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

Descrizione del progetto

Una piattaforma di modellizzazione automatizzata per il trasporto biologico a variazione dinamica

I modelli computazionali «in silico» sono sempre più utilizzati per comprendere sistemi biologici complessi e accelerare la ricerca medica. Le equazioni differenziali ordinarie (EDO) rappresentano un punto fermo in tal senso; ciononostante, le migliori sono in grado di modellare il trasporto solo in regime stazionario, dato l’intenso lavoro di costruzione manuale, il che limita la comprensione di complessi meccanismi di trasporto che cambiano dinamicamente. Il progetto AUTOMATHIC, finanziato dal CER, mira a invertire questa situazione sviluppando una struttura automatizzata che combina modellizzazione matematica e apprendimento automatico al fine di identificare le strutture ODE appropriate, stimarne i parametri e valutare in modo efficiente i modelli risultanti. Concentrandosi sul modo in cui le cellule renali trasportano le tossine e i farmaci, AUTOMATHIC utilizzerà questo quadro allo scopo di identificare le modalità con cui ridurre al minimo la tossicità dei farmaci, con l’obiettivo di sviluppare terapie più sicure nei pazienti affetti da malattia renale cronica.

Obiettivo

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. 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 as 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. 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 interrogation of large dynamic models of cell transport and signaling; 2) crucial knowledge about PT transport and metabolism; 3) a dynamic PT model to optimize nephrotoxicity protocols, drug dosing and screening.