Q: What is this dog thinking about?. He’s thinking about two things: 1. Saturable processes. 2. Solving the FDG model.

Slides:



Advertisements
Similar presentations
Chapter 6 Differential Equations
Advertisements

Fund BioImag : Two compartment modeling 1.What is compartmental modeling ? 2.How can tracer kinetics be mathematically described ? 3.How do 2-deoxyglucose.
Chapter 7 Chem 341 Suroviec Fall I. Introduction The structure and mechanism can reveal quite a bit about an enzyme’s function.
Chapter 13 MIMs - Mobile Immobile Models. Consider the Following Case You have two connected domains that can exchange mass
ANALYSIS OF PET STUDIES PET Basics Course 2006 Turku PET Centre Vesa Oikonen
Psychometrics, Dynamics, and Functional Data Analysis Jim Ramsay Jim Ramsay McGill University “The views “The views.
Computational Biology, Part 17 Biochemical Kinetics I Robert F. Murphy Copyright  1996, All rights reserved.
Experiment 6 Amount of Dye in a Sports Drink. Goal To make a Beer’s Law standard curve To use the standard curve (and spectrophotometry) to determine.
Maximum likelihood Conditional distribution and likelihood Maximum likelihood estimations Information in the data and likelihood Observed and Fisher’s.
ANALYSIS OF PET STUDIES Turku PET Centre V Oikonen PET Raw Data (sinogram) Results Parametric Sinogram PET Image Parametric Image Regional TACs.
Correlation 2 Computations, and the best fitting line.
What are some of the issues in choosing bolus vs bolus+infusion? Which one predicts equilibrium coefficients? Which one achieves equilibrium? Which one.
Governing equation for concentration as a function of x and t for a pulse input for an “open” reactor.
Lecture 9 Interpolation and Splines. Lingo Interpolation – filling in gaps in data Find a function f(x) that 1) goes through all your data points 2) does.
Need to know in order to do the normal dist problems How to calculate Z How to read a probability from the table, knowing Z **** how to convert table values.
Nonlinear pharmacokinetics
Lecture 17 Interaction Plots Simple Linear Regression (Chapter ) Homework 4 due Friday. JMP instructions for question are actually for.
One-compartment open model: Intravenous bolus administration
10 Equations in Biology: Michaelis-Menten Kinetics.
Probability, Bayes’ Theorem and the Monty Hall Problem
Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 12: Multiple and Logistic Regression Marshall University.
Applications of Calculus. The logarithmic spiral of the Nautilus shell is a classical image used to depict the growth and change related to calculus.
Copyright © 2012 Pearson Education. All rights reserved Copyright © 2012 Pearson Education. All rights reserved. Chapter 10 Sampling Distributions.
Laplace transformation
Physics 114: Lecture 15 Probability Tests & Linear Fitting Dale E. Gary NJIT Physics Department.
Louisiana Tech University Ruston, LA Slide 1 Krogh Cylinder Steven A. Jones BIEN 501 Wednesday, May 7, 2008.
Week 4 - Biopharmaceutics and Pharmacokinetics
Highly Correlated Measures of Insulin Sensitivity Thomas Lotz 1, J Geoffrey Chase 1, Kirsten A McAuley 3, Jessica Lin 1, Geoffrey M Shaw 2, Chris E Hann.
Evidence and scenario sensitivities in naïve Bayesian classifiers Presented by Marwan Kandela & Rejin James 1 Silja Renooij, Linda C. van der Gaag, "Evidence.
Dynamics. Chapter 1 Introduction to Dynamics What is Dynamics? Dynamics is the study of systems in which the motion of the object is changing (accelerating)
Gokaraju Rangaraju College of Pharmacy
PROBABILITY & STATISTICAL INFERENCE LECTURE 6 MSc in Computing (Data Analytics)
Modelling tutorial – ESCTAIC 2012 Stephen E. Rees Center for Model-based Medical Decision Support, Aalborg University, Denmark.
Presentation Schedule. Homework 8 Compare the tumor-immune model using Von Bertalanffy growth to the one presented in class using a qualitative analysis…
Computational Biology, Part 15 Biochemical Kinetics I Robert F. Murphy Copyright  1996, 1999, 2000, All rights reserved.
Chapter 6.3: Enzyme Kinetics CHEM 7784 Biochemistry Professor Bensley.
Chapter 2 Describing Motion: Kinematics in One Dimension.
PHARMACOKINETIC MODELS
Long Term Verification of Glucose-Insulin Regulatory System Model Dynamics THE 26th ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE.
Modeling with a Differential Equation
Today we will deal with two important Problems: 1.Law of Mass Action 2. Michaelis Menten problem. Creating Biomodel in Vcell we will solve these two problems.
© Department of Statistics 2012 STATS 330 Lecture 20: Slide 1 Stats 330: Lecture 20.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 22 Comparing Two Proportions.
J OURNAL C LUB : Deoni et al. One Component? Two Components? Three? The Effect of Including a Nonexchanging ‘‘Free’’ Water Component in mcDESPOT. Jan 14,
Chapter 15 – CTRW Continuous Time Random Walks. Random Walks So far we have been looking at random walks with the following Langevin equation  is a.
Radiometric Normalization Spring 2009 Ben-Gurion University of the Negev.
Chapter 2 Describing Motion: Kinematics in One Dimension © 2014 Pearson Education, Inc.
Lecture – 3 The Kinetics Of Enzyme-Catalyzed Reactions Dr. AKM Shafiqul Islam
A.B.Madhan Kumar Mentor: Dr. Charles M. Laymon Department of Radiology
Reaction Engineering.
Linearized models in PET Vesa Oikonen Turku PET Centre – Modelling workshop Modelling workshop
Lab: principles of protein purification
Dynamics: Newton’s Laws of Motion. Force A force is a push or pull. An object at rest needs a force to get it moving; a moving object needs a force to.
Properties of the Steady State. Sensitivity Analysis “Metabolic Control Analysis” Flux and Concentation Control Coefficients:
Lecture 8: Measurement Errors 1. Objectives List some sources of measurement errors. Classify measurement errors into systematic and random errors. Study.
GENERATION OF PARAMETRIC IMAGES PROSPECTS PROBLEMS Vesa Oikonen Turku PET Centre
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 23, Slide 1 Chapter 24 Comparing Means.
The inference and accuracy We learned how to estimate the probability that the percentage of some subjects in the sample would be in a given interval by.
The accuracy of averages We learned how to make inference from the sample to the population: Counting the percentages. Here we begin to learn how to make.
Segmentation of 3D microPET Images of the Rat Brain by Hybrid GMM and KDE Tai-Been Chen Department of Medical Imaging and Radiological Science,
Chapter 3 Describing Motion: Kinematics in One Dimension.
Compartmental Modelling
Physiology for Engineers
OK. What if we don’t care about the kinetics of a particular tracee molecule (e.g., glucose). Rather, we care about counting up the number of one type.
Kinetics, Modeling Oct 19, 2009 Casarett and Doull,
Dosimetry and Kinetics
Kinetics, Modeling Oct 15, 2006 Casarett and Doull,
Describing Motion: Kinematics in One Dimension
Presentation transcript:

