Generating Boolean networks with a prescribed attractor structure

MOTIVATION: Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). RESULTS: The long-run behavior of a BN is characterized by its attractors. The rest of the state transition diagram is partitioned into level sets, the j-th level set being composed of all states that transition to one of the attractor states in exactly j transitions. We present two algorithms for the attractor inverse problem. The attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Algorithm complexity and performance are analyzed. The algorithmic solutions have immediate application. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion can be used to test a design algorithm: randomly select a set of states to be used as data states; generate a BN possessing the selected states as attractors, perhaps with some added requirements such as constraints on the number of predictors and the level structure; apply the design algorithm; and check the concordance between the attractor states of the designed network and the data states. AVAILABILITY: The software and supplementary material is available at http://gsp.tamu.edu/Publications/BNs/bn.htm     

Superior feature-set ranking for small samples using bolstered error estimation

MOTIVATION: Ranking feature sets is a key issue for classification, for instance, phenotype classification based on gene expression. Since ranking is often based on error estimation, and error estimators suffer to differing degrees of imprecision in small-sample settings, it is important to choose a computationally feasible error estimator that yields good feature-set ranking. RESULTS: This paper examines the feature-ranking performance of several kinds of error estimators: resubstitution, cross-validation, bootstrap and bolstered error estimation. It does so for three classification rules: linear discriminant analysis, three-nearest-neighbor classification and classification trees. Two measures of performance are considered. One counts the number of the truly best feature sets appearing among the best feature sets discovered by the error estimator and the other computes the mean absolute error between the top ranks of the truly best feature sets and their ranks as given by the error estimator. Our results indicate that bolstering is superior to bootstrap, and bootstrap is better than cross-validation, for discovering top-performing feature sets for classification when using small samples. A key issue is that bolstered error estimation is tens of times faster than bootstrap, and faster than cross-validation, and is therefore feasible for feature-set ranking when the number of feature sets is extremely large.     

Decrypting the biochemical function of an essential gene from Streptococcus pneumoniae using ThermoFluor technology

The protein product of an essential gene of unknown function from Streptococcus pneumoniae was expressed and purified for screening in the ThermoFluor affinity screening assay. This assay can detect ligand binding to proteins of unknown function. The recombinant protein was found to be in a dimeric, native-like folded state and to unfold cooperatively. ThermoFluor was used to screen the protein against a library of 3000 compounds that were specifically selected to provide information about possible biological functions. The results of this screen identified pyridoxal phosphate and pyridoxamine phosphate as equilibrium binding ligands (K(d) approximately 50 pM, K(d) approximately 2.5 microM, respectively), consistent with an enzymatic cofactor function. Several nucleotides and nucleotide sugars were also identified as ligands of this protein. Sequence comparison with two enzymes of known structure but relatively low overall sequence homology established that several key residues directly involved in pyridoxal phosphate binding were strictly conserved. Screening a collection of generic drugs and natural products identified the antifungal compound canescin A as an irreversible covalent modifier of the enzyme. Our investigation of this protein indicates that its probable biological role is that of a nucleoside diphospho-keto-sugar aminotransferase, although the preferred keto-sugar substrate remains unknown. These experiments demonstrate the utility of a generic affinity-based ligand binding technology in decrypting possible biological functions of a protein, an approach that is both independent of and complementary to existing genomic and proteomic technologies.     

Electron cryomicroscopy and bioinformatics suggest protein fold models for rice dwarf virus

The three-dimensional structure of rice dwarf virus was determined to 6.8 A resolution by single particle electron cryomicroscopy. By integrating the structural analysis with bioinformatics, the folds of the proteins in the double-shelled capsid were derived. In the outer shell protein, the uniquely orientated upper and lower domains are composed of similar secondary structure elements but have different relative orientations from that of bluetongue virus in the same Reoviridae family. Differences in both sequence and structure between these proteins may be important in defining virus-host interactions. The inner shell protein adopts a conformation similar to other members of Reoviridae, suggesting a common ancestor that has evolved to infect hosts ranging from plants to animals. Symmetry mismatch between the two shells results in nonequivalent, yet specific, interactions that contribute to the stability of this large macromolecular machine.     

Growing genetic regulatory networks from seed genes

MOTIVATION: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. RESULTS: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. AVAILABILITY: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm     

The effect of febrile temperatures on biologic actions of interferons: abrogation of suppression of delayed-type hypersensitivity and antibody production

Interferons (IFN) have a complex immunoregulatory effect on all cells of the immune system. In most cases in which IFN had an enhancing effect, the suggested mechanism was inhibition of the generation or activity of suppressor cells. In the present study, we examined the effect of IFN on suppression of the delayed-type hypersensitivity (DTH) response. Suppression was induced with a low antigen dose of sheep erythrocytes (SRBC), and IFN was found to abrogate both the suppressed state and the transferability of this state. Cyclophosphamide had the same effect. However, the in vitro generation of suppressor cells was not altered by the addition of IFN to the culture medium at a normal temperature (37 degrees C). To reconcile the disparity between the successful anti-suppressive action of IFN in vivo compared with its failure in vitro, we considered the possibility that the pyrogenic action of IFN in vivo might create the optimal thermal environment for its anti-suppressive action. Indeed, when IFN was then tested in vitro at a febrile temperature (39.3 degrees C), it completely blocked the generation of suppressor cells. On the other hand, once suppressor cells were generated at 37 degrees C, IFN had no effect on their ability to suppress a fresh culture either at 37 degrees C or at 39.3 degrees C. IFN also had no effect on the generation of helper cells at either temperature, but help was greatly enhanced by high temperature alone. In vivo, we found our IFN preparation to be pyrogenic and observed that an anti-pyretic drug given before and during antigen stimulation abrogated the anti-suppressive effect of IFN. We suggest, therefore, that the febrile state induced by IFN promotes its action on suppressor cells.