DoD 2012.B SBIR Solicitation

ST12B-001: Advanced Materials and Methods for Biospecimen Collection for Infectious Disease

Description: OBJECTIVE: Develop advanced materials and technologies for swab or swab-like collection of bio-specimens that can be used by a minimally-trained individual, and shipped/stored under ambient conditions. These technologies would advance methods to collect bio-specimens, such as naso/oropharyngeal swabs, for the diagnosis of some respiratory diseases. Developed swabs should be optimized for bios-pecimen collection and recovery with materials that maintain and preserve activity of viral or bacterial targets, while reducing the need for cold chain requirements (eg, compatible with an ambient temperature methods). Swabs should be compatible with standard clinical analytical methods in the centralized reference or biomedical research laboratory. DESCRIPTION: Swab-based collection is standard practice for direct bio-specimen collection in a variety of clinical applications. The type of swab or collection material, the collection method, and the sample elution method may all influence the detection of clinical analyte and may artificially bias the clinical interpretation. This effort seeks development of swab or swab-like materials and/or collection methods that are specifically designed for such clinical applications. Applicability of the direct collection technique to bios-pecimens appropriate for infectious disease detection as the front-end for analysis in a clinical and/or research workflow, is preferred. Key attributes desired are: high efficiency collection, ease of use for collection, reproducibility of collection, and optimized analyte recovery for downstream analysis within a centralized reference or research laboratory environment. Maximized recovery and activity of nucleic acids, proteins, viable whole cells, active viruses and bacteria is critical. Materials that preserve these components in the swab or in a secondary material without cold chain requirements (to include transport and storage) are of specific interest. Proposers may focus on the swab material and/or on optimization of buffers/materials for storage and/or recovery. Methods and materials that ensure complete recovery of all captured analytes, such as through a dissolvable matrix that does not interfere with downstream analytical technologies or permit the shipment and culture of cells at a remote laboratory, are of particular interest. Proposers are encouraged to consider methods and technologies compatible with Clinical Laboratory Improvement Amendment (CLIA)-waiver6, good laboratory practices (GLP), and good manufacturing practice (GMP) procedures. Proposers may integrate a diagnostic test into device; however, collection, preservation, and recovery for the broadest clinical applications are desired. PHASE I: Demonstrate feasibility of swab methods, materials or integrated technologies. Proposers should address the quantitative advantages of the method compared to current commercially available swabs, as well as the complete anticipated operating procedure for use to include direct bio-specimen collection technique off an individual, swab drying time or secondary media introduction (if necessary), analyte (efficiency and function), and compatibility with downstream analyses. Proposers should demonstrate initial designs and performance, and project Phase II capabilities. Phase I efforts should justify the applicability to settings such as point of care and home use, and consideration of the U.S. Food & Drug Administration (FDA) regulations is encouraged. PHASE II: Phase II efforts should quantify all performance parameters related to the stated objectives, including quantity of bio-specimen collected, recovery efficiency for various analyte types (nucleic acids, proteins, active virus, etc), and performance with downstream assays. If sample preservation is pursued, performers should quantify recovery and analyte integrity following ambient temperature storage of known duration. Manufacturing designs and costs should be considered for all components of the device. Device potential for FDA clearance as a bio-specimen collection device for home use or physician office use should be described. PHASE III DUAL USE APPLICATIONS: The technology to be developed would enable reproducible bio-specimen collection outside of a major clinical facility and therefore would have significant impact on the clinical diagnostic market and pharmaceutical research. There is a significant commercial market for medical diagnostics and home-use physician-office based diagnostic testing is a growing element of this market. The developed technology would allow collection and preservation of bio-specimens in such settings, and enable clinically valid diagnostic testing, and remote clinical trials. The technology to be developed is critical for DoD, as many medics have minimal training. Development of a FDA-approved bio-specimen collection and storage device would enable reliable samples to be collected and shipped for analyses, even from patients located in remote/deployment settings. Potential customers include Military Health System - Defense Medical Research and Development Program (MHS DMRDP), Military Infectious Diseases Research Program (MIDRP), and Defense Threat Reduction Agency (DTRA). REFERENCES: 1) J. Norris, K. Manning, S. Linke, J. Ferrance, and J. Landers. Expedited, Chemically Enhanced Sperm Cell Recovery from Cotton Swabs for Rape Kit Analysis. J. Forensic Sciences, 52 (4) 2007, 800-805. 2) Mulrennan S, Tempone SS, Ling ITW, Williams SH, Gan G-C, et al. 2010 Pandemic Influenza (H1N1) 2009 Pneumonia: CURB-65 Score for Predicting Severity and Nasopharyngeal Sampling for Diagnosis Are Unreliable. PLoS ONE 5(9): e12849. 3) Anne J. Blaschke, Mandy A. Allison, Lindsay Meyers, Margarita Rogatcheva, Caroline Heyrend, Brittany Mallin, Marjorie Carter, Bonnie LaFleur, Trenda Barney, Mark A. Poritz, Judy A. Daly, Carrie L. Byington. Non-invasive sample collection for respiratory virus testing by multiplex PCR. Journal of Clinical Virology. 52(3), 2011, 210-214. 4) Susanna Esposito, Claudio Giuseppe Molteni, Cristina Daleno, Antonia Valzano, Laura Cesati, Laura Gualtieri, Claudia Tagliabue, Samantha Bosis and Nicola Principi. Comparison of nasopharyngeal nylon flocked swabs with universal transport medium and rayon-bud swabs with a sponge reservoir of viral transport medium in the diagnosis of pediatric influenza. Journal of Medical Microbiology. 59(1), 2010, 96-99. 5) Joann L. Cloud, Weston Hymas, Karen C. Carroll. Impact of Nasopharyngeal Swab Types on Detection of Bordetella pertussis by PCR and Culture J Clin Microbiol. 40(10): 2002, 38383840. 6) CLIA: http://wwwn.cdc.gov/clia/regs/toc.aspx