Q: What is this dog thinking about?

He’s thinking about two things: 1. Saturable processes. 2. Solving the FDG model.

Why use FDG in particular? Product of phosphorylation by hexokinase reaction gets trapped in cell. Accumulation of metabolic product is a measure of glucose usage. 1. Assumption: FDG acts just like glucose - this is not exactly true. (The ‘lumped constant’ arises to correct for differences.)

Glucose and Deoxy-Glucose Uptake and Metabolism

Keep in mind… What we want is a measure of GLUCOSE metabolism. Not a measure of FDG phosphorylation. How do we get from a model of FDG uptake to a value of GLUCOSE metabolism? 1.Use a single measurement technique which descends from the autoradiographic method in animals. 2.Or… Solve model of FDG uptake in terms of K 1 *, k 2 *, k 3 * 2. Fit PET data to FDG model. 3. Relate LCMRglc to K 1 *, k 2 *, k 3 *

from Herscovitch chapter in Valk et al, Springer, 2003 Cp = measured concentration of glucose (assumed constant) Cp*(t) =measured, time-varying concentration of FDG in plasma C(T) = tissue concentration of FDG, measured at time T (only) LC = “lumped constant” – measured in other animals/people; reconciles glucose with FDG other terms: can be calculated from integral of the FDG curve in plasma and parameters for FDG measured in other animals/people.

where do these terms come from?

‘Sokoloff’ model 1. X * --- designates FDG. 2. Assumes that there is NO dephosphorylation of FDG-6-P over course of scan (i.e., k4 = 0).

What’s the solution to the FDG model (aka the ‘Sokoloff’ model)? Just another modified Blood Flow model.

