CHEMOMETRICS IN VIRTUAL CELL

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CHEMOMETRICS IN VIRTUAL CELL
Analytical/Radio/Nuclear (ARN) Seminar

• NILESH RAUT

DEPARTMENT OF CHEMISTRY UNIVERSITY OF KENTUCKY
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OVERVIEW

• Chemometrics basics.
• Virtual cell basics.
• Components of virtual cell model.
• Use of virtual cell model in Ca2 transport.
• Use of chemometrics in virtual cell modeling of
  Ran transport.
• Results.
• Conclusions.

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The Need

• To understand the overall design principle of
  complex biological systems.
• To understand transport phenomenon within cell.
• To aid in genomics and proteomics studies.
• To develop full understanding of mechanisms
  underlying a cell biological event.
• To overcome communication problem between
  chemists and chemometricians

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Chemometrics

• Extracting chemically relevant information from
  data produced in chemical experiments.
• Makes use of mathematical model.
• Structure the chemical problem to a form that can
  be expressed as a mathematical relationship.
• A chemical model (M) relates experimental
  variables (X) to each other, and it also has a
  statistical model (E) associated with it.

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Chemometrics

• The statistical model also describes variability,
  noise of the data obtained from chemical model.
• X M E.
• i.e. Data Chemical Model Noise.
• More imphasis is to be given on the chemical
  model representing a situation.

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Virtual Cell What is it? And How is it done?

• Computational framework for modeling cell
  biological processes.
• Models are constructed from biochemical and
  electrophysical data.
• Couples chemical kinetics, membrane fluxes and
  diffusions.
• Resultant equations are solved numerically.

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System architecture for virtual cell
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System architecture for virtual cell

• Modeling framework gives biological abstractions
  necessary to model and simulate cellular
  physiology
• Mathematics framework Provides a general purpose
  solver for mathematical problems in the
  application domain of computational cellular
  physiology.

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Components of Physiological Model 1. Cellular
Structure

• Represents mutually exclusive regions in cell.
• Compartments 3D volumetric regions.
• Membranes 2D surfaces separating compartments
  and filaments.
• Filaments 1D contours lying within single
  compartment.
• Can also contain molecular species and reactions
  describing those species.

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Components of Physiological Model 2. Molecular
Species

• Within cellular structures.
• Behavior of molecular species
• Diffusion within compartments, membranes, etc.
• Directed motion along filaments.
• Flux between compartments through membranes.
• Advections between cellular structures.

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Components of Physiological Model 3. Reactions
and Fluxes

• Complete description of stoichiometry and
  kinetics of biochemical reactions.
• Associated with a single cellular structure.
• Stoichiometry in terms of reactants, products
  and catalysts related to species in a cellular
  structure.
• Kinetics specified as mass action kinetics.

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Specifications of Cellular Geometry

• Describes the behavior of cellular system.
• Defines morphology of the cell, and its spatially
  resolvable organelles.
• Taken directly from experimental images (from
  pixel density).

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Design of Virtual Cell
Interplay between model development and
experiment during modeling process
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Design of Virtual Cell

• Inputs to the model can be derived from the
  users own experiments as well as the literature.
• Physiology includes the topological arrangements
  of compartments and membranes, the molecules
  associated with each of these, and the reactions
  between the molecules.
• Geometry can be derived from either analytical
  expressions or from an experimental image
  acquired from a microscope.
• Numbers represent the relative surface densities
  of the BKR.

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Use of Virtual Cell in Ca2 transport
The pathway for bradykinin-induced calcium
release in differentiated neuroblastoma cells.
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Use of Virtual Cell in Ca2 transport

• Bradykinin (BK) binds to its receptor (BKR) in
  the plasma membrane.
• Sets off a G-protein cascade, activates
  phospholipase C (PLC), hydrolyzes the
  glycerolphosphate bond in phosphatidylinositol
  bisphosphate (PIP2), releases IP3 from the
  membrane.
• IP3R is a calcium channel that is triggered to
  open when IP3 is bound and when calcium itself
  binds to an activation site.
• Calcium released binds to calcium buffers (B) in
  the cytosol including the fluorescent calcium
  indicator.
• Finally, calcium is pumped back into the ER via a
  calcium ATPase (SERCA).

