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  • 1.
    Feldwisch-Drentrup, Hinnerk
    et al.
    University of Freiburg.
    Staniek, Matthaeus
    University of Bonn.
    Schulze-Bonhage, Andreas
    University of Freiburg.
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin. Linköpings universitet, Hälsouniversitetet.
    Dickten, Henning
    University of Bonn.
    Elger, Christian E
    University of Bonn.
    Schelter, Bjoern
    University of Aberdeen.
    Lehnertz, Klaus
    University of Bonn.
    Identification of preseizure states in epilepsy: a data-driven approach for multichannel EEG recordings2011Ingår i: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 5, nr 32Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure - so-called measure profiles - for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.

  • 2.
    Hug, S.
    et al.
    Helmholtz Zentrum Munchen, Germany Technical University of Munich, Germany .
    Raue, A.
    Helmholtz Zentrum Munchen, Germany University of Freiburg, Germany .
    Hasenauer, J.
    Helmholtz Zentrum Munchen, Germany .
    Bachmann, J.
    German Cancer Research Centre, Germany .
    Klingmueller, U.
    German Cancer Research Centre, Germany .
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för cellbiologi. Linköpings universitet, Hälsouniversitetet.
    Theis, F .J.
    Helmholtz Zentrum Munchen, Germany Technical University of Munich, Germany .
    High-dimensional Bayesian parameter estimation: Case study for a model of JAK2/STAT5 signaling2013Ingår i: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134, Vol. 246, nr 2, s. 293-304Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this work we present results of a detailed Bayesian parameter estimation for an analysis of ordinary differential equation models. These depend on many unknown parameters that have to be inferred from experimental data. The statistical inference in a high-dimensional parameter space is however conceptually and computationally challenging. To ensure rigorous assessment of model and prediction uncertainties we take advantage of both a profile posterior approach and Markov chain Monte Carlo sampling. We analyzed a dynamical model of the JAK2/STAT5 signal transduction pathway that contains more than one hundred parameters. Using the profile posterior we found that the corresponding posterior distribution is bimodal. To guarantee efficient mixing in the presence of multimodal posterior distributions we applied a multi-chain sampling approach. The Bayesian parameter estimation enables the assessment of prediction uncertainties and the design of additional experiments that enhance the explanatory power of the model. This study represents a proof of principle that detailed statistical analysis for quantitative dynamical modeling used in systems biology is feasible also in high-dimensional parameter spaces.

  • 3.
    Killmann, M
    et al.
    University of Medical Centre Freiburg, Germany .
    Sommerlade, L
    University of Freiburg, Germany .
    Mader, W
    University of Freiburg, Germany .
    Timmer, Jens
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi.
    Schelter, B
    University of Freiburg, Germany .
    Inference of time-dependent causal influences in Networks2012Ingår i: Biomedizinische Technik (Berlin. Zeitschrift), ISSN 1862-278X, E-ISSN 0013-5585, Vol. 57Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We address the challenge of detecting time-variant interactions in multivariate systems. Inferring Granger-causal interactions between processes promises to gain deeper insights into mechanisms underlying network phenomena, e. g., in the neurosciences. Renormalized partial directed coherence (rPDC) has been introduced as a means to investigate Granger causality in such multivariate systems. When using rPDC a major challenge is the reliable estimation of parameters in vector autoregressive processes. For time-varying connections a time-resolved estimation of the coefficients is mandatory. We show that the State Space Model in combination with the Kalman filter is a powerful tool for estimating time-variate AR process parameters.

