Christopher J. Savoie

An Adaptive Agent Oriented Software Architecture

Method and agent network architecture for processing a subject message, where each agent has a view of its own domain of responsibility. An initiator agent which receives a user-input request and does not itself have a relevant interpretation policy, queries its downchain agents whether the queried agent considers such message to be in its domain of responsibility. Each queried agent recursively determines whether it has an interpretation policy of its own that applies to the request, and if not, further queries its own further downchain neighboring agents. The further agents eventually respond to such further queries, thereby allowing the first-queried agents to respond to the initiator agent. The recursive invocation of this procedure ultimately determines one or more paths through the network from the initiator agent to one more more leaf agents. The request is then transmitted down the path(s), with each agent along the way taking any local action thereon and passing the message on to the next agent in the path. In the event of a contradiction, the network is often able to resolve many of such contradictions according to predetermined algorithms. If it cannot resolve a contradiction automatically, it learns new interpretation policies necessary to interpret the subject message properly. Such learning preferably includes interaction with the user (but only to the extent necessary), and preferably localizes the learning close to the correct leaf agent in the network.

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

USE OF GENE NETWORKS FOR IDENTIFYING AND VALIDATING DRUG TARGETS

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

Understanding endothelial cell apoptosis: what can the transcriptome, glycome and proteome reveal?

Endothelial cell (EC) apoptosis may play an important role in blood vessel development, homeostasis and remodelling. In support of this concept, EC apoptosis has been detected within remodelling vessels in vivo, and inactivation of EC apoptosis regulators has caused dramatic vascular phenotypes. EC apoptosis has also been associated with cardiovascular pathologies. Therefore, understanding the regulation of EC apoptosis, with the goal of intervening in this process, has become a current research focus. The protein-based signalling and cleavage cascades that regulate EC apoptosis are well known. However, the possibility that programmed transcriptome and glycome changes contribute to EC apoptosis has only recently been explored. Traditional bioinformatic techniques have allowed simultaneous study of thousands of molecular signals during the process of EC apoptosis. However, to progress further, we now need to understand the complex cause and effect relationships among these signals. In this article, we will first review current knowledge about the function and regulation of EC apoptosis including the roles of the proteome transcriptome and glycome. Then, we assess the potential for further bioinformatic analysis to advance our understanding of EC apoptosis, including the limitations of current technologies and the potential of emerging technologies such as gene regulatory networks.

Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles

We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-alpha, which is a known target of fenofibrate.

Use of BONSAI decision trees for the identification of potential MHC Class I peptide epitope motifs.

Recognition of short peptides of 8 to 10 mer bound to MHC class I molecules by cytotoxic T lymphocytes forms the basis of cellular immunity. While the sequence motifs necessary for binding of intracellular peptides to MHC have been well studied, little is known about sequence motifs that may cause preferential affinity to the T cell receptor and/or preferential recognition and response by T cells. Here we demonstrate that computational learning systems can be useful to elucidate sequence motifs that affect T cell activation. Knowledge of T cell activation motifs could be useful for targeted vaccine design or immunotherapy. With the BONSAI computational learning algorithm, using a database of previously reported MHC bound peptides that had positive or negative T cell responses, we were able to identify sequence motif rules that explain 70% of positive T cell responses and 84% of negative T cell responses.

An HLA-binding-motif-aided peptide epitope library: A novel library design for the screening of HLA-DR4-restricted antigenic peptides recognized by CD4 + T cells

AbstractSusceptibility to a series of autoimmune diseases is strongly associated with particular HLA class II alleles. Identification of T cell clones and antigenic epitopes bound by HLA class II molecules involved in autoimmune diseases is critical to understanding the etiology of these HLA class II-associated diseases. However, establishment of T cell clones in autoimmune diseases is difficult because the antigenic peptides are unknown. Peptide library methods which include all possible peptide sequences offer a potentially powerful tool for the detection of cross-reactive antigenic peptides recognized by T cells. Here, we reduced the number of peptides per mixture by utilizing the known binding motifs of peptides for the HLA-DRB1*0405 molecule and evaluated the effectiveness of this library design. Each library mixture evoked a strong proliferative response in the unprimed peripheral blood lymphocytes (PBL) from HLA-DRB1*0405-positive donors but little or no response in the PBL from HLA-DRB1*0405-negative donors. The library also detected antigenic peptides that activated three antigen-specific T cell lines restricted by HLA-DRB1*0405, with different specificities. The motif-based approach thus presents a powerful method for monitoring T cells in large, heterogeneous T cell populations and is useful for the identification of the mimic peptide epitopes of T cell lines and clones.

