Integrating patient-specific information into logic models of complex diseases: Application to acute myeloid leukemia

Alessandro Palma, Marta Iannuccelli, Ilaria Rozzo, Luana Licata, Livia Perfetto, Giorgia Massacci, Luisa Castagnoli, Gianni Cesareni, Francesca Sacco

Research output: Contribution to journalArticlepeer-review


High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with pa-tient-specific data should be explored to help bedside decisions.

Original languageEnglish
Article number117
Pages (from-to)1-15
Number of pages15
JournalJournal of Personalized Medicine
Issue number2
Publication statusPublished - Feb 2021


  • Acute myeloid leukemia
  • Boolean networks
  • Logic modelling
  • Signaling

ASJC Scopus subject areas

  • Medicine (miscellaneous)

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