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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

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The smart route towards an in silico liver

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?


Systems biology background
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


The dream of systems biology
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


The need for vertical integration
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


Glucose homeostasis
Glucose Homeostasis

  • The liver performs many functions

    • Bile synthesis,

    • detoxification …

  • We concentrate on glucose homeostasis.

    • Medically important

    • Diabetes!


Energy storage a metaphor
Energy Storage – A metaphor

Ion gradients

ATP

Glucose

Glycogen

Fat

Change

Notes

Current Account

Savings Account

Lock-in Account


Energy banking transactions

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


Liver systems biology is hard

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



Problems with simplification

Hill parameter

4

256

Problems with simplification

Analytical

result


Control analysis

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.


Applicability of simple models
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.


Multicellularity heterogeneity and intercellular communication

Hepatocytes vary

Systematically

Randomly

Hepatocytes communicate

Through gap junctions

Junction amounts and types vary dynamically

Multicellularity, heterogeneity and intercellular communication


The impact of intercellular communication
The impact of intercellular communication

Bottom quartile performance

  • Synergy

  • Robustness

  • Predictability

  • Synchronisation

  • Efficiency

Gap junction density


Control of communication
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?


When do glucagon and insulin cooperate

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.


The homeostatic basin
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)


Communication sharpens system s homeostatic response
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


Impact of connexin control on energy usage
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?



Hybrid complexity
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


What are the success criteria for a model
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?







What on earth are these oscillations
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.


Importance of liver to ultradian oscillations
Importance of liver to ultradian oscillations

Pancreas

Liver

Or:

Liver

Pancreas

Or even:

Pancreas

Liver


Experiments on hepatocyte hepatocyte glucose oscillations

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


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