Systems Biology in the Context of Big Data and Networks

Altaf-Ul-Amin, Md.; Afendi, Farit Mochamad; Kiboi, Samuel; Kanaya, Shigehiko

doi:10.1155/2014/428570

Cited by 81 publications

(56 citation statements)

References 90 publications

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“…The inherent complexity in the molecular interaction networks results in various components yielding an emergent property essential for normal functioning. Recognition of the components required for specific functional properties and their perturbation results in alteration in the phenotypic response which results in complex diseases, are essential to identify desired traits (Altaf-Ul- Amin et al, 2014;De Vleesschauwer et al, 2014;Doncheva et al, 2012;Mackay et al, 2009;Xu et al, 2014).…”

Section: Systems Biology In Agricultural Productivitymentioning

confidence: 99%

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

Kumar¹,

Pathak²,

Gupta

et al. 2015

OMICS: A Journal of Integrative Biology

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In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use wellorganized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes.

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Section: Systems Biology In Agricultural Productivitymentioning

confidence: 99%

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

Kumar¹,

Pathak²,

Gupta

et al. 2015

OMICS: A Journal of Integrative Biology

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“…In addition to the difficulty of integrating an increasing body of knowledge comes the inherent complexity of biological systems themselves [5][6][7][8][9][10]: this is where computational tools can help owing to their integrative power [11][12][13][14][15][16][17][18]. This interplay between wet-lab and computational biology is synergistic rather than competitive [19].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Boolean networks with continuous logical operators and edge tuning

Poret

Monteiro²,

Boissel³

2019

Preprint

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Due to the scarcity of quantitative details about biological phenomena, quantitative modeling in systems biology can be compromised, especially at the subcellular scale. One way to get around this is qualitative modeling because it requires few to no quantitative information. One of the most popular qualitative modeling approaches is the Boolean network formalism. However, Boolean models allow variables to take only two values, which can be too simplistic in some cases. The present work proposes a modeling approach derived from Boolean networks where continuous logical operators are used and where edges can be tuned. Using continuous logical operators allows variables to be more finely valued while remaining qualitative. To consider that some biological interactions can be slower or weaker than other ones, edge states are also computed in order to modulate in speed and strength the signal they convey. The proposed formalism is illustrated on a toy network coming from the epidermal growth factor receptor signaling pathway. The obtained simulations show that continuous results are produced, thus allowing finer analysis. The simulations also show that modulating the signal conveyed by the edges allows to incorporate knowledge about the interactions they model. The goal is to provide enhancements in the ability of qualitative models to simulate the dynamics of biological networks while limiting the need of quantitative information.

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“…This finding indicates that it is possible to classify other species, such as plants, based on metabolite-content similarity. With the development of plants metabolomics and big data biology, it is now possible to investigate the metabolite content of plants on a cross-class level [17, 18]. …”

Section: Introductionmentioning

confidence: 99%

Novel Approach to Classify Plants Based on Metabolite-Content Similarity

Liu

Abdullah

Huang

et al. 2017

BioMed Research International

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Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward's method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.

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Systems Biology in the Context of Big Data and Networks

Cited by 81 publications

References 90 publications

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

Enhancing Boolean networks with continuous logical operators and edge tuning

Novel Approach to Classify Plants Based on Metabolite-Content Similarity

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