  • 4.
    Klatt, Juliane
    et al.
    University of Freiburg, Germany.
    Feldwisch-Drentrup, Hinnerk
    University of Freiburg, Germany.
    Ihle, Matthias
    University Hospital of Freiburg, Germany.
    Navarro, Vincent
    CHU Pitie-Salpetriere, Paris, France.
    Neufang, Markus
    University Hospital of Freiburg, Germany.
    Teixeira, Cesar
    University of Coimbra, Portugal .
    Adam, Claude
    CHU Pitie-Salpetriere, Paris, France.
    Valderrama, Mario
    University of Paris 06, France .
    Alvarado-Rojas, Catalina
    University of Paris 06, France .
    Witon, Adrien
    University of Paris 06, France .
    Le Van Quyen, Michel
    University of Paris 06, France .
    Sales, Francisco
    University of Coimbra, Portugal .
    Dourado, Antonio
    University of Coimbra, Portugal .
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi. Linköpings universitet, Hälsouniversitetet.
    Schulze-Bonhage, Andreas
    University Hospital of Freiburg, Germany.
    Schelter, Bjoern
    University of Freiburg, Germany.
    The EPILEPSIAE database: An extensive electroencephalography database of epilepsy patients2012Ingår i: Epilepsia, ISSN 0013-9580, E-ISSN 1528-1167, Vol. 53, nr 9, s. 1669-1676Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012. We illustrate its content and scope. The EPILEPSIAE database provides long-term EEG recordings of 275 patients as well as extensive metadata and standardized annotation of the data sets. It will adhere to the current standards in the field of prediction and facilitate reproducibility and comparison of those studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and other fields.

  • 5.
    Kreutz, Clemens
    et al.
    University of Freiburg, Germany.
    Raue, Andreas
    University of Freiburg, Germany Helmholtz Zentrum Munchen, Germany .
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi. Linköpings universitet, Hälsouniversitetet.
    Likelihood based observability analysis and confidence intervals for predictions of dynamic models2012Ingår i: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 6, nr 120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Predicting a systems behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible. less thanbrgreater than less thanbrgreater thanResults: In this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. less thanbrgreater than less thanbrgreater thanConclusions: The presented methodology allows the propagation of uncertainty from experimental to model predictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at http://www.fdmold.uni-freiburg.de/(similar to)ckreutz/PPL.

  • 6.
    Maiwald, Thomas
    et al.
    Centre Syst Biol, Germany Harvard University, MA USA .
    Blumberg, Julie
    University Hospital, Germany Harvard University, MA USA .
    Raue, Andreas
    Centre Syst Biol, Germany .
    Hengl, Stefan
    Centre Syst Biol, Germany .
    Schilling, Marcel
    German Cancer Research Centre, Germany .
    Sy, Sherwin K. B.
    University of Florida, USA .
    Becker, Verena
    Harvard University, USA University of Heidelberg, Germany .
    Klingmueller, Ursula
    German Cancer Research Centre, Germany University of Heidelberg, Germany .
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi. Linköpings universitet, Hälsouniversitetet.
    In silico labeling reveals the time-dependent label half-life and transit-time in dynamical systems2012Ingår i: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 6, nr 13Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Mathematical models of dynamical systems facilitate the computation of characteristic properties that are not accessible experimentally. In cell biology, two main properties of interest are (1) the time-period a protein is accessible to other molecules in a certain state - its half-life - and (2) the time it spends when passing through a subsystem - its transit-time. We discuss two approaches to quantify the half-life, present the novel method of in silico labeling, and introduce the label half-life and label transit-time. The developed method has been motivated by laboratory tracer experiments. To investigate the kinetic properties and behavior of a substance of interest, we computationally label this species in order to track it throughout its life cycle. The corresponding mathematical model is extended by an additional set of reactions for the labeled species, avoiding any double-counting within closed circuits, correcting for the influences of upstream fluxes, and taking into account combinatorial multiplicity for complexes or reactions with several reactants or products. A profile likelihood approach is used to estimate confidence intervals on the label half-life and transit-time. Results: Application to the JAK-STAT signaling pathway in Epo-stimulated BaF3-EpoR cells enabled the calculation of the time-dependent label half-life and transit-time of STAT species. The results were robust against parameter uncertainties. Conclusions: Our approach renders possible the estimation of species and label half-lives and transit-times. It is applicable to large non-linear systems and an implementation is provided within the PottersWheel modeling framework (http://www.potterswheel.de).

