Change search
Refine search result
12 1 - 50 of 62
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Berezowski, M.
    et al.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Grzywacz, R.
    Increased performance through autonomous oscillations in tubular reactors with recycle2001In: In|ynieria Chemiczna i Procesowa, ISSN 0208-6425, Vol. 22, no 3B, p. 235-240Article in journal (Refereed)
    Abstract [pl]

    The paper treats the dynamics of tubular reactors in which a fraction of the product is recycled and mixed with the fresh feed. The results presented in this paper show that autonomous limit cycles may drastically increase the performance of the reactor, and that limit cycle behavior therefore may be something that should be sought rather than avoid.

  • 2. Berezowski, M.
    et al.
    Ptaszek, P.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Dynamics of heat-integrated pseudohomogeneous tubular reactors with axial dispersion2000In: Chemical Engineering and Processing, ISSN 0255-2701, E-ISSN 1873-3204, Vol. 39, no 2, p. 181-188Article in journal (Refereed)
    Abstract [en]

    The study concerns a theoretical analysis of pseudohomogeneous autothermal tubular reactors with axial dispersion of mass and heat. Based on analysis and simulations it is demonstrated that such a system can generate complex oscillatory profiles of temperature and concentration-periodic or chaotic. These profiles, especially those of aperiodic character, can seriously impair the performance of the system. The effect of three parameters on the reactor dynamics is studied, namely, the cooling medium temperature, the Lewis number and the Peclet number. We consider only relatively small values of the Lewis number in this paper.

  • 3.
    Carlemalm, Hong Cui
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Chapter B5 Design for controllability of integrated plants2004In: The Integration of Process Design and Control, Elsevier, 2004, no C, p. 306-325Chapter in book (Refereed)
  • 4.
    Carlemalm, Hong Cui
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Design for Controllability of Integrated Plants2004In: The Integration of Process Design and Control / [ed] P. Seferlis, M Georgiadis, Elsevier, 2004Chapter in book (Refereed)
  • 5.
    Carlemalm, Hong Cui
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Process Design for Reduced Disturbance Sensitivity of Integrated Plants2004In: Proceedings of IFAC DYCOPS 7, Boston, USA, 2004Conference paper (Refereed)
  • 6.
    Cui Carlemalm, Hong
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Performance limitations in decentralized control2002In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 12, no 4, p. 485-494Article in journal (Refereed)
    Abstract [en]

    In decentralized control of multivariable systems. the system is decomposed into a number of subsystems and individual controllers are designed for each subsystem. Advantages of such decomposition include reduced modelling requirements and case of implementation. However, a potential disadvantage is a reduction in achievable control performance due to restricted controller structure. In this paper we consider performance limitations from non-minimum phase transmission zeros in decentralized control. In particular, we derive conditions on when closing the loop around one subsystem moves transmission zeros of other subsystems across the imaginary axis. Such zero crossings may occur regardless of the existence of non-minmum phase behavior in the open-loop system, and may, therefore, represent performance limitations specific to the use of decentralized controllers.

  • 7. Cui, H.
    et al.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Optimal design of buffers in plants with recycling2002In: Pulp & paper Canada, ISSN 0316-4004, Vol. 103, no 6, p. 25-29Article in journal (Refereed)
    Abstract [en]

    Buffer tanks are commonly employed to dampen the effect of disturbances in chemical processes. In order to minimize the number and size of tanks, the buffers Should be designed to Mainly handle disturbances that cannot be effectively handled by a feedback Control system. In this paper we consider optimal design of buffers in plants with recycle of material and/or energy. Such recycling is becoming common in process plants, and typically increases the disturbance sensitivity significantly, and hence the potential need for buffers. We Show that the location of the buffer is a crucial decision in recycle systems, and derive simple model-based tools that Call be used to determine both the optimal Size and location of buffers ill recycle systems.

  • 8.
    Garcia-Gabin, Winston
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Combining high-level glucose models with intracellular insulin signaling models for improved glucose controlIn: Journal of Diabetes Science and Technology, E-ISSN 1932-2968Article in journal (Refereed)
  • 9.
    Garcia-Gabin, Winston
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Multilevel model based glucose control for type-1 diabetes patients2013In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, p. 3917-3920Conference paper (Refereed)
    Abstract [en]

    Diabetes is a disease that involves alterationsat multiple biological levels, ranging from intracellular sig-nalling to organ processes. Since glucose homeostasis is theconsequence of complex interactions that involve a numberof factors, the control of diabetes should be based on amultilevel analysis. In this paper, a novel approach to designof closed-loop glucose controllers based on multilevel models ispresented. A control scheme is proposed based on combininga pharmacokinetic/pharmacodynamic model with an insulinsignal transduction model for type 1 diabetes mellitus patients.Based on this, an insulin feedback control schemes is designed.Two main advantages of explicitly utilizing information at theintracellular level were obtained. First, significant reductionof hypoglycaemic risk by reducing the undershoot in glucoselevels in response to added insulin. Second, robust performancefor inter-patient changes, demonstrated through application ofthe multilevel control strategy to a well establishedin silicopopulation of diabetic patients.

