Monday + Tuesday summary

1. Monday + Tuesday summary By Frederic, Martijn, Hans All Yellow slides have been seen/OK’d by participants

2. Setting the stage for multiscale modelling:From defunct molecules to cancer prognosis • Heterogeneity between tumors is a key feature; more and better tools are needed to diagnose the heterogeneity between patients. Make a reference model and personalize this on the basis of patient specific data • Heterogeneity at the single gene level is much stronger than at the phenotypic level • A cancer phenotype, and its persistence is selected for. The selection product thereby depends on the surrounding tissue, hence the host, which is not even constant. • The most important function may be energy, or energy, carbon and nitrogen, the essentiality of which a cell cannot mutate against.

3. Selection of cells within a tumor also depends on spatial heterogeneity and selection pressure changes with time. Samples from a tumor are often not representative. • Tumor diversity comes from stochasticity at the levels of mutations, low-molecule numbers (transcription-translation), bistability, epigenetics. • DNA changes are more persistent, unless selected against during tumorigenesis. Some epigenetics and metabolic states (glycogen) are semi-persistent.

4. Neither from-molecule-analysis will be effective by itself, nor from-phenotype-analysis. Multiscalemodelling will need to bridge the two. • Pathology should be made more quantitative, linked up with models, and molecular information should be weighed together with the imaging data. This may require reclassification of diseases. • Therapy decisions and their effects should be followed up for later testing of our models.

5. Multiscalemodeling • Subcellular level by ODE model. Agent based (1 cell=1 agent) model for tissue level. Oxygen distribution through PDEs. Blood flow important • Take the perspective of only a few substances mediating between the scales (e.g. intracellular and extracellular). • Approach metabolism (mass transfer) distinctly from signalling. • Model granularity depends on question asked • Model reduction is important, but • fully detailed model would be more useful than experimental reality because the former can be interrogated more easily in terms of predicting effects of therapy

6. Oxygenation/ROS • Oxygen gradient depends on respiration which in turn determines oxygen and glucose levels. • Tumor cells can overcome many challenges through adaptation except the energy issue • Make models that specify the selective pressures; mutation rates do not matter, mutation fixation vis-a-vis the cells’ environment, does.

7. Tumor and bystanders • 98% of metastases can be classified in terms of origin • This may imply that the future metastatic capability can be predicted from information in the primary tumor. This could empower individualized therapy. This constitutes a program of research of modelling to predict metastatic potential. Problem: knowledge of metastases is limited due to focus on primary tumors. • Decide what to model: model functionally (e.g. motility to understand metastasis, or metabolism for tumor-cell survival), from there tumor anatomy, pathways, molecules.

8. Modeling signaling pathways • static vs dynamic (transient, sustained responses) • feedback circuitry • link to metabolic network; e.g. nutrient-binding: metabolites can bind GPCRs; insulin signaling • cell context dependency

9. Genome-scale metabolic modeling • Technical motivation: - cell lines may not describe the tumor - metabolic data (fluxes) hard to measure - transcriptomicdata readily available • Mapping between scales; gene expression <> flux scale. Somewhat predictive. • because of network functional organization (stoichiometric constraints) cells have to choose between proliferation and consolidation (ROS protection) • higher growth rate correlates with longer survival

10. Molecular dynamics • Talin two-state modelling/membrane interactions shold be possible • It is unclear at this stage whether a reduced number of conformations (showing two states) emerges.

11. How to bridge pathway level with cell level (and up)? • Transparent black-box models {Consider the intracellular networks in high detail (150 000 types of molecule) in transparent black box with a limited number of inputs and outputs (e.g. 70 of each); Input/output to and from cells is limited (<150 species?); Model the intracellular as how all molecules affect the transfer functions that lead from inputs to outputs.}. We must be able to define a limited number of parameters that can be accurately measured (e.g. concentration of metabolites homogeneous in cells) and make simplifications to be able to answer this problem. • Computational research agenda to test coarse graining strategy { Show at MD level that proteins essentially live in a small number of conformation-areas. Then model proteins as existing in small number of states., each with distinct activity. Model pathway in terms of activities. Show that the heterogeneity stemming form the above converges to limited heterogeneity of pathway: treat next level in terms of limited number of pathways. Show that this leads to limited number of cell states that are frequent. Build tissue in terms of these. Etcetera; this all computational, although in parallel experimental validation will be useful. Issue is how to transfer parameters between levels} • Robustness may (or may not), alleviate heterogeneity/stochasticity problems at higher scales. The extent to which complexity can be reduced remains to be shown . Some models may already oversimplify Life. . {Completely detailed models (Markus Covert, Cell 150, 2012) versus understandable models (Palsson); Yet biology may be so complex that models need to be complex? }

12. The call for proposals this would define • Develop multiscale models that predict metastatic activity/success on the basis of excised tumor material, as well as utility of and type of personalized anti-metastatic therapy • Develop multiscale models of tumor energetics/survival inclusive of oxygen, glucose, pH gradients, that take tumor cell evolutionary success and intratumor heterogeneity into account and suggest new personalized network-based drug targets. • Perhaps set this up competitively:

13. Proposal of modelling competition • Modelling competition? Like Asilomar’s CASP; homology/threading modelling • DREAM project exists in systems biology: top down modelling; the new proposal could be for bottom-up modelling. • NIH: Simon Kasif (BU, George Church); funds experiments that test model predictions. • Specific data set plus questions plus modelling types need to be defined (because existing modelling methods are complementary rather than parallel) • Question could be applied (e.g. medical) or fundamentally scientific (e.g. Warburg effect). Question should not be too complex/complicated • But this may not be enough because we cannot define what an appropriate dataset would be. May not be multiscale. • iGEM may be a good analogy; BUT this is more difficult to model than MD is. Questions may need to be limited in complexity. May be too expensive. • Link with hospitals? But should then be professional.