| 1. |
- Bendrioua, Loubna, et al.
(författare)
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Yeast AMP-activated protein kinase monitors glucose concentration changes and absolute glucose levels
- 2014
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Ingår i: Journal of Biological Chemistry. - 0021-9258 .- 1083-351X. ; 289:18, s. 12863-12875
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Tidskriftsartikel (refereegranskat)abstract
- Background: Little is known about the signaling dynamics of AMP-activated protein kinase. Results: We define the dynamics of yeast AMPK signaling under different glucose concentrations. Conclusion: The Snf1-Mig1 signaling system monitors glucose concentration changes and absolute glucose levels to adjust the metabolism to a wide range of conditions. Significance: This description of AMPK signaling dynamics will stimulate studies defining the integration of signaling and metabolism. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.
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| 2. |
- Meyer, René, et al.
(författare)
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Heterogeneous kinetics of AKT signaling in individual cells are accounted for by variable protein concentration
- 2012
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Ingår i: Frontiers in Physiology. - : Frontiers Media SA. - 1664-042X. ; 3:451
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Tidskriftsartikel (refereegranskat)abstract
- In most solid cancers, cells harboring oncogenic mutations represent only a sub-fraction of the entire population. Within this sub-fraction the expression level of mutated proteins can vary significantly due to cellular variability limiting the efficiency of targeted therapy. To address the causes of the heterogeneity, we performed a systematic analysis of one of the most frequently mutated pathways in cancer cells, the phosphatidylinositol 3 kinase (PI3K) signaling pathway. Among others PI3K signaling is activated by the hepatocyte growth factor (HGF) that regulates proliferation of hepatocytes during liver regeneration but also fosters tumor cell proliferation. HGF mediated responses of PI3K signaling were monitored both at the single cell and cell population level in primary mouse hepatocytes and in the hepatoma cell line Hepa1_6. Interestingly, we observed that the HGF mediated AKT responses at the level of individual cells is rather heterogeneous. However, the overall average behavior of the single cells strongly resembled the dynamics of AKT activation determined at the cell population level. To gain insights into the molecular cause for the observed heterogeneous behavior of individual cells, we employed dynamic mathematical modeling in a stochastic framework. Our analysis demonstrated that intrinsic noise was not sufficient to explain the observed kinetic behavior, but rather the importance of extrinsic noise has to be considered. Thus, distinct from gene expression in the examined signaling pathway fluctuations of the reaction rates has only a minor impact whereas variability in the concentration of the various signaling components even in a clonal cell population is a key determinant for the kinetic behavior.
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| 3. |
- Almquist, Joachim, 1980, et al.
(författare)
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A nonlinear mixed effects approach for modeling the cell-to-cell variability of Mig1 dynamics in yeast.
- 2015
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- The last decade has seen a rapid development of experimental techniques that allow data collection from individual cells. These techniques have enabled the discovery and characterization of variability within a population of genetically identical cells. Nonlinear mixed effects (NLME) modeling is an established framework for studying variability between individuals in a population, frequently used in pharmacokinetics and pharmacodynamics, but its potential for studies of cell-to-cell variability in molecular cell biology is yet to be exploited. Here we take advantage of this novel application of NLME modeling to study cell-to-cell variability in the dynamic behavior of the yeast transcription repressor Mig1. In particular, we investigate a recently discovered phenomenon where Mig1 during a short and transient period exits the nucleus when cells experience a shift from high to intermediate levels of extracellular glucose. A phenomenological model based on ordinary differential equations describing the transient dynamics of nuclear Mig1 is introduced, and according to the NLME methodology the parameters of this model are in turn modeled by a multivariate probability distribution. Using time-lapse microscopy data from nearly 200 cells, we estimate this parameter distribution according to the approach of maximizing the population likelihood. Based on the estimated distribution, parameter values for individual cells are furthermore characterized and the resulting Mig1 dynamics are compared to the single cell times-series data. The proposed NLME framework is also compared to the intuitive but limited standard two-stage (STS) approach. We demonstrate that the latter may overestimate variabilities by up to almost five fold. Finally, Monte Carlo simulations of the inferred population model are used to predict the distribution of key characteristics of the Mig1 transient response. We find that with decreasing levels of post-shift glucose, the transient response of Mig1 tend to be faster, more extended, and displays an increased cell-to-cell variability.
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| 4. |
- Almquist, Joachim, 1980, et al.
