This 3-year postdoc position is part of a larger European collaboration in the Horizon 2020 program.
We are seeking a postdoctoral researcher to work on an innovative research project in a highly interdisciplinary team. For this project you will focus on the use of mathematical and computational methods to expand the concept and applicability of disease networks, both theoretically and computationally. The main objective is to achieve the ability to combine multiple datasets, such as clinical cohort studies, and data types (e.g., genomics, proteomics, exposome) into a single analysis of multi-morbidity disease states. These analyses specifically focus on the link between cardiovascular diseases and clinical depression as two often intertwined diseases states.
There is a growing consensus that the complexities underlying the onset of disease of an individual cannot be understood by traditional, reductionistic methods alone. A shift in research paradigm towards complex systems, network thinking, and multivariate analysis is considered imperative to move this field forward. In particular, a major challenge in the management of disease states is multi-morbidity, i.e., the presence of two or more diseases at the same time. Patients with multiple diseases are more difficult to treat, but the pathways underlying multi-morbid states are poorly understand.
The EU project, of which you will be part, is centered around well-phenotyped longitudinal patient cohorts and population studies, containing a large amount of clinical and epidemiological information on cardiovascular disease and depression, including risk factors, metabolic and mental comorbidities, multiple omics datasets (including genomics, epigenomics, transcriptomics, lipidomics/metabolomics and targeted proteomics already available) and other biomarkers. These data are generated over the years through investments at the national and European level and provide a unique opportunity. In order to characterize the biological pathways and networks associated with CVD-depression multimorbidities these data will be explored through the application of multidimensional state-of-the-art methodologies combining bioinformatics, computational biology, mathematics and statistics, new algorithms and systems medicine approaches, with the aim to unwind shared causative mechanisms and identify novel biomarkers. These analyses will be complemented by studies of monocyte/macrophage/microglial function in patients’ cells in the laboratory and experimental animal and cell-based models in order to confirm causality and further refine the molecular mechanisms involved.
You will focus specifically on identifying as well as modeling (mathematical/computational) the dynamics of multi-morbidities. This will be done through extending the concept of disease networks (see for instance this article for an accessible introduction and relevant references). The first manner in which we plan to extend it is to enrich the statistical associations, used as part of the method, with multivariate information-theoretic measures, specifically the novel concept of synergistic information (see for instance this article). The second manner is to extend the network concept to a multiplex network, due to integrating different types of studies into a single analysis. Finally, we will attempt to identify causal links and causally important nodes in the networks by existing techniques (see this article and this article). All of this is to be integrated in a set of methods and tools which the domain experts in our project can apply to (other) datasets. During the development of the theory, methods, and tools, you are expected to closely collaborate with domain experts in order to ensure relevance and validity of your approach.
|Keywords||disease networks; multivariate statistics; biomedicine; biochemistry; exposome; omics datasets; causality; multiplex networks;|
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