| 1. |
- Bendrioua, Loubna, et al.
(författare)
-
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)
-
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)
-
Sensitivity Equations Provide More Robust Gradients and Faster Computation of the FOCE Approximation to the Population Likelihood
- 2015
-
Ingår i: Proceedings of the 24th Annual meeting of the Population Approach Group in Europe, PAGE2015.
-
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
-
Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 42:3, s. 191-209
-
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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| 11. |
- Andersson, R., et al.
(författare)
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Dose-response-time modelling: Second-generation turnover model with integral feedback control
- 2016
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Ingår i: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 81, s. 189-200
-
Tidskriftsartikel (refereegranskat)abstract
- © 2015 Elsevier B.V. All rights reserved. This study presents a dose-response-time (DRT) analysis based on a large preclinical biomarker dataset on the interaction between nicotinic acid (NiAc) and free fatty acids (FFA). Data were collected from studies that examined different rates, routes, and modes of NiAc provocations on the FFA time course. All information regarding the exposure to NiAc was excluded in order to demonstrate the utility of a DRT model. Special emphasis was placed on the selection process of the biophase model. An inhibitory Imax-model, driven by the biophase amount, acted on the turnover rate of FFA. A second generation NiAc/FFA model, which encompasses integral (slow buildup of tolerance - an extension of the previously used NiAc/FFA turnover models) and moderator (rapid and oscillatory) feedback control, was simultaneously fitted to all time courses in normal rats. The integral feedback control managed to capture an observed 90% adaptation (i.e., almost a full return to baseline) when 10 days constant-rate infusion protocols of NiAc were used. The half-life of the adaptation process had a 90% prediction interval between 3.5-12 in the present population. The pharmacodynamic parameter estimates were highly consistent when compared to an exposure-driven analysis, partly validating the DRT modelling approach and suggesting the potential of DRT analysis in areas where exposure data are not attainable. Finally, new numerical algorithms, which rely on sensitivity equations to robustly and efficiently compute the gradients in the parameter optimization, were successfully used for the mixed-effects approach in the parameter estimation.
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| 12. |
- 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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| 13. |
- Anguelova, Milena, 1978, et al.
(författare)
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An Efficient Method for Structural Identiability Analysis of Large Dynamic Systems
- 2012
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Ingår i: 16th IFAC Symposium on System Identification. - 1474-6670. - 9783902823069 ; 16:1, s. 941-946
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Konferensbidrag (refereegranskat)abstract
- Ordinary differential equation models often contain a large number of parameters that must be determined from measurements by parameter estimation. For a parameter estimation procedure to be successful, there must be a unique set of parameters that can have produced the measured data. This is not the case if a model is not structurally identifiable with the given set of outputs selected as measurements. We describe the implementation of a recent probabilistic semi-numerical method for testing local structural identifiability based on computing the rank of a numerically instantiated Jacobian matrix (observability/identifiability matrix). To obtain this, matrix parameters and initial conditions are specialized to random integer numbers, inputs are specialized to truncated random integer coefficient power series, and the corresponding output of the state space system is computed in terms of a truncated power series, which then is utilized to calculate the elements of a Jacobian matrix. To reduce the memory requirements and increase the speed of the computations all operations are done modulo a large prime number. The method has been extended to handle parametrized initial conditions and is demonstrated to be capable of handling systems in the order of a hundred state variables and equally many parameters on a standard desktop computer.
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| 14. |
- Anguelova, Milena, 1978, et al.
(författare)
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Minimal output sets for identifiability
- 2012
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Ingår i: Mathematical Biosciences. - : Elsevier BV. - 1879-3134 .- 0025-5564.
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Tidskriftsartikel (refereegranskat)abstract
- Ordinary differential equation models in biology often contain a large number of parameters that must be determined from measurements by parameter estimation. For a parameter estimation procedure to be successful, there must be a unique set of parameters that can have produced the measured data. This is not the case if a model is not uniquely structurally identifiable with the given set of outputs selected as measurements. In designing an experiment for the purpose of parameter estimation, given a set of feasible but resource-consuming measurements, it is useful to know which ones must be included in order to obtain an identifiable system, or whether the system is unidentifiable from the feasible measurement set. We have developed an algorithm that, from a user-provided set of variables and parameters or functions of them assumed to be measurable or known, determines all subsets that when used as outputs give a locally structurally identifiable system and are such that any output set for which the system is structurally identifiable must contain at least one of the calculated subsets. The algorithm has been implemented in Mathematica and shown to be feasible and efficient. We have successfully applied it in the analysis of large signalling pathway models from the literat
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| 15. |
- Arnarsson, Ívar Örn, 1988, et al.
