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New methodologies for automated modeling of the dynamic behavior of large biological networks

CORDIS · observation · Publication date unknown

lly hinder their application in quantitative toxicity assessment in key industrial settings like drug development. 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, focusing on cell transport and signaling, which is the timely leap forward needed to create large dynamic models and transform the field. 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 set-up to measure the essential time series data required for model calibration and validation. The anticipated outcomes of AUTOMATHIC are: 1) a next-generation, integrated framework for automated model structure identification and parameter estimation that will become the new standard for the creation and int

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR