cell model calibration using nmr based metabonomics kranthi varala advisor : peter ortoleva capstone...

CELL MODEL CALIBRATION

Using NMR based metabonomics

Kranthi VaralaAdvisor : Peter OrtolevaCapstone Project Spetember 2004

Kranthi Varala - Capstone Project

Cell models and simulators

Cell models study cell behavior and cell response

Powerful predictive tools

Simulators have to be able to predict behavior

accurately

Stochastic, Flux balance and Kinetic simulators exist


Background – Karyote

Karyote is a compartmentalized, kinetic cell

simulator (http://ruby.chem.indiana.edu)

Kinetic model superior to stochastic models

(Gillespie solutions) and Flux balance analysis

Harder to build and calibrate model


Motivation

Utilization of NMR data

Adapt our information theory approach to use

established experimental measurements

Utilization of multiplex data

Concurrent usage of different kinds of data


Nuclear Magnetic Resonance (NMR)

Chemicals (metabolites) with 13C can be detected

Position of the peaks is always constant and unique for

a given molecule

Position marked in ppm (ratio from original signal)

Inte

nsity

13C Spectrum for Toluene (http://www.cis.rit.edu/htbooks/nmr/inside.htm)


NMR based metabonomics

Intensity of peak is measure of its concentration in sample

Recent advances in NMR enhanced amplitude sensitivity

Reproducibility in the range of +/- 0.2-1.0% is reported in

the NMR community

Single cell isolation techniques help separation of a single

cell which can then be ruptured and its contents sampled


Spectrum complexity increases rapidly Dense spectra often have overlapping peaks Inversion of spectrum to metabolite

concentrations difficult

13C spectrum of Mountain DewImage: www.acts.org/roland/mt.dew

1H spectrum of one protein

Spectrum Complexity


Current approaches to NMR based Metabonomics Many papers published recently deal with the inversion problem. Deconstructing the

spectral intensities into concentrations.

Pre-processing spectrum

Normalization

Remove water, TMSP etc. peaks

Log scaling

Statistical analysis

Multivariate Analysis

Molecular Factor analysis

Most solutions computationally intensive


Simplification of spectral complexity

1H spectra are too dense to process. 13C spectra sparser but still

overwhelming

13C spectra have a wider spectral range(~200ppm) compared to 1H

(~15ppm)

Our solution is to grow cells in 13C enriched media to enhance 13C

spectra which are inherently sparse

Spectra from these cells will show peaks only for those metabolites that

are synthesized through metabolism using 13C medium components


Avoiding inversion

Faster processing, less computation

Generate synthetic NMR from metabolite concentrations

Spectral database for common metabolites

Predicted concentrations from Karyote translated to

spectrum


Synthetic NMR - Our approach

Conversion factor is provided by addition of a reference

compound

Known concentration of reference compound carefully added

to sample prior to data acquisition

Concentration of metabolite peaks computed as ratio against

the reference peak


Parameters in Karyote

List of parameters in Karyote

Initial concentration of metabolite

Rate of reaction

Equilibrium constant of reaction

Rate of transport across membrane


Calibration

Measure of intracellular metabolite levels gives

valuable information to calibrate a cell reaction-

transport model

Time series data ideal, discrete data can also be used

Information theory calibrates model by adjusting

parameters iteratively


Information theory (IT)

Probability based formulation to

calibrate cell model (Sayyed-

Ahmad et al. 2003)

Error minimization techniques to

calibrate kinetic parameters

Uncertainty of the system is

limited only by available data

In principle different data types

can be used in error computation


IT workflow


Making IT modular

IT built to use direct metabolite concentration data

Make each data type a module

Code optimized towards this end. IT can now accept any

kind of data if its parsing and error computation modules

are provided

NMR developed as a module for the core IT program


IT using NMR data


NMR error computation

Comparison of 2 spectra as line data

Inherently simplifies the spectrum by ignoring lines that need not be

compared

Allows computation without complete knowledge of spectra for all

species in the cell

Error computed as difference between synthetic and experimental

NMR


Error surface


Error surface-oscillatory model


Dual data schemes and cross-cell analysis

One cell model can be used to understand another less understood but

related cell

Spectral data obtained from both cells and processed to discover the

underlying functional differences between the two networks

Algorithm starts with the defined cell model and adjusts parameters on

subsequent iterations to match the spectrum for the new cell type

Typical example is comparing a normal to a mutated cell

Comparison between two organisms is also plausible


ResultsCell Model Parameter Initial guess Optimized value Correct Value Error %

4 metabolites, 2 reaction & transport model

Equillibrium Constant

1e-4 3e-4 3e-4 - 9e-3 0

4 metabolites, 2 reaction & transport model

Rate of reaction 0.0001 0.01 0.01 0

7 metabolites, oscillatory model

Rate of reaction 1e-4 2.5e-4 1e-5 96

Trypanosoma Equilibrium constant

1 110 126.41 13

Trypanosoma Rate of reaction 1e-9 1.28e-10 1.4e -10 8.57


ReferencesSayyed-Ahmad A, Tuncay K, Ortoleva P. American Chemical Society, Jun 30 2003

Sterin M, Cohen S, Mardor Y, Berman E, Ringel I. Cancer Research 61, Oct 15 2001

Eads C, Furnish C, Noda I, Juhlin K, Cooper A, Morrall S. Analytical Chemistry., 76(7)

Mar 9 2004

Lenz E.M, Bright J, Wilson I.D, Morgan S.R, Nash A.F.P Journal of Pharm. And

biomed. Anal. 33(5) Dec 5 2003

Reo N.V. Drug and chemical toxicology 25(4) 2002

Atlas of Carbon-13 NMR data Breitmaier E, Haas G, Voelter. W. Heyden & Son, 1979

13 C NMR spectroscopy Breitmaier E, Voelter W. Verlag Chemie 1974

The Aldrich library of 13 C and 1 H FT-NMR spectra. Pouchert C.J, Behnke J, Aldrich

chemical company Inc. 1993


Acknowledgements

Peter Ortoleva

Sun Kim

Abdallah Sayyed-Ahmad

Haixu Tang

John Tomaszewski

cell model calibration using nmr based metabonomics kranthi varala advisor : peter ortoleva capstone...

Documents

model slide

intensive slide

h spectrum

peaks inversion of spectrum

c spectra sparser

h spectra

cell model calibration

dense spectra