Analysis of Gene Networks for Drug Target Discovery and Validation

Understanding responses of the cellular system for a dosing molecule is one of the most important problems in pharmacogenomics. In this chapter, we describe computational methods for identifying and validating drug target genes based on the gene networks estimated from microarray gene expression data. We use two types of microarray gene expression data: gene disruptant microarray data and time-course drug response microarray data. For this purpose, the information of gene networks plays an essential role and is unattainable from clustering methods, which are the standard for gene expression analysis. The gene network is estimated from disruptant microarray data by the Bayesian network model, and then the proposed method automatically identifies sets of genes or gene regulatory pathways affected by the drug. We use an actual example from analysis of Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information toward drug target discovery.

Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis

BackgroundApoptosis is a critical process in endothelial cell (EC) biology and pathology, which has been extensively studied at protein level. Numerous gene expression studies of EC apoptosis have also been performed, however few attempts have been made to use gene expression data to identify the molecular relationships and master regulators that underlie EC apoptosis. Therefore, we sought to understand these relationships by generating a Bayesian gene regulatory network (GRN) model.ResultsECs were induced to undergo apoptosis using serum withdrawal and followed over a time course in triplicate, using microarrays. When generating the GRN, this EC time course data was supplemented by a library of microarray data from EC treated with siRNAs targeting over 350 signalling molecules.The GRN model proposed Vasohibin-1 (VASH1) as one of the candidate master-regulators of EC apoptosis with numerous downstream mRNAs. To evaluate the role played by VASH1 in EC, we used siRNA to reduce the expression of VASH1. Of 10 mRNAs downstream of VASH1 in the GRN that were examined, 7 were significantly up- or down-regulated in the direction predicted by the GRN.Further supporting an important biological role of VASH1 in EC, targeted reduction of VASH1 mRNA abundance conferred resistance to serum withdrawal-induced EC death.ConclusionWe have utilised Bayesian GRN modelling to identify a novel candidate master regulator of EC apoptosis. This study demonstrates how GRN technology can complement traditional methods to hypothesise the regulatory relationships that underlie important biological processes.

Changes at the floor of the peptide-binding groove induce a strong preference for proline at position 3 of the bound peptide: molecular dynamics simulations of HLA-A*0217.

We report on molecular dynamics simulations of major histocompatibility complex (MHC)-peptide complexes. Class I MHC molecules play an important role in cellular immunity by presenting antigenic peptides to cytotoxic T cells. Pockets in the peptide-binding groove of MHC molecules accommodate anchor side chains of the bound peptide. Amino acid substitutions in MHC affect differences in the peptide-anchor motifs. HLA-A*0217, human MHC class I molecule, differs from HLA-A*0201 only by three amino acid residues substitutions (positions 95, 97, and 99) at the floor of the peptide-binding groove. A*0217 showed a strong preference for Pro at position 3 (p3) and accepted Phe at p9 of its peptide ligands, but these preferences have not been found in other HLA-A2 ligands. To reveal the structural mechanism of these observations, the A*0217-peptide complexes were simulated by 1000 ps molecular dynamics at 300 K with explicit solvent molecules and compared with those of the A*0201-peptide complexes. We examined the distances between the anchor side chain of the bound peptide and the pocket, and the rms fluctuations of the bound peptides and the HLA molecules. On the basis of the results from our simulations, we propose that Pro at p3 serves as an optimum residue to lock the dominant anchor residue (p9) tightly into pocket F and to hold the peptide in the binding groove, rather than a secondary anchor residue fitting optimally the complementary pocket. We also found that Phe at p9 is used to occupy the space created by replacements of three amino acid residues at the floor within the groove. These findings would provide a novel understanding in the peptide-binding motifs of class I MHC molecules.