  • 10.
    Garcia-Gabin, Winston
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers2013In: Journal of Diabetes Science and Technology, E-ISSN 1932-2968, Vol. 7, no 1, p. 193-205Article in journal (Refereed)
    Abstract [en]

    Glucose homeostasis is the result of complex interactions across different biological levels. This multilevel characteristic should be considered when analyzing and designing closed-loop glucose control algorithms. Classic control schemes use only a pharmacokinetic-pharmacodynamic (PKPD) perspective to describe the gluco-regulatory system. A multilevel model combining a PKPD model with an insulin signaling model is proposed for patients with type 1 diabetes mellitus T1DM (T1DM). The PKPD Dalla Man model for T1DM is expanded to include an intracellular level involving insulin signaling to control glucose uptake through glucose transporter type 4 (GLUT4) translocation. A model-based controller is then designed and used as an example to illustrate the feasibility of the proposal. Two significant results were obtained for the controller explicitly utilizing multilevel information. No hypo-glycemic events were registered and an excellent performance for interpatient variability was achieved. Controller performance was evaluated using two indexes. The glucose was kept inside the range (70-180) mg/dl more than 99% of the time, and the intrapatient variability measured using control variability grid analysis was solid with 90% of the population inside the target zone. Multilevel models open new possibilities for designing glucose control algorithms. They allow controllers to take into account variables that have a strong influence on glucose homeostasis. A model-based controller was used for demonstrating how improved knowledge of the multilevel nature of diabetes increases the robustness and performance of glucose control algorithms. Using the proposed multi-level approach, a reduction of the hypoglycemic risk and robust behaviour for intrapatient variability was demonstrated.

  • 11.
    Hagrot, Erika
    et al.
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Oddsdottir, Hildur Aesa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Hosta, Joan Gonzalez
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Chotteau, Veronique
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model-Application to amino acid depletion in CHO cell culture2017In: Journal of Biotechnology, ISSN 0168-1656, E-ISSN 1873-4863, Vol. 259, p. 235-247Article in journal (Refereed)
  • 12.
    Hagrot, Erika
    et al.
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Oddsdóttir, Hildur Aesa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Hosta, Joan Gonzalez
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Chotteau, Veronique
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model - Application to amino acid depletion in CHO cell culture2016In: Journal of Biotechnology, ISSN 0168-1656, E-ISSN 1873-4863, Vol. 228, p. 37-49Article in journal (Refereed)
    Abstract [en]

    Mammalian cell lines are characterized by a complex and flexible metabolism. A single model that could describe the variations in metabolic behavior triggered by variations in the culture conditions would be a precious tool in bioprocess development. In this paper, we introduce an approach to generate a poly-pathway model and use it to simulate diverse metabolic states triggered in response to removal, reduction or doubling of amino acids in the culture medium of an antibody-producing CHO cell line. Macro-reactions were obtained from a metabolic network via elementary flux mode enumeration and the fluxes were modeled by kinetic equations with saturation and inhibition effects from external medium components. Importantly, one set of kinetic parameters was estimated using experimental data of the multiple metabolic states. A good fit between the model and the data was obtained for the majority of the metabolites and the experimentally observed flux variations. We find that the poly-pathway modeling approach is promising for the simulation of multiple metabolic states.

  • 13.
    Hagrot, Erika
    et al.
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Oddsdóttir, Hildur Æsa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Hosta, Joan Gonzalez
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Chotteau, Veronique
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model - Application to CHO cell cultureManuscript (preprint) (Other academic)
  • 14.
    Hagrot, Erika
    et al.
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Oddsdóttir, Hildur Æsa
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Hosta, Joan Gonzalez
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Chotteau, Véronique
    KTH, School of Biotechnology (BIO), Industrial Biotechnology.
    Retraction notice to “Poly-pathway model, a novel approach to simulate multiple metabolic states by reaction network-based model – Application to amino acid depletion in CHO cell culture” (Journal of Biotechnology (2016) 228 (37–39)(S0168165616301213)(10.1016/j.jbiotec.2016.03.015))2018In: Journal of Biotechnology, ISSN 0168-1656, E-ISSN 1873-4863, Vol. 265Article in journal (Refereed)
    Abstract [en]

    This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). The authors of the paper wish to retract the paper due to the discovery of a calculation error in the processing of the raw data. The discovered error concerns the calculation of the specific uptake/secretion rates for several metabolites in one of the experimental conditions, i.e. glutamine omission (called Q0). In other words, in Figure 2, the variations of the metabolic fluxes for the condition Q0 are not correct. When this error is corrected, the resulting mathematical model changes (in particular for the results associated with Q0 conditions), several figures and tables are modified, and the interpretation of the fluxes in Q0 has to be slightly modified. Therefore the authors wish to retract the article. However, the error does not affect the modelling approach or the methodology presented in the article. Therefore, a revised version with the correct data has since been published: http://www.sciencedirect.com/science/article/pii/S0168165617302663. We apologize to the scientific community for the need to retract the article and the inconvenience caused.

