In silico labeling reveals the time-dependent label half-life and transit-time in dynamical systems.

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).

Mathematical modeling of biochemical systems with PottersWheel.

The program PottersWheel has been developed to provide an intuitive and yet powerful framework for data-based modeling of dynamical systems like biochemical reaction networks. Its key functionality is multi-experiment fitting, where several experimental data sets from different laboratory conditions are fitted simultaneously in order to improve the estimation of unknown model parameters, to check the validity of a given model, and to discriminate competing model hypotheses. New experiments can be designed interactively. Models are either created text-based or using a visual model designer. Dynamically generated and compiled C files provide fast simulation and fitting procedures. Each function can either be accessed using a graphical user interface or via command line, allowing for batch processing within custom Matlab scripts. PottersWheel is designed as a Matlab toolbox, comprises 250,000 lines of Matlab and C code, and is freely available for academic usage at www.potterswheel.de .

Rapid phospho-turnover by receptor tyrosine kinases impacts downstream signaling and drug binding.

Epidermal growth factor receptors (ErbB1-4) are oncogenic receptor tyrosine kinases (RTKs) that regulate diverse cellular processes. In this study, we combine measurement and mathematical modeling to quantify phospho-turnover at ErbB receptors in human cells and to determine the consequences for signaling and drug binding. We find that phosphotyrosine residues on ErbB1 have half-lives of a few seconds and therefore turn over 100-1000 times in the course of a typical immediate-early response to ligand. Rapid phospho-turnover is also observed for EGF-activated ErbB2 and ErbB3, unrelated RTKs, and multiple intracellular adaptor proteins and signaling kinases. Thus, the complexes formed on the cytoplasmic tail of active receptors and the downstream signaling kinases they control are highly dynamic and antagonized by potent phosphatases. We develop a kinetic scheme for binding of anti-ErbB1 drugs to receptors and show that rapid phospho-turnover significantly impacts their mechanisms of action.

Are prodromes preictal events? A prospective PDA-based study.

Up to 29% of patients with epilepsy report "prodromal" sensations more than 30 minutes prior to seizures. We developed and implemented an objective methodology to prospectively assess the sensitivity and specificity of these subjective experiences using personal digital assistants (PDAs). The key property, in contrast to paper-based diaries, is the internal recording of the patient's entering time of prodromes and seizures. Of 500 patients with epilepsy interviewed, 31 claimed to sense prodromal symptoms at least 30 minutes before seizure onset. Eleven of them agreed to participate in a 4-week study to objectively measure their prospective prediction performance. In 9 patients returning data, the majority of prodrome entries were not followed by seizures or were identified only retrospectively. Statistical analysis revealed that no patient could outperform a nonspecific random predictor when predicting seizures based on the occurrence of prodromes, and that the group performance matched precisely the expected result for a by-chance prediction. These results question the predictive value of "prodromes" and the specificity of their occurrence in the preictal period.

Covering a broad dynamic range: information processing at the erythropoietin receptor.

Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

Theoretical and experimental analysis links isoform-specific ERK signalling to cell fate decisions.

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. Model analysis showed bow-tie-shaped signal processing and inherently transient signalling for cytokine-induced ERK signalling. Sensitivity analysis predicted that, through a feedback-mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over-expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions.

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering.

BACKGROUND: Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. RESULTS: We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. CONCLUSION: We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.

Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues.

Dynamical modeling and multi-experiment fitting with PottersWheel.