  • 7.
    Ramb, Rebecca
    et al.
    University of Freiburg, Germany .
    Eichler, Michael
    Maastricht University, Netherlands .
    Ing, Alex
    University of Aberdeen, Scotland .
    Thiel, Marco
    University of Aberdeen, Scotland .
    Weiller, Cornelius
    University Hospital Freiburg, Germany .
    Grebogi, Celso
    University of Aberdeen, Scotland University of Freiburg, Germany .
    Schwarzbauer, Christian
    University of Aberdeen, Scotland .
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för cellbiologi. Linköpings universitet, Hälsouniversitetet.
    Schelter, Bjoern
    University of Freiburg, Germany University of Aberdeen, Scotland University of Freiburg, Germany University of Medical Centre Freiburg, Germany .
    The impact of latent confounders in directed network analysis in neuroscience2013Ingår i: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 371, nr 1997Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the analysis of neuroscience data, the identification of task-related causal relationships between various areas of the brain gives insights about the network of physiological pathways that are active during the task. One increasingly used approach to identify causal connectivity uses the concept of Granger causality that exploits predictability of activity in one region by past activity in other regions of the brain. Owing to the complexity of the data, selecting components for the analysis of causality as a preprocessing step has to be performed. This includes predetermined-and often arbitrary-exclusion of information. Therefore, the system is confounded by latent sources. In this paper, the effect of latent confounders is demonstrated, and paths of influence among three components are studied. While methods for analysing Granger causality are commonly based on linear vector autoregressive models, the effects of latent confounders are expected to be present also in nonlinear systems. Therefore, all analyses are also performed for a simulated nonlinear system and discussed with regard to applications in neuroscience.

  • 8.
    Raue, Andreas
    et al.
    University of Freiburg, Germany .
    Kreutz, Clemens
    University of Freiburg, Germany .
    Joachim Theis, Fabian
    Technical University of Munich, Germany .
    Timmer, Jens
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi.
    Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability2013Ingår i: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 371, nr 1984Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Increasingly complex applications involve large datasets in combination with nonlinear and high-dimensional mathematical models. In this context, statistical inference is a challenging issue that calls for pragmatic approaches that take advantage of both Bayesian and frequentist methods. The elegance of Bayesian methodology is founded in the propagation of information content provided by experimental data and prior assumptions to the posterior probability distribution of model predictions. However, for complex applications, experimental data and prior assumptions potentially constrain the posterior probability distribution insufficiently. In these situations, Bayesian Markov chain Monte Carlo sampling can be infeasible. From a frequentist point of view, insufficient experimental data and prior assumptions can be interpreted as non-identifiability. The profile-likelihood approach offers to detect and to resolve non-identifiability by experimental design iteratively. Therefore, it allows one to better constrain the posterior probability distribution until Markov chain Monte Carlo sampling can be used securely. Using an application from cell biology, we compare both methods and show that a successive application of the two methods facilitates a realistic assessment of uncertainty in model predictions.

  • 9.
    Rausenberger, Julia
    et al.
    University of Freiburg.
    Tscheuschler, Anke
    University of Freiburg.
    Nordmeier, Wiebke
    University of Tubingen.
    Wuest, Florian
    University of Freiburg.
    Timmer, Jens
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi.
    Schaefer, Eberhard
    University of Freiburg.
    Fleck, Christian
    University of Freiburg.
    Hiltbrunner, Andreas
    University of Tubingen.
    Photoconversion and Nuclear Trafficking Cycles Determine Phytochrome As Response Profile to Far-Red Light2011Ingår i: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 146, nr 5, s. 813-825Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Phytochrome A (phyA) is the only photoreceptor in plants, initiating responses in far-red light and, as such, essential for survival in canopy shade. Although the absorption and the ratio of active versus total phyA are maximal in red light, far-red light is the most efficient trigger of phyA-dependent responses. Using a joint experimental-theoretical approach, we unravel the mechanism underlying this shift of the phyA action peak from red to far-red light and show that it relies on specific molecular interactions rather than on intrinsic changes to phyAs spectral properties. According to our model, the dissociation rate of the phyA-FHY1/FHL nuclear import complex is a principle determinant of the phyA action peak. The findings suggest how higher plants acquired the ability to sense far-red light from an ancestral photoreceptor tuned to respond to red light.