  • 15.
    Hellgren, Mikko
    et al.
    Department of Medical Biochemistry and Biophysics, Karolinska Institutet.
    Nordling, Torbjörn E M
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Höög, Jan Olov
    Department of Medical Biochemistry and Biophysics, Karolinska Institutet.
    Multi-level modelling of the parallell metabolism of ethanol and retinol, with implications for foetal alcohol syndrome2008In: The 9th International Conference on Systems Biology (ICSB-2008) in Gothenburg (Sweden): Abstract book, 2008Conference paper (Refereed)
    Abstract [en]

    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.

  • 16.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Cedersund, Gunnar
    On parametric sensitivity and structural robustness of cellular functions - The oscillatory metabolism of activated Neutrophils2005In: 2005 44th IEEE Conference on Decision and Control & European Control Conferenc, 2005, p. 3681-3686Conference paper (Refereed)
    Abstract [en]

    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.

  • 17.
    Jacobsen, Elling
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Nordling, Torbjörn
    National Cheng Kung University, Taiwan.
    Robust Target Identification in Drug Discovery2016In: 11th IFAC DYCOPS-CAB, Elsevier, 2016Conference paper (Refereed)
    Abstract [en]

    A key step in the development of new pharmaceutical drugs is that of identifying direct targets of the bioactive compounds, and distinguishing these from all other gene products that respond indirectly to the drug targets. Currently dominating approaches to this problem are based on often time consuming and costly experimental methods aimed at locating physical bindings of the corresponding small molecule to proteins or DNA sequences. In this paper we consider target identification based on time-series expression data of the corresponding gene regulatory network, using perturbation with the active compound only. As we show, the problem of identifying the direct targets can then be cast as a linear regression problem and, in principle, be accomplished with a number of samples equal to the number of involved genes and bioactive compounds. However, the regression matrix will typically be highly ill-conditioned and the target identification therefore prone even to small measurement uncertainties. In order to provide a label of confidence for the target identification, we consider conditions that can be used to quantify the robustness of the identification of individual drug targets with respect to uncertainty in the expression data. For this purpose, we cast the uncertain regression problem as a robust rank problem and employ SVD or the structured singular value to compute the robust rank. The proposed method is illustrated by application to a small scale gene regulatory network synthesised in yeast to serve as a benchmark problem in network inference

  • 18.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    The Role of Feedback in Intracellular Networks2006Conference paper (Other academic)
  • 19.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Cedersund, G.
    Structural robustness of biochemical network models-with application to the oscillatory metabolism of activated neutrophils2008Article in journal (Refereed)
    Abstract [en]

    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.

  • 20.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Nenchev, Vladislav
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Robustification as a tool in modeling biochemical reaction networks2010In: Proceedings of IFAC DYCOPS 2010, Leuven, Belgium, 2010, p. 338-343Conference paper (Refereed)
    Abstract [en]

    Biological functions have evolved to become robust against a multitude of perturbations such as gene mutations, intracellular noise and changes in the physical and chemical environment. This robustness should be reflected in models of the underlying biochemical networks, and robustness analysis is frequently employed in validating models of intracellular biochemical reaction networks. However, at present there are no tools or guidelines available to support postulation of model modifications that can serve to improve the robustness. Herein we propose a method based on computing the sensitivity of the robustness with respect to generic dynamic perturbations applied to the individual network edges. To quantify robustness we compute the smallest simultaneous change in the activity of the network nodes that induces a bifurcation in the network, resulting in a qualitative change in the network behavior. The focus is on biological functions related to bistable switches and sustained oscillations, and the proposed methodology is demonstrated through application to metabolic oscillations in white blood cells and bistable switching in MAPK signal transduction.

  • 21.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Nenchev, Vladislav
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Robustification as a tool in modeling biochemical reaction networks2011In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 21, no 10, p. 1517-1525Article in journal (Refereed)
    Abstract [en]

    Biological functions have evolved to become robust against a multitude of perturbations such as gene mutations, intracellular noise and changes in the physical and chemical environment. This robustness should be reflected in models of the underlying biochemical networks, and robustness analysis has consequently been established as an important tool for model validation in systems biology. However, while robustness analysis can be used to invalidate a given model, it does not support the postulation of model modifications that can serve to improve the robustness. Herein we propose a method for this purpose, based on computing the sensitivity of the robustness with respect to generic dynamic perturbations applied to the individual network edges. To quantify robustness we compute the smallest simultaneous change in the activity of the network nodes that induces a bifurcation in the network, resulting in a qualitative change in the network behavior. The proposed method can be used to identify network interactions with the most significant impact on the overall robustness of a given function. By considering the impact of adding new nodes and edges, the method can also be used to postulate the presence of important unmodeled components and interactions. The focus here is on biological functions related to bistable switches and sustained oscillations, and the proposed methodology is demonstrated through application to metabolic oscillations in white blood cells and bistable switching in MAPK signal transduction. The classical Goodwin model of oscillations in a simple gene regulatory network is used for illustration throughout the paper.

