Building with a scaffold: emerging strategies for high- to low-level cellular modeling

doi:10.1016/S0167-7799(03)00115-X

Trends in Biotechnology

Volume 21, Issue 6, June 2003, Pages 255-262

https://doi.org/10.1016/S0167-7799(03)00115-X Get rights and content

Abstract

Computational cellular models are becoming crucial for the analysis of complex biological systems. An important new paradigm for cellular modeling involves building a comprehensive scaffold of molecular interactions and then mining this scaffold to reveal a hierarchy of signaling, regulatory and metabolic pathways. We review the important trends that make this approach feasible and describe how they are spurring the development of models at multiple levels of abstraction. Pathway maps can be extracted from the scaffold using ‘high-level’ computational models, which identify the key components, interactions and influences required for more detailed ‘low-level’ models. Large-scale experimental measurements validate high-level models, whereas targeted experimental manipulations and measurements test low-level models.

Section snippets

Descending from the scaffold to high- and low-level pathway models

A good way to visualize this pathway mapping procedure is as a descent through a series of models at increasing levels of detail and decreasing levels of abstraction (Box 1). At the highest level of abstraction, the goal is to analyse the complete interaction scaffold to extract the basic components and connectivity of the pathway of interest. We term these connectivity-driven models ‘High Level’ or ‘Level One’ (L1) pathway models. At a more detailed level, we might wish to supplement the

Systematic experiments for characterizing networks and states

Signaling and regulatory pathways consist of some number of components, such as genes, proteins and small molecules, wired together in a complex network of intermolecular interactions. Recent technological developments are enabling us to define and interrogate these pathways more directly and systematically than ever before, using two complementary approaches. First, it is now possible systematically to measure the molecular interactions themselves, by screening for protein–protein, protein–DNA

Extracting L1 models from the interaction scaffold

To arrive at an L1 model of a pathway or cellular process of interest, data on molecular interactions and states are integrated in a multi-tiered pathway mapping strategy (Fig. 1). First, the global molecular interaction scaffold is constructed from systematic measurements of protein–protein interactions, protein–DNA interactions and/or metabolic reactions. In the case of budding yeast, a maximal set might include 14 941 protein–protein interactions (catalogued in the DIP™ database), 5631

From L1 to L2 models

At the other end of the level spectrum (L2 models), one wishes to build models with predictive capability for cell behavior that are physicochemical in nature and based on low level molecular detail. For instance, consider an altered DNA sequence leading to a modified protein structure, which in turn yields an altered rate constant in a cell signaling process governing cell proliferation, differentiation or migration. Can we predict how that sequence change would propagate to a change in cell

Coverage versus leverage: the two phases of experimental design

The systematic pathway mapping approach has important implications for experimental design. Just as pathway modeling has multiple levels (L1 and L2), so experiments to specify and validate these models are performed in distinct modes or phases (A and B; see Fig. 3). The goal of the initial phase (phase A) is to perform experiments that are comprehensive in nature, to stimulate the pathway of interest broadly and to perturb all of its components. We might consider all perturbations of a certain

Perspectives

Where are pathway modeling efforts headed in the future? It is revealing that, outside biotechnology and the pharmaceutical industry, nearly every sector of manufacturing depends on computer simulation and modeling for product development. Circuit manufacturers rely on computer aided design (CAD) tools to model the wiring of transistors and other circuit components as well as their two- and three-dimensional layouts on the silicon wafer. Likewise, automotive engineers can estimate how many

Acknowledgements

We are indebted to J. Lauffenburger, J. Doyle and O. Ozier for inspiring discussions and for very helpful comments on the manuscript. T.I. was generously supported by a research fellowship from Pfizer; D.L. was supported by grants from DARPA and NIGMS.

References (47)

S.F. Altschul
Basic local alignment search tool
J. Mol. Biol.
(1990)
L. Guarente
Synthetic enhancement in gene interaction: a genetic tool come of age
Trends Genet.
(1993)
I. Xenarios et al.
Protein interaction databases
Curr. Opin. Biotechnol.
(2001)
C.M. Deane
Protein interactions: two methods for assessment of the reliability of high throughput observations
Mol. Cell Proteomics
(2002)
J.L. Griffin
Choline containing metabolites during cell transfection: an insight into magnetic resonance spectroscopy detectable changes
FEBS Lett.
(2001)
S. Huang et al.
Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks
Exp. Cell Res.
(2000)
P.A. DiMilla
Mathematical model for the effects of adhesion and mechanics on cell migration speed
Biophys. J.
(1991)
P.D. Karp
Pathway databases: a case study in computational symbolic theories
Science
(2001)
M. Hucka
The ERATO systems biology workbench: enabling interaction and exchange between software tools for computational biology
Pac. Symp. Biocomput.
(2002)
S. Fields et al.
A novel genetic system to detect protein–protein interactions
Nature
(1989)

P. Uetz

A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

Nature

(2000)

A.C. Gavin

Functional organization of the yeast proteome by systematic analysis of protein complexes

Nature

(2002)

Y. Ho

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry

Nature

(2002)

J.C. Rain

The protein–protein interaction map of Helicobacter pylori

Nature

(2001)

A.J. Walhout

Protein interaction mapping in C. elegans using proteins involved in vulval development

Science

(2000)

G.D. Bader

BIND – the biomolecular interaction network database

Nucleic Acids Res.

(2001)

T.I. Lee

Transcriptional regulatory networks in Saccharomyces cerevisiae

Science

(2002)

G. MacBeath et al.

Printing proteins as microarrays for high-throughput function determination

Science

(2000)

H. Zhu

Global analysis of protein activities using proteome chips

Science

(2001)

B.B. Haab

Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions

Genome Biol.

(2001)

J.L. DeRisi

Exploring the metabolic and genetic control of gene expression on a genomic scale

Science

(1997)

S.P. Gygi

Quantitative analysis of complex protein mixtures using isotope-coded affinity tags

Nat. Biotechnol.

(1999)

H. Zhou

A systematic approach to the analysis of protein phosphorylation

Nat. Biotechnol.

(2001)

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Trends in Biotechnology

Building with a scaffold: emerging strategies for high- to low-level cellular modeling

Abstract

Section snippets

Descending from the scaffold to high- and low-level pathway models

Systematic experiments for characterizing networks and states

Extracting L1 models from the interaction scaffold

From L1 to L2 models

Coverage versus leverage: the two phases of experimental design

Perspectives

Acknowledgements

J. Mol. Biol.

Trends Genet.

Curr. Opin. Biotechnol.

Mol. Cell Proteomics

FEBS Lett.

Exp. Cell Res.

Biophys. J.

Pathway databases: a case study in computational symbolic theories

Science

The ERATO systems biology workbench: enabling interaction and exchange between software tools for computational biology

Pac. Symp. Biocomput.

A novel genetic system to detect protein–protein interactions

Nature

A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

Nature

Functional organization of the yeast proteome by systematic analysis of protein complexes

Nature

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry

Nature

The protein–protein interaction map of Helicobacter pylori

Nature

Protein interaction mapping in C. elegans using proteins involved in vulval development

Science

BIND – the biomolecular interaction network database

Nucleic Acids Res.

Transcriptional regulatory networks in Saccharomyces cerevisiae

Science

Printing proteins as microarrays for high-throughput function determination

Science

Global analysis of protein activities using proteome chips

Science

Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions

Genome Biol.

Exploring the metabolic and genetic control of gene expression on a genomic scale

Science

Quantitative analysis of complex protein mixtures using isotope-coded affinity tags

Nat. Biotechnol.

A systematic approach to the analysis of protein phosphorylation

Nat. Biotechnol.