BackgroundMost of the modeling performed in the area of systems biology aims at achieving a quantitative description of the intracellular pathways within a "typical cell". However, in many biologically important situations even clonal cell populations can show a heterogeneous response. These situations require study of cell-to-cell variability and the development of models for heterogeneous cell populations.ResultsIn this paper we consider cell populations in which the dynamics of every single cell is captured by a parameter dependent differential equation. Differences among cells are modeled by differences in parameters which are subject to a probability density. A novel Bayesian approach is presented to infer this probability density from population snapshot data, such as flow cytometric analysis, which do not provide single cell time series data. The presented approach can deal with sparse and noisy measurement data. Furthermore, it is appealing from an application point of view as in contrast to other methods the uncertainty of the resulting parameter distribution can directly be assessed.ConclusionsThe proposed method is evaluated using artificial experimental data from a model of the tumor necrosis factor signaling network. We demonstrate that the methods are computationally efficient and yield good estimation result even for sparse data sets.

The spindle assembly checkpoint is a conserved signalling pathway that protects genome integrity. Given its central importance, this checkpoint should withstand stochastic fluctuations and environmental perturbations, but the extent of and mechanisms underlying its robustness remain unknown. We probed spindle assembly checkpoint signalling by modulating checkpoint protein abundance and nutrient conditions in fission yeast. For core checkpoint proteins, a mere 20% reduction can suffice to impair signalling, revealing a surprising fragility. Quantification of protein abundance in single cells showed little variability (noise) of critical proteins, explaining why the checkpoint normally functions reliably. Checkpoint-mediated stoichiometric inhibition of the anaphase activator Cdc20 (Slp1 in Schizosaccharomyces pombe) can account for the tolerance towards small fluctuations in protein abundance and explains our observation that some perturbations lead to non-genetic variation in the checkpoint response. Our work highlights low gene expression noise as an important determinant of reliable checkpoint signalling.

DNA methylation maintenance by DNMT1 is an essential process in mammals but molecular mechanisms connecting DNA methylation patterns and enzyme activity remain elusive. Here, we systematically analyzed the specificity of DNMT1, revealing a pronounced influence of the DNA sequences flanking the target CpG site on DNMT1 activity. We determined DNMT1 structures in complex with preferred DNA substrates revealing that DNMT1 employs flanking sequence-dependent base flipping mechanisms, with large structural rearrangements of the DNA correlating with low catalytic activity. Moreover, flanking sequences influence the conformational dynamics of the active site and cofactor binding pocket. Importantly, we show that the flanking sequence preferences of DNMT1 highly correlate with genomic methylation in human and mouse cells, and 5-azacytidine triggered DNA demethylation is more pronounced at CpG sites with flanks disfavored by DNMT1. Overall, our findings uncover the intricate interplay between CpG-flanking sequence, DNMT1-mediated base flipping and the dynamic landscape of DNA methylation.

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Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.

New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.

BackgroundIn mammalian cells protein-lipid interactions at the trans-Golgi network (TGN) determine the formation of vesicles, which transfer secretory proteins to the cellular membrane. This process is regulated by a complex molecular network including protein kinase D (PKD), which is directly involved in the fission of transport vesicles, and its interaction with the ceramide transfer protein CERT that transports ceramide from the endoplasmic reticulum to the TGN.ResultsHere we present a novel quantitative kinetic model for the interactions of the key players PKD, phosphatidylinositol 4-kinase III beta (PI4KIII β) and CERT at the TGN membranes. We use sampling-based Bayesian analysis and perturbation experiments for model calibration and validation.ConclusionsOur quantitative predictions of absolute molecular concentrations and reaction fluxes have major biological implications: Model comparison provides evidence that PKD and CERT interact in a cooperative manner to regulate ceramide transfer. Furthermore, we identify active PKD to be the dominant regulator of the network, especially of CERT-mediated ceramide transfer.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0147-1) contains supplementary material, which is available to authorized users.