  • 22.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Nenchev, Vladislav
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Structural Robustness Analysis Provides Hypotheses on Missing Links in Network Models of Intracellular Oscillators and Bistable Switches2010In: Proceedings of The 11th International Conference on Systems Biology (ICSB-2010) in Edinburgh (UK):, 2010Conference paper (Refereed)
  • 23.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Nordling, Torbjörn
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Robust Inference of Gene Regulatory Networks2012Conference paper (Refereed)
    Abstract [en]

    To successfully infer the biochemical network that underly a given biological function, two problems must be resolved. First, sufficiently informative data that allow discrimination between alternative models corresponding to different network structures must be recorded. Second, the ''correct'' network model with a structure including only the active interactions must be selected based on the recorded data set. In this work we address both these problems within the framework of robust inference. We first address the problem in a deterministic framework and show that determination of the existence of a specific interaction, or directed network edge, can be reduced to a rank test on a matrix constructed from available perturbation and response data. To deal with uncertainty, we introduce a norm-bounded set around the nominal data points in the sample space and assume the true response of the system is within this set. A similar uncertainty description is employed to describe uncertainties in the applied perturbations. Determination of the existence of a specific interaction under uncertainty can then be formulated as a robust rank problem, which can be solved using results from robust control theory. The proposed method provides necessary and sufficient conditions for the existence of a directed network edge under the assumption that the true response is within the given uncertainty set. Similarly, network edges can be determined with a robustness margin, i.e., the size of the uncertainty set for which the edge can identified with confidentiality. An important outcome of the method is determination of interactions for which the available data set does not contain sufficient information to infer existence or non-existence with confidence. Furthermore, based on well known results from linear algebra, we show how specific perturbation experiments can be designed to generate data that enable inference of a specific edge at a given level of confidence.

  • 24.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. National Cheng Kung Universit, Taiwan.
    Nordling, Torbjörn E. M.
    KTH, School of Electrical Engineering (EES), Automatic Control. National Cheng Kung Universit, Taiwan.
    Robust Target Identification for Drug Discovery2016In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 49, no 7, p. 815-820Article in journal (Refereed)
    Abstract [en]

    A key step in the development of new pharmaceutical drugs is that of identifying direct targets of the bioactive compounds, and distinguishing these from all other gene products that respond indirectly to the drug targets. Currently dominating approaches to this problem are based on often time consuming and costly experimental methods aimed at locating physical bindings of the corresponding small molecule to proteins or DNA sequences. In this paper we consider target identification based on time-series expression data of the corresponding gene regulatory network, using perturbation with the active compound only. As we show, the problem of identifying the direct targets can then be cast as a linear regression problem and, in principle, be accomplished with a number of samples equal to the number of involved genes and bioactive compounds. However, the regression matrix will typically be highly ill-conditioned and the target identification therefore prone even to small measurement uncertainties. In order to provide a label of confidence for the target identification, we consider conditions that can be used to quantify the robustness of the identification of individual drug targets with respect to uncertainty in the expression data. For this purpose, we cast the uncertain regression problem as a robust rank problem and employ SVD or the structured singular value to compute the robust rank. The proposed method is illustrated by application to a small scale gene regulatory network synthesised in yeast to serve as a benchmark problem in network inference.

  • 25.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Trané, Camilla
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Structural Robustness of Biochemical Networks: Quantifying Robustness and Identifying Fragilities2010In: Control Theory and Systems Biology / [ed] Pablo A. Iglesias, Brian P. Ingalls, MIT Press, 2010, 1Chapter in book (Refereed)
  • 26.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Trané, Camilla
    KTH, School of Electrical Engineering (EES), Automatic Control.
    The use of Dynamic Perturbations to Uncover the Role of Multiple Loops in the Mammalian Circadian Clock2006In: Proceedings of 2006 International Workshop on Systems Biology, Dublin, Ireland, 2006Conference paper (Refereed)
  • 27.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Trané, Camilla
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Using dynamic perturbations to identify fragilities in biochemical networks2010In: International Journal of Robust and Nonlinear Control, ISSN 1049-8923, E-ISSN 1099-1239, Vol. 20, no 9, p. 1027-1046Article in journal (Refereed)
    Abstract [en]

    Biological functions at the cellular level result from interactions between genes, proteins and metabolites within complex biochemical reaction networks. Identifying specific and localized fragilities in such networks is important to understand the source of biological malfunctions, and to devise strategies for fighting disease states that have developed robustness. Herein we consider a method based on robust control theoretic concepts for identifying network fragilities. In particular, we consider adding static or dynamic perturbations to the network edges, i.e. the direct binary interactions within the network, and compute the smallest perturbation that induces a bifurcation, and hence a qualitative change, in the network function. The proposed method can also serve as a powerful modeling tool in that it can be used to identify parts of a model that require more detailed descriptions of the underlying processes. The method is demonstrated by application to models of sustained oscillations in the glycolytic pathway and bistability in the mitogen-activated protein kinase (MAPK) signal transduction.

