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  • Review Article
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Kinetic modelling approaches to in vivo imaging

Nature Reviews Molecular Cell Biology volume 2, pages 898–907 (2001)Cite this article

Key Points

  • In vivo imaging is routinely used to probe the dynamic behaviour of proteins and cellular compartments. These methods generate large, kinetically complex data sets, which often cannot be intuitively interpreted.

  • High-density data sets contain information that can be used to probe complex systems. Cellular processes that make up complex systems can be displayed as diagrams.

  • Translating cell-biological diagrams into kinetic models is based on standard principles of chemical kinetics. Quantitative models allow simulation of the diagram's response to a given experimental protocol and comparison of its predictions to experimental data.

  • Comparison of model predictions and experimental data permits quantitative hypothesis testing for even very complex hypotheses. An abundance of software tools and databases is available to support this work.

  • The numerical analysis techniques, optimization and parameter estimation, can be used to give each hypothesis its best chance to simultaneously account for all the available experimental data.

  • Alternative hypotheses can be quickly tested. Parameter values can be extracted from experimental data to quantify the three classes of biological processes: transformation, translocation, and binding. Biophysical properties such as diffusion coefficients, rate constants, steady-state distribution ratios, fluxes and residence times are among the most useful quantities that can be obtained.

  • Data collected using GFP-labelled proteins in living cells are particularly well suited to kinetic analysis. This is especially true in the context of the various photobleaching protocols, such as FRAP and FLIP, and will become even more profound as new developments in GFP technology become available.

  • As cellular process diagrams become more and more complex, it becomes essential to build databases for models as well as for experimental data. The new fields of integrative bioinformatics and pathway databases lie at the intersection of kinetic modelling and database technology.

  • The burgeoning fields of biological modelling, computational cell biology and systems biology will need a standard language for exchange of models among software tools. Nascent standards have been proposed in the form of the XML-based tools, CellML and SBML.

Abstract

The ability to visualize protein dynamics and biological processes by in vivo microscopy is revolutionizing many areas of biology. These methods generate large, kinetically complex data sets, which often cannot be intuitively interpreted. The combination of dynamic imaging and computational modelling is emerging as a powerful tool for the quantitation of biophysical properties of molecules and processes. The new discipline of computational cell biology will be essential in uncovering the pathways, mechanisms and controls of biological processes and systems as they occur in vivo.

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Figure 1: Kinetic microscopy methods.
Figure 2: FRAP for the measurement of protein binding in vivo.
Figure 3: Role of kinetic models in quantitative hypothesis testing.

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Author information

Authors and Affiliations

  1. BioInformatics Services, Rockville, 20854, Maryland, USA

    Robert D. Phair

  2. National Cancer Institute, National Institutes of Health, Bethesda, 20892, Maryland, USA

    Tom Misteli

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DATABASES

Swiss-Prot:

GFP

FURTHER READING

Simulation and Modelling Software: SAAM Institute

Berkeley Madonna

Gepasi

StochSim

MATLAB

Virtual Cell

WinSAAM

XPPAut

E-Cell

Online textbook

Integrative Bioinformatics

Databases

PathDB

KEGG

BIND

WIT

EMP

EcoCyc

Klotho

Standards

CellML

SBML

Glossary

CONFOCAL MICROSCOPY

A microscopy method used to obtain a thin optical section through a specimen.

MULTI-PHOTON MICROSCOPY

A microscopy method that uses the simultaneous absorbance of several low-energy electrons to generate an optical section through a specimen.

FLUOROPHORE

A small molecule or a part of a larger molecule that can be excited by light to emit fluorescence.

STEADY STATE

An open system, the content of which is held constant by a continuous input. Here, the output equals the input.

DIFFUSION COEFFICIENT

A measure to characterize the speed with which a particular molecule moves in a particular medium when driven by random thermal agitation.

PARAMETER

The numerical constant that determines the absolute speed of a process. A first-order process is characterized by a single parameter, the rate constant. A process that is governed by a Michaelis–Menten equation is characterized by two parameters, Vmax and Km.

STOCHASTIC SYSTEMS

A dynamic system, the processes of which are characterized by a probability distribution. The stochastic system theory is particularly important when the abundance of molecules in a particular state falls below the deterministic limit, about 100 molecules per cell.

FRACTALS

These are objects that provide more and more features as the resolution of the observation increases. These finer features show statistical self-similarity as seen in biological branching patterns, ion-channel currents and heart rate.

CHAOS

A deterministic system (for example, some systems of nonlinear differential equations), the output of which seems random, but is not. Such systems show a surprising sensitivity to initial conditions.

ANALYSIS OF VARIANCE

A statistical procedure for testing for differences among the means of several populations. It partitions the total sample variance among several specific sources to carry out the test on means.

SUM OF EXPONENTIALS

An algebraic expression that is made up of exponentials. In a first-order system, the time-course solution for every state can be precisely mimicked by the sum of exponentials that correspond to the number of states in the system.

POLYNOMIAL

Algebraic expressions that are made up of more than one term — for example, mx + b.

SUM OF GAUSSIANS

An approximation by weighted sums of normal distributions, or Gaussians, each characterized by two parameters — a mean and a variance — to describe a data set.

STATE

The generic name used here to identify those variables that change with time and for which differential equations are written.

ALLOSTERIC REGULATION

A modification of a process by a molecule that binds to an enzyme or a transporter or another protein at a site other than its active, or catalytic, site.

RATE LAWS

Algebraic expressions for the flux through a given pathway.

PROCESS

The generic name for events that bring about changes in one or more states.

EXTENSIBLE MARKUP LANGUAGE

A method for putting structured data in a text file so that applications receive not only unambiguous data but also unambiguous context. XML documents are not meant to be read, except by software.

INTEGRATIVE BIOINFORMATICS

The intersection of kinetic modelling and database technology, a combination that becomes essential as cell biologists move to analyse larger and more complex molecular genetic control systems.

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Phair, R., Misteli, T. Kinetic modelling approaches to in vivo imaging. Nat Rev Mol Cell Biol 2, 898–907 (2001). https://doi.org/10.1038/35103000

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