CELL MODEL CALIBRATION

Using NMR based metabonomics

Kranthi Varala Advisor : Peter Ortoleva Capstone Project Spetember 2004

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 Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

Nuclear Magnetic Resonance (NMR)

13 C Spectrum for Toluene (http://www.cis.rit.edu/htbooks/nmr/inside.htm)  Chemicals (metabolites) with 13 C can be detected  Position of the peaks is always constant and unique for a given molecule  Position marked in ppm (ratio from original signal) Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

Spectrum Complexity

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

1 H spectrum of one protein  Spectrum complexity increases rapidly  Dense spectra often have overlapping peaks  Inversion of spectrum to metabolite concentrations difficult Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

Simplification of spectral complexity

 1 H spectra are too dense to process.

13 C spectra sparser but still overwhelming  13 C spectra have a wider spectral range(~200ppm) compared to 1 H (~15ppm)  Our solution is to grow cells in 13 C enriched media to enhance 13 C spectra which are inherently sparse  Spectra from these cells will show peaks only for those metabolites that are synthesized through metabolism using 13 C medium components Kranthi Varala - Capstone Project

Avoiding inversion

 Faster processing, less computation  Generate synthetic NMR from metabolite concentrations  Spectral database for common metabolites  Predicted concentrations from Karyote translated to spectrum Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

Parameters in Karyote List of parameters in Karyote

 Initial concentration of metabolite  Rate of reaction  Equilibrium constant of reaction  Rate of transport across membrane Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

IT workflow

Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

IT using NMR data

Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

Error surface

Kranthi Varala - Capstone Project

Error surface-oscillatory model

Kranthi Varala - Capstone Project

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 Kranthi Varala - Capstone Project

Results

Cell Model Parameter Initial guess Optimized value Correct Value Error % 4 metabolites, 2 reaction & transport model 4 metabolites, 2 reaction & transport model 7 metabolites, oscillatory model Equillibrium Constant Rate of reaction Rate of reaction 1e-4 0.0001

1e-4 Trypanosoma Trypanosoma Equilibrium constant 1 Rate of reaction 1e-9 3e-4 0.01

2.5e-4 110 1.28e-10 3e-4 - 9e-3 0.01

1e-5 126.41

1.4e -10 0 0 96 13 8.57

Kranthi Varala - Capstone Project

References

Sayyed-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 Kranthi Varala - Capstone Project

Acknowledgements

 Peter Ortoleva  Sun Kim  Abdallah Sayyed-Ahmad  Haixu Tang  John Tomaszewski Kranthi Varala - Capstone Project