Overview
The nine partners from academia and industry, that originate from six European countries, support and
drive the novel scientific movement of ‘biointelligence’ which reflects the conversion of biotechnology,
information technology including AI and automation technology. It is this biointelligence mindset that
makes up the core of BIOS and that accelerates strain and bioprocess engineering for producing novel
fine chemicals and commodities in a sustainable manner. BIOS makes intensive use of hybrid learning
to fully exploit the experimental data which are generated in an automated fashion. The project brings
together experts from molecular and synthetic biology, systems metabolic engineering, microbiology,
biochemical and bioprocess engineering, chemistry, and life cycle assessment.
The dedicated team has one collective goal: to replace current fossil routes by sustainable microbial
processes with the help of the biointelligent mindset. Soon, progress of BIOS will be made publicly
available through different channels.
WP 1 - Project Management
Lead:
University of Stuttgart & LifeGlimmer GmbH
Objectives:
a) to provide a sound and flexible structure for operational management and technical vitality of the project, encompassing management tasks on contractual, financial, legal, technical, administrative and ethical levels.
b) to provide and foresee a data management plan for the project according to FAIR principles
c) to initiate and supervise the subcontracting for the semantic BIOS communication platform
c) to provide an effective risk management strategy to avoid deviations from the work plan.
d) to respond to opportunities through an active innovation management
WP 2 - Digital Twinning
Lead:
University of Stuttgart
Objectives:
The creation of a Digital Twin of cellular and bioprocesses resulting robust and scalable biomanufacturing. Hybrid digital twins merging AI and mechanistic models will be created by:
a) acquisition of (existing/new) sensor, performance, and stress data
b) constraining protein formation with maximum metabolic capacities
c) detailed kinetic and process modelling
d) testing different AI approaches to complement mechanistic modelling
e) merging AI and mechanistic models
f) deploy gene manipulation sets for strain engineering
g) implementing self-sufficient control systems in autonomous bio-manufacturing approaches for the regulation of nutrient supply and environmental conditions
h) developing an approach for actionable knowledge to narrow the feasible variable and parameter space so as to identify when change in either strain or environmental conditions must occur.
WP 3 - Bioware Engineering
Lead:
Wageningen University
Objectives:
To develop and apply the tools for bioware engineering by
a) developing dedicated genetic and genome engineering tools for P. putida
b) automating those tools in robotics (laying the basis for extension to other Gram-negative bacteria)
c) developing a small-scale electroporation device for robotic application
d) applying those tools for targeted strain engineering
e) standardize procedures & installing check points
WP 4 - Biosensors & -metrics
Lead:
VTT Technical Research Centre of Finland
Objectives:
To access and use quantitative metrics of biosensing by
a) engineering promoter strength and measuring quantitative read-outs
b) at line measuring in vivo transcripts
c) installing standardization protocols to ensure data exchangeability
WP 5 - Adaptive Laboratory Evolution (ALE) & Stress Tests
Lead:
University of Tartu
Objectives:
To ensure robust strain design by
a) analysing engineered P. putida stress response to substrates, intermediates and products of the test cases
b) increasing roubustness and toughening strain performance by smart ALE selection
c) recycling cellular long-term response to bioware engineering
d) establish an AI-derived automated platform for optimization of ALE experimental design and execution
WP 6 - Hybrid Learning
Lead:
MICALIS Institute
Objectives:
To learn from experimental feedback by
a) finetuning structures & parameters of mechanistic models
b) training AI approaches
c) defining best interaction modes between mechanistic models and AI
WP 7 - Valorisation
Lead:
Ingenza Ltd
Objectives:
To exploit cellular production capacities in decentralized self-controlled bioprocesses by
a) engineering production strains for the production of terpenoids, tailored polymers, and methacrylates
b) developing a bioprocess with promising production strains
c) using hybrid digital twins for self-controlled autonomous production
WP 8 - Life Cycle Engineering
Lead:
Fraunhofer Institute
Objectives:
To balance and quantify the ecological impact of the innovative approach by carrying out life cycle analysis (LCA) of the novel bioproduction approaches by comparison of the novel bio-manufacturing approach with a conventional manufacturing method of the selected product or a comparable product in terms of sustainability.
WP 8 - Life Cycle Engineering
Lead:
Fraunhofer Institute
Objectives:
To balance and quantify the ecological impact of the innovative approach by carrying out life cycle analysis (LCA) of the novel bioproduction approaches by comparison of the novel bio-manufacturing approach with a conventional manufacturing method of the selected product or a comparable product in terms of sustainability.
WP 10 - Ethics
University of Stuttgart