SubsidieMeestersNew methodologies for automated modeling of the dynamic behavior of large biological networks
AUTOMATHIC aims to develop an automated framework for ODE modeling of cell transport and signaling to enhance drug safety and optimize therapies for chronic kidney disease patients.
Projectdetails
Introduction
In silico models aim to capture and elucidate the complex and emergent interactions of biological systems, with the goal of expediting research and potential clinical translation. For example, ordinary differential equation (ODE) models of toxin and drug transport are being developed to bring safer therapies to chronic kidney disease patients.
Limitations of Current Models
Despite recent progress, these cutting-edge ODEs only model transport in steady state and remain limited regarding the amount and complexity of dynamic transport mechanisms. It is often not clear which kinetic relation is most suitable. This limitation is due to the manual and labor-intensive approaches to construct the ODEs, which critically hinder their application in quantitative toxicity assessment in key industrial settings like drug development.
Project Overview
In AUTOMATHIC, I will leverage and combine my expertise in mathematical modeling and machine learning to develop an integrated framework for automated ODE structure identification, parameter estimation, and model evaluation. The focus will be on cell transport and signaling, which is the timely leap forward needed to create large dynamic models and transform the field.
Methodology
I will test and illustrate the capabilities of the developed framework by exploring the dynamics and regulation of proximal tubule (PT) toxin and drug transport. I will use the dynamic PT model to define novel therapeutic regimens that minimize toxin accumulation, in combination with a state-of-the-art in vitro setup to measure the essential time series data required for model calibration and validation.
Anticipated Outcomes
The anticipated outcomes of AUTOMATHIC are:
- A next-generation, integrated framework for automated model structure identification and parameter estimation that will become the new standard for the creation and interrogation of large dynamic models of cell transport and signaling.
- Crucial knowledge about PT transport and metabolism.
- A dynamic PT model to optimize nephrotoxicity protocols, drug dosing, and screening.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.500.000 |
| Totale projectbegroting | € 1.500.000 |
Tijdlijn
| Startdatum | 1-12-2024 |
| Einddatum | 30-11-2029 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITEIT MAASTRICHTpenvoerder
Land(en)
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