TY - JOUR A1 - Omranian, Sara A1 - Nikoloski, Zoran A1 - Grimm, Dominik G. T1 - Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward BT - present state, challenges, and the way forward JF - Computational and structural biotechnology journal N2 - Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). KW - Protein Complex Prediction KW - Protein-Protein interaction network KW - Network KW - Clustering Algorithms KW - Network embedding Y1 - 2022 U6 - https://doi.org/10.1016/j.csbj.2022.05.049 SN - 2001-0370 VL - 20 SP - 2699 EP - 2712 PB - Research Network of Computational and Structural Biotechnology (RNCSB) CY - Gotenburg ER - TY - JOUR A1 - Omranian, Nooshin A1 - Kleessen, Sabrina A1 - Tohge, Takayuki A1 - Klie, Sebastian A1 - Basler, Georg A1 - Müller-Röber, Bernd A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran T1 - Differential metabolic and coexpression networks of plant metabolism JF - Trends in plant science N2 - Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used. KW - plant specialized metabolism KW - metabolic networks KW - gene coexpression KW - differential network analysis Y1 - 2015 U6 - https://doi.org/10.1016/j.tplants.2015.02.002 SN - 1360-1385 VL - 20 IS - 5 SP - 266 EP - 268 PB - Elsevier CY - London ER - TY - JOUR A1 - Larhlimi, Abdelhalim A1 - Blachon, Sylvain A1 - Selbig, Joachim A1 - Nikoloski, Zoran T1 - Robustness of metabolic networks a review of existing definitions JF - Biosystems : journal of biological and information processing sciences N2 - Describing the determinants of robustness of biological systems has become one of the central questions in systems biology. Despite the increasing research efforts, it has proven difficult to arrive at a unifying definition for this important concept. We argue that this is due to the multifaceted nature of the concept of robustness and the possibility to formally capture it at different levels of systemic formalisms (e.g, topology and dynamic behavior). Here we provide a comprehensive review of the existing definitions of robustness pertaining to metabolic networks. As kinetic approaches have been excellently reviewed elsewhere, we focus on definitions of robustness proposed within graph-theoretic and constraint-based formalisms. KW - Robustness KW - Metabolic networks KW - Graph theory KW - Constraint-based approaches Y1 - 2011 U6 - https://doi.org/10.1016/j.biosystems.2011.06.002 SN - 0303-2647 VL - 106 IS - 1 SP - 1 EP - 8 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Laitinen, Roosa A. E. A1 - Nikoloski, Zoran T1 - Genetic basis of plasticity in plants JF - Journal of experimental botany N2 - The ability of an organism to change its phenotype in response to different environments, termed plasticity, is a particularly important characteristic to enable sessile plants to adapt to rapid changes in their surroundings. Plasticity is a quantitative trait that can provide a fitness advantage and mitigate negative effects due to environmental perturbations. Yet, its genetic basis is not fully understood. Alongside technological limitations, the main challenge in studying plasticity has been the selection of suitable approaches for quantification of phenotypic plasticity. Here, we propose a categorization of the existing quantitative measures of phenotypic plasticity into nominal and relative approaches. Moreover, we highlight the recent advances in the understanding of the genetic architecture underlying phenotypic plasticity in plants. We identify four pillars for future research to uncover the genetic basis of phenotypic plasticity, with emphasis on development of computational approaches and theories. These developments will allow us to perform specific experiments to validate the causal genes for plasticity and to discover their role in plant fitness and evolution. KW - Genetic architecture KW - GWA KW - GxE interaction KW - hub genes KW - plant adaptation KW - plasticity KW - variance Y1 - 2018 U6 - https://doi.org/10.1093/jxb/ery404 SN - 0022-0957 SN - 1460-2431 VL - 70 IS - 3 SP - 739 EP - 745 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Basler, Georg A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran T1 - Advances in metabolic flux analysis toward genome-scale profiling of higher organisms JF - Bioscience reports : communications and reviews in molecular and cellular biology N2 - Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies. Y1 - 2018 U6 - https://doi.org/10.1042/BSR20170224 SN - 0144-8463 SN - 1573-4935 VL - 38 PB - Portland Press (London) CY - London ER - TY - JOUR A1 - Arnold, Anne A1 - Nikoloski, Zoran T1 - A quantitative comparison of Calvin-Benson cycle models JF - Trends in plant science N2 - The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. Y1 - 2011 U6 - https://doi.org/10.1016/j.tplants.2011.09.004 SN - 1360-1385 VL - 16 IS - 12 SP - 676 EP - 683 PB - Elsevier CY - London ER -