1 / 28

CHEMOMETRICS IN VIRTUAL CELL

CHEMOMETRICS IN VIRTUAL CELL. Analytical/Radio/Nuclear (ARN) Seminar. NILESH RAUT. DEPARTMENT OF CHEMISTRY UNIVERSITY OF KENTUCKY. OVERVIEW. Chemometrics basics. Virtual cell basics. Components of virtual cell model. Use of virtual cell model in Ca 2+ transport.

abram
Télécharger la présentation

CHEMOMETRICS IN VIRTUAL CELL

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CHEMOMETRICS IN VIRTUAL CELL Analytical/Radio/Nuclear (ARN) Seminar NILESH RAUT DEPARTMENT OF CHEMISTRY UNIVERSITY OF KENTUCKY

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. System architecture for virtual cell

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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).

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

  14. Design 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.

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

  16. 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).

  17. 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.

  18. 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.

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

  20. Kinetic 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.

  21. Kinetic Studies of Ran Transport • Results of injection of FL-Ran: Nuclear accumulation of FL-Ran in BHK-21 cells after cytosolic injection.

  22. 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.

  23. 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)

  24. 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.

  25. 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.

  26. 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.

  27. References • Alicia E. Smith, Boris M. Slepchenko, James C. Schaff, Leslie M. Loew, Ian G. Macara Science295, 488-491 (2002). • Svante Wold Chemometrics and Intelligent Laboratory Systems 30, 109-115 (1995). • Stanislaw Gorski, Tom Misteli Journal of Cell Science118(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 Sciences23(4), 158-159 (2002).

  28. THANK YOU

More Related