Machine learning in bioprocess development: From promise to practice

arXiv (Cornell University)

Abstract

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Authors 5

1. Laura M. Helleckes Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute for Bio-and Geosciences (IBG-1) , Forschungszentrum Jülich GmbH , 52428 Jülich , Germany

   RWTH Aachen University , Templergraben 55 , 52062 Aachen

2. Johannes Hemmerich

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Bio-and Geosciences (IBG-1) , Forschungszentrum Jülich GmbH , 52428 Jülich , Germany

3. Wolfgang Wiechert Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute for Bio-and Geosciences (IBG-1) , Forschungszentrum Jülich GmbH , 52428 Jülich , Germany

   RWTH Aachen University , Templergraben 55 , 52062 Aachen

4. Eric von Lieres
5. Alexander Grünberger

   Bielefeld University

   Affiliation as printed

   Center for Biotechnology (CeBiTec) , Bielefeld University , Universitätsstr. 25 , 33615 Bielefeld , Germany

   Multiscale Bioengineering , Technical Faculty , Bielefeld University , Universitätsstr. 25 , 33615 Bielefeld , Germany