| 1. |
- Anguelova, M., et al.
(author)
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Conservation laws and unidentifiability of rate expressions in biochemical models
- 2007
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In: IET SYSTEMS BIOLOGY. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 1:4, s. 230-237
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Journal article (peer-reviewed)abstract
- New experimental techniques in bioscience provide us with high-quality data allowing quantitative mathematical modelling. Parameter estimation is often necessary and, in connection with this, it is important to know whether all parameters can be uniquely estimated from available data, (i.e. whether the model is identifiable). Dealing essentially with models for metabolism, we show how the assumption of an algebraic relation between concentrations may cause parameters to be unidentifiable. If a sufficient data set is available, the problem with unidentifiability arises locally in individual rate expressions. A general method for reparameterisation to identifiable rate expressions is provided, together with a Mathematica code to help with the calculations. The general results are exemplified by four well-cited models for glycolysis.
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| 2. |
- Eriksson, Olivia, et al.
(author)
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Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit
- 2009
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In: IET systems biology. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 3:2, s. 113-129
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Journal article (peer-reviewed)abstract
- Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein-protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a -tearing-and-zooming- approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between state-variables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits.
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| 3. |
- Greese, Bettina, et al.
(author)
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Influence of cell-to-cell variability on spatial pattern formation
- 2012
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In: IET Systems Biology. - : Institution of Engineering and Technology (IET). - 1751-8857 .- 1751-8849. ; 6:4, s. 143-153
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Journal article (peer-reviewed)abstract
- Many spatial patterns in biology arise through differentiation of selected cells within a tissue, which is regulated by a genetic network. This is specified by its structure, parameterisation and the noise on its components and reactions. The latter, in particular, is not well examined because it is rather difficult to trace. The authors use suitable local mathematical measures based on the Voronoi diagram of experimentally determined positions of epidermal plant hairs (trichomes) to examine the variability or noise in pattern formation. Although trichome initiation is a highly regulated process, the authors show that the experimentally observed trichome pattern is substantially disturbed by cell-to-cell variations. Using computer simulations, they find that the rates concerning the availability of the protein complex that triggers trichome formation plays a significant role in noise-induced variations of the pattern. The focus on the effects of cell noise yields further insights into pattern formation of trichomes. The authors expect that similar strategies can contribute to the understanding of other differentiation processes by elucidating the role of naturally occurring fluctuations in the concentration of cellular components or their properties.
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| 4. |
- Gustafsson, Mika, et al.
(author)
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Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
- 2009
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In: IET SYSTEMS BIOLOGY. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 3:4, s. 219-228
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Journal article (peer-reviewed)abstract
- Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.
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| 5. |
- Huss, Mikael, et al.
(author)
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Currency and commodity metabolites : their identification and relation to the modularity of metabolic networks
- 2007
-
In: IET Systems Biology. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 1:5, s. 280-285
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Journal article (peer-reviewed)abstract
- The large-scale shape and function of metabolic networks are intriguing topics of systems biology. Such networks are on one hand commonly regarded as modular (i.e. built by a number of relatively independent subsystems), but on the other hand they are robust in a way not necessarily expected of a purely modular system. To address this question, we carefully discuss the partition of metabolic networks into subnetworks. The practice of preprocessing such networks by removing the most abundant substances, 'currency metabolites', is formalized into a network-based algorithm. We study partitions for metabolic networks of many organisms and find cores of currency metabolites and modular peripheries of what we call 'commodity metabolites'. The networks are found to be more modular than random networks but far from perfectly divisible into modules. We argue that cross-modular edges are the key for the robustness of metabolism.
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| 6. |
- Kutalic, Zoltan, et al.
(author)
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S-system parameter estimation for noisy metabolic profiles using Newton-flow analysis
- 2007
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In: IET Systems Biology. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 1:3, s. 174-180
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Journal article (peer-reviewed)abstract
- Biochemical systems are commonly modelled by systems of ordinary differentialequations (ODEs). A particular class of such models called S-systems have recently gained popu-larity in biochemical system modelling. The parameters of an S-system are usually estimated fromtime-course profiles. However, finding these estimates is a difficult computational problem.Moreover, although several methods have been recently proposed to solve this problem for idealprofiles, relatively little progress has been reported for noisy profiles. We describe a specialfeature of a Newton-flow optimisation problem associated with S-system parameter estimation.This enables us to significantly reduce the search space, and also lends itself to parameter esti-mation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems.In addition, we propose an extension of our method that allows the detection of network topologiesfor small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competingmethods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.
