The department of Cell Biology-Inspired Tissue Engineering (cBITE) at the MERLN Institute for Technologyinspired Regenerative Medicine at Maastricht University in the Netherlands invites applications for a postdoctoral position. The post-doctoral researcher will perform cutting-edge research in computational modeling methods applied to drug-toxin interactions in the kidney. Chronic kidney disease (CKD) is one of the top ten causes of death worldwide. Gradual decline in kidney function leads to accumulation of metabolic waste products (toxins) normally excreted by the kidneys, contributing to progression of CKD and its comorbidities, in particular cardiovascular disease. To alleviate the CKD complications, patients are routinely treated with many drugs, such as antihypertensive and cholesterol-lowering agents. However, the transporter proteins in kidney cells that handle these drugs are also responsible for secreting the toxins, and may, therefore, not be able to handle the competing demands. Considering the multitude of CKD drugs, toxins and transporter proteins and their potential interactions, investigating drug-toxin interactions is experimentally time consuming and difficult to accomplish. In this project, funded by the Dutch Kidney Foundation (https://nierstichting.nl/), we propose to integrate computational and experimental expertise with state-of-the-art technologies to address investigate drug-toxin interactions in the kidney and improve CKD drug treatments.

Project description: 

• Computational modeling of drug-toxin transport in the proximal tubule; (competitive) binding kinetics;
• Sensitivity analysis;
• Analysis and integration of various in vitro/in vivo data for model calibration;
• Parameter optimization.

Application deadline: 29 June 2023

More information:

https://www.academictransfer.com/en/328781/postdoc-position-merln-computational-modeling-of-drug-toxin-interactions-in-the-kidney/

Research Project

O-Health is a Consolidator grant from the European Research Council, awarded by the European Commission under the HORIZON Action. It is led by Prof. Jérôme Noailly, from the BCN MedTech research unit of the Department of Information and Communication Technologies (DTIC), at the Universitat Pompeu Fabra (UPF), Barcelona, Spain.

Non-communicable diseases (NCD) that involve load-bearing organs emerge silently according to complex mechanisms that are likely to involve inter-disease systemic communications. Clinical explorations cannot apprehend such intricate emergence, O-Health postulates that multiscale in silico models can. Digital twin technologies for health have progressed a lot in the last decades, but multi-disease transversal modelling at the body scale is yet to be developed. It requires to couple small to large-scale model components with appropriate balance of phenomenological and mechanistic approaches, to cope with overwhelming biological complexity, preserve interpretability and incorporate real-world data. This is the niche of O-Health that proposes a scalable ecosystem of multiscale NCD models interrelated through a body-wide systemic model of low-grade inflammation. The project tackles such vertical and transversal physiology-based computational modelling through four major NCD, lung emphysema, atherosclerosis, intervertebral disc degeneration and knee osteoarthritis that affect load-bearing organs at different anatomical locations. Each NCD model will vertically share predicted variables with an interface model of endothelial cell dysfunction that communicates with a transversal model of body-wide systemic communications. The O-health ecosystem shall be modular and interoperable. Models and simulations will combine finite element models at the organ /tissue scales and systems biology models at lower scales, with machine learning models for the incorporation of real-world data out of population cohorts. Interoperability shall be ensured through declarative standard languages such as Field and Systems Biology Mark-up Languages.

Description of the position

The successful Candidate will be a senior postdoctoral researcher, with proven experience in data science and machine learning. He/She will be based at DTIC, UPF and will be responsible for the management and mining of health data collected for the O-Health project, out of existing patient and population cohorts. Scientific and technical competencies are necessary in interpretable machine learning, nonlinear unsupervised learning, as well as in federated and transfer learning. Advanced programming skills and experience in handling health and/or biomedical data are required. Knowledge of data management plans will be valued. The Researcher will become progressively responsible for the data science operations of the BMMB, to properly mine real-world health data and couple the latter to the physics- and biology-based models, developed by the other researchers of the group for interpretable predictive modelling. While a high level of independence is expected, (s)he shall report periodically to PI, to ensure (i) that the timelines of the project and deliverables are met, and (ii) that the project results can be properly disseminated through scientific and medical congresses and journals. As such, communication, teamwork, and leadership skills will be chiefly important.

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