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Biomedical subjects

William S Hlavacek

Publications and source records attributed to William S Hlavacek.

18 recordsLinked to original sources
PubMed2006

Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites.

MOTIVATION: Our knowledge of metabolism is far from complete, and the gaps in our knowledge are being revealed by metabolomic detection of small-molecules not previously known to exist in cells. An important challenge is to determine the reactions in which these compounds participate, which can lead to the identification of gene products responsible for novel metabolic pathways. To address this challenge, we investigate how machine learning can be used to predict potential substrates and products of oxidoreductase-catalyzed reactions. RESULTS: We examined 1956 oxidation/reduction reactions in the KEGG database. The vast majority of these reactions (1626) can be divided into 12 subclasses, each of which is marked by a particular type of functional group transformation. For a given transformation, the local structures of reaction centers in substrates and products can be characterized by patterns. These patterns are not unique to reactants but are widely distributed among KEGG metabolites. To distinguish reactants from non-reactants, we trained classifiers (linear-kernel Support Vector Machines) using negative and positive examples. The input to a classifier is a set of atomic features that can be determined from the 2D chemical structure of a compound. Depending on the subclass of reaction, the accuracy of prediction for positives (negatives) is 64 to 93% (44 to 92%) when asking if a compound is a substrate and 71 to 98% (50 to 92%) when asking if a compound is a product. Sensitivity analysis reveals that this performance is robust to variations of the training data. Our results suggest that metabolic connectivity can be predicted with reasonable accuracy from the presence or absence of local structural motifs in compounds and their readily calculated atomic features. AVAILABILITY: Classifiers reported here can be used freely for noncommercial purposes via a Java program available upon request.

Algorithms↗
PubMed2006

Rules for modeling signal-transduction systems.

Formalized rules for protein-protein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactions. A set of rules can be as easy to read as a diagrammatic interaction map, but unlike most such maps, rules have precise interpretations. Rules can be processed to automatically generate a mathematical or computational model for a system, which enables explanatory and predictive insights into the system's behavior. Rules are independent units of a model specification that facilitate model revision. Instead of changing a large number of equations or lines of code, as may be required in the case of a conventional mathematical model, a protein interaction can be introduced or modified simply by adding or changing a single rule that represents the interaction of interest. Rules can be defined and visualized by using graphs, so no specialized training in mathematics or computer science is necessary to create models or to take advantage of the representational precision of rules. Rules can be encoded in a machine-readable format to enable electronic storage and exchange of models, as well as basic knowledge about protein-protein interactions. Here, we review the motivation for rule-based modeling; applications of the approach; and issues that arise in model specification, simulation, and testing. We also discuss rule visualization and exchange and the software available for rule-based modeling.

Computer Simulation↗
PubMed2005

A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity.

We consider a model of early events in signaling by the epidermal growth factor (EGF) receptor (EGFR). The model includes EGF, EGFR, the adapter proteins Grb2 and Shc, and the guanine nucleotide exchange factor Sos, which is activated through EGF-induced formation of EGFR-Grb2-Sos and EGFR-Shc-Grb2-Sos assemblies at the plasma membrane. The protein interactions involved in signaling can potentially generate a diversity of protein complexes and phosphoforms; however, this diversity has been largely ignored in models of EGFR signaling. Here, we develop a model that accounts more fully for potential molecular diversity by specifying rules for protein interactions and then using these rules to generate a reaction network that includes all chemical species and reactions implied by the protein interactions. We obtain a model that predicts the dynamics of 356 molecular species, which are connected through 3749 unidirectional reactions. This network model is compared with a previously developed model that includes only 18 chemical species but incorporates the same scope of protein interactions. The predictions of this model are reproduced by the network model, which also yields new predictions. For example, the network model predicts distinct temporal patterns of autophosphorylation for different tyrosine residues of EGFR. A comparison of the two models suggests experiments that could lead to mechanistic insights about competition among adapter proteins for EGFR binding sites and the role of EGFR monomers in signal transduction.