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Output of Virtual Cell

• Left column shows the experimental
• calcium changes following addition of
• BK at time 0 s in a differentiated
• N1E-115 neuroblastoma cell.
• Center column displays the output
• of the Virtual Cell simulation.
• Right column displays the output of
• the simulation for IP3.
• Hence permits simulation permits
• estimation of the spatiotemporal
• distribution of molecules that are not
• accessible experimentally.

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Chemometric Studies of Ran Transport Setup

• Ran is guanine nucleotide triphosphatase.
• Two cellular compartments cytosol and nucleus.
• Behavior under consideration Flux of Ran.
• Flux rate is calculated as a product of
  permeability constant and concentration
  difference across nuclear envelope.
• For visualization aid, recombinant protein was
  modified with a fluorescent maleimide.

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Kinetic Studies of Ran Transport
Fine solid lines denote reversible interactions,
dashed lines indicate enzyme-mediated reactions,
and bold, double-headed arrows indicate flux.
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Kinetic Studies of Ran Transport

• NTF Nuclear transport factor. RCC Ran exchange
  factor.
• In the cytosolic compartment, RanGDP associates
  with NTF2 to form the NTF2RanGDP complex.
• Nuclear NTF2RanGDP decomposes to NTF2 and
  RanGDP.
• Interaction of RCC1 with NTF2 or RanGDP produced
  25 RanGDP and 75 RanGTP, to account for the
  estimated GTP/GDP ratio in the cell.
• RanGTP associates with transport cargo Carriers
  to form a CarrierRanGTP complex.
• Cytosolic Carrier RanGTP associates with RanBP1
  to form a CarrierRanGTPBP1 complex. RanGAP
  interacts with CarrierRanGTPBP1 complex to form
  BP1, RanGDP, and Carrier.

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Kinetic Studies of Ran Transport

• Results of injection of FL-Ran

Nuclear accumulation of FL-Ran in BHK-21 cells
after cytosolic injection.
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Virtual Cell Modeling of Ran Transport

• 3D geometry from experimental images is used.
• Microinjection is modeled as a brief localized
  increase of the cytosolic FL-Ran concentration.
• Result 3D simulation resembles experimental
  FL-Ran nuclear import and diffusion through
  cytosol.

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Comparison of Virtual Cell Modeling and
Experimental Result
Comparison of Ran transport in a time series for
an FL-Ran nuclear import at initial cytosolic
conc. 1µM (in gray) with a sample plane from a 3D
spatial model of Ran transport (In color)
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Analysis of Compartmental model for Ran Transport
Transients for simulated endogenous nuclear
species concentrations, followed over time during
recovery from addition of 1µM FL-Ran to cytosol
compartment.
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In Vivo Analysis of Ran Import and Shuttling
A Time courses for nuclear accumulation of wild
type FL-Ran for the indicated initial cytosolic
concentrations. B Fluorescence loss in
photobleaching (FLIP) on FL-Ran at steady-state
in micro-injected BHK-21 cells. Boxed area was
repetitively photobleached.
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Conclusions

• Virtual cell has broad applicability in
  biological systems.
• Chemometric methods are an important tool in
  predicting the results.
• It serves as a confirmative test for a particular
  biological reactions.
• Failures in obtaining results using chemometric
  methods, insures that the thought process is not
  yet perfect.

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References

• Alicia E. Smith, Boris M. Slepchenko, James C.
  Schaff, Leslie M. Loew, Ian G. Macara Science
  295, 488-491 (2002).
• Svante Wold Chemometrics and Intelligent
  Laboratory Systems 30, 109-115 (1995).
• Stanislaw Gorski, Tom Misteli Journal of Cell
  Science 118(18), 4083-4092 (2005).
• Boris M. Slepchenko, James C. Schaff, Ian Macara,
  Leslie M. Loew TRENDS in Cell Biology 13(11),
  570-576 (2003).
• Leslie M. Loew and James C. Schaff TRENDS in Cell
  Biology 19(10), 401-406 (2001).
• Zoltan Szallasi TRENDS In Pharmacological
  Sciences 23(4), 158-159 (2002).

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• THANK YOU