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

Descripción del proyecto

Plataforma automatizada de modelización de cambios dinámicos en el transporte biológico

Los modelos computacionales «in silico» se utilizan cada vez más para comprender sistemas biológicos complejos y acelerar la investigación médica. Las ecuaciones diferenciales ordinarias (EDO) son un elemento básico, pero las mejores solo modelizan el transporte en estado estacionario, dado el intenso trabajo de construcción manual. Esto limita la comprensión de los complejos mecanismos de transporte que cambian dinámicamente. El equipo del proyecto AUTOMATHIC, financiado por el Consejo Europeo de Investigación, pretende transformar esta situación elaborando un marco automatizado que combine la modelización matemática y el aprendizaje automático para identificar las estructuras de EDO adecuadas, estimar sus parámetros y evaluar con eficacia los modelos resultantes. Centrándose en cómo las células renales transportan toxinas y fármacos, en AUTOMATHIC se utilizará este marco para identificar formas de minimizar la toxicidad de los medicamentos con vistas a unos tratamientos más seguros en pacientes con enfermedad renal crónica.

Objetivo

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.