Tanya Zlateva

MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles.

Abstract MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes. NetMHCpan and NetMHCIIpan are used as prediction engines. The 13 HLA Class I supertypes are A1, A2, A3, A24, B7, B8, B27, B44, B58, B62, C1, and C4. The 13 HLA Class II DR supertypes are DR1, DR3, DR4, DR6, DR7, DR8, DR9, DR11, DR12, DR13, DR14, DR15, and DR16. In total, MULTIPRED2 enables prediction of peptide binding to 1077 variants representing 26 HLA supertypes. MULTIPRED2 has visualization modules for mapping promiscuous T-cell epitopes as well as those regions of high target concentration – referred to as T-cell epitope hotspots. Novel graphic representations are employed to display the predicted binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/.

DR BACA: dynamic role based access control for Android

Android as an open platform dominates the booming mobile market. However its permission mechanism is inflexible and often results in over-privileged applications. This in turn creates severe security issues. Aiming to support the Principle of Least Privilege, we propose and implement a Dynamic Role Based Access Control for Android (DR BACA) model to enhance Android security, particularly in corporate environment. Our system offers multi-user management on Android mobile devices comparable to traditional workstations, and provides fine-grained Role Based Access Control (RBAC) to enhance Android security at both the application and permission level. Moreover, by leveraging context-aware capabilities of mobile devices and Near Field communication (NFC) technology, our solution supports dynamic RBAC to provide more flexible access control while still being able to mitigate some of the most serious security risks on mobile devices. The DR BACA system can easily be managed, even in large business environments with many mobile devices. We show that our DR BACA system can be deployed and used with ease. With a proper security policy, our evaluation shows that DR BACA can effectively mitigate the security risks posed by both malicious and vulnerable non-malicious applications while incurring only a small overall system overhead.

Identities, Anonymity and Information Warfare

We discuss the primarily role of anonymity and identity manipulation in information warfare. We contend that those who engage in information warfare have very similar goals as those involved in cyber-crime and cyber-terrorism. Today Internet-based commerce has become global, representing a significant component of the world market. Network-based personal communications services are rapidly becoming the method of choice for many nations. In fact many critical infrastructure components are managed and controlled remotely. Yet these various capabilities are usurped by cyber-warriors, terrorists, or other criminals.

A Course on Computer and Network Security: Teaching Online Versus Face-to-Face

The paper presents an overview of the Computer and Network Security course offered through distance education division as part of the online degree program. Topics presented in the online format are compared with those presented in a traditional curriculum in the face-to-face format. The pros and cons of each of the formats are discussed. Unique to the online course are weekly discussion topics that require each student’s participation and the follow-ups to postings of other students. A distinguishing aspect of the online course is a three week based case study assignment exploring a practical security framework encountered in real companies.

Visualization tools for presenting and analysis of global landscapes of vaccine targets

Immunology produces large amounts of complex and hierarchical data. The overwhelming quantities and complexity of these data present a challenge for immunologists trying to interpret results, extract useful information, and derive new knowledge. Visualization plays an increasingly important role in the process of analyzing and understanding immunological data. We employed two visualization modules — heat map and stack graph, to be embedded in MULTIPRED2, a computational system for antigenic analysis and support of large-scale vaccine studies. We have described two complementary modules that display complex information on antigenic targets from proteins. The heat map enables visualization of large number of HLA variants, their groupings within the supertypes, and identification of antigenic regions within a query protein sequence. The stack graph is an interactive tool that presents predicted immunogenicity of a query protein across multiple HLA supertypes at human population level. The stack graph enables zooming in and out facilitating visualization at desired level of detail. Both visualization tools present a large amount of information in a single graphical display. The goal of visualization tools is to help immunologists and vaccine researchers to gain rapid insight into the data using comprehensive but clear graphic representation and summarization.

Probe design optimization of HLA microarray: Data cleaning of probe signals from cDNA tiling microarray: Outlier detection, noise reduction, and identification of uninformative probes in HLA typing application

Custom made tiling cDNA microarrays have been developed for high resolution HLA genotyping. The array comprises tens of thousands of sequence-specific oligonucleotide probes (SSOP) that hybridize with HLA sequences. Sophisticated methods for analyzing the multidimensional complex data are needed due to large amounts of data generated from the microarray experiments. Moreover, the inconsistencies, noise, and outliers in the probe signals add additional complexities. We proposed data cleaning methods for the identification of uninformative and misinformative probes to improve the performance of HLA typing process.