Abstract: Application of computer simulation for predicting cancer progression/remission/recurrence is still underestimated by clinicians. This is mainly due to the lack of tumor modeling approaches, which are both reliable and realistic computationally. We propose and describe here the concept of a viable prediction/correction system for predicting cancer dynamics, very similar, in spirit, to that used in weather forecast and climate modeling. It is based on the supermodeling technique where the supermodel consists of a few coupled instances (sub-models) of a generic coarse-grained tumor model. Consequently, the latent and fine-grained cancer properties not included in the generic model, e.g. reflecting microscopic phenomena and other unpredictable events influencing tumor dynamics, are hidden in sub-models coupling parameters, which can be learned from incoming real data. Thus instead of matching hundreds of parameters for multi-scale tumor models by using complicated scales-bridging and data adaptation schemes, we need to fit only several values of coupling coefficients between sub-models to the current tumor status. Here, we propose a supermodel based, prediction/correction scheme that can be further employed for planning anti-cancer therapy and drug treatment, being continually updated by incoming diagnostic data.

Abstract: The long term outcome of Coronary Artery Bypass Graft (CABG) surgery remains unsatisfactory to this day. Despite years of improvements in surgical techniques and therapies administered, re-occlusion of the graft is experienced in 10-12% of the cases within just few months (Motwani JC, 1998). We suggest that an efficient post-surgical therapy might be found at the genetic level. Accordingly, we propose a multiscale model that is able to replicate the healing of the graft and detail the level of impact of targeted clusters of genes on the graft’s healing. A key feature of our integrated model is its capability of linking the genetic, cellular and tissue levels with feedback bridges in such a way that every single variation from an equilibrium point is reflected on all the other elements, creating a highly organized loop. Once validated on experimental data, our model offers the possibility to test several gene therapies that aim to improve the patency of the graft lumen in advance. Being able to anticipate the outcome will speed up the development of an efficient therapy and may lead to prolonged life expectancy of the graft.

Abstract: Today's modelling approaches in systems medicine are increasingly multiscale, containing two or more submodels, each of which operates on different temporal and/or spatial scales (Hunter,2008). In addition, as these models become increasingly sophisticated, they tend to be run as multiscale computing applications using computational infrastructures such as clusters, supercomputers, grids or clouds. Constructing, validating and deploying such applications is far from trivial, and communities in different scientific disciplines have chosen very diverse approaches to address these challenges (Groen,2014;Borgdorff,2013). Within this presentation we reflect on the use of Multiscale Computing within the context of Open Multiscale Systems Medicine (OpenMultiMed) COST action and related developments. Multiscale computing is widely applied within this area, and instead of summarizing the field as a whole we will highlight a set of challenges that we believe are of key relevance to the systems medicine community. Among these we will highlight key multiscale computing challenges in the context of healthcare and assisted living settings, systems medicine data analytics, effectively exploiting cloud and HPC infrastructures, and the use of Multiscale Computing in relation to the Internet of Things. Note: This abstract is part of a paper-in-progress project by Multiscale Computing Working Group in the OpenMultiMed project, where we reflect on current advances and seek to formulate a vision on Multiscale Computing specific to this COST project. We appreciate any points raised by the referees or during the presentation, and intend to take those to heart for our future activities. (Hunter:2008) Peter J Hunter, Edmund J Crampin, and Poul MF Nielsen. Bioinformatics, multiscale modeling and the iups physiome project. Briefings in bioinformatics, 9(4):333–343, 2008. (Groen:2014) Derek Groen, Stefan J Zasada, and Peter V Coveney. Survey of multiscale and multiphysics applications and communities. Computing in Science & Engineering, 16(2):34–43, 2014. (Borgdorff:2013) Joris Borgdorff, Jean-Luc Falcone, Eric Lorenz, Carles Bona-Casas, Bastien Chopard, and Alfons G Hoekstra. Foundations of distributed multiscale computing: Formalization, specification, and analysis. Journal of Parallel and Distributed Computing, 73(4):465–483, 2013.

Abstract: The mechanism that controls embryonic branching is not fully understood. Of all proposed mechanism, only a Turing pattern-based model succeeds in predicting the location of newly emerging branches during lung and kidney branching morphogenesis. Turing models are based on at least two coupled non-linear reaction-diffusion equations. In case of the lung model, the two components (ligands and receptors) are produced in two different tissue layers [1]. Thus the ligand is produced in the outer mesenchymal layer and the receptor is produced in the inner, branching epithelial layer; the diffusion of receptors is restricted to this epithelial layer. So far, numerical instabilities due to highly complex mesh deformations limit the maximal rounds of branching that can be simulated in an ALE-based framework. A recently developed Phase-Field-based framework [2], shows promising results for the simulation of consecutive 3D branching events. In this talk, I will present our Phase-Field-based framework for simulating the inner epithelial and the outer mesenchymal layer, how we coupled the reaction-diffusion equations to the diffuse / implicit domain as well as the incorporation of additional equations representing further components influencing the growth of a mammalian lung. [1] D. Menshykau et al., "An interplay of geometry and signaling enables robust lung branching morphogenesis.", Development 141(23): 4526-4536, 2014 [2] LD. Wittwer et al., "Simulating Organogenesis in COMSOL: Phase-Field Based Simulations of Embryonic Lung Branching Morphogenesis.", Proceedings of the 2016 COMSOL Conference in Munich, 2016