  • 10.
    Sa Ferreira, Karine
    et al.
    University of Freiburg.
    Kreutz, Clemens
    University of Freiburg.
    MacNelly, Sabine
    University Hospital Freiburg.
    Neubert, Karin
    University of Freiburg.
    Haber, Angelika
    University of Freiburg.
    Bogyo, Matthew
    Stanford University.
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin. Linköpings universitet, Hälsouniversitetet.
    Borner, Christoph
    University of Freiburg.
    Caspase-3 feeds back on caspase-8, Bid and XIAP in type I Fas signaling in primary mouse hepatocytes2012Ingår i: Apoptosis (London), ISSN 1360-8185, E-ISSN 1573-675X, Vol. 17, nr 5, s. 503-515Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The TNF-R1 like receptor Fas is highly expressed on the plasma membrane of hepatocytes and plays an essential role in liver homeostasis. We recently showed that in collagen-cultured primary mouse hepatocytes, Fas stimulation triggers apoptosis via the so-called type I extrinsic signaling pathway. Central to this pathway is the direct caspase-8-mediated cleavage and activation of caspase-3 as compared to the type II pathway which first requires caspase-8-mediated Bid cleavage to trigger mitochondrial cytochrome c release for caspase-3 activation. Mathematical modeling can be used to understand complex signaling systems such as crosstalks and feedback or feedforward loops. A previously published model predicted a positive feedback loop between active caspases-3 and -8 in both type I and type II FasL signaling in lymphocytes and Hela cells, respectively. Here we experimentally tested this hypothesis in our hepatocytic type I Fas signaling pathway by using wild-type and XIAP-deficient primary hepatocytes and two recently characterized, selective caspase-3/-7 inhibitors (AB06 and AB13). Caspase-3/-7 activity assays and quantitative western blotting confirmed that fully processed, active p17 caspase-3 feeds back on caspase-8 by cleaving its partially processed p43 form into the fully processed p18 species. Our data do not discriminate if p18 positively or negatively influences FasL-induced apoptosis or is responsible for non-apoptotic aspects of FasL signaling. However, we found that caspase-3 also feeds back on Bid and degrades its own inhibitor XIAP, both events that may enhance caspase-3 activity and apoptosis. Thus, potent, selective caspase-3 inhibitors are useful tools to understand complex signaling circuitries in apoptosis.

  • 11.
    Schelker, M
    et al.
    University of Freiburg, Germany.
    Raue, A
    University of Freiburg, Germany.
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi. Linköpings universitet, Hälsouniversitetet.
    Kreutz, C
    University of Freiburg, Germany.
    Comprehensive estimation of input signals and dynamics in biochemical reaction networks2012Ingår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 28, nr 18, s. I529-I534Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function. less thanbrgreater than less thanbrgreater thanParameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation. For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedure is suboptimal since the input uncertainties are not considered in the estimation process which often leads to overoptimistic confidence intervals of the inferred parameters and the model dynamics. less thanbrgreater than less thanbrgreater thanResults: This article presents a new approach which includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously. We applied this comprehensive approach to an illustrative model with simulated data and compared it to alternative methods. Statistical analyses revealed that our method improves the prediction of the model dynamics and the confidence intervals leading to a proper coverage of the confidence intervals of the dynamic parameters. The method was applied to the JAK-STAT signaling pathway.

