In silico patient: systems medicine approach to inborn errors of metabolism

Metabolism is a set of chemical reactions that convert nutrients into energy and building blocks for growth or remove toxic end products. Most reactions are catalysed by specialised proteins, called enzymes, which are encoded in our genes. If such a gene is mutated, it may produce an inactive enzyme and lead to diseases. These are called inborn errors of metabolism. All the chemical reactions in our body, together with their enzymes, form a functional blueprint of an individual. This can be translated into mathematical terms. Eventually, we may create mathematical models that include patient-specific information. Such a “digital twin” can then be used to analyse the molecular blueprint of an individual patient, and to discover potential new biomarkers and targets for therapies.
In her PhD thesis, Agnieszka Wegrzyn describes several ways to optimise and apply various computational modelling approaches for the analysis of patient data and disease mechanisms. She predicts new biomarkers for Refsum disease (a disease in which the food component phytanic acid is not degraded) and for vitamin B2 deficiency. Also, she presents a new model for brain metabolism in phenylketonuria patients, linking diet with serotonin and dopamine metabolism. Based on this model, she proposes a diet optimisation to reduce neurological symptoms in these patients. This model can also be applied to other neurodegenerative diseases, such as Alzheimer’s or Parkinson’s Disease, depression, or autism.