4 resultados para regulatory design
Resumo:
This chapter explores how the EU is a largely overlooked exporter of normative power through its facilitation and use of clinical trials data produced abroad for the marketing of safe pharmaceuticals at home; a move that helps to foster the growing resort to pharmaceuticals as a fix for public health problems. This is made possible by the EU’s (de)selection of international ethical frameworks in preference to the international technical standards it co-authors with other global regulators. Clinical trials abroad underscore how ethics are contingent and revisable in light of market needs, producing weak protections for the vulnerable subjects of EU law. I argue that these components and effects of the regime are ultimately about that which undergirds, shapes and directs regulatory design. That is, I point to the use, infiltration, perpetuation and extension of market-oriented ideas, values and rationalities into formally non-market domains like biomedical knowledge production and public health. I explain how these are central to efforts at producing and legitimating the EU, its related imagined socio-political order based on a more innovative, profitable and competitive pharmaceutical sector in order to foster economic growth, jobs and prosperity, and with them the project of European integration. ‘Bioethics as risk’ is highlighted as a way to reshape and redirect the regulatory regime in ways that are more consistent with the spirit and letter of the ethical standards (and through them the human rights) the EU claims to uphold.
Resumo:
The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.
Resumo:
The purpose of this study is to compare the inferability of various synthetic as well as real biological regulatory networks. In order to assess differences we apply local network-based measures. That means, instead of applying global measures, we investigate and assess an inference algorithm locally, on the level of individual edges and subnetworks. We demonstrate the behaviour of our local network-based measures with respect to different regulatory networks by conducting large-scale simulations. As inference algorithm we use exemplarily ARACNE. The results from our exploratory analysis allow us not only to gain new insights into the strength and weakness of an inference algorithm with respect to characteristics of different regulatory networks, but also to obtain information that could be used to design novel problem-specific statistical estimators.
Resumo:
Background
Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.
Results
In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.
Conclusions
For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.