| 1. |
- 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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| 2. |
- 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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| 3. |
- Almquist, Joachim, 1980
(författare)
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Kinetic Models in Life Science — Contributions to Methods and Applications
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Kinetic models in life science combine mathematics and biology to answer questions from areas such as cell biology, physiology, biotechnology, and drug development. The idea of kinetic models is to represent a biological system by a number of biochemical reactions together with mathematical expressions for the reaction kinetics, i.e., how fast the reactions occur. This defines a set of mass balance differential equations for the modeled biochemical variables, whose solution determines the variables' temporal dynamics. Good kinetic models describe, predict, and enable understanding of biological systems, and provide answers to questions which are otherwise technically challenging, unethical, or expensive to obtain directly from experiments. This thesis investigates the workflow for building and using kinetic models. Briefly, the model question determines a suitable mathematical framework for the mass balance equations, prior knowledge informs selection of relevant reactions and kinetics, and unknown parameters are estimated from experimental data. A validated model is used for simulation and analysis, which is interpreted to gain biological insights. Three kinetic models were created to illustrate the workflow. First, a model of the antiplatelet drug ticagrelor and the investigational antidote MEDI2452 was developed for the mouse. The model unraveled the biological mechanisms of the pharmacokinetic interaction and predicted free ticagrelor plasma concentration, thereby contributing to the pharmaceutical development of MEDI2452. Second, a model of the Kv1.5 potassium ion channel was integrated within an existing electrophysiological model of a canine atrial cell. The effect of Kv1.5 block on the action potential was simulated, which improved understanding of blocking mechanisms and enabled assessing pharmacological treatment of atrial fibrillation. Third, a nonlinear mixed effects (NLME) model, with population-level distributions of kinetic parameters, was successfully used to describe cell-to-cell variability of the yeast transcription factor Mig1. This model demonstrated the innovative idea of applying NLME modeling to single cell data.Two studies of kinetic model-building methods are also presented. First, a novel parameter estimation algorithm for NLME models is explained. It computes exact gradients using sensitivity-equations, and represents a substantial advancement over its predecessor. Second, a modeling framework is proposed that combines stochastic differential equations with NLME modeling. This promising framework extends the current scope of NLME models by considering uncertainty in the model dynamics.
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| 4. |
- Almquist, Joachim, 1980, et al.
(författare)
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Overexpressing cell systems are a competitive option to primary adipocytes when predicting in vivo potency of dual GPR81/GPR109A agonists
- 2018
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Ingår i: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 114, s. 155-165
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Tidskriftsartikel (refereegranskat)abstract
- Mathematical models predicting in vivo pharmacodynamic effects from in vitro data can accelerate drug discovery, and reduce costs and animal use. However, data integration and modeling is non-trivial when more than one drug-target receptor is involved in the biological response. We modeled the inhibition of non-esterified fatty acid release by dual G-protein-coupled receptor 81/109A (GPR81/GPR109A) agonists in vivo in the rat, to estimate the in vivo EC50 values for 12 different compounds. We subsequently predicted those potency estimates using EC 50 values obtained from concentration-response data in isolated primary adipocytes and cell systems overexpressing GPR81 or GPR109A in vitro. A simple linear regression model based on data from primary adipocytes predicted the in vivo EC50 better than simple linear regression models based on in vitro data from either of the cell systems. Three models combining the data from the overexpressing cell systems were also evaluated: two piecewise linear models defining logical OR- and AND-circuits, and a multivariate linear regression model. All three models performed better than the simple linear regression model based on data from primary adipocytes. The OR-model was favored since it is likely that activation of either GPR81 or GPR109A is sufficient to deactivate the cAMP pathway, and thereby inhibit non-esterified fatty acid release. The OR-model was also able to predict the in vivo selectivity between the two receptors. Finally, the OR-model was used to predict the in vivo potency of 1651 new compounds. This work suggests that data from the overexpressing cell systems are sufficient to predict in vivo potency of GPR81/GPR109A agonists, an approach contributing to faster and leaner drug discovery.