  • 28.
    Jacobsen, Elling W.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Zhang, Shuoqi
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Elucidating the Design Principles of Signal Transduction Networks: Application to Dopamine and Calcium signaling in reinforcement learning2011In: Proceedings of IFAC World Congress 2011, Milan, Italy, 2011, p. 10436-10441Conference paper (Refereed)
    Abstract [en]

    The biochemical networks underlying biological functions are in general highly complex. An important aim of systems biology is to provide mechanistic insight into how the different interactions within the network give rise to specific behaviors and properties. In this paper we consider the use of structured dynamic perturbations applied to the network nodes and edges for elucidating the most important interactions in signal transduction networks. Signal transduction networks mediate extracellular and intracellular signals to the nucleus, resulting in an appropriate response by the gene regulatory network. The most important characteristic of signal transduction networks is usually the specific temporal amplification of signals. As a case study we consider the intracellular signaling pathways that underlie reinforcement learning in striatum brain cells. It has recently been found that these networks respond to Dopamine and Calcium signals in a fashion which is strongly dependent on the signal shape, and the hypothesis is that this is related to the existence of a resonant feedback loop within the network. By systematically perturbing the nodes and edges of the network using general dynamic perturbations, affecting both the strength and phase lag of the direct interactions within the network, we are able to identify the most important components and interactions underlying the ''resonant'' signal amplification. Based on this we derive a reduced order model of the network, with retained physical states, from which we can show that the apparant resonance is caused by two parallel pathways with opposing effects and widely different time-constants. We postulate that this is a sound architecture for signal amplification of mid-frequency signals based on the fact that the robustness can be made almost arbitrarily large, as compared to resonant feedback loops that are inherently unrobust.

  • 29.
    Liu, Yi
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Error Detection and Control in Grey-Box Modelling of Distributed Parameter Processes2004In: Proceedings of IFAC DYCOPS 7, Boston, USA, 2004Conference paper (Refereed)
  • 30.
    Liu, Yi
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Grey-Box Modelling of Distributed Parameter Processes - Application to Chromatography2004In: EUROPEAN SYMPOSIUM ON COMPUTER-AIDED PROCESS ENGINEERING - 14, 2004Conference paper (Refereed)
  • 31.
    Liu, Yi
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    On the use of reduced order models in bifurcation analysis of distributed parameter systems2004In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 28, no 1-2, p. 161-169Article in journal (Refereed)
    Abstract [en]

    Bifurcation theory provides a powerful tool for analyzing the nonlinear dynamic behavior of process systems. However, although the theory in principle applies to lumped as well as distributed parameter processes, it is in practice necessary to reduce the order of distributed (partial differential equations, PDE) models prior to application of the theory. As shown in this paper, simply applying some ad hoc discretization method such as finite differences or finite elements, can result in spurious bifurcations and erroneous predictions of stability. To enable detection of such anomalities, and to aid in the selection of a proper model order, we propose a method for estimating the error introduced by the model reduction. Apart from simply providing a label of confidence in the results of the bifurcation analysis, the estimated error can be used to improve the quality of the reduced order model. For this purpose we propose a method based on dynamically moving the discretization mesh such as to minimize the discretization error. The proposed method is based on principles from feedback control, and is both very simple and highly robust compared with existing so-called moving mesh methods. As an application we consider bifurcation analysis of a heat-integrated fixed-bed reactor.

  • 32.
    Nordling, Torbjörn E M
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Design of perturbations is the key to inference of tumour specific gene regulation2010Other (Other academic)
    Abstract [en]

    Tumour development requires alteration of the normal gene regulation of involved cell types. Mapping of these alterations and inference of the resulting local disease network is therefore crucial to improve our understanding of tumour progression and develop novel cures. Based on the number of known alterations and subtypes of each form of cancer, we assume that the network inference needs to be based on subtype and cell specific expression data to obtain the necessary specific knowledge. We have identified design of perturbations as the key to successful inference of such locally altered gene regulatory networks. Analysis of published gene expression data sets reveal that the variation in expression is concentrated to significantly fewer “characteristic modes” (Holter et al. 2000) or “eigengenes” (Alter et al. 2000) than both the number of recorded assays and the number of measured genes. In other words, the responses obtained in standard experiments are typically concentrated to a subset of the gene space. This is an advantage when considering modelling for predicting gene responses to external perturbations, since the model only needs to capture the characteristic modes correctly for this purpose. However, it seriously hampers network inference, since it implies that models with widely different network structure are practically indistinguishable based on standard response data. To infer the structure we need to design specific perturbations that yield a sufficiently strong signal also for perturbations that are attenuated by the system, i.e., excite the weak modes of the network. The perturbations needed depend on the unknown system and we have therefore developed an iterative design, which we here demonstrate on two published gene expression data sets (Lorenz et al. 2009, Gardner et al. 2003). Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A. 2000 Aug 29; 97(18): 10101-6. Gardner TS, di Bernardo D, Lorenz D, Collins JJ, Inferring genetic networks and identifying compound mode of action via expression profiling. Science. 2003 Jul 4; 301(5629): 102-5. Holter NS, Mitra M, Maritan A, Cieplak M, Banavar JR, Fedoroff NV. Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci U S A. 2000 Jul 18; 97(15): 8409-14. Lorenz DR, Cantor CR, Collins JJ. A network biology approach to aging in yeast. Proc Natl Acad Sci U S A. 2009 Jan 27; 106(4): 1145-50.

