Bayesian Inference of Gene Regulatory Networks : From Parameter Estimation to Experimental Design

To learn the structure of gene regulatory networks is an interesting and important topic in systems biology. This structure could be used to specify key regulators and this knowledge may be used to develop new drugs which affect the expression of these regulators. However, the inference of gene regulatory networks, especially from time-series data is a challenging task. This is due to the limited amount of given data which additionally contain a lot of noise. These data cause from the technical point of view for the parameter estimation procedure problems like the non-identifiability and sloppiness of parameters. To address these difficulties, in these thesis new methods for both, the parameter estimation task and the experimental design for gene regulatory networks, are developed for a non-linear ordinary differential equations model, which use a Bayesian procedure and generate samples of the underlying distribution of the parameters. These distributions are of high interest, since they do not provide only one network structure but give all network structures that are consistent with the given data. And all of these structures can then be examined in more detail. The proposed method for Bayesian parameter estimation uses smoothing splines to circumvent the numerical integration of the underlying system of ordinary differential equations, which is usually used for parameter estimation procedures in systems of ordinary differential equations. An iterative Hybrid Monte Carlo and Metropolis-Hastings algorithm is used to sample the model parameters and the smoothing factor. This new method is applied to simulated data, which shows that it is able to reconstruct the topology of the underlying gene regulatory network with high accuracy. The approach was also applied to real experimental data, a synthetic designed 5-gene network (the DREAM 2 Challenge #3 data) and outperforms other methods. For the Bayesian experimental design step, a full Bayesian approach was used which does not use any parametric assumption of the posterior distribution, nor linearizes around a point estimate. To make the full Bayesian approach computationally manageable, maximum entropy sampling is used together with a population-based Markov chain Monte Carlo algorithm. The approach was applied to simulated and real experimental data, the DREAM 2 Challenge #3 data, and outperforms the usage of random experiments and a classical experimental design method

Right to Access Information as a Collective-Based Approach to the GDPR’s Right to Explanation in European Law

This article presents a perspective which focuses on the right to access information as a mean to ensure a non-discriminatory character of algorithms by providing an alternative to the right to explanation implemented in the General Data Protection Regulation (GDPR). I adopt the evidence-based assumption that automated decision-making technologies have an inherent discriminatory potential. The example of a regulatory means which to a certain extent addresses this problem is the approach based on privacy protection in regard to the right to explanation. The Articles 13-15 and 22 of the GDPR provide individual users with certain rights referring to the automated decision-making technologies. However, the right to explanation not only may have a very limited impact, but it also focuses on individuals thus overlooking potentially discriminated groups. Because of this, the article offers an alternative approach on the basis of the right to access information. It explores the possibility of using this right as a tool to receive information on the algorithms determining automated decision-making solutions. Tracking an evolution of the interpretation of Article 10 of the Convention for the Protection of Human Right and Fundamental Freedoms in the relevant case law aims to illustrate how the right to access information may become a collective- based approach towards the right to explanation. I consider both, the potential of this approach, such as its more collective character e.g. due to the unique role played by the media and NGOs in enforcing the right to access information, as well as its limitations

Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling

<p>Abstract</p> <p>Background</p> <p>The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable.</p> <p>Results</p> <p>We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed.</p> <p>Conclusions</p> <p>Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters, which holds interesting prospects for experimental design purposes.</p

Kinome profiling of regulatory T cells : a closer look into a complex intracellular network

Regulatory T cells (Treg) are essential for T cell homeostasis and maintenance of peripheral tolerance. They prevent activation of auto-reactive T effector cells (Teff) in the context of autoimmunity and allergy. Otherwise, Treg also inhibit effective immune responses against tumors. Besides a number of Treg-associated molecules such as Foxp3, CTLA-4 or GARP, known to play critical roles in Treg differentiation, activation and function, the involvement of additional regulatory elements is suggested. Herein, kinase activities seem to play an important role in Treg fine tuning. Nevertheless, our knowledge regarding the complex intracellular signaling pathways controlling phenotype and function of Treg is still limited and based on single kinase cascades so far. To gain a more comprehensive insight into the pathways determining Treg function we performed kinome profiling using a phosphorylation-based kinome array in human Treg at different activation stages compared to Teff. Here we have determined intriguing quantitative differences in both populations. Resting and activated Treg showed an altered pattern of CD28-dependent kinases as well as of those involved in cell cycle progression. Additionally, significant up-regulation of distinct kinases such as EGFR or CK2 in activated Treg but not in Teff not only resemble data we obtained in previous studies in the murine system but also suggest that those specific molecular activation patterns can be used for definition of the activation and functional state of human Treg. Taken together, detailed investigation of kinome profiles opens the possibility to identify novel molecular mechanisms for a better understanding of Treg biology but also for development of effective immunotherapies against unwanted T cell responses in allergy, autoimmunity and cancer

