TY - JOUR A1 - Girbig, Dorothee A1 - Selbig, Joachim A1 - Grimbs, Sergio T1 - A MATLAB toolbox for structural kinetic modeling JF - Bioinformatics N2 - Structural kinetic modeling (SKM) enables the analysis of dynamical properties of metabolic networks solely based on topological information and experimental data. Current SKM-based experiments are hampered by the time-intensive process of assigning model parameters and choosing appropriate sampling intervals for MonteCarlo experiments. We introduce a toolbox for the automatic and efficient construction and evaluation of structural kinetic models (SK models). Quantitative and qualitative analyses of network stability properties are performed in an automated manner. We illustrate the model building and analysis process in detailed example scripts that provide toolbox implementations of previously published literature models. Y1 - 2012 U6 - https://doi.org/10.1093/bioinformatics/bts473 SN - 1367-4803 VL - 28 IS - 19 SP - 2546 EP - 2547 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Nikoloski, Zoran A1 - Grimbs, Sergio A1 - Klie, Sebastian A1 - Selbig, Joachim T1 - Complexity of automated gene annotation JF - Biosystems : journal of biological and information processing sciences N2 - Integration of high-throughput data with functional annotation by graph-theoretic methods has been postulated as promising way to unravel the function of unannotated genes. Here, we first review the existing graph-theoretic approaches for automated gene function annotation and classify them into two categories with respect to their relation to two instances of transductive learning on networks - with dynamic costs and with constant costs - depending on whether or not ontological relationship between functional terms is employed. The determined categories allow to characterize the computational complexity of the existing approaches and establish the relation to classical graph-theoretic problems, such as bisection and multiway cut. In addition, our results point out that the ontological form of the structured functional knowledge does not lower the complexity of the transductive learning with dynamic costs - one of the key problems in modern systems biology. The NP-hardness of automated gene annotation renders the development of heuristic or approximation algorithms a priority for additional research. KW - Complexity KW - Gene function prediction KW - External structural measures KW - Transductive learning Y1 - 2011 U6 - https://doi.org/10.1016/j.biosystems.2010.12.003 SN - 0303-2647 VL - 104 IS - 1 SP - 1 EP - 8 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Basler, Georg A1 - Grimbs, Sergio A1 - Ebenhöh, Oliver A1 - Selbig, Joachim A1 - Nikoloski, Zoran T1 - Evolutionary significance of metabolic network properties JF - Interface : journal of the Royal Society N2 - Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone. KW - metabolic networks KW - significance KW - randomization KW - null model KW - centrality Y1 - 2012 U6 - https://doi.org/10.1098/rsif.2011.0652 SN - 1742-5689 VL - 9 IS - 71 SP - 1168 EP - 1176 PB - Royal Society CY - London ER - TY - JOUR A1 - Töpfer, Nadine A1 - Caldana, Camila A1 - Grimbs, Sergio A1 - Willmitzer, Lothar A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran T1 - Integration of genome-scale modeling and transcript profiling reveals metabolic pathways underlying light and temperature acclimation in arabidopsis JF - The plant cell N2 - Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism. Y1 - 2013 U6 - https://doi.org/10.1105/tpc.112.108852 SN - 1040-4651 VL - 25 IS - 4 SP - 1197 EP - 1211 PB - American Society of Plant Physiologists CY - Rockville ER - TY - JOUR A1 - Bulik, Sascha A1 - Grimbs, Sergio A1 - Huthmacher, Carola A1 - Selbig, Joachim A1 - Holzhutter, Hermann G. T1 - Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws : a promising method for speeding up the kinetic modelling of complex metabolic networks N2 - Kinetic modelling of complex metabolic networks - a central goal of computational systems biology - is currently hampered by the lack of reliable rate equations for the majority of the underlying biochemical reactions and membrane transporters. On the basis of biochemically substantiated evidence that metabolic control is exerted by a narrow set of key regulatory enzymes, we propose here a hybrid modelling approach in which only the central regulatory enzymes are described by detailed mechanistic rate equations, and the majority of enzymes are approximated by simplified (nonmechanistic) rate equations (e.g. mass action, LinLog, Michaelis-Menten and power law) capturing only a few basic kinetic features and hence containing only a small number of parameters to be experimentally determined. To check the reliability of this approach, we have applied it to two different metabolic networks, the energy and redox metabolism of red blood cells, and the purine metabolism of hepatocytes, using in both cases available comprehensive mechanistic models as reference standards. Identification of the central regulatory enzymes was performed by employing only information on network topology and the metabolic data for a single reference state of the network [Grimbs S, Selbig J, Bulik S, Holzhutter HG & Steuer R (2007) Mol Syst Biol3, 146, doi:10.1038/msb4100186]. Calculations of stationary and temporary states under various physiological challenges demonstrate the good performance of the hybrid models. We propose the hybrid modelling approach as a means to speed up the development of reliable kinetic models for complex metabolic networks. Y1 - 2009 UR - http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291742-4658 U6 - https://doi.org/10.1111/j.1742-4658.2008.06784.x SN - 1742-464X ER - TY - JOUR A1 - Neigenfind, Jost A1 - Grimbs, Sergio A1 - Nikoloski, Zoran T1 - On the relation between reactions and complexes of (bio)chemical reaction networks JF - Journal of theoretical biology N2 - Robustness of biochemical systems has become one of the central questions in systems biology although it is notoriously difficult to formally capture its multifaceted nature. Maintenance of normal system function depends not only on the stoichiometry of the underlying interrelated components, but also on the multitude of kinetic parameters. Invariant flux ratios, obtained within flux coupling analysis, as well as invariant complex ratios, derived within chemical reaction network theory, can characterize robust properties of a system at steady state. However, the existing formalisms for the description of these invariants do not provide full characterization as they either only focus on the flux-centric or the concentration-centric view. Here we develop a novel mathematical framework which combines both views and thereby overcomes the limitations of the classical methodologies. Our unified framework will be helpful in analyzing biologically important system properties. KW - Metabolic network KW - Mass action system KW - Flux coupling analysis KW - Chemical reaction network theory Y1 - 2013 U6 - https://doi.org/10.1016/j.jtbi.2012.10.016 SN - 0022-5193 VL - 317 IS - 2 SP - 359 EP - 365 PB - Elsevier CY - London ER - TY - JOUR A1 - Basler, Georg A1 - Grimbs, Sergio A1 - Nikoloski, Zoran T1 - Optimizing metabolic pathways by screening for feasible synthetic reactions JF - Biosystems : journal of biological and information processing sciences N2 - Background: Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze. Results: Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coil, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis. Conclusions: While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering. KW - Metabolic networks KW - Optimization KW - Mass-balanced reactions KW - Synthetic biology Y1 - 2012 U6 - https://doi.org/10.1016/j.biosystems.2012.04.007 SN - 0303-2647 VL - 109 IS - 2 SP - 186 EP - 191 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Childs, Dorothee A1 - Grimbs, Sergio A1 - Selbig, Joachim T1 - Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns JF - Bioinformatics N2 - Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system's Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase. Y1 - 2015 U6 - https://doi.org/10.1093/bioinformatics/btv243 SN - 1367-4803 SN - 1460-2059 VL - 31 IS - 12 SP - 214 EP - 220 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Grimbs, Sergio A1 - Arnold, Anne A1 - Koseska, Aneta A1 - Kurths, Jürgen A1 - Selbig, Joachim A1 - Nikoloski, Zoran T1 - Spatiotemporal dynamics of the Calvin cycle multistationarity and symmetry breaking instabilities JF - Biosystems : journal of biological and information processing sciences N2 - The possibility of controlling the Calvin cycle has paramount implications for increasing the production of biomass. Multistationarity, as a dynamical feature of systems, is the first obvious candidate whose control could find biotechnological applications. Here we set out to resolve the debate on the multistationarity of the Calvin cycle. Unlike the existing simulation-based studies, our approach is based on a sound mathematical framework, chemical reaction network theory and algebraic geometry, which results in provable results for the investigated model of the Calvin cycle in which we embed a hierarchy of realistic kinetic laws. Our theoretical findings demonstrate that there is a possibility for multistationarity resulting from two sources, homogeneous and inhomogeneous instabilities, which partially settle the debate on multistability of the Calvin cycle. In addition, our tractable analytical treatment of the bifurcation parameters can be employed in the design of validation experiments. KW - Multistationarity KW - Calvin cycle KW - Algebraic geometry KW - Bifurcation parameters KW - Biomass Y1 - 2011 U6 - https://doi.org/10.1016/j.biosystems.2010.10.015 SN - 0303-2647 VL - 103 IS - 2 SP - 212 EP - 223 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Larhlimi, Abdelhalim A1 - Basler, Georg A1 - Grimbs, Sergio A1 - Selbig, Joachim A1 - Nikoloski, Zoran T1 - Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks JF - Bioinformatics N2 - Motivation: Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases. Results: We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called 'stoichiometric capacitance'. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic system's changes. Y1 - 2012 U6 - https://doi.org/10.1093/bioinformatics/bts381 SN - 1367-4803 VL - 28 IS - 18 SP - I502 EP - I508 PB - Oxford Univ. Press CY - Oxford ER -