  • 33.
    Nordling, Torbjörn E. M.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Experiment design for optimal excitation of gene regulatory networks2006Conference paper (Refereed)
    Abstract [en]

    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.

  • 34.
    Nordling, Torbjörn E. M.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Experiment Design for Proper Excitation of Gene Regulatory Networks2007In: Proceedings of Foundations of Systems Biology in Engineering (FOSBE), 2nd Conference, 2007Conference paper (Refereed)
    Abstract [en]

    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.

  • 35.
    Nordling, Torbjörn E. M.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Experiment Design for Systematic Excitation of Gene Regulatory Networks2006Conference paper (Refereed)
  • 36.
    Nordling, Torbjörn E. M.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Ill-Conditioning: A Property of Bio Networks2008In: Proceedings of 2nd q-Bio Conference on Cellular Information Processing, 2008, 2008Conference paper (Refereed)
    Abstract [en]

    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.

  • 37.
    Nordling, Torbjörn E. M.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Inference of interampatte gene regulatory networks: with application to apoptosis signalling2008In: The 9th International Conference on Systems Biology (ICSB-2008) in Gothenburg (Sweden): Abstract book, Gothenburg, Sweden: University Of Gothenburg, Curran Associates, Inc. , 2008Conference paper (Other academic)
    Abstract [en]

    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.

  • 38.
    Nordling, Torbjörn E M
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Interampatteness - A generic property of biochemical networks2009In: IET Systems Biology, ISSN 1751-8849, Vol. 3, no 5, p. 388-403Article in journal (Refereed)
    Abstract [en]

    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.

  • 39.
    Nordling, Torbjörn E M
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Invalidating models of gene regulatory networks: the implications of characteristic and weak modes for network inference2009In: Proceedings of Conference on Engineering Principles in Biological Systems: Hinxton, UK, Engineering Principles in Biological systems conference, Hinxton (UK) , 2009Conference paper (Refereed)
    Abstract [en]

    Analysis of published gene expression data sets reveal that the variation in expression is concentrated to significantly fewer ‘characteristic modes’ or ‘eigengenes’ than both the number of recorded assays and the number of measured genes. In other words, the responses obtained in standard experiments are typically concentrated to a subset of the gene space. This is an advantage when considering modelling for predicting gene responses to external perturbations, since the model only needs to capture the characteristic modes correctly for this purpose. However, it seriously hampers network inference, since it implies that models with widely different network structure are practically indistinguishable based on standard response data. Furthermore, as we show here, the presence of characteristics modes implies that it is easy to validate and hard to invalidate false model structures. The information required to invalidate a false model is hidden in the weak modes that contribute only weakly to the gene response data and therefore are largely hidden in the measurement noise. Here we use two published gene expression data sets and an in silico gene regulatory network to illustrate the principal differences between validation and invalidation of models of gene regulatory networks. All three systems have a high degree of interampatteness (see ref.), i.e. some perturbations are amplified while others are attenuated by the system. The response of an interampatte system to random perturbations can be desccribed well based on the characteristic modes only, implying that it is easy to validate any model that predicts the characteristic modes correctly. To invalidate a model we need to design specific perturbations that yield a sufficiently strong signal also for perturbations that are attenuated by the system, i.e., that excites the weak modes of the network. From a biological perspective it is trivial to realize that amplification and attenuation of perturbations are equally important for biological function, and hence a proper model should be able to predict both the weak and characteristic modes correctly. We stress that the common assumption that the quality of a model can be judged based on its ability to predict response data only holds for systems with a low degree of interampatteness. Nordling TEM, Jacobsen EW Interampatteness–a generic property of biochemical networks. IET Systems Biology, 2009, in press.

  • 40.
    Nordling, Torbjörn E. M.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    On Sparsity As a Criterion in Reconstructing Biochemical Networks2011In: Proceedings of the 18th International Federation of Automatic Control (IFAC) World Congress, 2011, 2011, p. 11672-11678Conference paper (Refereed)
    Abstract [en]

    A common problem in inference of gene regulatory networks from experimental response data is the relatively small number of samples available in relation to the number of nodes/states. In many cases the identification problem is underdetermined and prior knowledge is required for the network reconstruction. A specific prior that has gained widespread popularity is the assumption that the underlying network is sparsely connected. This has led to a flood of network reconstruction algorithms based on subset selection and regularization techniques, mainly adopted from the statistics and signal processing communities. In particular, methods based on \ell_1 and \ell_2-penalties on the interaction strengths, such as LASSO, have been widely proposed and applied. We briefly review some of these methods and discuss their suitability for inferring the structure of biochemical networks. A particular problem is the fact that these methods provide little or no information on the uncertainty of individual identified edges, combined with the fact that the identified networks usually have a large fraction of false positives as well as false negatives.To partly overcome these problems we consider conditions that can be used to classify edges into those that can be uniquely determined based on a given incomplete data set, those that cannot be uniquely determined due to collinearity in the data and those for which no information is available. Apart from providing a label of confidence for the individual edges in the identified network, the classification can be used to improve the reconstruction by employing standard unbiased identification methods to the identifiable edges while employing sparse approximation methods for the remaining network. The method is demonstrated through application to a synthetic network in yeast which has recently been proposed for in vivo assessment of network identification methods.

