The smart route towards an in silico liver.

1. The smart route towards an in silico liver. James Hetherington Systems biology – the hope of simulation. Introduction to glucose homeostasis The benefits and challenges of simplification Scale crossing: the cell-tissue divide Larger models – hybrid complexity When is a model really useful?

2. Systems biology - background • Molecular biology revolution • Enormous increase in our understanding of biology at its smallest scales. • Massive amount of data collected • No comparable increase in understanding of physiology. • Information revolution • Enormous increase in ability to process information • Experience in handling massive datasets • Complexity revolution • Innovations in application of mathematics to complex systems

3. The dream of systems biology • Bring computation, mathematics, and biology together by modelling physiological systems • Make molecular data biomedically useful • Hope for simulations of biological systems to provide for: • Drug discovery • Advances in diagnosis and treatment

4. The need for vertical integration • We have data at the molecular scale • Genes • Proteins • Messenger chemicals • We want to understand the systems scale • Tissues • Organs • Systems • We need to bridge the gap • Signalling pathways and networks • Cells

5. Glucose Homeostasis • The liver performs many functions • Bile synthesis, • detoxification … • We concentrate on glucose homeostasis. • Medically important • Diabetes!

6. Energy Storage – A metaphor Ion gradients ATP Glucose Glycogen Fat Change Notes Current Account Savings Account Lock-in Account

7. Glycogen Glycogen breakdown Glucose Cellular signal processing Glucose export Liver cell Hormone receptors Glucagon Hormone binding to receptor Pancreas notices change in glucose Bloodstream Energy banking transactions. • Glucagon • withdrawal hormone, or “hungry hormone” • produced by the pancreas in response to low blood glucose levels, • Instructs liver to turn glycogen into glucose • Insulin • investment hormone, or “full-up hormone” plays the reverse role. • Keeps glucose within bounds • Homeostasis • Diseases of this process include diabetes

8. Glycogen Glycogen Gap junction Glucose Glucose Cellular signal processing Other cell responds Liver cell Hormone receptors Hormone Hormone binding to receptor Bloodstream Liver systems biology is hard • Chemical, not electrical or mechanical • Cells not uniform across liver • Random variation • Systematic variation • Cells work together • Gap junctions (connexins) • Liver not isolated, but works together with pancreas • Wide variety of spatial and temporal scales involved

9. Simplifying calcium oscillations

10. Hill parameter 4 256 Problems with simplification Analytical result

11. Strong effect in simulation. Strong effect in both models. Strong effect in simple model No strong effect Control analysis Treatment effecting • Control analysis pinpoints changes to the system that produce large changes in behaviour. • Simplified models and simulations tend to agree at this level. Volume ratio ER : cytoplasm ER pump strength Threshold for calcium to close ER channel ER calcium channel leak rate Observation Oscillation period Fraction rising Minimum conc. Maximum conc.

12. Applicability of simple models • Agreement of simple model with detailed simulation is statistically significant. • Control analysis has been used by others to produce medically relevant results for metabolic models. • Can we use this technique to predict drug toxicities from our simple models? • If control analysis shows that a treatment will have a strong effect, we can warn against possible toxic effects.

13. Hepatocytes vary Systematically Randomly Hepatocytes communicate Through gap junctions Junction amounts and types vary dynamically Multicellularity, heterogeneity and intercellular communication

14. The impact of intercellular communication Bottom quartile performance • Synergy • Robustness • Predictability • Synchronisation • Efficiency Gap junction density

15. Control of communication • If communication is so good, why do hepatocytes control the level of it? • Metabolic efficiency. • Robustness. • We care not just for philosophical reasons, but because we need to know in order to model it well. • Can the observed variations in connexin levels be explained functionally?

16. Connexin 26 20 h 8 h Glucagon Glucagon +Insulin When do glucagon and insulin cooperate? • They’re usually opposed to each other, but: • glucagon – increases connexin levels • insulin – increases gap junction formation • A paradox? Collaborators – Marta Varela Rey, Sachie Yamaji, Anne Warner.

17. The homeostatic basin With Communication • Analogy to driven oscillations in a potential well • Cellular variability softens the “potential well” • Communication hardens the well Glucose consumption rate Restoring force Without Communication Glucose level (Displacement)

18. Communication sharpens system’s homeostatic response. 3 Variability in blood glucose 2.5 Without Communication 2 1.5 1 With Communication 0.5 0.5 1 1.5 2 Variability in diet

19. Impact of connexin control on energy usage • Without communication • More variation • Cheaper on energy Alternating hormones are used to detect a variable environment when homeostasis is more important than saving energy. Blood Glucose / arbitrary units ® Time / seconds ® • With communication • Smaller variation • More energy consumption Evolutionary consequences?

20. Modelling in greater detail…

21. Hybrid complexity • Glycogen receptor model • Full enzyme kinetics for each protein • Calcium model • Simplified version of well-known model • Glycogenolysis control model • “Fuzzy Logic” system • Pancreas model • Simple relaxation terms

22. What are the success criteria for a model? • Model performs as expected • Can almost always get this by twiddling parameters • Did it work without changing parameters too far? • Yes – almost no need to change component model parameters • But – needed to adjust scalings between equivalent variables in different models, (including time scalings) • Some structural changes to models were needed. • Model generates interesting science • Questions that need to be answered • Parameters that need more accurate values • Experiments that should be performed • Functional hypotheses. • Maps between disease and parametric model changes • Model complexity appropriate to results • Can a simpler model be found with the same explanatory power?

23. Succesful homeostasis

24. Failed homeostasis: oversensitivity

25. Underfeeding.

26. Failed homeostasis: undersensitivity

27. Varying insulin sensitivity

28. What on earth are these oscillations? • Ultradian insulin oscillations • Third-fastest class of insulin oscillations • Existing models focus on pancreas physiology. Experimental results from Glucon Inc.

29. Importance of liver to ultradian oscillations Pancreas Liver Or: Liver Pancreas Or even: Pancreas Liver

30. Control Glycogenolysis Glucagon 2 nM 2.5 2 Glucose secreted relative to the control 1.5 1 0.5 10 20 30 45 60 90 120 time (mins) Experiments on hepatocyte-hepatocyte glucose oscillations