In vitro cytotoxicity of injectable and biodegradable poly(propylene fumarate)-based networks: unreacted macromers,cross-linked networks,and degradation products

This study evaluates the in vitro biocompatibility of an injectable and biodegradable polymeric network based on poly(propylene fumarate) (PPF) and the cross-linking agent PPF-diacrylate (PPF-DA). Using a methyl tetrazolium (MTT) assay, the effect of the concentrations of PPF and PPF-DA on the cytotoxicity of its unreacted macromers, cross-linked networks, and degradation products was examined. The influence of network structure properties on cell viability and attachment to the cross-linked material was also investigated. The unreacted macromers exhibited a time- and dose-dependent cytotoxic response that increased with more PPF-DA in the mixture. Conversely, the cross-linked networks formed with more PPF-DA did not demonstrate an adverse response because increases in conversion and cross-linking density prevented the extraction of toxic products. Fibroblast attachment was observed on the PPF/PPF-DA networks with the highest double bond conversions. The degradation products, obtained from the complete breakdown of the networks in basic conditions, displayed a dose-dependent cytotoxic response. These results show that there are concerns regarding the biocompatibility of injectable, biodegradable PPF/PPF-DA networks but also sheds light onto potential mechanisms to reduce the cytotoxic effects.     

Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge

Background  

Cellular processes are controlled by gene-regulatory networks. Several computational methods are currently used to learn the structure of gene-regulatory networks from data. This study focusses on time series gene expression and gene knock-out data in order to identify the underlying network structure. We compare the performance of different network reconstruction methods using synthetic data generated from an ensemble of reference networks. Data requirements as well as optimal experiments for the reconstruction of gene-regulatory networks are investigated. Additionally, the impact of prior knowledge on network reconstruction as well as the effect of unobserved cellular processes is studied.     

An error model for protein quantification

MOTIVATION: Quantitative experimental data is the critical bottleneck in the modeling of dynamic cellular processes in systems biology. Here, we present statistical approaches improving reproducibility of protein quantification by immunoprecipitation and immunoblotting. RESULTS: Based on a large data set with more than 3600 data points, we unravel that the main sources of biological variability and experimental noise are multiplicative and log-normally distributed. Therefore, we suggest a log-transformation of the data to obtain additive normally distributed noise. After this transformation, common statistical procedures can be applied to analyze the data. An error model is introduced to account for technical as well as biological variability. Elimination of these systematic errors decrease variability of measurements and allow for a more precise estimation of underlying dynamics of protein concentrations in cellular signaling. The proposed error model is relevant for simulation studies, parameter estimation and model selection, basic tools of systems biology. AVAILABILITY: Matlab and R code is available from the authors on request. The data can be downloaded from our website www.fdm.uni-freiburg.de/~ckreutz/data.     

Data-based identifiability analysis of non-linear dynamical models

MOTIVATION: Mathematical modelling of biological systems is becoming a standard approach to investigate complex dynamic, non-linear interaction mechanisms in cellular processes. However, models may comprise non-identifiable parameters which cannot be unambiguously determined. Non-identifiability manifests itself in functionally related parameters, which are difficult to detect. RESULTS: We present the method of mean optimal transformations, a non-parametric bootstrap-based algorithm for identifiability testing, capable of identifying linear and non-linear relations of arbitrarily many parameters, regardless of model size or complexity. This is performed with use of optimal transformations, estimated using the alternating conditional expectation algorithm (ACE). An initial guess or prior knowledge concerning the underlying relation of the parameters is not required. Independent, and hence identifiable parameters are determined as well. The quality of data at disposal is included in our approach, i.e. the non-linear model is fitted to data and estimated parameter values are investigated with respect to functional relations. We exemplify our approach on a realistic dynamical model and demonstrate that the variability of estimated parameter values decreases from 81 to 1% after detection and fixation of structural non-identifiabilities.     

Finite Sample Properties of the Maximum Likelihood Estimator and of Likelihood Ratio Tests in Hidden Markov Models

Hidden Markov models were successfully applied in various fields of time series analysis, especially for analyzing ion channel recordings. The maximum likelihood estimator (MLE) has recently been proven to be asymptotically normally distributed. Here, we investigate finite sample properties of the MLE and of different types of likelihood ratio tests (LRTs) by means of simulation studies. The MLE is shown to reach the asymptotic behavior within sample sizes that are common for various applications. Thus, reliable estimates and confidence intervals can be obtained. We give an approximative scaling function for the estimation error for finite samples, and investigate the power of different LRTs suitable for applications to ion channels, including tests for superimposed hidden Markov processes. Our results are applied to physiological sodium channel data.     

Early detection of upper airway obstructions by analysis of acoustical respiratory input impedance

Repetitive occurrence of partial or total upper airway obstruction characterizes several respiratory dysfunctions such as the obstructive sleep apnea syndrome (OSAS). In OSAS patients, pharyngeal collapses are linked to a decrease in upper airway muscle activity during sleep which causes decreased upper airway wall stiffness. Continuous positive airway pressure (CPAP) is recommended as the treatment of choice. Advancements in CPAP therapy require early detection of respiratory events in real time to adapt the level of the applied pressure to airway collapsibility. The forced oscillation technique (FOT) is a noninvasive method which reflects patients' airway patency by measuring respiratory impedance. The aim of this study was to evaluate by a mathematical model of the respiratory system if FOT can provide an early detection index of total or partial upper airway obstruction. Furthermore, the simulation should suggest which characteristic features are relevant for early apnea detection in measured clinical data. The respiratory system has been treated as a series of cylindrical segments. The oropharynx analog of the model allows simulation of upper airway collapse, mimicking the situation in patients with OSAS. We calculated the input impedance for different degrees of upper airway obstruction ranging from unobstructed airways to total occlusion. Furthermore, we simulated different upper airway wall compliances. We compared the simulation with real data. The results of the study suggest that FOT is a valuable tool for assessing the degree of upper airway obstruction in patients with OSAS. Especially, the phase angle of the impedance seems to be a potentially useful tool for early apnea detection by assessing the upper airway wall collapsibility. Received: 23 July 1998 / Accepted in revised form: 26 January 1999     

On identification of Na+ channel gating schemes using moving-average filtered hidden Markov models

 Transitions between distinct kinetic states of an ion channel are described by a Markov process. Hidden Markov models (HMM) have been successfully applied in the analysis of single ion channel recordings with a small signal-to-noise ratio. However, we have recently shown that the anti-aliasing low-pass filter misleads parameter estimation. Here, we show for the case of a Na⁺ channel recording that the standard HMM do neither allow parameter estimation nor a correct identification of the gating scheme. In particular, the number of closed and open states is determined incorrectly, whereas a modified HMM considering the anti-aliasing filter (moving-average filtered HMM) is able to reproduce the characteristic properties of the time series and to perform gating scheme identification. Received: 11 February 1999 / Revised version: 18 June 1999 / Accepted: 21 June 1999     

A fast, efficient and stereoselective synthesis of hydroxy-pyrrolidines

A five-step, protecting group free synthesis of 2,3-cis substituted hydroxy-pyrrolidines is presented. Key steps in the synthesis are the chemoselective formation of a primary amine via a Vasella reductive amination using ammonia as the nitrogen source, and the stereoselective formation of a cyclic carbamate from an alkenylamine. Improvement of the reductive amination, by way of the use of α-picoline borane as a more environmentally benign reducing agent, is also presented.     

Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling

Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.