  • 41.
    Nordling, Torbjörn E. M.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sparsity is a means and not an aim in inference of gene regulatory networks2010In: Proceedings of The 11th International Conference on Systems Biology (ICSB-2010), 2010Conference paper (Refereed)
    Abstract [en]

    Availability of high-throughput gene expression data has lead to numerous attempts to infer network models of gene regulation based on expression changes. The low number of observations compared to the number of genes, the low signal-to-noise ratios, and the system being interampatte make the inference problem ill-posed and challenging. To solve the problem a majority of all published approaches resort to regularization, e.g. the LASSO penalty is used to find a sparse model. Regularization is known to introduce a bias, but its effect on inferred gene regulatory networks has hardly been investigated. In machine learning and compressed sensing, where regularization has been widely applied and studied, the objective is to reproduce a signal and the actual variable selection is of minor importance as long as the signal is reproduced well. In network inference, on the other hand, the variable selection is crucial since we want to identify the true topology of the network and a minimal number of links is not an aim per se. We first study the inference problem in a deterministic setting in order to gain insight and derive conditions on when the regularization causes false negative and positive links. By viewing the problem as a parameter identifiability problem, we establish three cases in which a subset of the parameters can be uniquely determined. Finally we devise conditions for invalidation of the inferred links using existing or additional data; resulting in an iterative procedure of inference and experiment design that significantly increases the confidence in the inferred network model.

  • 42. Schmidt, H.
    et al.
    Cho, K. H.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Identification of small scale biochemical networks based on general type system perturbations2005In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 272, no 9, p. 2141-2151Article in journal (Refereed)
    Abstract [en]

    New technologies enable acquisition of large data-sets containing genomic, proteomic and metabolic information that describe the state of a cell. These data-sets call for systematic methods enabling relevant information about the inner workings of the cell to be extracted. One important issue at hand is the understanding of the functional interactions between genes, proteins and metabolites. We here present a method for identifying the dynamic interactions between biochemical components within the cell, in the vicinity of a steady-state. Key features of the proposed method are that it can deal with data obtained under perturbations of any system parameter, not only concentrations of specific components, and that the direct effect of the perturbations does not need to be known. This is important as concentration perturbations are often difficult to perform in biochemical systems and the specific effects of general type perturbations are usually highly uncertain, or unknown. The basis of the method is a linear least-squares estimation, using time-series measurements of concentrations and expression profiles, in which system states and parameter perturbations are estimated simultaneously. An important side-effect of also employing estimation of the parameter perturbations is that knowledge of the system's steady-state concentrations, or activities, is not required and that deviations from steady-state prior to the perturbation can be dealt with. Time derivatives are computed using a zero-order hold discretization, shown to yield significant improvements over the widely used Euler approximation. We also show how network interactions with dynamics that are too fast to be captured within the available sampling time can be determined and excluded from the network identification. Known and unknown moiety conservation relationships can be processed in the same manner. The method requires that the number of samples equals at least the number of network components and, hence, is at present restricted to relatively small-scale networks. We demonstrate herein the performance of the method on two small-scale in silico genetic networks.

  • 43. Schmidt, H.
    et al.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Identifying feedback mechanisms behind complex cell behavior2004In: Control Systems Magazine, ISSN 0272-1708, Vol. 24, no 4, p. 91-102Article in journal (Refereed)
    Abstract [en]

    An analysis method based on linearization and decomposition of the network model was used to examine the early embryonic periodiccelldivisions of Xenopus eggs. The components andfeedbackinterconnections that generate periodic oscillations and bistability in the Xenopuscellcycle were identified. Thefeedbackmechanismsdriving the periodic oscillations in yeast glycolysis were also clarified.

  • 44. Schmidt, H.
    et al.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Selecting control configurations for performance with independent design2003In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 27, no 1, p. 101-109Article in journal (Refereed)
    Abstract [en]

    An important task in the design of decentralized control systems for multivariable plants is the choice of the structure of interconnections between manipulated variables and controlled outputs, i.e. the control configuration. Most tools available for this task, such as the RGA, address mainly the stability properties of the overall system. In this paper we focus on performance, and consider in particular the problem of selecting control structures that enable a desired performance to be achieved through independent tuning of the subsystems. We show that, for this task, the common assumption of perfect control within the bandwidths of the subsystems is a poor one. Based on this, a new measure of interactions, the decentralized relative gain (dRG), is proposed. Finally, it is stressed that the effect of interactions on the magnitude as well as on the phase lag of the subsystems should be considered when selecting control configurations for performance.