(författare)
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Kinetic models in industrial biotechnology - Improving cell factory performance
- 2014
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Ingår i: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 24, s. 38-60
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Tidskriftsartikel (refereegranskat)abstract
- An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.
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| 5. |
- Almquist, Joachim, 1980, et al.
(författare)
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Sensitivity Equations Provide More Robust Gradients and Faster Computation of the FOCE Approximation to the Population Likelihood
- 2015
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Ingår i: Proceedings of the 24th Annual meeting of the Population Approach Group in Europe, PAGE2015.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: The first order conditional estimation (FOCE) method [1] is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics [2]. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. Methods: We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models [3]. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. Results: The advantages of the approach are demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation [4]. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Conclusions: Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that signicant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization. References: [1] Wang Y. Derivation of various NONMEM estimation methods. J of Pharmacokin Pharmacodyn (2007) 34(5): 575-593. [2] Johansson ÅM, Ueckert S, Plan EL, Hooker AC, Karlsson MO. Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7. J of Pharmacokin Pharmacodyn (2014) 41(3):223-238. [3] Almquist J, Leander J, Jirstrand M. Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood. In press J of Pharmacokin Pharmacodyn (2015). [4] Tapani S, Almquist J, Leander J, Ahlström C, Peletier LA, Jirstrand M, Gabrielsson J. Joint Feedback Analysis Modeling of Nonesterified Fatty Acids in Obese Zucker Rats and Normal Sprague–Dawley Rats after Different Routes of Administration of Nicotinic Acid. J Pharmaceutical Sciences (2014), 103(8):2571–2584.
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| 6. |
- Almquist, Joachim, 1980, et al.
(författare)
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Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood
- 2015
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 42:3, s. 191-209
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Tidskriftsartikel (refereegranskat)abstract
- The first order conditional estimation (FOCE) method is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. The advantages of this approach were demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that significant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization.
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| 7. |
- Almqvist, Joachim E, 1980, et al.
(författare)
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A Kinetic Model of the Monocarboxylate Transporter MCT1 and its Interaction with Carbonic Anhydrase II
- 2010
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Ingår i: Journal of Computer Science and Systems Biology. - : OMICS Publishing Group. - 0974-7230. ; 3:5, s. 107-116
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Tidskriftsartikel (refereegranskat)abstract
- The enzyme carbonic anhydrase isoform II (CAII), catalyzing the hydration and dehy-dration of CO2, enhances transport activity of the monocarboxylate transporter isoform I (MCT1, SLC16A1) expressed in Xenopus oocytes by a mechanism that does not require CAII catalytic activity. In the present study, we have investigated the mechanism of the CAII induced increase in transport activity by using electrophysiological techniques and mathematical modeling of the MCT1 transport cycle. The model consists of six states arranged in cyclic fashion and features an ordered, mirrorsymmetric, binding mechanism, where binding and unbinding of the proton to the transport protein is considered to be the rate limiting step under physiological conditions. An explicit rate expression for the substrate flux is derived using model reduction techniques. By treating the pools of intra-and extracellular MCT1 substrates as dynamic states, the time dependent kinetics are obtained by integration, using the derived expression for the substrate flux. The simulations were compared with experimental data obtained from MCT1-expressing oocytes injected with different amounts of CAII. The model suggests that CAII increases the effective rate constants of the proton reactions, possibly by working as a proton antenna.
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| 8. |
- Almqvist, Joachim E, 1980, et al.
(författare)
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Modeling the Effect of Kv1.5 Block on the Canine Action Potential
- 2010
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Ingår i: Biophysical Journal. - : Elsevier BV. - 0006-3495 .- 1542-0086. ; 99:9, s. 2726-2736
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Tidskriftsartikel (refereegranskat)abstract
- A wide range of ion channels have been considered as potential targets for pharmacological treatment of atrial fibrillation. The Kv1.5 channel, carrying the IKur current, has received special attention because it contributes to repolarization in the atria but is absent or weakly expressed in ventricular tissue. The dog serves as an important animal model for electrophysiological studies of the heart and mathematical models of the canine atrial action potential (CAAP) have been developed to study the interplay between ionic currents. To enable more-realistic studies on the effects of Kv1.5 blockers on the CAAP in silico, two continuous-time Markov models of the guarded receptor type were formulated for Kv1.5 and subsequently inserted into the Ramirez-Nattel-Courtemanche model of the CAAP. The main findings were: 1), time- and state-dependent Markov models of open-channel Kv1.5 block gave significantly different results compared to a time- and state-independent model with a downscaled conductance; 2), the outcome of Kv1.5 block on the macroscopic system variable APD90 was dependent on the precise mechanism of block; and 3), open-channel block produced a reverse use-dependent prolongation of APD90. This study suggests that more-complex ion-channel models are a prerequisite for quantitative modeling of drug effects.
