Multiscale Modeling of Complex Bio-chemical Systems

Multiscale Modeling of Complex Bio-chemical Systems - Presentation at the Microbiology Colloquium - Medellin, Colombia - April 27th 2016

1. Multiscale Modeling of Complex Bio-chemical Systems Hugo Hernández, Dr.rer.nat . Microbiology Colloquium Medellín, April 27 th 2016 1

2. 2 Multiscale Complexity Chris Tolworthy Time- scale range : From femtoseconds to billions of years Lenght scale range : From subatomic particles to distance between galaxies

3. Example : Complexity in Polymers 3 10.3390/polym5020751 Hernandez , H. (2008) Multiscale simulation of heterophase polymerization. Application to the synthesis of multicomponent colloidal polymer particles. Dr.rer.nat . Dissertation. Universität Potsdam, Germany.

4. Example : Complexity in Food Processes 4 Atomistic / Quantum scale Molecular scale Macromolecular scale Cellular scale Microscopic scale Macroscopic scale Unit Operation scale Processing plant scale Macroscopic scale Organ system scale Molecular scale Macromolecular scale Microscopic scale Cellular scale

5. Example : Complexity of infectious diseases 5 Fig. taken from : 10.1016/j.mbs.2015.04.004 From molecular mechanims of disease to global spreading

6. A more complex picture ! Atomistic /Quantum scale Molecular scale Metabolic scale Gene scale Biomacromolecular scale Intracellular scale Global population scale Local population scale Individual scale Organ / organ system scale Tissue scale Cellular scale EPIDEMIOLOGY CLINICAL MEDICINE BIOCHEMISTRY + OMICS CHEMISTRY BIOLOGY 6

7. Systems Biology 7 Multidisciplinary work Fig. taken from: 10.1126/science.1069492 Systems biology aims to describe and understand the operation of complex biological systems and ultimately to develop predictive models of human disease.

8. Multiscale Modeling Multiscale modeling is the bridge connecting the different computational methods employed at individual scales 8

9. Multiscale Integration 9 Fig. taken from: 10.1179/1743280410Y.0000000002

10. 10 Hunter, P. J., Crampin , E. J., & Nielsen , P. M. (2008). Bioinformatics , multiscale modeling and the IUPS Physiome Project. Briefings in bioinformatics , 9(4), 333-343. Example : Multiscale nature of the Heart Relevant scales

11. Example : Multiscale nature of the Heart 11 Hunter, P. J., Crampin , E. J., & Nielsen , P. M. (2008). Bioinformatics , multiscale modeling and the IUPS Physiome Project. Briefings in bioinformatics , 9(4), 333-343.

12. Example : Modeling a Heart attack 12 Noble, D. (2002). Modeling the heart--from genes to cells to the whole organ. Science, 295(5560), 1678-1682. The whole organ simulations required many hours of computation using supercomputers!!!

13. Example : Tumor growth 13 Zhang, L., Athale , C. A., & Deisboeck , T. S. (2007). Development of a three -dimensional multiscale agent-based tumor model : simulating gene- protein interaction profiles , cell phenotypes and multicellular patterns in brain cancer . Journal of theoretical biology , 244(1), 96-107. The ratio of migrating to proliferating cells is strongly affected by the concentration of phospholipase C  (PLC).

14. 14 Robertson, et al. (2007). Multiscale computational analysis of Xenopus laevis morphogenesis reveals key insights of systems-level behavior . BMC systems biology , 1(1), 46. Signaling proteins ( fibronectin ) affect cellular movement and overall tissue-level behavior during morphogenesis Example : Tissue Morphogenesis

15. OPPORTUNITIES FOR MULTISCALE MODELING IN SYSTEMS BIOLOGY

16. Novel approaches to persistent disease control 16 Fig. taken from:10.1038/nrmicro1919 How can TB bacteria membranes be destroyed inside macrophages ? Which factors trigger granuloma breakdown ? How can it be prevented ? How does the growth rate of M. tuberculosis affect persistence ? A deeper understanding of the persistence mechanisms is needed ! Relevant scales

17. Strategies against multidrug-resistant microorganims 17 Natural selection is even faster for microorganisms than for larger species! Focus should be placed on indirect , less aggresive strategies for controlling pathogenesis ! Adaptation of a microorganism to an antibiotic measured by the “Vitality value” Fig. taken from: 10.3389/fmicb.2013.00004

18. Strategies against multidrug-resistant microorganims - Examples 18 How can we emulate the effect of sickle cell for preventing malaria? Fig. taken from: 10.1186/1475-2875-12-317 Mechanisms by which sickle cell trait protect against malaria

19. Strategies against multidrug-resistant microorganims - Examples Drugs designed to interfere with signal transduction networks ! 19 Adaptation to environmental stimuli in bacteria is mediated primarily through the expression of transcriptional regulators By blocking these signals, pathogens will not be able to adapt to stressing environments Fig. taken from: 10.1128/MMBR.05004-11

20. Design of smarter drug delivery systems 20 Use of extracellular and intracellular environments as triggers for drug release Certain cytokines are released by cells in the presence of disease . These cytokines might be used as triggers for a controlled release of antibiotics at the right time and at the right place. Fig. taken from: 10.1021/nn5070343

21. Increasing the effectiveness of antibiotics 21 How can the diffusion of antibiotics into pathogens be enhanced ? Fig. taken from : 10.1016/j.bpj.2009.10.045

22. Predictive modeling of human immune system 22 Opportunities for understanding and improving the response of the immune system against diseases ! Fig. taken from : 10.1016/j.it.2008.08.006

23. Epidemics prevention 23 Figures taken from:10.1038/srep08980 Evaluation of different vaccination policies Different prevention strategies can be evaluated for identifying the most promising alternatives Multiscale model

24. Contact : [email protected] Peter, C., & Kremer, K. (2009). Multiscale simulation of soft matter systems–from the atomistic to the coarse-grained level and back. Soft Matter, 5(22), 4357-4366. 24 Thank you very much for your attention!