of most biological systems. In a cell, the structure of the interactions between genes, proteins, and metabolites has a crucial role in maintaining the cell's functionality and viability in presence of external perturbations and noise. Despite advances in characterizing the robustness of biological systems, most of the current efforts have been focused on studying homogeneous molecular networks in isolation, such as protein-protein or gene regulatory networks, neglecting the interactions among different molecular substrates. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network.We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, and protein-protein interaction layer and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system's robustness, defined as its influence over the global network. We find that highly influential genes are enriched in essential and cancer genes, confirming the central role of these genes in critical cellular processes. Further, we determine that the metabolic layer is more vulnerable to perturbations involving genes associated to metabolic diseases. By comparing the robustness of the network to multiple randomized network models, we find that the real network is comparably or more robust than expected in the random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within or between layers.These results provide new insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.The recent development of high throughput omics technologies has facilitated the extensive profiling of the different molecular strata composing living organisms, such as the transcriptome, epigenome, and proteome, providing a more comprehensive picture of the detailed molecular composition of cellular systems. However, cellular processes are not only driven by individual molecules but also by the interplay between them. These interactions are conventionally modeled as context-specific molecular interaction networks [1], such as gene regulatory networks [2], protein-protein interaction (PPI) networks [3], and metabolic networks [4,5]. Such network-based analysis [6] has become an effective and widely used tool in the analysis of cellular systems. While the study of the static topology of these networks has been successful in various applications, such as disease gene prioritization [7][8][9], disease biomarkers discovery [10], and disease diagnosis and subtyping [11], substantial insights can be gained by analyzing the properties of d...

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Towards a data-integrated cell

Cited by 56 publications

References 53 publications

Nonlinear Structural Fusion for Multiplex Network

Nonlinear Structural Fusion for Multiplex Network

A biochemical network modeling of a whole-cell

Robustness and lethality in multilayer biological molecular networks

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