Modelling gene or protein networks or pathways in disease states is a recent field of endeavour. Proteins usually function in interdependent networks. The goal of model development is to understand inter-relationships among the members of a protein network or family in a disease state. Precise models enable prediction of the biological responses to a perturbation, and detection of novel associations among members76
Model development is a mathematical exercise. Two types of mathematical approach are being implemented in static and dynamic biological systems. The standard approach in biology has been reductionist analysis — division of the system into component variables and ‘solving’ a differential equation for each with empirical data77
. Although biological systems are generally complex and nonlinear, much of our current biological knowledge has been derived from this type of deterministic, reduction-ist analysis78
. Furthermore, disease states are frequently associated with linear dynamics79
(or, more accurately, with the breakdown of multi-scale fractal complexity). For these reasons, reductionist methods are likely to remain useful for the foreseeable future for quantitative prediction of responses to perturbation of networks. Notable recent examples of progress are the development of dynamic models of yeast responses to hyperosmolar shock80
and treatment of chronic myeloid leukaemia with imatinib81
. The second, newer approach to proteomic modelling is finite-state modelling, which uses the changes in gene3,4
or protein expression76
that follow perturbation of a particular node to identify the topology of complex networks. This approach, although relatively recently introduced to systems biology76,82
, has proven useful in other nonlinear systems and enables detection of novel associations among elements76
or assessment of network robustness.
Current biological experimentation, particularly for multiplexed protein measurement, mandates that these modelling approaches must have the flexibility to use noisy, incomplete data. When modelling networks, pathways and disease states, the technological specifications should include the following: assays for many of the major elements in that network or pathway (typically 25–200 proteins); measurement of those within the biological dynamic range with reasonable precision (coefficients of variation of ≤15%); and the ability to measure many samples (≥500) without confounding run-to-run imprecision. The technologies of choice for this application are immunofluorescent bead or planar arrays. Experiments typically involve following the time course or dose response in multiple individuals or following multiple treatments or interventions. As with ELISAs, fluorescent intensities are converted back to mass units using standard curves, and several replicate observations are made for each protein and sample to calculate standard errors of values73
One field of research that is being dramatically affected by this use of protein arrays is the characterization of humoral immunity in common allergies34,47,83-87
, autoimmune disorders31,32,49,88-92
and infectious diseases33,38,95-99
. Comprehensive characterization of changes in humoral immunity is starting to become possible through the development of protein chips containing hundreds or thousands of potential allergens, autoantigens or epitopes arrayed as microscopic spots on planar glass slides (). Arrays are incubated with serum, then washed, ‘developed’ by incubation with a fluorescently tagged anti-immunoglobulin (for example, anti-IgE for allergy detection) or anti-immunoglobulin subtype, and quantified in a fluorescence scanner. Antigen arrays are simple to develop and calibrate. Hundreds of antibody specificities can be screened simultaneously by class, subclass and titre.
Clinical uses of such protein arrays include disease classification on the basis of reactive or autoreactive epitopes, monitoring disease progression by measuring the change in epitope dynamics, immunoglobulin class or sub-class switching, and monitoring disease activity by analysis of the antibody titre. In limited comparisons with traditional ‘monoplex’ in vitro
diagnostics, multiplexed antigen arrays have shown similar or improved diagnostic sensitivity and specificity83,85
. Although studies so far have been limited to descriptions of states or differences between states, antigen arrays have the power to enable development of quantitative mathematical models of the dynamics of humoral immune systems in health and disease100,101
. Furthermore, the development of high-throughput peptide expression systems has the potential to greatly expand the repertoire of such studies by allowing a broad array of epitopes to be surveyed simultaneously for humoral immune responses.
A related, highly innovative development is the construction of microarrays of substrates for protein activities or protein modifications28,36,50,51,102-110
. Rather than measuring multiplexed protein abundance changes between states or in disease processes, such arrays measure changes in both specific protein activity and in global patterns of activity.
As noted above, a second area in which protein arrays are enabling new understanding of networks, pathways and disease states is in cytokine biology43,45,111-114
. Multiplexed, immunoassay arrays allow quantitative, comprehensive measurement of cytokine families and networks. Cytokine arrays require less multiplexing than antigen arrays, but are technically more demanding, having the following requirements: they must be sandwich immunoassays (with antibodies capable of detecting two epitopes); pg ml−1
sensitivity; and a dynamic range of 3 logs (without detectable cross reactivity at the lower quantitation limits). Formats available for multiplexed cytokine measurement include bead arrays, planar glass arrays and nitrocellulose membrane arrays. Such arrays enable the multiplexed measurement of time course and dose response of cytokine production by specific leukocyte lineages following treatment with various lectins or ionophores or in disease states. These studies are essential for the validation of findings from gene expression or shotgun mass spectrometry studies, and are crucial for the development of mathematical models of cytokine networks. Such models permit prediction of responses to various stimuli and downstream changes in bioactivities. One such study examined cytokine network responses of dendritic cells to in vitro
treatment with defensins114
. RNAse, which was included in the study as a control treatment, was, unexpectedly, found to elicit dose-dependent secretion of pro-inflammatory cytokines. Subsequent studies confirmed that RNAse A superfamily proteins activate dendritic cells in a manner similar to that of tumour-necrosis factor — a hitherto unknown property.
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