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CHEMOMETRICS IN VIRTUAL CELL

NILESH RAUT

Analytical/Radio/Nuclear (ARN) Seminar

DEPARTMENT OF CHEMISTRYUNIVERSITY OF KENTUCKY

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OVERVIEWOVERVIEW

• 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 NeedThe 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 CellVirtual 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 cellSystem architecture for virtual cell

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System architecture for virtual cellSystem 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 ModelComponents 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 ModelComponents 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 GeometrySpecifications 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 CellDesign of Virtual Cell

Interplay between model development and experiment during modeling process

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Design of Virtual CellDesign of Virtual Cell

• Inputs to the model can be derived from the user’s 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 CaUse of Virtual Cell in Ca2+2+ transport transport

The pathway for bradykinin-induced calcium release in differentiated neuroblastoma cells.

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Use of Virtual Cell in CaUse of Virtual Cell in Ca2+2+ transport 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 CellOutput 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 Chemometric Studies of Ran TransportTransport: Setup: 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 TransportKinetic 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 TransportKinetic Studies of Ran Transport• NTF: Nuclear transport factor. RCC: Ran exchange

factor.• In the cytosolic compartment, RanGDP associates with

NTF2 to form the NTF2:RanGDP complex.• Nuclear NTF2:RanGDP 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 Carrier:RanGTP complex.

• Cytosolic Carrier: RanGTP associates with RanBP1 to form a Carrier:RanGTP:BP1 complex. RanGAP interacts with Carrier:RanGTP:BP1 complex to form BP1, RanGDP, and Carrier.

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Kinetic Studies of Ran TransportKinetic 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 Virtual Cell Modeling of Ran TransportTransport

• 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 Comparison of Virtual Cell Modeling and Experimental ResultModeling 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 In Vivo Analysis of Ran Import and Shuttling 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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ConclusionsConclusions

1. Virtual cell has broad applicability in biological systems.

2. Chemometric methods are an important tool in predicting the results.

3. It serves as a confirmative test for a particular biological reactions.

4. Failures in obtaining results using chemometric methods, insures that the thought process is not yet perfect.

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ReferencesReferences1. Alicia E. Smith, Boris M. Slepchenko, James C. Schaff,

Leslie M. Loew, Ian G. Macara Science 295, 488-491 (2002).

2. Svante Wold Chemometrics and Intelligent Laboratory Systems 30, 109-115 (1995).

3. Stanislaw Gorski, Tom Misteli Journal of Cell Science 118(18), 4083-4092 (2005).

4. Boris M. Slepchenko, James C. Schaff, Ian Macara, Leslie M. Loew TRENDS in Cell Biology 13(11), 570-576 (2003).

5. Leslie M. Loew and James C. Schaff TRENDS in Cell Biology 19(10), 401-406 (2001).

6. Zoltan Szallasi TRENDS In Pharmacological Sciences 23(4), 158-159 (2002).

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THANK YOUTHANK YOU