(författare)
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Design Analytics is the Answer, But What Questions Would Product Developers Like to Have Answered?
- 2017
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Ingår i: Proceedings of the International Conference on Engineering Design, ICED. - 2220-4334 .- 2220-4342. ; 7:DS87-7, s. 71-80
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Konferensbidrag (refereegranskat)abstract
- There is a growing need for data expertise and data analysis. Companies are looking more towards analytics for improvement opportunities within the business and products. Data collection is growing at a fast pace and we need capabilities to be able to analyze it. The data volume that companies are sitting on makes this task even more important. The paper presents interviews performed with product developers who have worked on a large complex system development project. The findings explain questions and needs developers are facing and what answers they are looking for with data mining. By Identifying beneficial and meaningful outputs from data mining and data analytics, developers can be supported in making better decisions for a new designs/re-designs and ultimately make a superior robust product. The paper further accounts for 20 heterogeneous purposefully sample interviews, ranging in project roles from product development to manufacturing and testing.
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| 16. |
- Arnarsson, Ívar Örn, 1988, et al.
(författare)
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Modeling industrial engineering change processes using the design structure matrix for sequence analysis: a comparison of multiple projects
- 2020
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Ingår i: Design Science. - : Cambridge University Press (CUP). - 2053-4701. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- The problem at hand is that vast amount of data on industrial changes is captured and stored; yet the present challenge is to systematically retrieve and use them in a purposeful way. This paper presents an industrial case study where complex product development processes are modeled using the design structure matrix (DSM) to analyze engineering change requests sequences. Engineering change requests are documents used to initiate a change process to enhance a product. Due to the amount of changes made in different projects, engineers want to be able to analyze these change processes to identify patterns and propose the best practices. The previous work has not specifically explored modeling engineering change requests in a DSM to holistically analyze sequences. This case study analyzes engineering change request sequences from four recent industrial product development projects and compares patterns among them. In the end, this research can help to identify and guide process improvement work within projects.
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| 17. |
- Arnarsson, Ívar Örn, 1988, et al.
(författare)
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Natural language processing methods for knowledge management - Applying document clustering for fast search and grouping of engineering documents
- 2021
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Ingår i: Concurrent Engineering Research and Applications. - 1063-293X .- 1531-2003. ; 29:2, s. 142-152
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Tidskriftsartikel (refereegranskat)abstract
- Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.
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| 18. |
- Arnarsson, Ívar Örn, 1988, et al.
(författare)
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Supporting Knowledge Re-Use with Effective Searches of Related Engineering Documents - A Comparison of Search Engine and Natural Language-Based Processing Algorithms
- 2019
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Ingår i: Proceedings of the International Conference on Engineering Design, ICED. - : Cambridge University Press (CUP). - 2220-4334 .- 2220-4342. ; 2019-August, s. 2597-2606
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Konferensbidrag (refereegranskat)abstract
- Product development companies are collecting data in form of Engineering Change Requests for logged design issues and Design Guidelines to accumulate best practices. These documents are rich in unstructured data (e.g., free text) and previous research has pointed out that product developers find current it systems lacking capabilities to accurately retrieve relevant documents with unstructured data. In this research we compare the performance of Search Engine & Natural Language Processing algorithms in order to find fast related documents from two databases with Engineering Change Request and Design Guideline documents. The aim is to turn hours of manual documents searching into seconds by utilizing such algorithms to effectively search for related engineering documents and rank them in order of significance. Domain knowledge experts evaluated the results and it shows that the models applied managed to find relevant documents with up to 90% accuracy of the cases tested. But accuracy varies based on selected algorithm and length of query.
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| 19. |
- Arnarsson, Ívar Örn, 1988, et al.
(författare)
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Towards Big-Data Analysis of Deviation and Error Reports in Product Development Projects
- 2016
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Ingår i: Proceedings of NordDesign 2016, Trondheim, Norway. - 9781904670803 ; 2, s. 83-92
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Konferensbidrag (refereegranskat)abstract
- Large complex system development projects, such as complete truck development projects, take several years to carry out. They involve hundreds of engineers who develop tens of thousands of parts and millions of lines of codes. During a project, many design decisions often need to be changed due to emergence of new information. The bulk of these changes are requested late in the development process. It is known that changes late in the development process are very costly and run a risk of delaying the project. These changes are often well documented in databases, but, due to the complexity of the data, few companies analyze engineering change in a comprehensive and structured fashion. This paper argues that “big data” (specifically data mining) analysis tools can be applied for such analyses and proposes a process for carrying out the analysis and using the results for product and development process improvement. The paper further accounts for experiences gained from testing the approach on a dataset consisting of 4,000 deviation and error reports that were created during a truck development project.
