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- Hellgren, Mikko, 1972-, et al.
(författare)
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Multi-level modelling of the parallell metabolism of ethanol and retinol, with implications for foetal alcohol syndrome
- 2008
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Ingår i: The 9th International Conference on Systems Biology (ICSB-2008) in Gothenburg (Sweden). - 9781615673322
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Konferensbidrag (refereegranskat)abstract
- Objective: Models of the human metabolism are important for understanding diseases and could serve as a powerful tool in the drug discovery process. The complexity of even a unicellular organism is tremendous and most researchers have therefore limited their modelling efforts to bacteria, or single intracellular pathways. We studied the parallel metabolism of ethanol and retinol in humans, because of its suggested physiological importance for the development of foetal alcohol syndrome. Large ethanol intake will inhibit the conversion of retinol into retinoic acid, which is a crucial transcription factor during embryonic development. In this study the objective was to construct a quantitative model that connects phenotype observations at a population, organic and intracellular level with differences in genotype and ethanol metabolism, for further prediction of the influence on the foetus. Results: We constructed a multiple compartments model, which included a detailed desccription of the ethanol and retinol metabolism in hepatic cells for different genotypes. The model has been validated using published time-series measurements of ethanol, acetaldehyde and acetate concentrations in the blood. This model correctly accounts for differences in geno- and phenotype observed within the human population. Furthermore, the model shows that the retinol metabolism is decreased by ethanol ingestion, both via a reduced intracellular NAD+ concentration, and by an inhibition of alcohol and aldehyde dehydrogenases. Conclusions: We considered the problem of multi-level modelling with a human model for the ethanol and retinol metabolism in different compartments. This links intracellular mechanisms to macroscopic observations. The model explained the connection between geno- and phenotype differences observed at a population level. This model also shows a plausible relationship between ethanol and retinol metabolism for e.g. foetal alcohol syndrome.
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| 2. |
- Jacobsen, Elling W., et al.
(författare)
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On parametric sensitivity and structural robustness of cellular functions - The oscillatory metabolism of activated Neutrophils
- 2005
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Ingår i: 2005 44th IEEE Conference on Decision and Control & European Control Conferenc. ; , s. 3681-3686
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Konferensbidrag (refereegranskat)abstract
- Robustness of cellular functions is a key property of living organisms. Modelling and analysis of the genetic and biochemical networks underlying specific functions will enable quantification of the robustness as well as identification of the specific mechanisms providing robustness. Studies on cellular robustness has so far largely focused on parametric sensitivities, i.e., robustness of functions (behavior) with respect to changes in model parameters. In this paper we argue that robustness analysis of cellular models also should encompass structural robustness, i.e., robustness with respect to perturbations in the model structure. This is important not only to quantify the robustness of the cell functions themselves, but equally important, to gain knowledge about the quality of the model as such. In particular, if the model displays poor robustness against structural perturbations this serves as an indication of a potentially highly uncertain model and hence care must be exercised when interpreting the obtained parametric sensitivities. We here propose a simple method for analysing structural robustness of functions related to bistability and periodic oscillations in intracellular networks. The method is applied to a model of the oscillatory metabolism of activated neutrophils (white blood cells) recently proposed in Olsen et al., Biophys J, 84:69-81, 2003. The model is found to be highly robust against parametric uncertainties, but is shown to display poor structural robustness. Indeed, attempting to divide the model into compartments, with the aim of emulating spatial distributions that exist in vivo, results in a qualitatively different model prediction.
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| 5. |
- Liu, Yi, 1974-
(författare)
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Grey-box Identification of Distributed Parameter Systems
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis considers the problem of making dynamic models for industrial processes by combining physical modelling with experimental data. The focus is on distributed parameter systems, that is, systems for which the model structure involves partial differential equations (PDE). Distributed parameter systems are important in many applications, e.g., in chemical process systems and in intracellular biochemical processes, and involve for instance all forms of transport and transfer phenomena. For such systems, the postulated model structure usually requires a finite dimensional approximation to enable identification and validation using experimental data. The finite dimensional approximation involves translating the PDE model into a set of ordinary differential equations, and is termed model reduction. The objective of the thesis is two-fold. First, general PDE model reduction methods which are efficient in terms of model order for a given level of accuracy are studied. The focus here is on a class of methods called moving mesh methods, in which the discretization mesh is considered a dynamic degree of freedom that can be used for reducing the model reduction error. These methods are potentially highly efficient for model reduction of PDEs, but often suffer from stability and robustness problems. In this thesis it is shown that moving mesh methods can be cast as standard feedback control problems. Existing moving mesh methods are analyzed based on tools and results available from control theory, and plausible explanations to the robustness problems and parametric sensitivity experienced with these methods are provided. Possible remedies to these problems are also proposed. A novel moving finite element method, Orthogonal Collocation on Moving Finite Elements (OCMFE), is proposed based on a simple estimate of the model reduction error combined with a low order linear feedback controller. The method is demonstrated to be robust, and hence puts only small demands on the user. In the second part of the thesis, the integration of PDE model reduction methods with grey-box modelling tools available for finite dimensional models is considered. First, it is shown that the standard approach based on performing model reduction using some ad hoc discretization method and model order, prior to calibrating and validating the reduced model, has a number of potential pitfalls and can easily lead to falsely validated PDE models. To overcome these problems, a systematic approach based on separating model reduction errors from discrepancies between postulated model structures and measurement data is proposed. The proposed approach is successfully demonstrated on a challenging chromatography process, used for separation in biochemical production, for which it is shown that data collected at the boundaries of the process can be used to clearly distinguish between two model structures commonly used for this process.
