My central question: How to make biomedical predictions under uncertainty?

  • 11th Jun 2021

In clinical practice, a plethora of examinations is conducted to assess the state of a certain pathology. These span from blood sample analysis, clinical imaging (e.g. CT, MRI) and biopsy sampling are among the most important diagnostic and prognostic tools. Such medical data correspond to snapshots in time of the patient’s state, since current standard of care (SoC) is not based on emergent technologies of real-time measurements, such as liquid biopsies or biosensors. Moreover, clinical data refer to different biological… [...]

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Cell decision-making in multicellular systems

  • 16th Mar 2021

A bottom-up approach to translate multiscale clinical data into predictive model is to focus on the principles of cell decision-making. My starting point is how a single cell process microenvironmental information. By this approach I am aiming to address challenges (C2) and (C3), i.e. to enable integration of multi scale data into a predictive theory that circumvents lack of knowledge of the underlying regulation mechanisms. Regarding cells act as energetically constrained Bayesian inferrers of their intrinsic states (phenotype) from extrinsic… [...]

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Combination of mechanistic modelling and machine learning

  • 22nd Feb 2021

Biomedical problems are highly complex and multi-dimensional. Typically, only a small part of the relevant variables/data can be modelled by virtue of mechanistic modelling, since it hampered by the lack of the exact knowledge of the involved phenomena (C2). Typical non-modellable variables are -omics data, since the underlying regulatory networks dynamics are largely unknown and their coupling with higher scales is also non trivial (C3). Mechanistic models are great in analyzing the qualitative dynamic behavior of the system but rarely… [...]

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Understanding the impact of cancer and immune cell plasticity on tumor progression and therapy resistance

  • 29th Jun 2021

Cancer is no more a disease of cells than a traffic jam is a disease of cars. A lifetime of study of the internal-combustion engine would not help anyone understand our traffic problems.-D. W. Smithers (1962) Tumor heterogeneity is the result of the dynamic interplay of tumor and stromal/microenvironmental cells, which in turn is pivotal for therapy resistance. An example of cancer cell adaptation, particularly to nutrient fluctuations, is the migration/proliferation plasticity, or Go-or-Grow,  that I pioneered in exemplifying its… [...]

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Multiscale modelling for multicellular systems

  • 27th Jul 2021

My main goal is developing multiscale mathematical methods for modelling multicellular systems such as tissues, bacterial colonies, tumours etc. Many mathematical models rely on phenomenological relationships between model parameters and variables. While these relationships may be fitted to experimental data, they do not incorporate functions of directly measurable quantities at the cell scal. Multiscale models equip directly measurable quantities at the cell scale inform the model parameters at the continuum scale through upscaling techniques making multiscale models, in principle, predictive… [...]

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