Systems-immunology platform for model development 

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In a nutshell

In Subtopic 1, artificial intelligence (big data analysis and computational modelling) is being used to build an open-access and cloud-based platform to predict if a vaccine candidate is effective and more likely to succeed in later stages of vaccine development.

The scientific and technical objectives of this subtopic are:

  • Develop methods for modelling and quantifying the heterogeneous baseline of human adaptive immunity

  • Develop methods for prediction of antigen and pathogen features most likely to induce protective immunity and the anticipated immune responses to those features

  • Combine baseline and immune receptor level methods for assessing, simulating, and predicting individualized protective adaptive immune responses

  • Integrate methods developed into an open-access front-/back-end infrastructure for the in silico vaccine efficacy prediction platform

  • Validate and perform case studies of the in silico prediction platform

Academic leader: Gunnveig Grødeland, University of Oslo

Industry co-leader: Guglielmo Roma, GSK

Project Manager: Luisa Borgianni, Sclavo Vaccines Association

ST1 consists of a highly ambitious team that is led by Gunnveig Grødeland (UiO). She has previously brought a novel vaccine from preclinical design to clinical testing, and has a strong background in vaccine development and immunology.


The industry co-leader, Guglielmo Roma (GSK), works on the development of ML methods to address vaccine-related questions, like antigen identification, monoclonal antibody discovery, vaccines mechanisms of action identification, vaccines coverage prediction, vaccines effectiveness among others. Further, the team includes Victor Greiff (UiO) who has made key discoveries within the immune repertoire field and developed novel ML approaches. Morten Nielsen (DTU) focuses on the development of ML methods to identify patterns in complex biological systems, and has invented several methods for the prediction of HLA-I and II antigen presentation. Michael Meyer-Hermann (HZI) is a world-leading expert in mathematical modelling of germinal centres. 


Artur Rocha (INESC-TEC) is an expert in software architecture, interoperability and FAIR. Ademar Aguiar (INESC-TEC) will contribute his expertise on design of complex software systems and also on agile methodologies for software development. ENPICOM BV was established to bring innovative products in the field of immunogenomics to the market, and has developed a TCR/BCR repertoire immune-sequencing data analysis platform – the IGX platform – to support the development, patient stratification and treatment monitoring of immunotherapies. In sum, the ST1 team will use state-of-the-art knowledge and methods for development of in silico tools that can predict vaccine efficacy.


Main WP objective


Research and innovation tools for systems biology platform development

Develop the research and innovation tools needed for development of an integrated in silico platform for predicting vaccine efficacy

Led by Victor Greiff, University of Oslo


Development of in silico cloud-based platform for predicting vaccine efficacy

Develop a unified front-end and back-end for the scientific platform developed in WP3

Led by Artur Rocha, INESC TEC - Institute for Systems and Computer Engineering, Technology and Science


In silico predictive platform: End user and clinical validations

Validate and perform case studies of the in silico prediction platform

Led by Gunnveig Grødeland, University of Oslo