@misc{OmranianNikoloskiGrimm2022, author = {Omranian, Sara and Nikoloski, Zoran and Grimm, Dominik G.}, title = {Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward}, series = {Computational and structural biotechnology journal}, volume = {20}, journal = {Computational and structural biotechnology journal}, publisher = {Research Network of Computational and Structural Biotechnology (RNCSB)}, address = {Gotenburg}, issn = {2001-0370}, doi = {10.1016/j.csbj.2022.05.049}, pages = {2699 -- 2712}, year = {2022}, abstract = {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/).}, language = {en} } @misc{BaslerFernieNikoloski2018, author = {Basler, Georg and Fernie, Alisdair R. and Nikoloski, Zoran}, title = {Advances in metabolic flux analysis toward genome-scale profiling of higher organisms}, series = {Bioscience reports : communications and reviews in molecular and cellular biology}, volume = {38}, journal = {Bioscience reports : communications and reviews in molecular and cellular biology}, publisher = {Portland Press (London)}, address = {London}, issn = {0144-8463}, doi = {10.1042/BSR20170224}, pages = {11}, year = {2018}, abstract = {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.}, language = {en} } @misc{LaitinenNikoloski2018, author = {Laitinen, Roosa A. E. and Nikoloski, Zoran}, title = {Genetic basis of plasticity in plants}, series = {Journal of experimental botany}, volume = {70}, journal = {Journal of experimental botany}, number = {3}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0022-0957}, doi = {10.1093/jxb/ery404}, pages = {739 -- 745}, year = {2018}, abstract = {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.}, language = {en} } @misc{OmranianKleessenTohgeetal.2015, author = {Omranian, Nooshin and Kleessen, Sabrina and Tohge, Takayuki and Klie, Sebastian and Basler, Georg and M{\"u}ller-R{\"o}ber, Bernd and Fernie, Alisdair R. and Nikoloski, Zoran}, title = {Differential metabolic and coexpression networks of plant metabolism}, series = {Trends in plant science}, volume = {20}, journal = {Trends in plant science}, number = {5}, publisher = {Elsevier}, address = {London}, issn = {1360-1385}, doi = {10.1016/j.tplants.2015.02.002}, pages = {266 -- 268}, year = {2015}, abstract = {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.}, language = {en} } @misc{LarhlimiBlachonSelbigetal.2011, author = {Larhlimi, Abdelhalim and Blachon, Sylvain and Selbig, Joachim and Nikoloski, Zoran}, title = {Robustness of metabolic networks a review of existing definitions}, series = {Biosystems : journal of biological and information processing sciences}, volume = {106}, journal = {Biosystems : journal of biological and information processing sciences}, number = {1}, publisher = {Elsevier}, address = {Oxford}, issn = {0303-2647}, doi = {10.1016/j.biosystems.2011.06.002}, pages = {1 -- 8}, year = {2011}, abstract = {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.}, language = {en} } @misc{ArnoldNikoloski2011, author = {Arnold, Anne and Nikoloski, Zoran}, title = {A quantitative comparison of Calvin-Benson cycle models}, series = {Trends in plant science}, volume = {16}, journal = {Trends in plant science}, number = {12}, publisher = {Elsevier}, address = {London}, issn = {1360-1385}, doi = {10.1016/j.tplants.2011.09.004}, pages = {676 -- 683}, year = {2011}, abstract = {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.}, language = {en} }