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androulakis ip

Towards semi-mechanistic models of disease progression: A translational systems biology approach Ioannis (Yannis) P. Androulakis Biomedical Engineering and Chemical & Biochemical Engineering, Rutgers University Department of Surgery, UMDNJ-RWJ Medical School. androulakis ip. NIH Funded.

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androulakis ip

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  1. Towards semi-mechanistic models of disease progression: A translational systems biology approachIoannis (Yannis) P. AndroulakisBiomedical Engineering and Chemical & Biochemical Engineering, Rutgers UniversityDepartment of Surgery, UMDNJ-RWJ Medical School androulakis ip NIH Funded

  2. From phenomenological relationships to semi-mechanistic representations • (TSB) Mathematical formalisms which use mechanistic information and basic knowledge in order to simulate behaviours at the organism level providing a mechanistic basis for pathophysiology • Link outcomes (clinical responses) to processes (cellular mechanisms) • What should be monitored (state variables/markers) • How are the states variables interconnected (network topology) • How can the dynamics be inferred (progression dynamics) • From causal relationships to progression models through vignettes in • Interpreting data • Interpreting models

  3. Hierarchy of models, data and methods Models (experimental) Data Biochemical (cellular, tissue and host level) Outcome (physiological at the systemic level) Models (computational) Phenomenological models (semi)-mechanistic models Knowledge representation models Methods (computational) Statistical correlations PLSR Bayesian models ODE models

  4. Context dependent causal relationshipsTissue-specific gene expression

  5. Context dependent causal relationshipsCirculating cytokines

  6. Integration across layers of informationGene expression, signalling, cytokines

  7. Dynamics: A step beyond causal relationshipsFrom genes …

  8. Dynamics: A step beyond causal relationshipsto clusters …

  9. Dynamics: A step beyond causal relationshipsto functions ….

  10. Dynamics: A step beyond causal relationshipsPoints of divergence from “homeostasis” • While the system diverges from homeostasis, not “everybody” diverges • at the same time • at the same rate • in the same direction

  11. (disease) Progression as evolution along dynamic trajectories Homeostatic regulatory mechanisms adequately control certain systemic responses Homeostatic training with limited data

  12. Disease progression as evolution along dynamic trajectories

  13. Disease progression as evolution along dynamic trajectories State variables are “close”, yet outcomes differ substantially

  14. Disease progression as evolution along dynamic trajectories • “poor man’s” clinical trial • Parameter sampling simulates population heterogeneity • Same initiating response induces responses which “live” on well defined manifolds

  15. Disease progression as evolution along dynamic trajectories Markers reproducing systemic dynamics

  16. Intrinsic computational model dynamics precede (predicted) biomarker changes • Can computational prediction of critical bifurcation events precede (and inform) outcome? • The biomarker becomes an interpretation of the dynamics Can computational prediction of critical bifurcation guide evaluation?

  17. A multi-scale translational systems biology model of human endotoxmiea

  18. 18 RR(t+1) RR(t+1) RR(t) RR(t)

  19. 19 Holzheimer et al., SHOCK 2002

  20. 20

  21. Suppressed rhythms and the HPA axis Disrupted chronobiology has profound implications in the context of (i) indicator of disease state; (ii) instigator of disease; (iii) compromising factor (chronic stress)

  22. Semi-mechanistic computational models can, potentially, enable • the integration of information across multiple levels (cellular, systemic, physiologic) • the in silico assessment of heterogeneity

  23. The digital patient (T. Buchman, MD) Mathematical models • can establish links across multiple scales (i.e., genomic, proteomic, metabolomic) • encourage the subtle distinction between outcomes (clinical response) and processes (cellular mechanisms). The latter are most likely responsible for individualized responses Computationalmodel that capture mechanistic information, at a reasonable level • can help establish complex markers characteristic of disease progression • can form the basis of in silicoclinical trials reflecting heterogeneities at various levels Mood disorders and cytokines, loss of rhythms and circadian disruption, sleep loss, stress (physical and psychological)  Patient history

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