| 1. |
- 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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| 2. |
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| 3. |
- 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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| 4. |
- 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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| 5. |
- Boianelli, Alessandro, et al.
(författare)
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Cross-Species Translation of Biophase Half-Life and Potency of GalNAc-Conjugated siRNAs
- 2022
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Ingår i: Nucleic Acid Therapeutics. - : Mary Ann Liebert. - 2159-3337 .- 2159-3345. ; 32:6, s. 507-512
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Tidskriftsartikel (refereegranskat)abstract
- Small interfering RNAs (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs to treat liver diseases. Understanding how pharmacokinetics and pharmacodynamics translate is pivotal for in vivo study design and human dose prediction. However, the literature is sparse on translational data for this modality, and pharmacokinetics in the liver is seldom measured. To overcome these difficulties, we collected time-course biomarker data for 11 GalNAc-siRNAs in various species and applied the kinetic-pharmacodynamic modeling approach to estimate the biophase (liver) half-life and the potency. Our analysis indicates that the biophase half-life is 0.6-3 weeks in mouse, 1-8 weeks in monkey, and 1.5-14 weeks in human. For individual siRNAs, the biophase half-life is 1-8 times longer in human than in mouse, and generally 1-3 times longer in human than in monkey. The analysis indicates that the siRNAs are more potent in human than in mouse and monkey.
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| 6. |
- Gennemark, Peter, 1974, et al.
(författare)
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A simple mathematical model of adaptation to high osmolarity in yeast
- 2006
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Ingår i: In silico biology. - 1434-3207 .- 1386-6338. ; 6:0018
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Tidskriftsartikel (refereegranskat)abstract
- We present a simple ordinary differential equation (ODE) model of the adaptive response to an osmotic shock in the yeast Saccharomyces cerevisiae. The model consists of two main components. First, a biophysical model describing how the cell volume and the turgor pressure are affected by varying extra-cellular osmolarity. The second component describes how the cell controls the biophysical system in order to keep turgor pressure, or equivalently volume, constant. This is done by adjusting the glycerol production and the glycerol outflow from the cell. The complete model consists of 4 ODEs, 3 algebraic equations and 10 parameters. The parameters are constrained from various literature sources and estimated from new and previously published absolute time series data on intra-cellular and total glycerol. The qualitative behaviour of the model has been successfully tested on data from other genetically modified strains as well as data for different input signals. Compared to a previous detailed model of osmoregulation, the main strength of our model is its lower complexity, contributing to a better understanding of osmoregulation by focusing on relationships which are obscured in the more detailed model. Besides, the low complexity makes it possible to obtain more reliable parameter estimates.
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| 7. |
- Gennemark, Peter, 1974, et al.
(författare)
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Benchmarks for identification of ordinary differential equations from time series data
- 2009
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1460-2059 .- 1367-4803 .- 1367-4811. ; 25:6, s. 780-786
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: In recent years, the biological literature has seen a significant increase of reported methods for identifying both structure and parameters of ordinary differential equations (ODEs) from time series data. A natural way to evaluate the performance of such methods is to try them on a sufficient number of realistic test cases. However, weak practices in specifying identification problems and lack of commonly accepted benchmark problems makes it difficult to evaluate and compare different methods. Results: To enable better evaluation and comparisons between different methods, we propose how to specify identification problems as optimization problems with a model space of allowed reactions (e.g. reaction kinetics like Michaelis - Menten or S-systems), ranges for the parameters, time series data and an error function. We also define a file format for such problems. We then present a collection of more than 40 benchmark problems for ODE model identification of cellular systems. The collection includes realistic problems of different levels of difficulty w.r.t. size and quality of data. We consider both problems with simulated data from known systems, and problems with real data. Finally, we present results based on our identification algorithm for all benchmark problems. In comparison with publications on which we have based some of the benchmark problems, our approach allows all problems to be solved without the use of supercomputing.
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| 8. |
- Gennemark, Peter, 1974, et al.
(författare)
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Efficient algorithms for ordinary differential equation model identification of biological systems
- 2007
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Ingår i: IET Systems Biology. - 1751-8849. ; 1:2, s. 120-129
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Tidskriftsartikel (refereegranskat)abstract
- Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.
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| 9. |
- Gennemark, Peter, 1974, et al.
(författare)
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Improved Parameter Estimation for Completely Observed Ordinary Differential Equations with Application to Biological Systems
- 2009
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Ingår i: LEcture Notes in Computer Science: 7th International Conference on Computational Methods in Systems Biology, CMSB 2009; Bologna; Italy; 31 August 2009 through 1 September 2009. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349. - 9783642038440 ; LNCS 5688, s. 205-217
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Konferensbidrag (refereegranskat)abstract
- We consider parameter estimation in ordinary differential equations (ODEs) from completely observed systems, and describe an improved version of our previously reported heuristic algorithm (IET Syst. Biol., 2007). Basically, in that method, estimation based on decomposing the problem to simulation of one ODE, is followed by estimation based on simulation of all ODEs of the system. The main algorithmic improvement compared to the original version, is that we decompose not only to single ODEs, but also to arbitrary subsets of ODEs, as a complementary intermediate step. The subsets are selected based on an analysis of the interaction between the variables and possible common parameters. We evaluate our algorithm on a number of well-known hard test problems from the biological literature. The results show that our approach is more accurate and considerably faster compared to other reported methods on these problems. Additionally, we find that the algorithm scales favourably with problem size.
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| 10. |
- Gennemark, Peter, 1974
(författare)
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Modeling and identification of biological systems with emphasis on osmoregulation in yeast
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with two topics in the area of systems biology. Thefirst topic, model identification, concerns the problem ofautomatically identifying a mathematical model of a biochemical systemfrom experimental data. We present algorithms for model selection andparameter estimation that identify both the structure and theparameters of a differential equation model from experimental data.The algorithms are designed to handle problems of realistic size,where reactions can be non-linear in the parameters and where data canbe sparse and noisy. To achieve computational efficiency, parametersare estimated for one equation at a time, giving a fast and accurateparameter estimation algorithm compared to other algorithms in theliterature. The model selection is done with an efficient heuristicsearch algorithm, where the structure is built incrementally. The mainstrengths of our algorithms are that a complete model, and not only astructure, is identified, and that they are considerably fastercompared to other identification algorithms.The second topic concerns mathematical modeling of osmoregulation inthe yeast \emph{Saccharomyces cerevisiae}. This system involves thebiophysical and biochemical responses of a cell when it is exposed toan osmotic shock. We present two different differential equationmodels based on experimental data of this system. The first model isa detailed model taking into account an extensive amount of moleculardetail, while the second is a simple model with less detail. Wedemonstrate that both models agree well with experimental data onwild-type cells. Moreover, the models predict the behavior of othergenetically modified strains and input signals.
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