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| 7. |
- Larsson, Christer, 1958, et al.
(author)
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Flux balance analysis for ethylene formation in genetically engineered Saccharomyces cerevisiae
- 2011
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In: IET Systems Biology. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 5:4, s. 245-251
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Journal article (peer-reviewed)abstract
- Biosynthesis of ethylene (ethene) is mainly performed by plants and some bacteria and fungi, via two distinct metabolic routes. Plants use two steps, starting with S-adenosylmethionine, while the ethylene-forming microbes perform an oxygen dependent reaction using 2-oxoglutarate and arginine. Introduction of these systems into Saccharomyces cerevisiae was studied in silico. The reactions were added to a metabolic network of yeast and flux over the two networks was optimised for maximal ethylene formation. The maximal ethylene yields obtained for the two systems were similar in the range of 7-8-mol ethylene/10-mol glucose. The microbial metabolic network was used for testing different strategies to increase the ethylene formation. It was suggested that supplementation of exogenous proline, using a solely NAD-coupled glutamate dehydrogenase, and using glutamate as the nitrogen source, could increase the ethylene formation. Comparison of these in silico results with published experimental data for yeast expressing the microbial system confirmed an increased ethylene formation when changing nitrogen source from ammonium to glutamate. The theoretical analysis methods indicated a much higher maximal yield per glucose for ethylene than was experimentally observed. However, such high ethylene yields could only be obtained with a concomitant very high respiration (per glucose). Accordingly, when ethylene production was optimised under the additional constraint of restricted respiratory capacity (i.e. limited to experimentally measured values) the theoretical maximal ethylene yield was much lower at 0.2/10 mol glucose, and closer to the experimentally observed values.
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| 8. |
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| 9. |
- Nordling, Torbjörn E M, 1979-, et al.
(author)
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Interampatteness - A generic property of biochemical networks
- 2009
-
In: IET Systems Biology. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 3:5, s. 388-403
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Journal article (peer-reviewed)abstract
- Analysis of gene expression data sets reveals that the variation in expression is concentrated to significantly fewer 'characteristic modes' or 'eigengenes' than the number of both recorded assays and measured genes. Previous works have stressed the importance of these characteristic modes, but neglected the equally important weak modes. Herein a generic system property - interampatteness - is defined that explains the previous feature, and assigns equal weight to the characteristic and weak modes. An interampatte network is characterised by strong INTERactions enabling simultaneous AMPlification and ATTEnuation of different signals. It is postulated that biochemical networks are interampatte, based on published experimental data and theoretical considerations. Existence of multiple time-scales and feedback loops is shown to increase the degree of interampatteness. Interampatteness has strong implications for the dynamics and reverse engineering of the network. One consequence is highly correlated changes in gene expression in response to external perturbations, even in the absence of common transcription factors, implying that interampatte gene regulatory networks erroneously may be assumed to have co-expressed/ co-regulated genes. Data compression or reduction of the system dimensionality using clustering, singular value decomposition, principal component analysis or some other data mining technique results in a loss of information that will obstruct reconstruction of the underlying network.
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| 10. |
- W. Jacobsen, E., et al.
(author)
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Structural robustness of biochemical network models-with application to the oscillatory metabolism of activated neutrophils
- 2008
-
In: IET SYSTEMS BIOLOGY. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 2:1, s. 39-47
-
Journal article (peer-reviewed)abstract
- Sensitivity of biochemical network models to uncertainties in the model structure, with a focus on autonomously oscillating systems, is addressed. Structural robustness, as defined here, concerns the sensitivity of the model predictions with respect to changes in the specific interactions between the network components and encompass, for instance, uncertain kinetic models, neglected intermediate reaction steps and unmodelled transport phenomena. Traditional parametric sensitivity analysis does not address such structural uncertainties and should therefore be combined with analysis of structural robustness. Here a method for quantifying the structural robustness of models for systems displaying sustained oscillations is proposed. The method adopts concepts from robust control theory and is based on adding dynamic perturbations to the network of interacting biochemical components. In addition to providing a measure of the overall robustness, the method is able to identify specific network fragilities. The importance of considering structural robustness is demonstrated through an analysis of a recently proposed model of the oscillatory metabolism in activated neutrophils. The model displays small parametric sensitivities, but is shown to be highly unrobust to small perturbations in some of the network interactions. Identification of specific fragilities reveals that adding a small delay or diffusion term in one of the involved reactions, likely to exist in vivo, completely removes all oscillatory behaviour in the model.
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