Supermodel of melanoma dynamics Witold Dzwinel 1, Adrian Kłusek 1 and Oleg V. Vasilyev 2 AGH University of Science and Technology, Department of Computer Science, Poland University of Colorado at Boulder, Department of Mechanical Engineering, US

2 Motivation Cancer cannot anticipate the future Predicting cancer dynamics scenarios → progression, regression/remission and recurrence. Planning cancer treatment. Using computer models of cancer dynamics in personalized therapy.

3 Multiscale cancer models Countless of interrelated microscopic and macroscopic factors. Around a hundred variable parameters. Complex initial conditions. Unknown influence of complex environment. i r r e d u c i b l e o v e r f i t t e d i l l - c o n d i t i o n e d

4 Multiscale model SCENARIO

5 Coupled dynamical systems stuttgart.de/research/pso_opt/pso_anim_de.php#pso_anim

6 How it works.

7 Supermodel – toy example

8 Supermodeling of climate/weather Supermodel → integration of a few complex atmosphere and ocean models Considerably better predictions! It can follow greenhouse effect absent in the model (hidden in training data) SUMO EU FET Project → Can we do the same for cancer simulation

9 Melanoma model Continuous - discrete single phase model [Welter and Rieger, Chaplain et al.]  mitosis/apoptosis/necrosis, angiogenesis, heterogeneity, vessels remodeling, blood pressure 7 dynamical variables of concentration fields f( r,t)  Tumor cells  ECM  Endothelial cells (vascularization)  TAF  Oxygen  Fibronectin  ECM degradation enzyme ~30 free parameters

10 Melanoma simulation

11 Melanoma supermodel Instead of 34 parameters 6 coupling factors

12 Results

13 Results

14 Synchronization error

15 Synchronization error (variance)

16 Gompertz law

17 Postulate

18 Correction/Prediction → tumor growth

19 Prediction/Correction→ tumor regression

20 Conclusions → Postulates It is possible to obtain reliable prognoses about cancer dynamics by using its supermodel. There exist a generic coarse-grained computer model of cancer → a computational framework for developing high quality supermodels Real data adaptation can be achieved by using a prediction/correction learning scheme The latent fine-grained tumor features e.g. microscopic processes and other unpredictable events accompanying its proliferation not included in the model, are hidden in incoming real data.

21 This research is financed by project DEC2013/10/M/ST6/00531.