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| 9. |
- Andersson, Robert, et al.
(författare)
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Dose-response-time data analysis of nicotinic acid-induced changes in non-esterified fatty acids in rats
- 2014
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Ingår i: In proceedings of PKUK 2014.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: Structural identifiability concerns whether the parameters in a postulated model structure can be uniquely determined given the input and output functions to and from that model. What this means in practice is that if a model is structurally unidentifiable, the model structure itself allows a subset (or all) of the model parameters to vary while the model output remains unchanged. Conclusions drawn from such a model are potentially unreliable. For instance, if the estimated value of Emax is of interest, but if Emax is a member of the subset of unidentifiable parameters as a result of the model structure, the estimated value of Emax is effectively meaningless. For deterministic models, there exist several different structural identifiability analysis techniques for both linear and nonlinear systems. However, little has been done on the identifiability analysis of models having a mixed-effects framework. Here the main challenge comes from the fact that, apart from having a deterministic part describing the typical individual, there is also an additional statistical sub-model describing the random effects for the parameters and the covariance between them. In population modelling, these parameters represent the variability in the population. Since estimation of the variability is often one of the main goals in population modelling, it is important to determine whether these parameters can be uniquely determined or otherwise. This motivates the need to extend the concept of structural identifiability for deterministic models to non-deterministic models such as mixed-effects models.Aim: To develop ways of analysing structural identifiability in mixed-effects models. Methods: In statistics, and in particular statistical inference, there exist problems which are similar to those encountered in parameter estimation for mixed-effect models. In this work, we make use of these similarities and use these relevant relations to study structural identifiability in mixed-effects models.Results: Some initial results from a structural identifiability analysis on a particular mixed-effects model structure are presented.
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| 10. |
- Andersson, Robert, et al.
(författare)
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Dose-response-time modelling - Second generation turnover model with integral feedback control
- 2015
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Ingår i: Proceedings of the 24th Annual meeting of the Population Approach Group in Europe, PAGE2015.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: To demonstrate the utility of a dose-response-time (DRT) model using a large preclinical biomarker dataset of nicotinic acid (NiAc) induced changes on free fatty acids (FFA).Methods: Data were collected from studies where different rates, routes, and modes of NiAc provocations on the FFA time course had been tested [1]. All information of the exposure were excluded in order to use a DRT approach. Different models structures, describing the biophase kinetics, were assessed and quantitatively and qualitatively compared. The modeled biophase drug amount was assumed to act as the `driving force`of an inhibitory Imax-model which acted on the turnover of FFA. An integral feedback controller was used to model the slow adaptation process that forces FFA levels back to baseline values under long-term NiAc provocations. Finally, new numerical algorithms were applied, which rely on sensitivity equations to robustly and efficiently compute the gradients of the approximate population likelihood function in mixed-effects modelling [2].Results: The DRT model successfully captured the behaviour of all FFA time courses. The model predicted 90% adaptation within four days of constant-rate infusions of NiAc, using rates that lead to therapeutic concentrations. High consistency of the pharmacodynamic parameters was shown when compared to an exposure-driven study by Tapani et al. [3].Conclusions: The versatility of the DRT approach was shown by successfully fitting a DRT model to all FFA time courses. Different feedback mechanisms were described, using moderator compartments and integral feedback control. The consistency in the pharmacodynamic parameters, when comparing to an exposure-driven approach, demonstrates the utility of DRT analysis in a wider context.References:[1] Ahlström C. Modelling of tolerance and rebound in normal and diseased rats. Dissertation, University of Gothenburg. 2011.[2] Almquist J, Leander J, Jirstrand M. Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood. J Pharmacokin Pharmacodyn. 2015.[3] Tapani S, Almquist J, Leander J, Ahlström C, Peletier LA, Jirstrand M, Gabrielsson J. Joint feedback analysis modeling of nonesterified fatty acids in obese Zucker rats and normal Sprague-Dawley rats after different routes of administration of nicotinic acid. J Pharm Sci. 2014.
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