Animals↗
PubMed2005

Multiple functions of a feed-forward-loop gene circuit.

The feed-forward-loop (FFL), a network motif in genetic regulatory networks, involves two transcription factors (TFs): one regulates the expression of the second, and both TFs regulate the expression of an effector gene. Analysis of FFL design principles has been initiated, but the functional significance of the FFL is still unclear. In theoretical studies so far, the TFs are assumed to interact with different signals, which is common. However, we have found examples of FFLs in Escherichia coli in which both TFs interact with the same signal. These examples belong to the type 2 incoherent class of FFLs, in which each TF acts exclusively as a repressor of transcription. Here, we analyze mathematical models of this class of circuits, examining a comprehensive array of subclasses that differ in the way a signal modulates the activities of the TFs. Through parameter variation, we characterize statistically how input/output (I/O) behavior and temporal responsiveness are predicted to depend on the wiring of signal interactions in a circuit. We find that circuits can exhibit any of 13 qualitatively distinct steady-state I/O patterns, including inducible and repressible patterns. Some subclasses exhibit as many as six patterns. Transient pulses are also possible, and the response of a circuit to a signal may be either faster or slower than that of a gene circuit in which there is only one TF. Our results provide a catalog of functions for a class of FFL circuits, whose subclasses have different breadths of possible behaviors and different typical behaviors.

Escherichia coli↗
PubMed2004

Interaction of a monoclonal IgE-specific antibody with cell-surface IgE-Fc epsilon RI: characterization of equilibrium binding and secretory response.

Aggregation of FcepsilonRI, the high-affinity cell-surface receptor for IgE antibody, is required for degranulation of basophils and mast cells, but not all receptor aggregates elicit this cellular response. The stereochemical constraints on clusters of FcepsilonRI that are able to signal cellular responses, such as degranulation, have yet to be fully defined. To improve our understanding of the properties of FcepsilonRI aggregates that influence receptor signaling, we have studied the interaction of 23G3, a rat IgG(1)(kappa) IgE-specific monoclonal antibody, with IgE-FcepsilonRI complexes on rat mucosal-type mast cells (RBL-2H3 line). We find that 23G3 is a potent secretagogue. This property and the structural features of 23G3 (two symmetrically arrayed IgE-specific binding sites) make 23G3 a potentially valuable reagent for investigating the relationship between FcepsilonRI clustering and FcepsilonRI-mediated signaling events. To develop a mathematical model of 23G3-induced aggregation of FcepsilonRI, we used fluorimetry and flow cytometry to quantitatively monitor equilibrium binding of FITC-labeled 23G3 intact Ab and its Fab' fragment to cell-surface IgE. The results indicate that IgE bound to FcepsilonRI expresses two epitopes for 23G3 binding; that 23G3 binds IgE resident on the cell surface with negative cooperativity; and that 23G3 appears to induce mostly but not exclusively noncyclic dimeric aggregates of FcepsilonRI. There is no simple relationship between receptor aggregation at equilibrium and the degranulation response. Further studies are needed to establish how 23G3-induced aggregation of IgE-FcepsilonRI correlates with cellular responses.

Animals↗
PubMed2004

BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains.

BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction.

Algorithms↗
PubMed2003

The complexity of complexes in signal transduction.

Many activities of cells are controlled by cell-surface receptors, which in response to ligands, trigger intracellular signaling reactions that elicit cellular responses. A hallmark of these signaling reactions is the reversible nucleation of multicomponent complexes, which typically begin to assemble when ligand-receptor binding allows an enzyme, often a kinase, to create docking sites for signaling molecules through chemical modifications, such as tyrosine phosphorylation. One function of such docking sites is the co-localization of enzymes with their substrates, which can enhance both enzyme activity and specificity. The directed assembly of complexes can also influence the sensitivity of cellular responses to ligand-receptor binding kinetics and determine whether a cellular response is up- or downregulated in response to a ligand stimulus. The full functional implications of ligand-stimulated complex formation are difficult to discern intuitively. Complex formation is governed by conditional interactions among multivalent signaling molecules and influenced by quantitative properties of both the components in a system and the system itself. Even a simple list of the complexes that can potentially form in response to a ligand stimulus is problematic because of the number of ways signaling molecules can be modified and combined. Here, we review the role of multicomponent complexes in signal transduction and advocate the use of mathematical models that incorporate detail at the level of molecular domains to study this important aspect of cellular signaling.