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| 20. |
- Baaz, Marcus, 1993, et al.
(författare)
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A Model Based Approach for Translation in Oncology - From Xenografts to RECIST
- 2022
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A major problem in drug development is translating results from preclinical studies to the clinical setting. Therefore, we ev alu ate the translational potential of semi mechanistic tumor models (based on xenograft data) to predict clinical oncology results (RECIST data). Two commonly used translational methods are evaluated: (1) replacement with human PK, and (2) allometric scaling of PD pa rameters. We then compute optimal scaling coefficients given the observed clinical data and relate them to the standard allom etr ic exponents in method (2). The analysis is performed for three drug combinations: binimetinib/encorafenib (shown below), binime tin ib/ribociclib, and cetuximab/encorafenib.
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| 21. |
- Baaz, Marcus, 1993, et al.
(författare)
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Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology
- 2023
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Ingår i: Bmc Cancer. - 1471-2407. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. Methods We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. Results The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 mu g/mL of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. Conclusions A simulation- based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE- curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.
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| 22. |
- Baaz, Marcus, 1993, et al.
(författare)
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Model-based prediction of progression-free survival for combination therapies in oncology
- 2023
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Ingår i: Cpt-Pharmacometrics & Systems Pharmacology. - 2163-8306. ; 12:9, s. 1227-37
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Tidskriftsartikel (refereegranskat)abstract
- Progression-free survival (PFS) is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. After the completion of a clinical trial, a descriptive analysis of the patients' PFS is often performed post hoc using the Kaplan-Meier estimator. However, to perform predictions, more sophisticated quantitative methods are needed. Tumor growth inhibition models are commonly used to describe and predict the dynamics of preclinical and clinical tumor size data. Moreover, frameworks also exist for describing the probability of different types of events, such as tumor metastasis or patient dropout. Combining these two types of models into a so-called joint model enables model-based prediction of PFS. In this paper, we have constructed a joint model from clinical data comparing the efficacy of FOLFOX against FOLFOX + panitumumab in patients with metastatic colorectal cancer. The nonlinear mixed effects framework was used to quantify interindividual variability (IIV). The model describes tumor size and PFS data well, and showed good predictive capabilities using truncated as well as external data. A machine-learning guided analysis was performed to reduce unexplained IIV by incorporating patient covariates. The model-based approach illustrated in this paper could be useful to help design clinical trials or to determine new promising drug candidates for combination therapy trials.
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| 23. |
- Baaz, Marcus, 1993, et al.
(författare)
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Model-based Prediction of Progression-Free Survival for Combination Therapies in Oncology
- 2023
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: Extend a joint modeling approach for predicting progression-free survival (PFS) for monotherapies [1] to combination therapies. Test the model’s predictive capabilities by performing different cross-validations. Methods: PFS is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. Using the RECIST (version 1.1) guidelines all tumor lesions have to be accounted for by a combination of target and non-target lesions. A patient’s PFS time is set by target progression (TP) if there is at least a 20%- and 5-mm increase of the sum of the largest target diameters (SLD) compared to the nadir [2]. If the patient dies, a new lesion has appeared, or the non-target lesions are deemed unequivocal progressing the PFS time is instead set by non-target progression (NTP). If a patient leaves the trial before this occurs the patient is censored at that time point. We present a joint modeling approach for predicting PFS for combination therapies where we link the risk of adverse events such as e.g., tumor metastasis or death with the derivative of SLD. Thus, the joint model consists of both a tumor growth inhibition (TGI) model, for the SLD time series, and a time-to-event (TTE) model to model the risk of adverse events. In addition, a Weibull TTE model is used to account for dropout. Monolix [3] was used to calibrate the models with data coming from a clinical study comparing the efficacy of FOLFOX versus FOLFOX + panitumumab in metastatic colorectal cancer patients. The data were provided to us by ProjectDataSphere [4]. We did not have data for panitumumab given as a monotherapy and therefore assume that there were no interaction effects between the drugs. To adequately quantify the variability in the data the nonlinear mixed effects framework was used. After the models were calibrated they were combined