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Lecture #2 Basics of Kinetic Analysis
27

Lecture #2

Jan 11, 2016

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Lecture #2. Basics of Kinetic Analysis. Outline. Fundamental concepts The dynamic mass balances Some kinetics Multi-scale dynamic models Important assumptions. FUNDAMENTAL CONCEPTS. Fundamental Concepts. Time constants: measures of characteristic time periods Aggregate variables: - PowerPoint PPT Presentation
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Page 1: Lecture #2

Lecture #2

Basics of Kinetic Analysis

Page 2: Lecture #2

Outline

• Fundamental concepts• The dynamic mass balances• Some kinetics• Multi-scale dynamic models• Important assumptions

Page 3: Lecture #2

FUNDAMENTAL CONCEPTS

Page 4: Lecture #2

Fundamental Concepts

• Time constants: – measures of characteristic time periods

• Aggregate variables: – ‘pooling’ variables as time constants relax

• Transitions: – the trajectories from one state to the next

• Graphical representation: – visualizing data

Page 5: Lecture #2

Time Constants

• A measure of the time it takes to observe a significant change in a variable or process of interest

$

0 1 mo

save

balance

borrow

Page 6: Lecture #2

Aggregate Variables:primer on “pooling”

GluHK

ATP ADP

G6P F6PPGI PFK

ATP ADP

1,6FDP

“slow” “fast” “slow”

HK

ATP

Glu HPPFK

ATP

Time scale separation (TSS)Temporal decomposition

Aggregate poolHP= G6P+F6P

Page 7: Lecture #2

TransitionsTransition

homeostaticor

steady

Transient response:1 “smooth” landing2 overshoot3 damped oscillation4 sustained oscillation5 chaos

The subject ofnon-linear dynamics

1 2 3 4

Page 8: Lecture #2

Representing the Solution

fast slow

Glu

G6P

F6P

HP

Example:

Page 9: Lecture #2

THE DYNAMIC MASS BALANCES

Page 10: Lecture #2

Units on Key Quantities

Dynamic Mass Balance dxdt = S•v(x;k)

Dimensionlessmol/mol

Mass (or moles)per volume

per time

Mass (or moles) per

volume

1 mol ATP/1 mol glucose

mM/secM/sec

mMM

Example:

1/time, or1/time • conc.

sec-1

sec-1 M-1

Need to know ODEs and Linear Algebra for this class

Page 11: Lecture #2

Matrix Multiplication: refresher

( )( ) ( )+

=

s11•v1 + s12•v2 = dx1/dt

=

Page 12: Lecture #2

SOME KINETICS

Page 13: Lecture #2

Kinetics/rate laws =Sv(x;k)dxdt

Two fundamental types of reactions:

1) Linear

2) Bi-linear

xv

x+yv

Example: Hemoglobin

Actual

Lumped2+2 22

22

Special case

x+x

dimerization

+ 2

x,y ≥ 0, v ≥ 0

fluxes and concentrations are non-negative quantities

Page 14: Lecture #2

Mass Action Kinetics

rate ofreaction( ) collision frequency

v=kxa a<1 if collision frequency is hampered by geometry

v=kxayb a>1, opposite case or b>1

Restricted Geometry (rarely used)

Collision frequency concentration

Linear: v=kx; Bi-linear: v = kxy

Continuum assumption:

Page 15: Lecture #2

Kinetic Constants are BiologicalDesign Variables

•What determines the numerical value of a rate constant?•Right collision; enzymes are templates for the “right” orientation•k is a biologically determined variable. Genetic basis, evolutionary origin•Some notable protein properties:

•Only cysteine is chemically reactive (di-sulfur, S-S, bonds), •Proteins work mostly through hydrogen bonds and their shape,•Aromatic acids and arginine active (orbitals) •Proteins stick to everything except themselves•Phosporylation influences protein-protein binding•Prostetic groups and cofactors confer chemical properties

reaction

no reaction

Angle of Collision

Page 16: Lecture #2

Combining Elementary Reactions

Mass action ratio ()

G6P F6PPGI

Keq=[F6P]eq

[G6P]eq

=[F6P]ss

[G6P]ss

closed system open system

Keq

x1 x2

v+

v-vnet=v+-v-

vnet >0

vnet <0

vnet =0 equil

Reversible reactions

Equilibrium constant, Keq, is a physico-chemical quantityEquilibrium constant, Keq, is a physico-chemical quantity

Convert a reaction mechanism into a rate law:

S+E xv1

v-1

P+Eqssa

or qeav(s)=

VmsKm+s

v2

mechanism assumption rate law

Page 17: Lecture #2

MULTI-SCALE DYNAMIC MODELS

Page 18: Lecture #2

P AP+ +

Capacity: =2(ATP+ADP+AMP)

Occupancy: 2ATP+1ADP+0AMP

EC= ~ [0.85-0.90]occupancy

capacity

Example:

ATP=10, ADP=5, AMP=2

Occupancy =2•10+5=25Capacity=2(10+5+2)=34

2534

EC=

P baseP APP

High energy phosphate bond trafficking in cells

Page 19: Lecture #2

Kinetic Description

ATP+ADP+AMP=Atot

2ATP+ADP=total

inventoryof ~P

Slow

Intermediate Fast

pooling:

Page 20: Lecture #2

Time Scale Hierarchy•Observation•Physiological process

Examples: secATP

binding

minenergy

metabolism

daysadenosine carrier:

blood storage in RBC

Page 21: Lecture #2

Untangling dynamic response:modal analysis m=-1x’, pooling matrix p=Px’

log(x’(t))

Total Response Decoupled Response

time

mi

mi0log

m3; “slow”

m2; “intermediate”

m1; “fast”

Example:

x’: deviation variable

( )

Page 22: Lecture #2

IMPORTANT ASSUMPTIONS

Page 23: Lecture #2

The Constant Volume Assumption

M = V • xmol/cell vol/cell mol/vol

volu

me

conc

entra

tion

Total mass balancemol/cell/time

f = formation, d = degradation

=0 if V(t)=const

mol/vol/time

Page 24: Lecture #2

Osmotic balance:in=out; in=RTiXi

Electro-neutrality: iZiXZi=0

Fundamental physical constraints

Gluc

2lac

ATPADP

3K+ 2Na+

Hb-

Albumin-

membranes:typically permeable to anions

not permeable to cations

red blood cell

Page 25: Lecture #2

Two Historical Examples of Bad Assumptions

1. Cell volume doubling during division

modeling theprocess of cell

divisionbut

volumeassumed tobe constant

2. Nuclear translocation

NFc

VN

AN

VC

dNFc

dt=…-(AN/Vc)vtranslocation

dNFn

dt=…+(AN/VN)vtranslocation

Missing (A/V) parameters make mass lost during translocation

Page 26: Lecture #2

Hypotheses/Theories can be right or wrong…

Models have a third possibility;they can be irrelevant

Manfred Eigen

Also see:http://www.numberwatch.co.uk/computer_modelling.htm

Page 27: Lecture #2

Summary• i is a key quantity

• Spectrum of i time scale separation temporal decomposition

• Multi-scale analysis leads to aggregate variables• Elimination of a i reduction in dim from m m-1

– one aggregate or pooled variable, – one simplifying assumption (qssa or qea) applied

• Elementary reactions; v=kx, v=kxy, v≥0, x≥0, y≥0• S can dominate J; J=SG S ~ -GT

• Understand the assumptions that lead to dtdx =Sv(x;k)