Mohan Krishnamoorthy

Recurrent Signature Patterns in HIV-1 B Clade Envelope Glycoproteins Associated with either Early or Chronic Infections

Here we have identified HIV-1 B clade Envelope (Env) amino acid signatures from early in infection that may be favored at transmission, as well as patterns of recurrent mutation in chronic infection that may reflect common pathways of immune evasion. To accomplish this, we compared thousands of sequences derived by single genome amplification from several hundred individuals that were sampled either early in infection or were chronically infected. Samples were divided at the outset into hypothesis-forming and validation sets, and we used phylogenetically corrected statistical strategies to identify signatures, systematically scanning all of Env. Signatures included single amino acids, glycosylation motifs, and multi-site patterns based on functional or structural groupings of amino acids. We identified signatures near the CCR5 co-receptor-binding region, near the CD4 binding site, and in the signal peptide and cytoplasmic domain, which may influence Env expression and processing. Two signatures patterns associated with transmission were particularly interesting. The first was the most statistically robust signature, located in position 12 in the signal peptide. The second was the loss of an N-linked glycosylation site at positions 413–415; the presence of this site has been recently found to be associated with escape from potent and broad neutralizing antibodies, consistent with enabling a common pathway for immune escape during chronic infection. Its recurrent loss in early infection suggests it may impact fitness at the time of transmission or during early viral expansion. The signature patterns we identified implicate Env expression levels in selection at viral transmission or in early expansion, and suggest that immune evasion patterns that recur in many individuals during chronic infection when antibodies are present can be selected against when the infection is being established prior to the adaptive immune response.

A multiple-alignment based primer design algorithm for genetically highly variable DNA targets

BackgroundPrimer design for highly variable DNA sequences is difficult, and experimental success requires attention to many interacting constraints. The advent of next-generation sequencing methods allows the investigation of rare variants otherwise hidden deep in large populations, but requires attention to population diversity and primer localization in relatively conserved regions, in addition to recognized constraints typically considered in primer design.ResultsDesign constraints include degenerate sites to maximize population coverage, matching of melting temperatures, optimizing de novo sequence length, finding optimal bio-barcodes to allow efficient downstream analyses, and minimizing risk of dimerization. To facilitate primer design addressing these and other constraints, we created a novel computer program (PrimerDesign) that automates this complex procedure. We show its powers and limitations and give examples of successful designs for the analysis of HIV-1 populations.ConclusionsPrimerDesign is useful for researchers who want to design DNA primers and probes for analyzing highly variable DNA populations. It can be used to design primers for PCR, RT-PCR, Sanger sequencing, next-generation sequencing, and other experimental protocols targeting highly variable DNA samples.

Identification of broadly neutralizing antibody epitopes in the HIV-1 envelope glycoprotein using evolutionary models

BackgroundIdentification of the epitopes targeted by antibodies that can neutralize diverse HIV-1 strains can provide important clues for the design of a preventative vaccine.MethodsWe have developed a computational approach that can identify key amino acids within the HIV-1 envelope glycoprotein that influence sensitivity to broadly cross-neutralizing antibodies. Given a sequence alignment and neutralization titers for a panel of viruses, the method works by fitting a phylogenetic model that allows the amino acid frequencies at each site to depend on neutralization sensitivities. Sites at which viral evolution influences neutralization sensitivity were identified using Bayes factors (BFs) to compare the fit of this model to that of a null model in which sequences evolved independently of antibody sensitivity. Conformational epitopes were identified with a Metropolis algorithm that searched for a cluster of sites with large Bayes factors on the tertiary structure of the viral envelope.ResultsWe applied our method to ID50 neutralization data generated from seven HIV-1 subtype C serum samples with neutralization breadth that had been tested against a multi-clade panel of 225 pseudoviruses for which envelope sequences were also available. For each sample, between two and four sites were identified that were strongly associated with neutralization sensitivity (2ln(BF) > 6), a subset of which were experimentally confirmed using site-directed mutagenesis.ConclusionsOur results provide strong support for the use of evolutionary models applied to cross-sectional viral neutralization data to identify the epitopes of serum antibodies that confer neutralization breadth.

Toward smart manufacturing using decision analytics

This paper is focused on decision analytics for smart manufacturing. We consider temporal manufacturing processes with stochastic throughput and inventories. We demonstrate the use of the recently proposed concept of the decision guidance analytics language to perform monitoring, analysis, planning, and execution tasks. To support these tasks we define the structure of and develop modular reusable process component models, which represent data, decision/control variables, computation of functions, constraints, and uncertainty. The tasks are then implemented by posing declarative queries of the decision guidance analytics language for data manipulation, what-if prediction analysis, decision optimization, and machine learning.

A system and architecture for reusable abstractions of manufacturing processes

In this paper we report on the development of a system for managing a repository and conducting analysis and optimization on manufacturing performance models. The repository is designed to contain (1) unit manufacturing process performance models, (2) composite performance models representing production cells, lines, and facilities, (3) domain specific analytical views, and (4) ontologies and taxonomies. Initial implementation includes performance models for milling and drilling as well as a composite performance model for machining. These performance models formally capture (1) the metrics of energy consumption, CO2 emissions, tool wear, and cost as a function of process controls and parameters, and (2) the process feasibility constraints. The initial scope of the system includes (1) an Integrated Development Environment and its interface, and (2) simulation and deterministic optimization of performance models through the use of Unity Decision Guidance Management System.

Tree pruner: An efficient tool for selecting data from a biased genetic database

BackgroundLarge databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation or shared genotypes, is problematic. Selection on the basis of epidemiological variables may not achieve the desired properties. Available automated approaches to the selection of influenza genetic data make a tradeoff between speed and simplicity on the one hand and control over quality and contents of the dataset on the other hand. A poorly chosen dataset may be detrimental to subsequent analyses.ResultsWe developed a tool, Tree Pruner, for obtaining a dataset with desired evolutionary properties from a large, biased genetic database. Tree Pruner provides the user with an interactive phylogenetic tree as a means of editing the initial dataset from which the tree was inferred. The tree visualization changes dynamically, using colors and shading, reflecting Tree Pruner actions. At the end of a Tree Pruner session, the editing actions are implemented in the dataset.Currently, Tree Pruner is implemented on the Influenza Research Database (IRD). The data management capabilities of the IRD allow the user to store a pruned dataset for additional pruning or for subsequent analysis. Tree Pruner can be easily adapted for use with other organisms.ConclusionsTree Pruner is an efficient, manual tool for selecting a high-quality dataset with desired evolutionary properties from a biased database of genetic sequences. It offers an important alternative to automated approaches to the same goal, by providing the user with a dynamic, visual guide to the ongoing selection process and ultimate control over the contents (and therefore quality) of the dataset.

Autonomic smart manufacturing

Smart manufacturing (SM) systems have to optimise manufacturing activities at the machine, unit or entire manufacturing facility level as well as adapting the manufacturing process on the fly as required by a variety of conditions (e.g. machine breakdowns and/or slowdowns) and unexpected variations in demands. This paper provides a framework for autonomic smart manufacturing (ASM) that relies on a variety of models for its support: (1) a process model to represent machines, parst inventories and the flow of parts through machines in a discrete manufacturing floor; (2) a predictive queueing network model to support the analysis and planning phases of ASM; and (3) optimisation models to support the planning phase of ASM. In essence, ASM is an integrated decision support system for smart manufacturing that combines models of different nature in a seamless manner. As shown here, these models can be used to predict manufacturing time and the energy consumed by the manufacturing process, as well as for finding the machine settings that minimise the energy consumed or the manufacturing time subject to a variety of constraints using non-linear optimisation models. The diversity of models used affords different levels of integration and granularity in the decision-making process. An example of a car manufacturing process is used throughout the paper to explain the concepts and models introduced here.

Analysis and optimization in smart manufacturing based on a reusable knowledge base for process performance models

In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires the development of automatic methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by performing diagnostic tasks on a composite performance model.

Stochastic decision optimisation based on deterministic approximations of processes described as closed-form arithmetic simulation

Abstract We propose an efficient one-stage stochastic optimisation algorithm for the problem of finding process controls that minimise the expectation of cost while satisfying multiple deterministic and stochastic feasibility constraints with a given high probability. The proposed algorithm is based on a series of deterministic approximations to produce a candidate solution set and on a refinement step using stochastic simulations with optimal simulation budget allocation. We conduct an experimental study for a real-world manufacturing service network, which shows that the proposed algorithm significantly outperforms four popular simulation-based stochastic optimisation algorithms.

Modular Modeling and Optimization of Temporal Manufacturing Processes with Inventories

Smart manufacturing requires streamlining operations and optimizing processes at a global and local level. This paper considers temporal manufacturing processes that involve physical or virtual inventories of products, parts and materials that move through a network of subprocesses. The inventory levels vary with time and are a function of the configuration settings of the machines involved in the process. These environments require analysis, e.g., answering what-if questions, and optimization to determine optimal operating settings for the entire process. To address this problem, the paper proposes modular process components that can represent these manufacturing environments at various levels of granularity for performing what-if analysis and decision optimization queries. These components are extensible and reusable against which optimization and what-if questions can be posed. Additionally, the paper describes the steps to translate these complex components and optimization queries into a formal mathematical programming model, which is then solved by a commercial optimization solver.