Evolving Our Understanding ofThe Neural Control of Breathing

Jeff MendenhallCollege of William and Mary

Department of Applied Sciences, Room #314

Outline• Why Investigate Breathing• Review• Standard Model• Shortcomings of the Standard Model• The Next Step• Dealing with the Problem of Detailed Models• Where to from here

Our Motivation• Necessity of breathing

• Stroke/Disease-induced lesions can impair breathing

• CCHS and other disorders of the control of breathing

Outline• Why Investigate Breathing• Review• Standard Model• Shortcomings of the Standard Model• The Next Step• Dealing with the Problem of Detailed Models• Where to from here

System Overview

• Neural Control of Inspiration Takes Place in the preBötzinger Complex (preBötC) 1

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

preBötC

XII Nerve

Muscles

TerminologyInspiratory

burst (raw)

Inspiratory burst

(smoothed)

Inspiratory drive

potential

amplitude area

Outline• Why Investigate Breathing• Review• Standard Model• Shortcomings of the Standard Model• The Next Step• Dealing with the Problem of Detailed Models• Where to from here

Standard Model

• Assumptions: Effectively Isospatial

Currents Present:

INaP, INaF, IK, IL, Itonic-e, Isyn

• Predictions: “Pacemaker” neurons and INaP Essential for Network-Level Bursts

Outline• Why Investigate Breathing• Review• Standard Model• Shortcomings of the Standard Model• The Next Step• Dealing with the Problem of Detailed Models• Where to from here

Problems with the Standard Model I

• Assumptions: Effectively Isospatial

• Currents Present: INaP, INaF, IK, IL, Itonic-e

+ ICAN, Ih, IA, INMDA, IGABA

Problems with the Standard Model II

•Predictions: “Pacemaker” neurons and INaP are Essential for Network Functioning

-Pace, Mackay, Feldman, and Del Negro, J. Physiology, 582: 113-125 2007. 3

-Del Negro, Morgado-Valle. Mackay, and Feldman, J. Neuroscience, 25(2): 446-53.4

-Del Negro, Morgado-Valle, and Feldman, Neuron 34: 821- 30, 2002.5

Outline• Why Investigate Breathing• Review• Standard Model• Shortcomings of the Standard Model• The Next Step• Dealing with the Problem of Detailed Models• Where to from here

The Next Step I

• Correct Isospatial Assumption

• Use Realistic gNaP ConductancegNMDA

gAMPA

INaK

gK

gCa

gL (K,Na,Ca)

ICa-ATP

gA

gtonic

gelec

gCAN

CDendrite

gNaP

INaK

gK

gK(Ca)

gL(K,Na,Ca)

ICa-ATP

gH

gNaF

CSoma

gCa

EK,Na,Ca

ECa ECa

ECAN

ESyn-Ex

ESyn-Ex

EK

EK

EK EK

EH

EAv. Neighbor

ESyn-Ex

ENa

ENa

EK,Na,Ca

gCDendritic Compartment Somatic Compartment

• Add Other Currents

The Next Step II

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

• Add mGluR-IP3-Ca2+-ICAN pathway

The Next Step III:

• Add material balance for Ca2+ and Na+

Example:

Ca2+ BalanceJIP3

JCaER-Leak

JNa+/Ca2+

Exchanger

JCaER-Leak

JNa+/Ca2+

Exchanger

The Next Step IV• Add calcium microdomains

Outline• Why Investigate Breathing• Review• Standard Model• Shortcomings of the Standard Model• The Next Step• Dealing with the Problem of Detailed Models• Where to from here

The Problem: Too Many Poorly Constrained Parameters

Dendritic Compartment Somatic Compartment

gNMDA

gAMPA

INaK

gK

gCa

gL (K,Na,Ca)

ICa-ATP

gA

gtonic

gelec

gCAN

CDendrite

gNaP

INaK

gK

gK(Ca)

gL(K,Na,Ca)

ICa-ATP

gH

gNaF

CSoma

gCa

EK,Na,Ca

ECa ECa

ECAN

ESyn-Ex

ESyn-Ex

EK

EK

EK EK

EH

EAv. Neighbor

ESyn-Ex

ENa

ENa

EK,Na,Ca

gC

Methods: Evolving SolutionsStep 2: Sit back, relax, let the computer do the work

Methods: Evolving SolutionsStep 1: Teach the Fitness Function What is Important

Score: 100

Score: -5 (Kill)

Score: 40

Score: 50

Score: -30 (Kill)

Fitness Function

What Is a Fitness Function Anyway?

A weighted sum of fitness measures

Inside the Black Box

Traces

ScoresDetermine Kill Conditions

Spike/Burst Analyzer

TraceStatistics

Surviving Traces Stable, Bounded Linear Regression

Fitness Parameters

Advantages of Evolutionary Algorithm

• Efficiently Handles Large Parameter Spaces

• Yields Many Good Regions

• Approximates Their Boundaries

Preliminary Results

• Problem: Fit the current model to 4 experiment traces

• Number of Parameters: 110

Ideal Curve Some Evolved Solutions

Time

Voltage

Control Control Pulse

Riluzole ApaminPulse

Time

Voltage

Control Control Pulse

Riluzole ApaminPulse

Time

Voltage

Control Control Pulse

Riluzole ApaminPulse

Time

Voltage

Control Control Pulse

Riluzole ApaminPulse

Time

Voltage

Control Control Pulse

Riluzole ApaminPulse

V(Somatic)

Typical Burst

Control Riluzole

V(Somatic)

Typical Burst

Ca2+ From Stores

ICAN

Ca2+(Dend)

V (Dend)

Outline• Why Investigate Breathing• Review• Standard Model• Shortcomings of the Standard Model• The Next Step• Dealing with the Problem of Detailed Models• Where to from here

Future Directions

• Add More Experiments

• Adjust Parameter Ranges

• Make / Test Predictions

Acknowledgements

AcademicDr. Christopher Del Negro (C. W&M)

Dr. Pete Roper (U. Utah)

FinancialNSF Grant IOB-0616099

Suzzane Matthews Faculty Research Award

References

1. Smith, J.C., Ellenberger, H.H., Ballanyi, K., Richter, D.W. & Feldman, J.L. “Pre-Bötzinger complex: a brainstem region that may generate respiratory rhythm in mammals.” Science 254, 726-9 (1991).

2. Rekling, J.C., Champagnat, J. & Denavit-Saubie, M. (1996) “Electroresponsive properties and membrane potential trajectories of three types of inspiratory neurons in the newborn mouse brain stem in vitro.” J Neurophysiol 75, 795-810.

3. Ryland W. Pace, Devin D. Mackay, Jack L. Feldman, and Christopher A. Del Negro (2007). “Cellular And Synaptic Mechanisms That Generate Inspiratory Drive Potentials In Pre-Bötzinger Neurons In Vitro.” J. Physiology 582: 113-125 2007.

4. Del Negro, C. A., C. Morgado-Valle, et al. (2005). "Sodium and Calcium Current-Mediated Pacemaker Neurons and Respiratory Rhythm Generation." J. Neurosci. 25(2): 446-453.

5. Del Negro, C. A., N. Koshiya, et al. (2002). "Persistent sodium current, membrane properties and bursting behavior of pre-botzinger complex inspiratory neurons in vitro." J Neurophysiol 88(5): 2242-50.