Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Self-organized defensive behavior in honeybees

We investigated the defensive behavior of honeybees under controlled experimental conditions. During an attack on two identical targets, the spatial distribution of stings varied as a function of the total number of stings, evincing the classic “pitchfork bifurcation” phenomenon of nonlinear dynamics. The experimental results support a model of defensive behavior based on a self-organizing mechanism. The model helps to explain several of the characteristic features of the honeybee defensive response: (i) the ability of the colony to localize and focus its attack, (ii) the strong variability between different hives in the intensity of attack, as well as (iii) the variability observed within the same hive, and (iv) the ability of the colony to amplify small differences between the targets.

How amoeboids self-organize into a fruiting body: Multicellular coordination in Dictyostelium discoideum

When individual amoebae of the cellular slime mold Dictyostelium discoideum are starving, they aggregate to form a multicellular migrating slug, which moves toward a region suitable for culmination. The culmination of the morphogenesis involves complex cell movements that transform a mound of cells into a globule of spores on a slender stalk. The movement has been likened to a “reverse fountain,” whereby prestalk cells in the upper part form a stalk that moves downwards and anchors to the substratum, while prespore cells in the lower part move upwards to form the spore head. So far, however, no satisfactory explanation has been produced for this process. Using a computer simulation that we developed, we now demonstrate that the processes that are essential during the earlier stages of the morphogenesis are in fact sufficient to produce the dynamics of the culmination stage. These processes are cAMP signaling, differential adhesion, cell differentiation, and production of extracellular matrix. Our model clarifies the processes that generate the observed cell movements. More specifically, we show that periodic upward movements, caused by chemotactic motion, are essential for successful culmination, because the pressure waves they induce squeeze the stalk downwards through the cell mass. The mechanisms revealed by our model have a number of self-organizing and self-correcting properties and can account for many previously unconnected and unexplained experimental observations.

Forming classes by stimulus frequency: Behavior and theory

Visual classification is the way we relate to different images in our environment as if they were the same, while relating differently to other collections of stimuli (e.g., human vs. animal faces). It is still not clear, however, how the brain forms such classes, especially when introduced with new or changing environments. To isolate a perception-based mechanism underlying class representation, we studied unsupervised classification of an incoming stream of simple images. Classification patterns were clearly affected by stimulus frequency distribution, although subjects were unaware of this distribution. There was a common bias to locate class centers near the most frequent stimuli and their boundaries near the least frequent stimuli. Responses were also faster for more frequent stimuli. Using a minimal, biologically based neural-network model, we demonstrate that a simple, self-organizing representation mechanism based on overlapping tuning curves and slow Hebbian learning suffices to ensure classification. Combined behavioral and theoretical results predict large tuning overlap, implicating posterior infero-temporal cortex as a possible site of classification.

Synaptic pattern formation during cellular recognition

Cell–cell recognition often requires the formation of a highly organized pattern of receptor proteins (a synapse) in the intercellular junction. Recent experiments [e.g., Monks, C. R. F., Freiberg, B. A., Kupfer, H., Sciaky, N. & Kupfer, A. (1998) Nature (London) 395, 82–86; Grakoui, A., Bromley, S. K., Sumen, C., Davis, M. M., Shaw, A. S., Allen, P. M. & Dustin, M. L. (1999) Science 285, 221–227; and Davis, D. M., Chiu, I., Fassett, M., Cohen, G. B., Mandelboim, O. & Strominger, J. L. (1999) Proc. Natl. Acad. Sci. USA 96, 15062–15067] vividly demonstrate a complex evolution of cell shape and spatial receptor–ligand patterns (several microns in size) in the intercellular junction during immunological synapse formation. The current view is that this dynamic rearrangement of proteins into organized supramolecular activation clusters is driven primarily by active cytoskeletal processes [e.g., Dustin, M. L. & Cooper, J. A. (2000) Nat. Immunol. 1, 23–29; and Wulfing, C. & Davis, M. M. (1998) Science 282, 2266–2269]. Here, aided by a quantitative analysis of the relevant physico-chemical processes, we demonstrate that the essential characteristics of synaptic patterns observed in living cells can result from spontaneous self-assembly processes. Active cellular interventions are superimposed on these self-organizing tendencies and may also serve to regulate the spontaneous processes. We find that the protein binding/dissociation characteristics, protein mobilities, and membrane constraints measured in the cellular environment are delicately balanced such that the length and time scales of spontaneously evolving patterns are in near-quantitative agreement with observations for synapse formation between T cells and supported membranes [Grakoui, A., Bromley, S. K., Sumen, C., Davis, M. M., Shaw, A. S., Allen, P. M. & Dustin, M. L. (1999) Science 285, 221–227]. The model we present provides a common way of analyzing immunological synapse formation in disparate systems (e.g., T cell/antigen-presenting cell junctions with different MHC-peptides, natural killer cells, etc.).

Antagonism of WT1 activity by protein self-association.

Germline loss-of-function mutations at the Wilms tumor (WT) suppressor locus WT1 are associated with a predisposition to WTs and mild genital system anomalies. In contrast, germ-line missense mutations within the WT1 gene encoding the DNA-binding domain often yield a more severe phenotype consisting of WT, sexual ambiguity, and renal nephropathy. In this report, we demonstrate that the products of mutant alleles that impair DNA recognition can antagonize WT1-mediated transcriptional repression. We demonstrate that WT1 can self-associate in vitro and in vivo and that the responsible domain maps to the amino-terminal region of the protein. Oligomers of full-length protein form less efficiently or produce less stable complexes than oligomers between truncated polypeptides and full-length protein. Our data suggest a molecular mechanism to explain how WT1 mutations may act in deregulating cellular proliferation and differentiation.

Aod1, the immunoregulatory locus controlling abrogation of tolerance in neonatal thymectomy-induced autoimmune ovarian dysgenesis, maps to mouse chromosome 16.

Mice thymectomized at three days of age (D3Tx) develop during adulthood a variety of organ-specific autoimmune diseases, including autoimmune ovarian dysgenesis (AOD). The phenotypic spectrum of AOD is characterized by the development of anti-ovarian autoantibodies, oophoritis, and atrophy. The D3Tx model of AOD is unique in that disease induction depends exclusively on perturbation of the normal developing immune system, is T-cell-mediated, and is strain specific. For example, D3Tx A/J mice are highly susceptible to AOD, whereas C57BL/6J mice are resistant. After D3Tx, self ovarian antigens, expressed at physiological levels, trigger an autoimmune response capable of eliciting disease. The D3Tx model provides, therefore, the opportunity to focus on the mechanisms of self-tolerance that are relevant to disease pathogenesis. Previous studies indicate that the principal mechanisms involved in AOD susceptibility are genetically controlled and govern developmental processes associated with the induction and maintenance of peripheral tolerance. We report here the mapping of the Aod1 locus to mouse chromosome 16 within a region encoding several loci of immunologic relevance, including scid, Igl1, VpreB, Igll, Igl1r, Mtv6 (Mls-3), Ly-7, Ifnar, and Ifgt.