Single-cell Digital Twins for Cancer Preclinical Investigation

  • Marzia Di Filippo
  • Chiara Damiani
  • Marco Vanoni
  • Davide Maspero
  • Giancarlo Mauri
  • Lilia AlberghinaEmail author
  • Dario PesciniEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)


Laboratory models derived from clinical samples represent a solid platform in preclinical research for drug testing and investigation of disease mechanisms. The integration of these laboratory models with their digital counterparts (i.e., predictive mathematical models) allows to set up digital twins essential to fully exploit their potential to face the enormous molecular complexity of human organisms. In particular, due to the close integration of cell metabolism with all other cellular processes, any perturbation in cellular physiology typically reflect on altered cells metabolic profiling. In this regard, changes in metabolism have been shown, also in our laboratory, to drive a causal role in the emergence of cancer disease. Nevertheless, a unique metabolic program does not describe the altered metabolic profile of all tumour cells due to many causes from genetic variability to intratumour heterogeneous dependency on nutrients consumption and metabolism by multiple co-existing subclones. Currently, fluxomics approaches just match with the necessity of characterizing the overall flux distribution of cells within given samples, by disregarding possible heterogeneous behaviors. For the purpose of stratifying cancer heterogeneous subpopulations, quantification of fluxes at the single-cell level is needed. To this aim, we here present a new computational framework called single-cell Flux Balance Analysis (scFBA) that aims to set up digital metabolic twins in the perspective of being better exploited within a framework that makes also use of laboratory patient cell models. In particular, scFBA aims at integrating single-cell RNA-seq data within computational population models in order to depict a snapshot of the corresponding single-cell metabolic phenotypes at a given moment, together with an unsupervised identification of metabolic subpopulations.

Key words

Cancer heterogeneity Constraint-based modelling Single-cell RNA-seq 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Marzia Di Filippo
    • 1
    • 2
  • Chiara Damiani
    • 1
    • 2
    • 3
  • Marco Vanoni
    • 1
    • 3
  • Davide Maspero
    • 2
    • 4
  • Giancarlo Mauri
    • 1
    • 2
  • Lilia Alberghina
    • 1
    Email author
  • Dario Pescini
    • 1
    Email author
  1. 1.SYSBIO Centre of Systems BiologyMilanItaly
  2. 2.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanItaly
  3. 3.Department of Biotechnology and BiosciencesUniversity of Milano-BicoccaMilanItaly
  4. 4.Istituto Nazionale dei TumoriMilanItaly

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