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Our overall objective is to investigate and demonstrate how Systems Medicine can deliver a well-constructed pipeline for rational screening for synergistic drug combinations and become a foundation for clinical decision making for anti-cancer combination therapies under the precision oncology vision.

Our specific aims are to show that computer models of cancer cells can be:

  • automatically built if supported by an adequate source of biological knowledge, also named Knowledge Commons, a freely available resource of information about how proteins and other molecules in a cell together regulate biological processes (causal statements);
  • tailored to represent very specific cancer cells and tumours by use of molecular information of the particular cancer, or even general patient biomarkers;
  • used to identify the molecular mechanisms crucial to cancer progression;
  • used in multiscale approaches to predict the response of cancer cells to drugs and drug combinations;
  • supported through systematic scrutiny of epistemic and ethical-political conditions for well-constructed innovation systems for precision oncology.

Our multidisciplinary team involving researchers from the humanities, sciences and medicine will assess how research can enable not only a doable, but also a responsible, reflective and responsive innovation process for the Knowledge Commons, and thereby provide a key component for precision medicine.

Planned work
In our joint work we will:

  • Design a prototype Knowledge Commons that underpins computational simulations for precise and individual diagnosis and treatment of cancer.
  • Contribute to the Knowledge Commons by developing a high-quality, low-noise repository of causal statements that can be used as building blocks for cell-fate decision networks relevant to model disease mechanisms of specific cancers,
  • Identify cancer biomarker data necessary for understanding the patient-specific configuration of the network that drives a particular cancer, either from new or existing (public) data from cancer biobanks.
  • Develop automated building and refinement of logical (Boolean) models from causal statements tailored to specific cancer cells using their biomarker data and use these models to predict the effects of drugs singly or in sets on these specific cancer cells.
  • Test computationally generated predictions in cancer cell cultures in a high throughput manner enabled by robotic screening facilities.
  • Test promising sets of drugs in clinically relevant cancer models like mouse xenografts.
  • Support the design of a prototype for Knowledge Commons by developing and implementing a strategy for a Responsible Research and Innovation (RRI) mode of working.
  • Map the innovation system for the precision medicine research infrastructure, including knowledge commons infrastructures as a starting point for our RRI strategy, aming to identify key scientific and social bottlenecles and the responsibility challenges they pose.
  • Engage a broad stakeholder base to help identify normative drivers and scientific constraints, clarify available choices and anticiptate ramifications of these choices.
  • Investigate how our RRI strategy support scientific solutions, including its ability to withstand trials of moral engagement and scrutiny as we critically evaluate the limitations and possibilities of our proposed method.