  • 12.
    Sommerlade, Linda
    et al.
    University of Freiburg, Germany .
    Schelter, Bjoern
    University of Freiburg, Germany.
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi. Linköpings universitet, Hälsouniversitetet.
    Reinhard, Matthias
    University of Freiburg, Germany .
    GRADING OF DYNAMIC CEREBRAL AUTOREGULATION WITHOUT BLOOD PRESSURE RECORDINGS: A SIMPLE DOPPLER-BASED METHOD2012Ingår i: Ultrasound in Medicine and Biology, ISSN 0301-5629, E-ISSN 1879-291X, Vol. 38, nr 9, s. 1546-1551Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Transcranial Doppler sonography allows for noninvasive assessment of dynamic cerebral autoregulation. A wider clinical use of this approach has been hampered by the need for continuous arterial blood pressure (ABP) measurements. We describe a new method of a pure Doppler signal based estimation of dynamic autoregulation using heart rate (HR) and cerebral blood flow velocity (CBFV) information. The phase between these two signals was assessed from 0.1 Hz oscillations induced by regular breathing. We compared this new approach with the standard method (phase between ABP and CBFV oscillations) in 93 patients with unilateral severe carotid artery obstruction. On a group level, the phase HR-CBFV differed significantly between ipsi- and contralateral sides (p = 0.024) and correlated significantly with the standard phase ABP-CBFV (r = 0.369, p andlt; 0.001). The proposed method can, thus, detect impaired dynamic autoregulation in occlusive carotid artery disease using a single Doppler probe.

  • 13.
    Sommerlade, Linda
    et al.
    University of Freiburg.
    Thiel, Marco
    University of Aberdeen.
    Platt, Bettina
    University of Aberdeen.
    Plano, Andrea
    University of Aberdeen.
    Riedel, Gernot
    University of Aberdeen.
    Grebogi, Celso
    University of Aberdeen.
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin. Linköpings universitet, Hälsouniversitetet.
    Schelter, Bjoern
    University of Freiburg.
    Inference of Granger causal time-dependent influences in noisy multivariate time series2012Ingår i: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 203, nr 1, s. 173-185Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Inferring Granger-causal interactions between processes promises deeper insights into mechanisms underlying network phenomena, e.g. in the neurosciences where the level of connectivity in neural networks is of particular interest. Renormalized partial directed coherence has been introduced as a means to investigate Granger causality in such multivariate systems. A major challenge in estimating respective coherences is a reliable parameter estimation of vector autoregressive processes. We discuss two shortcomings typical in relevant applications, i.e. non-stationarity of the processes generating the time series and contamination with observational noise. To overcome both, we present a new approach by combining renormalized partial directed coherence with state space modeling. A numerical efficient way to perform both the estimation as well as the statistical inference will be presented.

  • 14.
    Steiert, Bernhard
    et al.
    University of Freiburg, Germany.
    Raue, Andreas
    University of Freiburg, Germany.
    Timmer, Jens
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi. Linköpings universitet, Hälsouniversitetet.
    Kreutz, Clemens
    University of Freiburg, Germany.
    Experimental Design for Parameter Estimation of Gene Regulatory Networks2012Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, nr 7Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best performing procedure at the DREAM6 Estimation of Model Parameters challenge. For fast and reliable parameter estimation, local deterministic optimization of the likelihood was applied. We analyzed identifiability and precision of the estimates by calculating the profile likelihood. Furthermore, the profiles provided a way to uncover a selection of most informative experiments, from which the optimal one was chosen using additional criteria at every step of the design process. In conclusion, we provide a strategy for optimal experimental design and show its successful application on three highly nonlinear dynamic models. Although presented in the context of the GRNs to be inferred for the DREAM6 challenge, the approach is generic and applicable to most types of quantitative models in systems biology and other disciplines.

  • 15.
    Teixeira, C A
    et al.
    University of Coimbra.
    Direito, B
    University of Coimbra.
    Feldwisch-Drentrup, H
    University of Freiburg.
    Valderrama, M
    UPMC Paris 6.
    P Costa, R
    University of Coimbra.
    Alvarado-Rojas, C
    UPMC Paris 6.
    Nikolopoulos, S
    UPMC Paris 6.
    Le Van Quyen, M
    UPMC Paris 6.
    Timmer, Jens
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Cellbiologi.
    Schelter, B
    University of Freiburg.
    Dourado, A
    University of Coimbra.
    EPILAB: A software package for studies on the prediction of epileptic seizures2011Ingår i: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 200, nr 2, s. 257-271Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A Matlab (R)-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. less thanbrgreater than less thanbrgreater thanSeizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. less thanbrgreater than less thanbrgreater thanEPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.

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