ErbB Receptors↗
PubMed2003

Design principles for regulator gene expression in a repressible gene circuit.

We consider the design of a type of repressible gene circuit that is common in bacteria. In this type of circuit, a regulator protein acts to coordinately repress the expression of effector genes when a signal molecule with which it interacts is present. The regulator protein can also independently influence the expression of its own gene, such that regulator gene expression is repressible (like effector genes), constitutive, or inducible. Thus, a signal-directed change in the activity of the regulator protein can result in one of three patterns of coupled regulator and effector gene expression: direct coupling, in which regulator and effector gene expression change in the same direction; uncoupling, in which regulator gene expression remains constant while effector gene expression changes; or inverse coupling, in which regulator and effector gene expression change in opposite directions. We have investigated the functional consequences of each form of coupling using a mathematical model to compare alternative circuits on the basis of engineering-inspired criteria for functional effectiveness. The results depend on whether the regulator protein acts as a repressor or activator of transcription at the promoters of effector genes. In the case of repressor control of effector gene expression, direct coupling is optimal among the three forms of coupling, whereas in the case of activator control, inverse coupling is optimal. Results also depend on the sensitivity of effector gene expression to changes in the level of a signal molecule; the optimal form of coupling can be physically realized only for circuits with sufficiently small sensitivity. These theoretical results provide a rationale for autoregulation of regulator genes in repressible gene circuits and lead to testable predictions, which we have compared with data available in the literature and electronic databases.

Gene Expression Regulation, Bacterial↗
PubMed2003

Investigation of early events in Fc epsilon RI-mediated signaling using a detailed mathematical model.

Aggregation of Fc epsilon RI on mast cells and basophils leads to autophosphorylation and increased activity of the cytosolic protein tyrosine kinase Syk. We investigated the roles of the Src kinase Lyn, the immunoreceptor tyrosine-based activation motifs (ITAMs) on the beta and gamma subunits of Fc epsilon RI, and Syk itself in the activation of Syk. Our approach was to build a detailed mathematical model of reactions involving Fc epsilon RI, Lyn, Syk, and a bivalent ligand that aggregates Fc(epsilon)RI. We applied the model to experiments in which covalently cross-linked IgE dimers stimulate rat basophilic leukemia cells. The model makes it possible to test the consistency of mechanistic assumptions with data that alone provide limited mechanistic insight. For example, the model helps sort out mechanisms that jointly control dephosphorylation of receptor subunits. In addition, interpreted in the context of the model, experimentally observed differences between the beta- and gamma-chains with respect to levels of phosphorylation and rates of dephosphorylation indicate that most cellular Syk, but only a small fraction of Lyn, is available to interact with receptors. We also show that although the beta ITAM acts to amplify signaling in experimental systems where its role has been investigated, there are conditions under which the beta ITAM will act as an inhibitor.

Amino Acid Motifs↗
PubMed2002

Kinetic proofreading in receptor-mediated transduction of cellular signals: receptor aggregation, partially activated receptors, and cytosolic messengers.

Signaling by the T cell receptor (TCR), and the related immunoreceptor Fc epsilon RI, is sensitive to ligand-receptor binding kinetics. Differences in the rate at which a ligand dissociates from a receptor cause disproportionate differences in signaling events and cellular responses to ligand-receptor engagement. Analysis of a simple mathematical model, developed by McKeithan (1995, Proc. Natl. Acad. Sci. USA, 92, 5042-5046), has indicated that such sensitivity to binding kinetics is expected if a bound receptor must complete a cascade of modifications before generating a productive signal. However, recent experiments show that some cellular responses mediated by immunoreceptors escape from the control of kinetic proofreading, in the sense that these responses do not exhibit the expected sensitivity to the lifetime of a ligand-receptor bond. Here, we use an extended form of the McKeithan model to investigate possible explanations for such exceptions to the kinetic proofreading rule. We examine cellular responses triggered by cytosolic messengers, which are activated by modified receptors, and responses triggered by receptors in intermediate states of modification, i.e., receptors that have not progressed through the full series of potential modifications. Receptor aggregation is also considered. We find that the expected relationship between ligand-receptor binding kinetics and cellular responses can change significantly when signal transduction depends on a messenger or a partially modified receptor. In particular, cellular responses triggered by a messenger, such as a transcription factor that translocates from the membrane to the nucleus after receptor-mediated activation, can be sensitive or insensitive to a change in the lifetime of a ligand-receptor bond, depending on the parameters that govern the activation and decay of a messenger.

Animals↗
PubMed2002

Retention of antigen on follicular dendritic cells and B lymphocytes through complement-mediated multivalent ligand-receptor interactions: theory and application to HIV treatment.

In HIV-infected patients, large quantities of HIV are associated with follicular dendritic cells (FDCs) in lymphoid tissue. During antiretroviral therapy, most of this virus disappears after six months of treatment, suggesting that FDC-associated virus has little influence on the eventual outcome of long-term therapy. However, a recent theoretical study using a stochastic model for the interaction of HIV with FDCs indicated that some virus may be retained on FDCs for years, where it can potentially reignite infection if treatment is interrupted. In that study, an approximate expression was used to estimate the time an individual virion remains on FDCs during therapy. Here, we determine the conditions under which this approximation is valid, and we develop expressions for the time a virion spends in any bound state and for the effect of rebinding on retention. We find that rebinding, which is influenced by diffusion, may play a major role in retention of HIV on FDCs. We also consider the possibility that HIV is retained on B cells during therapy, which like FDCs also interact with HIV. We find that virus associated with B cells is unlikely to persist during therapy.

Anti-HIV Agents↗
PubMed2002

Modeling the early signaling events mediated by FcepsilonRI.

We present a detailed mathematical model of the phosphorylation and dephosphorylation events that occur upon ligand-induced receptor aggregation, for a transfectant expressing FcepsilonRI, Lyn, Syk and endogenous phosphatases that dephosphorylate exposed phosphotyrosines on FcepsilonRI and Syk. Through model simulations we show how changing the ligand concentration, and consequently the concentration of receptor aggregates, can change the nature of a cellular response as well as its amplitude. We illustrate the value of the model in analyzing experimental data by using it to show that the intrinsic rate of dephosphorylation of the FcepsilonRI gamma immunoreceptor tyrosine-based activation motif (ITAM) in rat basophilic leukemia (RBL) cells is much faster than the observed rate, provided that all of the cytosolic Syk is available to receptors.

Animals↗
PubMed2002

A quantitative approach for studying IgE-FcepsilonRI aggregation.

Aggregation of cell surface receptors is a ubiquitous means of initiating signal transduction in many cellular systems. In this manuscript, we describe a combined theoretical and experimental approach based on multiparameter flow cytometry for measuring the time course of ligand induced aggregation of IgE-FcepsilonRI on RBL cells. By fluorescently labeling both the ligand and surface IgE (sIgE), we have developed an assay that permits us to simultaneously measure both occupancy of sIgE combining sites and association of antigen with the cell surface. This allows for a direct calculation of the degree of receptor aggregation present on the cell. By employing new mixing technologies developed for flow cytometry, we are able to look at aggregation in the sub second time domain. To extend our work, we have synthesized a new set of chemically well defined ligands (of valences 1-3) to use as probes in our studies. We show that the magnitude of the cellular response is dramatically increased as the valence of our ligand is raised from two to three.

Animals↗