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| 6. |
- Nordling, Torbjörn E. M., 1979-, et al.
(författare)
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Experiment design for optimal excitation of gene regulatory networks
- 2006
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Konferensbidrag (refereegranskat)abstract
- Identification of gene regulatory networks from quantitative data has attracted significant interest in recent years. The focus has mainly been on determining model structures and algorithms for fitting experimental data, while the problem of obtaining suitable experimental data largely has been neglected. In this work we focus on the problem of systematically designing in vivo/in vitro experiments that will yield the information needed to determine both the structure and dynamics of biochemical networks. As a first approximation we consider linear dynamic models valid in a particular physiological state. We propose an iterative design strategy, where selection of the perturbation, sampling time and number of samples in each experiment is based on available partial information about the system, i.e. an ill-conditioned or rank deficient measurement matrix. Three different sources of such deficiency exist: (i) unidirectionality intrinsic to the system, due to moiety conservation or strongly correlated variables, (ii) fast dynamic modes and (iii) incomplete excitation of the system. The former two can be identified and ￜlifted outￜ of the measurement matrix, while the latter require additional experimental data. Our experiment design strategy endeavours in each step to provide information perpendicular to the existing one. When all directions of the state space, spanned by the gene network, are present in the measurements matrix, the design emphasizes those directions where the least information has been obtained. Existing optimum design strategies are based on maximization of some measure of the Fisher information matrix (FIM). An a priori model of the system is needed to determine the FIM and hence good prior knowledge of the system is essential. Otherwise the design will give slow convergence, corresponding to an excessive number of experiments. Our approach requires no prior information and its effectiveness is here demonstrated through identification of in silico networks previously proposed in the literature.
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| 7. |
- Nordling, Torbjörn E. M., 1979-, et al.
(författare)
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Experiment Design for Proper Excitation of Gene Regulatory Networks
- 2007
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Ingår i: Proceedings of Foundations of Systems Biology in Engineering (FOSBE), 2nd Conference.
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Konferensbidrag (refereegranskat)abstract
- Feedback is ubiquitous in gene regulatory networks, and provide e.g., homeostasis and signal amplification. The presence of feedback has significant implications for network inference since it implies that the gene responses to perturbation experiments typically will be strongly correlated, leading to ill-conditioning of the measurement matrix. The ill-conditioning will represent a fundamental problem in network identification since it implies that some of the network interactions will be identified with gross errors. To overcome this problem, we propose herein a systematic iterative experiment design that ensures sufficient excitations of all network interactions. The method leads to combinatorial perturbation experiments, in which a number of genes are perturbed simultaneously. The effectiveness of the method is demonstrated by application to an in silico regulatory network.
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| 9. |
- Nordling, Torbjörn E. M., et al.
(författare)
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Ill-Conditioning : A Property of Bio Networks
- 2008
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Ingår i: Proceedings of 2nd q-Bio Conference on Cellular Information Processing, 2008.
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Konferensbidrag (refereegranskat)abstract
- Analysis of large gene expression datasetsshows that they all share the same feature; the variance isconcentrated to significantly fewer orthogonal directions thanthe applied perturbations span. Given that all perturbationsare of same magnitude, this shows that the underlyingnetworks are ill-conditioned. We establish ill-conditioning as ageneric property of biochemical networks, resulting from thefact that all networks need to provide both signal amplificationand disturbance attenuation. One consequence of illconditioningis the commonly observed co-expression of genes.
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| 10. |
- Nordling, Torbjörn E. M., 1979-, et al.
(författare)
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Inference of interampatte gene regulatory networks : with application to apoptosis signalling
- 2008
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Ingår i: The 9th International Conference on Systems Biology (ICSB-2008) in Gothenburg (Sweden). - Gothenburg, Sweden : University Of Gothenburg, Curran Associates, Inc..
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objective: Inference of gene regulatory networks (GRN) from quantitative expression data has the potential to reveal all interactions existing within a selected set of genes. However, microarray data typically only contain a few characteristic modes or eigengenes, even when a large number of arrays are recorded at varying experimental conditions. The reason and implications of this inherent rank deficiency has largely been neglected, even though rank deficiency caused by fewer experiments than measured genes has been addressed. We explain why the data in the former case are rank deficient, what it implies for network inference, and how to counteract it through experiment design. Results: We define interampatte systems as systems characterised by strong interactions necessary to both amplify and attenuate different signals at multiple time-scales. GRN are interampatte with strong directional dependence. This generic network property make microarray data rank deficient and gives rise to features observed as characteristic modes, eigengenes and co-expressed genes. While few modes imply that low order models can be used for data compression and prediction, it effectively prevents inference of causal interactions, since many sparse networks with completely different structure fit equally well to the dataset. We illustrate this problem using a previously published model of apoptosis signalling. Inference based on standard experiments, i.e. perturbing genes one-by-one, is shown to yield networks with the wrong structure although its predictive ability is validated using independent validation data. We present an iterative algorithm for experiment design that guarantees sufficient excitation of all network modes and demonstrate its effectiveness. Conclusions: Systematic design of perturbation experiments, where several genes are perturbed simultaneously in a controlled fashion, is necessary in order to infer the true structure of GRN from expression data. It is likely that many inferred network models with validated predictive properties have falsely identified gene interactions.
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