Solving the Sokoloff model Analytical expression for the extracellular compartment also called the “precursor pool” NOTE: for FDG, all these quantities are * ’ d

from Herscovitch chapter in Valk et al, Springer, 2003 Cp = measured concentration of glucose (assumed constant) Cp*(t) =measured, time-varying concentration of FDG in plasma C(T) = tissue concentration of FDG, measured at time T (only) LC = “lumped constant” – measured in other animals/people; reconciles glucose with FDG other terms: can be calculated from integral of the FDG curve in plasma and parameters for FDG measured in other animals/people.

so, in at least 4 of the subjects in the London et al paper, the authors are measuring a single time-point C(45-55) and converting that map of measured concentrations of FDG to CMRglc via the preceeding operational equation which requires the blood curve and population- average parameters for FDG

…lets think about the FDG model again for a moment…

Tracer Kinetics Puzzle How can a tracer be described by a first order kinetic process when we know that the tracee molecule follows Michaelis-Menten kinetics? A process may be saturable in terms of enzymes and the concentration of the tracee, but, for a given system, if the tracee is not perturbed, it remains at a single set-point on this curve. V tracee Operating point for system

Consider the K 1 C p term in FDG model… A ‘transporter’ molecule helps FDG across the blood-brain-barrier. Therefore, the uptake process from blood to tissue might be saturable and NOT first order in FDG concenctration. V tracee velocity of FDG transport (via the glucose transporter) from plasma, across BBB in presence of the competitor, glucose

Glucose-6-P inside the cell is also mediated by a specific enzyme, hexokinase. As long as glucose is at steady state, and we are not near maxing out the cell’s capacity to metabolize, then we use the same reasoning as in previous slide to assign a 1st order rate constant, k 3

What would max out cells ability to transport FDG in from blood? What happens? Lots of glucose in the blood! Transporter operates at different set-point. V tracee

Non-Fasted Care of Jeniece Nott, Ph.D., Ned Rouze, Ph.D. FDG images of Mouse Brain Fasted for 14 Hours

Care of Jeniece Nott, Ph.D. Uptake into brain varies with fasting state

The FDG model What do the (grey) boxes mean? State equations --- I.e., unknowns We need to write a balance eqn on each compartment.

Keep our goal in mind… Solving for GLUCOSE metabolic rate Solving the Sokoloff model - 3 But we assume that glucose is in steady state. but these k i are glucose parameters, not FDG parameters

How to relate LCMRglc = f(K 1, k 2, k 3 ) to LCMRglc = f(measured, estimated quantities)? Glucose parameters

Dynamic FDG scanning here the LC is the lumped constant that corrects K1, k2, k3 for K1*, k2*, k3* but in any case, this approach requires fitting all the data to a model

…so the question for interpretation of the London et al. paper is: Are cocaine addicts more like normals or like PD or Alzheimer’s patients?

What if we don’t want to solve it? There are ways to linearize it (called the ‘Patlak’ plot).

Interpretation: What does the parameter K 1 k 3 represent? (k 2 +k 3 ) ? Blood Door #2 Metabolism Door #3 k2k2 k3k3 Think Baye’s theorem…

? LCMRglc ~ p(metabolism | transport from blood) = ‘choose’ metabolism out of Sum of [return to blood+metabolism], given already transported =(k 3 /[k 2 +k 3 ])K 1

Why does the data keep going up? Because there’s no k ? Is this realistic? Over the time frame of the scan, perhaps.

irreversibility… like ‘early’ and ‘late’ depends on context. FDG may be effectively irreversible during a 2 hr scan… but not over 24 hrs.

Heterogeneity: what if our pixels are too large to measure a truly homogenous region? Say, we get white and gray matter in a single pixel.

We could include heterogeneity into the model. (Just as we included radioactive decay.) But this might mean too many parameters.

time Input Function What’s needed to Solve the Model? Input Function, P, Drives the Model. P

Questions 1.subjects (polydrug users!) in withdrawal from other drugs? 2.static analysis assumes that population parameters apply? and that lumped constant is valid across all subjects. 3.no data or details given for fitting of data and estimating paramters in 4 subjects 4.training sessions – are these good or bad? 5.how can it be “double-blind”? 6.preselected the cohort for big responders – is this fair? 7.how many slices on the NeuroEcat? 8.why all the detail about positioning by the orbitomeatal line? 9.no corrections for multiple comparisons 10.why do we need plasma glucose levels?... aha! 11.did they give enough cocaine – no one felt good, energetic, or anxious compared to saline 12.no statistically signif effect of drug (coc v sal) on “high” - should we worry about this? figure mis-labeled.

…well, as long as they were right handed!

The effects of cocaine: A shifting target over the course of addiction Linda J. Porrino, Hilary R. Smith, Michael A. Nader, Thomas J.R. Beveridge Center for the Neurobiological Investigation of Drug Abuse, Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC , USA Available online 4 September 2007 Can we do this with FDG in living monkeys? Why? Why not? is 5 days really “initial” is 100 days really “chronic”? What does Porrino think about the London paper – does it relate?