| 1. |
- Gennemark, Peter, 1974, et al.
(författare)
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ODEion- a software module for structural identification of ordinary differential equations
- 2014
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Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- In the systems biology field, algorithms for structural identification of ordinary differential equations (ODEs) have mainly focused on fixed model spaces like S-systems and/or on methods that require sufficiently good data so that derivatives can be accurately estimated. There is therefore a lack of methods and software that can handle more general models and realistic data. We present ODEion, a software module for structural identification of ODEs. Main characteristic features of the software are: • The model space is defined by arbitrary user-defined functions that can be nonlinear in both variables and parameters, such as for example chemical rate reactions. • ODEion implements computationally efficient algorithms that have been shown to efficiently handle sparse and noisy data. It can run a range of realistic problems that previously required a supercomputer. • ODEion is easy to use and provides SBML output. We describe the mathematical problem, the ODEion system itself, and provide several examples of how the system can be used. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219720013500157
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| 2. |
- Gennemark, Peter, 1974, et al.
(författare)
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Optimal Design in Population Kinetic Experiments by Set-Valued Methods
- 2011
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:4, s. 495-507
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Tidskriftsartikel (refereegranskat)abstract
- We propose a new method for optimal experimental design of population pharmacometric experiments based on global search methods using interval analysis; all variables and parameters are represented as intervals rather than real numbers. The evaluation of a specific design is based on multiple simulations and parameter estimations. The method requires no prior point estimates for the parameters, since the parameters can incorporate any level of uncertainty. In this respect, it is similar to robust optimal design. Representing sampling times and covariates like doses by intervals gives a direct way of optimizing with rigorous sampling and dose intervals that can be useful in clinical practice. Furthermore, the method works on underdetermined problems for which traditional methods typically fail.
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| 3. |
- Johansson, Carl-Christer, et al.
(författare)
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Population pharmacokinetic modeling and deconvolution of enantioselective absorption of eflornithine in the rat.
- 2013
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Ingår i: Journal of pharmacokinetics and pharmacodynamics. - : Springer Science and Business Media LLC. - 1573-8744 .- 1567-567X. ; 40:1, s. 117-28
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Tidskriftsartikel (refereegranskat)abstract
- Enantioselective pharmacokinetics and absorption of eflornithine in the rat was investigated using population pharmacokinetic modeling and a modified deconvolution method. Bidirectional permeability of L- and D-eflornithine was investigated in Caco-2 cells. The rat was administered racemic eflornithine hydrochloride as a single oral dose [40-3,000 mg/kg bodyweight (BW)] or intravenously (IV) (100-2,700 mg/kg BW infused over 60-400 min). Serial arterial blood samples were collected and L- and D-eflornithine were quantitated with a previously published chiral bioanalysis method. The D:L concentration ratio was determined in rat faeces. Intravenous L-and D-eflornithine plasma concentration-time data was analyzed using population pharmacokinetic modeling and described with a 3-compartment pharmacokinetic model with saturable binding to one of the peripheral compartments. Oral plasma concentration-time data was analyzed using a modified deconvolution method accounting for nonlinearities in the eflornithine pharmacokinetics. Clearance was similar for both enantiomers (3.36 and 3.09 mL/min). Oral bioavailability was estimated by deconvolution at 30 and 59% for L- and D-eflornithine. The D:L concentration ratio in feces was 0.49 and the Caco-2 cell permeability was similar for both enantiomers (6-10 × 10(-8) cm/s) with no evident involvement of active transport or efflux. The results presented here suggest that the difference in the bioavailability between eflornithine enantiomers is caused by a stereoselective difference in extent rather than rate of absorption. The presented modified deconvolution method made it possible to account for the non-linear component in the suggested three-compartment pharmacokinetic model thus rapidly estimating eflornithine oral bioavailability.
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| 4. |
- Jörnsten, Rebecka, 1971, et al.
(författare)
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Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
- 2011
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
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| 5. |
- Kattas, G.D., et al.
(författare)
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Structural identification of GMA models: Algorithm and model comparison
- 2010
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Ingår i: CMSB 2010 - Proceedings of the 8th International Conference on Computational Methods in Systems Biology. - New York, NY, USA : ACM. - 9781450300681 ; , s. 107-113
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Konferensbidrag (refereegranskat)abstract
- We propose a local search algorithm for structural identification of Generalized Mass Action (GMA) models from time course data. The algorithm has been implemented as part of our existing system for identification of dynamical systems. We compare this approach to existing alternatives in terms of analytical GMA models, analytical GMA models with parameter estimation from time course data, S-systems, and linear models. This is done on three new test problems designed to exhibit different characteristic properties of biochemical pathways, and which are defined with chemical rate reactions. By applying state-of-the-art algorithmic methods we are able to make a full investigation for the test problems also with noisy data. The results show that on the tested problems, our structural identification algorithm is able to find as good or better models than any of the other approaches. It can therefore be expected to be a useful tool for identifying models of unknown systems from time course data. All test problems are available on the web. Copyright 2010 ACM.
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| 6. |
- Kuhn, C., et al.
(författare)
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Modeling yeast osmoadaptation at different levels of resolution
- 2013
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Ingår i: Journal of Bioinformatics and Computational Biology. - : World Scientific Pub Co Pte Ltd. - 0219-7200 .- 1757-6334. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e. g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.
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| 7. |
- Kühn, Clemens, et al.
(författare)
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Modeling yeast osmoadaptation at different levels of resolution
- 2013
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Ingår i: Journal of Bioinformatics and Computational Biology. - 0219-7200 .- 1757-6334. ; 11:2, s. 1330001-
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Forskningsöversikt (refereegranskat)abstract
- We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e. g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.
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| 8. |
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| 9. |
- Trägårdh, Magnus, et al.
(författare)
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Input estimation in nonlinear dynamical systems for model – based drug discovery using optimal control techniques
- 2014
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Ingår i: In proceedings of PKUK 2014.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: In many pharmacokinetic (PK) applications, it is of interest to determine the input to a dynamical system, based only on sparse and noisy measurements. One typical case is when a drug is administered orally, and where there is a known PK model, but the absorption process is not well understood. When the model of the system is linear and time invariant, input estimation is referred to as deconvolution. Traditional deconvolution methods based on regularised regression can easily be solved in closed form for linear models. However, many PK models are nonlinear, e.g. as a result of saturable elimination. Therefore, being able to handle only the linear case is a severe restriction. Besides, input estimation methods have a much wider applicability than PK, and can be used in any pharmacodynamic (PD) or disease modelling problem where the input to a linear or nonlinear model needs to be determined. As an example, these methods are being considered for use in body weight modelling, estimating the energy intake from body weight measurements.Aim: To investigate, implement and benchmark techniques for input estimation (deconvolution) for the case when the underlying dynamical system is nonlinear.Methods: Key techniques from optimal control theory were applied: multiple shooting and collocation in combination with sensitivity analysis and automatic differentiation. The techniques were benchmarked on a previously published dataset measuring the plasma concentration of eflornithine in 26 rats after oral administration. Two choices of regularisation functions were used: Tikhonov (ridge regression) and Maximum Entropy.Results: The investigated methods worked robustly on the benchmark dataset, even when starting from very poor initial guesses. The multiple shooting methods needed 15 seconds on a standard workstation for a typical dataset, while collocation methods needed about 5 seconds.Conclusion: Optimal control methods make it possible to use traditional deconvolution methods even when the system is nonlinear.
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