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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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Unraveling the Pharmacokinetic Interaction of Ticagrelor and MEDI2452 (Ticagrelor Antidote) by Mathematical Modeling
- 2016
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Ingår i: CPT: Pharmacometrics and Systems Pharmacology. - : Wiley. - 2163-8306. ; 5:6, s. 313-323
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Tidskriftsartikel (refereegranskat)abstract
- The investigational ticagrelor-neutralizing antibody fragment, MEDI2452, is developed to rapidly and specifically reverse the antiplatelet effects of ticagrelor. However, the dynamic interaction of ticagrelor, the ticagrelor active metabolite (TAM), and MEDI2452, makes pharmacokinetic (PK) analysis nontrivial and mathematical modeling becomes essential to unravel the complex behavior of this system. We propose a mechanistic PK model, including a special observation model for post-sampling equilibration, which is validated and refined using mouse in vivo data from four studies of combined ticagrelor-MEDI2452 treatment. Model predictions of free ticagrelor and TAM plasma concentrations are subsequently used to drive a pharmacodynamic (PD) model that successfully describes platelet aggregation data. Furthermore, the model indicates that MEDI2452-bound ticagrelor is primarily eliminated together with MEDI2452 in the kidneys, and not recycled to the plasma, thereby providing a possible scenario for the extrapolation to humans. We anticipate the modeling work to improve PK and PD understanding, experimental design, and translational confidence.
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| 7. |
- 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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| 8. |
- Andersson, R., et al.
(författare)
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Modeling of free fatty acid dynamics: insulin and nicotinic acid resistance under acute and chronic treatments
- 2017
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1573-8744 .- 1567-567X. ; 44:3, s. 203-222
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Tidskriftsartikel (refereegranskat)abstract
- Nicotinic acid (NiAc) is a potent inhibitor of adipose tissue lipolysis. Acute administration results in a rapid reduction of plasma free fatty acid (FFA) concentrations. Sustained NiAc exposure is associated with tolerance development (drug resistance) and complete adaptation (FFA returning to pretreatment levels). We conducted a meta-analysis on a rich pre-clinical data set of the NiAc-FFA interaction to establish the acute and chronic exposure-response relations from a macro perspective. The data were analyzed using a nonlinear mixed-effects framework. We also developed a new turnover model that describes the adaptation seen in plasma FFA concentrations in lean Sprague-Dawley and obese Zucker rats following acute and chronic NiAc exposure. The adaptive mechanisms within the system were described using integral control systems and dynamic efficacies in the traditional model. Insulin was incorporated in parallel with NiAc as the main endogenous co-variate of FFA dynamics. The model captured profound insulin resistance and complete drug resistance in obese rats. The efficacy of NiAc as an inhibitor of FFA release went from 1 to approximately 0 during sustained exposure in obese rats. The potency of NiAc as an inhibitor of insulin and of FFA release was estimated to be 0.338 and 0.436 , respectively, in obese rats. A range of dosing regimens was analyzed and predictions made for optimizing NiAc delivery to minimize FFA exposure. Given the exposure levels of the experiments, the importance of washout periods in-between NiAc infusions was illustrated. The washout periods should be 2 h longer than the infusions in order to optimize 24 h lowering of FFA in rats. However, the predicted concentration-response relationships suggests that higher AUC reductions might be attained at lower NiAc exposures.
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| 9. |
- 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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| 10. |
- Cardilin, Tim, 1989, et al.
(författare)
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Evaluation and translation of combination therapies in oncology – A quantitative approach
- 2018
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Ingår i: European Journal of Pharmacology. - : Elsevier BV. - 0014-2999 .- 1879-0712. ; 834, s. 327-336
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Tidskriftsartikel (refereegranskat)abstract
- Quantitative techniques improve our understanding of tumor volume data for combination treatments and its translation across in vivo models and species. The focus of this paper is therefore on understanding in vivo data, highlighting key structural elements of pharmacodynamic tumor models, and challenging these methods from a translational point of view. We introduce the concept of Tumor Static Exposure (TSE) both for single and multiple combined anticancer agents. The TSE curve separates all possible exposure combinations into regions of tumor growth and tumor shrinkage. Moreover, the degree of curvature of the TSE curve indicates the degree of synergy or antagonism. We demonstrate the TSE approach by two case studies. The first examines a combination of the drugs cetuximab and cisplatin. The TSE curve associated with this combination reveals a weak synergistic effect, suggesting only modest gains from combination therapy. The second case study examines combinations of ionizing radiation and a radiosensitizing agent. In this case, the TSE curve exhibits a pronounced curvature, indicating a strong synergistic effect; tumor regression can be achieved at significantly lower exposure levels and/or radiation doses. Finally, an allometric approach to human dose prediction demonstrates the translational power of the model and the TSE concept. We conclude that the TSE approach, which embodies model-based measures of both drug (potency) and target properties (tumor growth rate), has a strong potential for ranking of compounds, supporting compound selection, and translating preclinical findings to humans.
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