Quantum-mechanical model of the Kerr-Newman black hole

We consider a Hamiltonian quantum theory of stationary spacetimes containing a Kerr-Newman black hole. The physical phase space of such spacetimes is just six-dimensional, and it is spanned by the mass $M$, the electric charge $Q$ and angular momentum $J$ of the hole, together with the corresponding canonical momenta. In this six-dimensional phase space we perform a canonical transformation such that the resulting configuration variables describe the dynamical properties of Kerr-Newman black holes in a natural manner. The classical Hamiltonian written in terms of these variables and their conjugate momenta is replaced by the corresponding self-adjoint Hamiltonian operator and an eigenvalue equation for the Arnowitt-Deser-Misner (ADM) mass of the hole, from the point of view of a distant observer at rest, is obtained. In a certain very restricted sense, this eigenvalue equation may be viewed as a sort of "Schr\"odinger equation of black holes". Our "Schr\"odinger equation" implies that the ADM mass, electric charge and angular momentum spectra of black holes are discrete, and the mass spectrum is bounded from below. Moreover, the spectrum of the quantity $M^2-Q^2-a^2$, where $a$ is the angular momentum per unit mass of the hole, is strictly positive when an appropriate self-adjoint extension is chosen. The WKB analysis yields the result that the large eigenvalues of $M$, $Q$ and $a$ are of the form $\sqrt{2n}$, where $n$ is an integer. It turns out that this result is closely related to Bekenstein's proposal on the discrete horizon area spectrum of black holes.Comment: 30 pages, 3 figures, RevTe

Haplotype reference consortium panel: Practical implications of imputations with large reference panels

Recently, the Haplotype Reference Consortium (HRC) released a large imputation panel that allows more accurate imputation of genetic variants. In this study, we compared a set of directly assayed common and rare variants from an exome array to imputed genotypes, that is, 1000 genomes project (1000GP) and HRC. We showed that imputation using the HRC panel improved the concordance between assayed and imputed genotypes at common, and especially, low-frequency variants. Furthermore, we performed a genome-wide association meta-analysis of vertical cup-disc ratio, a highly heritable endophenotype of glaucoma, in four cohorts using 1000GP and HRC imputations. We compared the results of the meta-analysis using 1000GP to the meta-analysis results using HRC. Overall, we found that using HRC imputation significantly improved P values (P = 3.07 × 10-61), particularly for suggestive variants. Both meta-analyses were performed in the same sample size, yet we found eight genome-wide significant loci in the HRC-based meta-analysis versus seven genome-wide significant loci in the 1000GP-based meta-analysis. This study provides supporting evidence of the new avenues for gene discovery and fine mapping that the HRC imputation panel offers

A modern scleractinian coral with a two-component calcite–aragonite skeleton

Until now, all of the ca. 1,800 known modern scleractinian coral species were thought to produce skeletons exclusively of aragonite. Asymbiotic Paraconotrochus antarcticus living in the Southern Ocean is the first example of an extant scleractinian that forms a two-component carbonate skeleton, with an inner structure made of high-Mg calcite and an outer structure composed of aragonite. This discovery adds support to the notion that the coral skeletal formation process is strongly biologically controlled. Mitophylogenomic analysis shows that P. antarcticus represents an ancient scleractinian clade, suggesting that skeletal mineralogy/polymorph of a taxon, once established, is a trait conserved throughout the evolution of that clade.One of the most conserved traits in the evolution of biomineralizing organisms is the taxon-specific selection of skeletal minerals. All modern scleractinian corals are thought to produce skeletons exclusively of the calcium-carbonate polymorph aragonite. Despite strong fluctuations in ocean chemistry (notably the Mg/Ca ratio), this feature is believed to be conserved throughout the coral fossil record, spanning more than 240 million years. Only one example, the Cretaceous scleractinian coral Coelosmilia (ca. 70 to 65 Ma), is thought to have produced a calcitic skeleton. Here, we report that the modern asymbiotic scleractinian coral Paraconotrochus antarcticus living in the Southern Ocean forms a two-component carbonate skeleton, with an inner structure made of high-Mg calcite and an outer structure composed of aragonite. P. antarcticus and Cretaceous Coelosmilia skeletons share a unique microstructure indicating a close phylogenetic relationship, consistent with the early divergence of P. antarcticus within the Vacatina (i.e., Robusta) clade, estimated to have occurred in the Mesozoic (ca. 116 Mya). Scleractinian corals thus join the group of marine organisms capable of forming bimineralic structures, which requires a highly controlled biomineralization mechanism; this capability dates back at least 100 My. Due to its relatively prolonged isolation, the Southern Ocean stands out as a repository for extant marine organisms with ancient traits.Mitogenome sequences data have been deposited in GenBank (MT409109). All other study data are included in the article text and supporting information