Markov chain Monte Carlo methods for parameter identification in systems biology models

First, I would like to thank Prof. Dr. Achim Tresch for giving me the opportunity to write this thesis and to work on three fascinating projects. I really appreciate all the fruitful discussions, his constant support and the excellent working atmosphere. I would also like to thank Prof. Dr. Patrick Cramer for being my doctoral supervisor. Furthermore, I would like to thank all the other members of my dissertation committee (Prof. Dr. Rainer Spang

Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions

Motivation: Cell fate decisions have a strong stochastic component. The identification of the underlying mechanisms therefore requires a rigorous statistical analysis of large ensembles of single cells that were tracked and phenotyped over time. Results: We introduce a probabilistic framework for testing elementary hypotheses on dynamic cell behavior using time-lapse cell-imaging data. Factor graphs, probabilistic graphical models, are used to properly account for cell lineage and cell phenotype information. Our model is applied to time-lapse movies of murine granulocyte-macrophage progenitor (GMP) cells. It decides between competing hypotheses on the mechanisms of their differentiation. Our results theoretically substantiate previous experimental observations that lineage instruction, not selection is the cause for the differentiation of GMP cells into mature monocytes or neutrophil granulocytes. Availability and implementation: The Matlab source code is available at http://treschgroup.de/Genealogies.html Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Quantitative analysis of processive RNA degradation by the archaeal RNA exosome

RNA exosomes are large multisubunit assemblies involved in controlled RNA processing. The archaeal exosome possesses a heterohexameric processing chamber with three RNase-PH-like active sites, capped by Rrp4- or Csl4-type subunits containing RNA-binding domains. RNA degradation by RNA exosomes has not been studied in a quantitative manner because of the complex kinetics involved, and exosome features contributing to efficient RNA degradation remain unclear. Here we derive a quantitative kinetic model for degradation of a model substrate by the archaeal exosome. Markov Chain Monte Carlo methods for parameter estimation allow for the comparison of reaction kinetics between different exosome variants and substrates. We show that long substrates are degraded in a processive and short RNA in a more distributive manner and that the cap proteins influence degradation speed. Our results, supported by small angle X-ray scattering, suggest that the Rrp4-type cap efficiently recruits RNA but prevents fast RNA degradation of longer RNAs by molecular friction, likely by RNA contacts to its unique KH-domain. We also show that formation of the RNase-PH like ring with entrapped RNA is not required for high catalytic efficiency, suggesting that the exosome chamber evolved for controlled processivity, rather than for catalytic chemistry in RNA decay

In Vitro Evolution of Allergy Vaccine Candidates, with Maintained Structure, but Reduced B Cell and T Cell Activation Capacity

Allergy and asthma to cat (Felis domesticus) affects about 10% of the population in affluent countries. Immediate allergic symptoms are primarily mediated via IgE antibodies binding to B cell epitopes, whereas late phase inflammatory reactions are mediated via activated T cell recognition of allergen-specific T cell epitopes. Allergen-specific immunotherapy relieves symptoms and is the only treatment inducing a long-lasting protection by induction of protective immune responses. The aim of this study was to produce an allergy vaccine designed with the combined features of attenuated T cell activation, reduced anaphylactic properties, retained molecular integrity and induction of efficient IgE blocking IgG antibodies for safer and efficacious treatment of patients with allergy and asthma to cat. The template gene coding for rFel d 1 was used to introduce random mutations, which was subsequently expressed in large phage libraries. Despite accumulated mutations by up to 7 rounds of iterative error-prone PCR and biopanning, surface topology and structure was essentially maintained using IgE-antibodies from cat allergic patients for phage enrichment. Four candidates were isolated, displaying similar or lower IgE binding, reduced anaphylactic activity as measured by their capacity to induce basophil degranulation and, importantly, a significantly lower T cell reactivity in lymphoproliferative assays compared to the original rFel d 1. In addition, all mutants showed ability to induce blocking antibodies in immunized mice.The approach presented here provides a straightforward procedure to generate a novel type of allergy vaccines for safer and efficacious treatment of allergic patients

MC EMiNEM Maps the Interaction Landscape of the Mediator

The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors

Prediction quality and influence of the Empirical Bayes procedure.

<p>(A) Prediction quality. Comparison of the sensitivity of MC EMiNEM and four alternative methods for four different noise levels (top) and four different signals graph sizes (bottom). The sensitivity is depicted on the y-axis, each frame corresponds to one parameter setting. Top: For a signals graph of 11 nodes, noisy data was generated such that for an optimal test with a type-I error (-level) of 5%, a type II error (-level) of , and would be achieved, respectively. Bottom: For a noise level corresponding to an error level of (, ), signals graph sizes of are investigated. We expect our application to range within the four central scenarios. The comparisons of sensitivities is a fair comparison of the prediction qualities since the specificities for all methods and parameter settings are located (see also Fig. S3.7 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi.1002568.s004" target="_blank">Text S1</a>). (B) Influence of the Empirical Bayes procedure. Here, for the standard setting and (, ). The x-axis shows the calculated marginal posterior values centered at (indicated by the dashed vertical line), on the y-axis the frequency is displayed. In the table, the percentages of signals graphs scoring higher than are provided, as well as the -distances (relative to the maximum).</p

Effects graph inferred from the Mediator data.

<p>Shown are the log-odds ratios which serve as MC EMiNEM's input. Genes that are likely to change in a given condition are depicted in red,and they are blue otherwise. Color saturation indicates the absolute value of the log-odds ratio (cf. Fig. S4.3 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi.1002568.s004" target="_blank">Text S1</a>). Rows correspond to Mediator perturbation experiments, columns correspond to genes, sorted according to their attachment to Mediator subunits. Mediator subunits are colored as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi-1002568-g003" target="_blank">Fig. 3</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002568#pcbi-1002568-g005" target="_blank">Fig. 5</a>.</p