  • 45.
    Schmidt, Henning
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jacobsen, Elling
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Linear systems approach to analysis of complex dynamic behaviours in biochemical networks2004In: IEE Proceedings - Systems Biology, ISSN 1741-2471, E-ISSN 1741-248X, Vol. 1, no 1, p. 149-158Article in journal (Refereed)
    Abstract [en]

    Central functions in the cell are often linked to complex dynamic behaviours, such as sustained oscillations and multistability, in a biochemical reaction network. Determination of the specific mechanisms underlying such behaviours isimportant, e.g. to determine sensitivity, robustness, and modelling requirements of given cell functions. In this work we adopt a systems approach to the analysis of complex behaviours in intracellular reaction networks, described byordinary differential equations with known kinetic parameters. We propose to decompose the overall system into a number of low complexity subsystems, and consider the importance of interactions between these in generating specific behaviours. Rather than analysing the network in a state corresponding to the complex non-linear behaviour, we move the system to the underlying unstable steady state, and focus on the mechanisms causing destabilisation of this steady state. This is motivated by the fact that all complex behaviours in unforced systems can be traced to destabilisation (bifurcation) of some steady state, andhence enables us to use tools from linear system theory to qualitatively analyse the sources of given network behaviours. One important objective of the present study is to see how far one can come with a relatively simple approach to the analysis of highly complex biochemical networks. The proposed method is demonstrated by application to a model of mitotic control in Xenopus frog eggs, and to a model of circadian oscillations in Drosophila. In both examples we are able to identify the subsystems, and the related interactions, which are instrumental in generating the observed complex non-linear behaviours.

  • 46.
    Schmidt, Henning
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Analysis of complex behaviors in integrated plants2004In: EUROPEAN SYMPOSIUM ON COMPUTER-AIDED PROCESS ENGINEERING - 14, 2004, Vol. 18, p. 805-810Conference paper (Refereed)
    Abstract [en]

    In this paper we propose a method, based on linear systems theory, which can be used to determine the feedback mechanisms within a plant that are the main sources of a given nonlinear behavior. This is important since it can be used to guide the plant design such as to remove undesirable behaviors. We focus here in particular on sustained oscillatory behaviors, and illustrate our method by applying it to two example systems.

  • 47.
    Schmidt, Henning
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    On The Decomposition of Dynamica Metabolic Networks2004In: Proceedings of 5th International Conference on systems Biology, Heidelberg, Germany (Book of Asbtracts), 2004Conference paper (Refereed)
  • 48.
    Schmidt, Henning
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jacobsen, Elling W.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Cho, Kwang H
    Identification of Feedback Loops in Metabolic Pathways2004In: Proceedings 5th International Conference on Systems Biology, Heidelberg, Germany (Book of Abstracts), 2004Conference paper (Refereed)
  • 49.
    Sivard, Gunilla
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. KTH, Centres, XPRES, Excellence in production research.
    Lindberg, Lars
    KTH, School of Industrial Engineering and Management (ITM).
    Shariatzadeh, Navid
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    The BIC Model – Towards Business Oriented Information Contexts2015In: Procedia CIRP / [ed] Elsevier B.V. Moshe Shpitalni, Anath Fischer and Gila Molcho, Elsevier, 2015, Vol. 36, p. 101-105Conference paper (Refereed)
    Abstract [en]

    Increasingly complex products and business models require support of increasingly complex information. In this paper we propose a new approach BIC, Business Information Context, to define contexts for accessing, viewing, and managing this complex information. BIC structures information based on key domains: business drivers, business processes, information entities, product characteristics, and information systems. We compare BIC with other and simpler approaches, like views and contexts used in the ISO 10303 (STEP) standards, design methodologies, and PLM systems. We illustrate the definition and use of BIC with an implementation of an application forprotecting a company’s intellectual property.

  • 50. Sohlberg, Björn
    et al.
    Jacobsen, Elling W.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Grey-Box Modeling: Branches and Experiences2008In: 17th World Congress, International Federation of Automatic Control, IFAC, 2008Conference paper (Refereed)
    Abstract [en]

    This paper deals with methods and experiences of incorporating a priori knowledge into mathematical models of industrial processes and systems. Grey box modelling has been developed in several directions and can be grouped into branches depending on the way a priori knowledge is handled. In this paper we divide grey box modelling into the following branches; constrained black box identification, semi-physical modelling, mechanistic modelling, hybrid modelling and distributed parameter modelling. Experiences from case studies demonstrate the different branches of grey box modelling procedures. In the applications, the grey box models have been used for model based control, soft sensors, process supervision and failure detection. Further, distributed parameter modelling presents a specific challenge in that it is difficult to distinguish model reduction errors from model-data discrepancies. By estimating the model reduction error and forming hypothesis tests based on the estimate, the problem can be dealt with effectively.

12 1 - 50 of 62
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf