PREDYCTBIO: PRomoting the Exploitation of DYnamic models with Computational Techniques in the BIOeconomy


Proyecto PID2020-113992RA-I00 financiado por MCIN/ AEI /10.13039/501100011033 (Funded by the Ministry of Science and Innovation – Agencia Estatal de Investigación, call “Proyectos de I+D+i – Retos de Investigación – RTI Tipo A”). 2021 - 2023. Endowment: 108.053 EUR

Project summary

It is easy to misinterpret the results obtained by dynamic models in the presence of structural deficiencies, insufficiently informative data, or inappropriate assumptions. These issues are common in many applications, especially in those that involve models of biological processes.

In order to extract correct insights from a model it is crucial to quantify the uncertainty associated with its variables and predictions. The scientific community has framed this problem using mathematical/engineering terms such as identifiability and observability. In virtue of the relationship between observation and control, such knowledge also informs the possibility of driving the system to a desired state (controllability) and the design of optimal intervention strategies for reaching a certain goal (optimal control). The techniques for performing these analyses have often been tailored to engineering disciplines in which they are relatively well established. However, other areas that are highly relevant to the bioeconomy and health sectors pose particularly challenging problems that cannot be satisfactorily addressed with current state of the art techniques.

In PREDYCTBIO we have identified three emerging bio-based application domains with large potential for benefitting from such advances: synthetic biology, biomedicine, and industrial biotechnology. Despite the differences among these fields, many biological systems exploited in them can be studied with ODE models. The specific challenges existing in each of the application areas motivates the development of systems identification and control methodologies. In these areas there is an unmet need for:

(I) Efficient computational procedures for assessing the ability of a dynamic model to answer questions such as: what is the value of the unmeasurable quantity K?, or, what will be the value of variable X at time t?

(II) Control engineering methods for assessing a system's ability to achieve a desired behaviour, and designing optimal interventions to reach that goal.

(III) Open source software tools that implement the above listed methodologies and that are widely applicable, easy to use, and computationally efficient.

PREDYCTBIO will develop techniques that provide significant advances over the state of the art in the tasks listed above. The project results will be exploited for building identifiable and predictive models with the appropriate level of complexity, which will in turn be used for optimizing interventions and processes.

Research team

Alejandro F. Villaverde (UVIGO, Systems & Control Engineering)

Antonio Barreiro (UVIGO, Systems & Control Engineering)

Work team

Sandra Díaz Seoane (UVIGO, Systems & Control Engineering)

Glenn Terje Lines (SIMULA Research Laboratory, Norway)

Antonis Papachristodoulou (University of Oxford, UK)

Hidde de Jong (INRIA, France)