to make predictions of PFS. The algorithm below summarizes how the predictions are performed, Generate artificial patients and simulate time series of SLD using the TGI model. Estimate time when SLD has increased by 20% and at least 5 mm for each patient. Construct individual survival curves and sample time of non-target progression event. Sample dropout times using the estimated Weibull distribution. Pick the time that occurs first for each patient, record the PFS trigger, and repeat it 1000 times. If dropout occurs first, the patient is censored that that time. From this procedure, we obtain both a median prediction along with a 95% confidence interval for the prediction. To both test the model’s predictive capabilities and validate the assumption of no interaction between the drugs we predicted the median PFS time for panitumumab given as a monotherapy and compared it with results from the ASPECCT study [5]. We also recalibrated the model with truncated data at 3,7, and 27 months and then made forward predictions of the remaining study. Results: All models were successfully calibrated to the data and validated based on, e.g., the precision of parameter estimates, individual fits, distribution of Empirical Bayes Estimates (EBEs), and analysis of residuals. Furthermore, the combined (PFS) model was able to describe the PFS for both treatment arms of the study. When we recalibrated the model with truncated data, the forward predictions were very good for both the 7 and 27 months truncation points. The prediction for the median PFS time for patients given only panitumumab was similar to what was found in the ASPECCT study. Conclusions: We successfully calibrated a joint model using clinical SLD and TTE data for a combination therapy. Using the model, we were able to first describe the PFS time of the same study well and then make model predictions. Predictions were performed on both truncated data sets and for data coming from a different study. In both cases, the model was shown to have good predictive capabilities. References: [1] Yu J, Wang N, Kågedal M. A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics. CPT Pharmacomet Syst Pharmacol 2020;9:177–84. https://doi.org/10.1002/psp4.12499. [2] Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1). Eur J Cancer 2009;45:228–47. https://doi.org/10.1016/j.ejca.2008.10.026. [3] Monolix 2021R2 Lixoft SAS, a Simulations Plus company. [4] Project Data Sphere 2022. https://www.projectdatasphere.org/. [5] Kim TW, Peeters M, Thomas A, Gibbs P, Hool K, Zhang J, et al. Impact of Emergent Circulating Tumor DNA RAS Mutation in Panitumumab-Treated Chemoresistant Metastatic Colorectal Cancer. Clin Cancer Res 2018;24:5602–9. https://doi.org/10.1158/1078-0432.CCR-17-3377.
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| 24. |
- Baaz, Marcus, 1993, et al.
(författare)
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Optimized scaling of translational factors in oncology: from xenografts to RECIST
- 2022
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Ingår i: Cancer Chemotherapy and Pharmacology. - : Springer Science and Business Media LLC. - 0344-5704 .- 1432-0843. ; 90:3, s. 239-250
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Tumor growth inhibition (TGI) models are regularly used to quantify the PK-PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response. Method To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data. Results The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of - 0.25. Conclusions We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.
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| 25. |
- Baaz, Marcus, 1993, et al.
(författare)
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Population Modeling of Toxicological Combination Effects
- 2022
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Aim: Nonlinear mixed effects (NLME) modeling is currently the state-of-the-art mathematical framework for analyzing population data in medicine. We aim to illustrate how NLME modeling and the Tumor Static Exposure (TSE) concept can be beneficial for analyzing the effects of combined pollutants in marine life. Results: TSE is defined as all drug exposure that results in tumor stasis and therefore separates the space of all exposures into a region of tumor growth and a region of tumor shrinkage. TSE is derived from the equations of the NLME model and when two drugs are investigated the TSE for the median individual can be illustrated in a diagram with each axis representing the exposure of one of the drugs. We apply a similar approach to a toxicological model that describes the combined toxicological effects of two pollutants on marine animals. The model is based on a set of ordinary differential equations and from these, we derive a curve, similar to TSE, which describes all exposure combinations that result in a critical toxicological event. We use simulated data to calibrate the model and illustrate how predictions of toxicity can be made on a population level. Discussion/Conclusions: Since all possible combinations of pollutants cannot be tested experimentally the modified version of the TSE-curve can be useful to explore how different combinations affect marine life populations. Thus, it could be used to rank which pollutants are most important to reduce in the oceans. The NLME framework provides a powerful method for analyzing time-series data and could increase the statistical power when analyzing data from animal studies. In addition, it allows for simulation-based analysis, which could help reduce the number of animal experiments.
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