MOTIVATION: Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental datasets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check the validity of a given model, and to discriminate competing model hypotheses. It requires high-performance integration of ordinary differential equations and robust optimization. RESULTS: We here present the comprehensive modeling framework Potters-Wheel (PW) including novel functionalities to satisfy these requirements with strong emphasis on the inverse problem, i.e. data-based modeling of partially observed and noisy systems like signal transduction pathways and metabolic networks. PW is designed as a MATLAB toolbox and includes numerous user interfaces. Deterministic and stochastic optimization routines are combined by fitting in logarithmic parameter space allowing for robust parameter calibration. Model investigation includes statistical tests for model-data-compliance, model discrimination, identifiability analysis and calculation of Hessian- and Monte-Carlo-based parameter confidence limits. A rich application programming interface is available for customization within own MATLAB code. Within an extensive performance analysis, we identified and significantly improved an integrator-optimizer pair which decreases the fitting duration for a realistic benchmark model by a factor over 3000 compared to MATLAB with optimization toolbox. AVAILABILITY: PottersWheel is freely available for academic usage at http://www.PottersWheel.de/. The website contains a detailed documentation and introductory videos. The program has been intensively used since 2005 on Windows, Linux and Macintosh computers and does not require special MATLAB toolboxes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Early detection of behavioral side effects of antiepileptic treatment using handheld computers.

OBJECTIVE: Treatment-emergent side effects are frequent events, particularly during the uptitration of antiepileptic drugs. So far, monitoring of such adverse events in outpatients has often been limited to intervals of weeks or months. We here report the application of a new device for temporally fine-grained assessment of objective well-being and cognitive performance using personal digital assistants (PDAs). METHODS: Twenty adult patients with epilepsy participated in this pilot study. Ten received add-on treatment with levetiracetam. Ten patients with constant medication served as a control group. Differences between groups with respect to self-rated cognitive condition, psychophysical condition, aggressiveness, and cognitive test performance in a concentration test assessed three times daily (morning, early afternoon, and evening), over the course of 6 days, were analyzed. RESULTS: Levetiracetam-treated patients manifested an early augmentation of self-rated aggressiveness, which increased in intensity over the course of days. Aggressiveness reached a maximum in the early afternoon across days. There were no major changes in cognitive performance, except for an increase in morning performance in the control group. CONCLUSIONS: This study demonstrates the feasibility of a new method of ambulatory assessment of behavioral and cognitive data during titration of antiepileptic drugs. Significant changes in aggressiveness under add-on treatment with levetiracetam were found to be dependent on the time of assessment during the day. These results suggest that PDA-based ambulatory monitoring of patients with epilepsy may be a promising tool for early detection of drug-related side effects and, thus, may constitute a significant improvement in patient care.

Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings.

OBJECTIVE: Retrospective evaluation and comparison of performances of a multivariate method for seizure detection and prediction on simultaneous long-term EEG recordings from scalp and intracranial electrodes. METHODS: Two multivariate techniques based on simulated leaky integrate-and-fire neurons were investigated in order to detect and predict seizures. Both methods were applied and assessed on 423h of EEG and 26 seizures in total, recorded simultaneously from the scalp and intracranially continuously over several days from six patients with pharmacorefractory epilepsy. RESULTS: Features generated from simultaneous scalp and intracranial EEG data showed a similar dynamical behavior. Significant performances with sensitivities of up to 73%/62% for scalp/invasive EEG recordings given an upper limit of 0.15 false detections per hour were obtained. Up to 59%/50% of all seizures could be predicted from scalp/invasive EEG, given a maximum number of 0.15 false predictions per hour. A tendency to better performances for scalp EEG was obtained for the detection algorithm. CONCLUSIONS: The investigated methods originally developed for non-invasive EEG were successfully applied to intracranial EEG. Especially, concerning seizure detection the method shows a promising performance which is appropriate for practical applications in EEG monitoring. Concerning seizure prediction a significant prediction performance is indicated and a modification of the method is suggested. SIGNIFICANCE: This study evaluates simultaneously recorded non-invasive and intracranial continuous long-term EEG data with respect to seizure detection and seizure prediction for the first time.

Dynamic pathway modeling: feasibility analysis and optimal experimental design.

A major challenge in systems biology is to evaluate the feasibility of a biological research project prior to its realization. Since experiments are animals-, cost- and time-consuming, approaches allowing researchers to discriminate alternative hypotheses with a minimal set of experiments are highly desirable. Given a null hypothesis and alternative model, as well as laboratory constraints like observable players, sample size, noise level, and stimulation options, we suggest a method to obtain a list of required experiments in order to significantly reject the null hypothesis model M0 if a specified alternative model MA is realized. For this purpose, we estimate the power to detect a violation of M0 by means of Monte Carlo simulations. Iteratively, the power is maximized over all feasible stimulations of the system using multi-experiment fitting, leading to an optimal combination of experimental settings to discriminate the null hypothesis and alternative model. We prove the importance of simultaneous modeling of combined experiments with quantitative, highly sampled in vivo measurements from the Jak/STAT5 signaling pathway in fibroblasts, stimulated with erythropoietin (Epo). Afterwards we apply the presented iterative experimental design approach to the Jak/STAT3 pathway of primary hepatocytes stimulated with IL-6. Our approach offers the possibility of deciding which scientific questions can be answered based on existing laboratory constraints. To be able to concentrate on feasible questions on account of inexpensive computational simulations yields not only enormous cost and time saving, but also helps to specify realizable, systematic research projects in advance.

Spatio-temporal patient-individual assessment of synchronization changes for epileptic seizure prediction.

OBJECTIVE: Abnormal synchronization of neurons plays a central role for the generation of epileptic seizures. Therefore, multivariate time series analysis techniques investigating relationships between the dynamics of different neural populations may offer advantages in predicting epileptic seizures. METHODS: We applied a phase and a lag synchronization measure to a selected subset of multicontact intracranial EEG recordings and assessed changes in synchronization with respect to seizure prediction. RESULTS: Patient individual results, group results, spatial aspects using focal and extra-focal electrode contacts as well as two evaluation schemes analyzing decreases and increases in synchronization were examined. Averaged sensitivity values of 60% are observed for a false prediction rate of 0.15 false predictions per hour, a seizure occurrence period of half an hour, and a prediction horizon of 10 min. For approximately half of all 21 patients, a statistically significant prediction performance is observed for at least one synchronization measure and evaluation scheme. CONCLUSIONS: The results indicate that synchronization changes in the EEG dynamics preceding seizures can be used for seizure prediction. Nevertheless, the underlying pathogenic mechanisms differ and both decreases and increases in synchronization may precede epileptic seizures depending on the structures investigated. SIGNIFICANCE: The prediction method, optimized values of intervention times, as well as preferred brain structures for the EEG recordings have to be determined for each patient individually offering the chance of a better patient-individual prediction performance.

Host cell responses induced by hepatitis C virus binding.

Initiation of hepatitis C virus (HCV) infection is mediated by docking of the viral envelope to the hepatocyte cell surface membrane followed by entry of the virus into the host cell. Aiming to elucidate the impact of this interaction on host cell biology, we performed a genomic analysis of the host cell response following binding of HCV to cell surface proteins. As ligands for HCV-host cell surface interaction, we used recombinant envelope glycoproteins and HCV-like particles (HCV-LPs) recently shown to bind or enter hepatocytes and human hepatoma cells. Gene expression profiling of HepG2 hepatoma cells following binding of E1/E2, HCV-LPs, and liver tissue samples from HCV-infected individuals was performed using a 7.5-kd human cDNA microarray. Cellular binding of HCV-LPs to hepatoma cells resulted in differential expression of 565 out of 7,419 host cell genes. Examination of transcriptional changes revealed a broad and complex transcriptional program induced by ligand binding to target cells. Expression of several genes important for innate immune responses and lipid metabolism was significantly modulated by ligand-cell surface interaction. To assess the functional relevance and biological significance of these findings for viral infection in vivo, transcriptional changes were compared with gene expression profiles in liver tissue samples from HCV-infected patients or controls. Side-by-side analysis revealed that the expression of 27 genes was similarly altered following HCV-LP binding in hepatoma cells and viral infection in vivo. In conclusion, HCV binding results in a cascade of intracellular signals modulating target gene expression and contributing to host cell responses in vivo. Reprogramming of cellular gene expression induced by HCV-cell surface interaction may be part of the viral strategy to condition viral entry and replication and escape from innate host cell responses.

Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction.

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory.

Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies.

PURPOSE: Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep-wake cycle. METHODS: In 21 patients, each with 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics. RESULTS: A dependency of false predictions on the time of day is shown. Renormalized to the duration of the period patients are asleep and awake, 86% of all false predictions occurred during sleep for the dynamic similarity index and 68% for the mean phase coherence, respectively. Combining two reference intervals, one during sleep and one in an awake state, the dynamic similarity index increases its performance by reducing the number of false predictions by almost 50% without major changes in sensitivity. No obvious dependence of false predictions was noted on visible epileptic activity, such as spikes, sharp waves, or subclinical ictal patterns. CONCLUSIONS: Changes in the EEG dynamics related to the sleep-wake cycle contribute to limits of specificity of both seizure-prediction methods investigated. This may provide a clue for improving prediction methods in general. The combination of reference states yields promising results and may offer opportunities to increase further the performance of prediction methods.

Computational processing and error reduction strategies for standardized quantitative data in biological networks.

High-quality quantitative data generated under standardized conditions is critical for understanding dynamic cellular processes. We report strategies for error reduction, and algorithms for automated data processing and for establishing the widely used techniques of immunoprecipitation and immunoblotting as highly precise methods for the quantification of protein levels and modifications. To determine the stoichiometry of cellular components and to ensure comparability of experiments, relative signals are converted to absolute values. A major source for errors in blotting techniques are inhomogeneities of the gel and the transfer procedure leading to correlated errors. These correlations are prevented by randomized gel loading, which significantly reduces standard deviations. Further error reduction is achieved by using housekeeping proteins as normalizers or by adding purified proteins in immunoprecipitations as calibrators in combination with criteria-based normalization. Additionally, we developed a computational tool for automated normalization, validation and integration of data derived from multiple immunoblots. In this way, large sets of quantitative data for dynamic pathway modeling can be generated, enabling the identification of systems properties and the prediction of targets for efficient intervention.

Gene expression profiling in polycythaemia vera: overexpression of transcription factor NF-E2.

Summary The molecular aetiology of polycythaemia vera (PV) remains unknown and the differential diagnosis between PV and secondary erythrocytosis (SE) can be challenging. Gene expression profiling can identify candidates involved in the pathophysiology of PV and generate a molecular signature to aid in diagnosis. We thus performed cDNA microarray analysis on 40 PV and 12 SE patients. Two independent data sets were obtained: using a two-step training/validation design, a set of 64 genes (class predictors) was determined, which correctly discriminated PV from SE patients. Separately 253 genes were identified to be upregulated and 391 downregulated more than 1.5-fold in PV compared with healthy controls (P < 0.01). Of the genes overexpressed in PV, 27 contained Sp1 sites: we therefore propose that altered activity of Sp1-like transcription factors may contribute to the molecular aetiology of PV. One Sp1 target, the transcription factor NF-E2 [nuclear factor (erythroid-derived 2)], is overexpressed 2- to 40-fold in PV patients. In PV bone marrow, NF-E2 is overexpressed in megakaryocytes, erythroid and granulocytic precursors. It has been shown that overexpression of NF-E2 leads to the development of erythropoietin-independent erythroid colonies and that ectopic NF-E2 expression can reprogram monocytic cells towards erythroid and megakaryocytic differentiation. Transcription factor concentration may thus control lineage commitment. We therefore propose that elevated concentrations of NF-E2 in PV patients lead to an overproduction of erythroid and, in some patients, megakaryocytic cells/platelets. In this model, the level of NF-E2 overexpression determines both the severity of erythrocytosis and the concurrent presence or absence of thrombocytosis.