ST12B-002: Forecasting Dynamic Group Behavior in Social Media

Description: OBJECTIVE: Develop automated tools that can (1) learn models of the dynamics of inter- as well as intra- group interactions in social media and (2) track the evolution of such dynamics and derive causal factors from online interaction data. DESCRIPTION: Social media have evolved from a platform that provides infrastructure that supports maintaining connections between friends to a platform that supports recruiting, collaborating, organizing, and competing for resources. Facebook has over 800M active users, 900M pages and groups, millions of new postings per day, and users are, on average, connected to 140 friends and 80 community pages. Many online communities enable the creation of virtual teams, which evolve over time. Among these communities and teams are terrorist and other criminal organizations. Previous research studying community interactions in social media has had limited success. Clustering and community detection algorithms only find groups of closely collaborating individuals [1], and are unable to track the changing state of roles and interacting networks or model the causes of interactions between people and communities. The models developed to forecast online interactions are limited to person-to-person scientific collaborations [2] and longer-term connections to interest groups [3]. Social media interactions are much more dynamic. Some teams form, organize, perform activities, and dissolve quickly. Team members are often heterogeneous, performing different roles and activities online and in the physical world. The impact of these teams on the social landscape, their interactions with other teams, the evolution of network state over time, and competition with other teams and communities has not been adequately researched. Due to the overwhelming deluge of data generated by users across social media platforms, this analysis cannot be done manually. One of the key insights from online collaboration research is that group dynamics are affected by many factors [4]. First, users often join the same group for varying reasons, possessing different knowledge, skills, and opinions, which affects their roles on the team and the interactions within the team. Second, interactions between groups and their members may result in changes to group structures and roles of individuals, producing mergers, switches and defections of the members to other teams. Finally, the teams"states and their activities evolve over time under influence of external factors. As people have limited resources to participate in online activities, their behaviors can be affected by team membership, motivating events, and shared knowledge. Many of these dynamics are due to the collaborative and competitive nature of online interactions. While collaborations in social media have been researched extensively, little attention has been paid to how the groups compete with each other for members and influence on opinions of other teams and communities. Understanding what affects such online behavior is needed for trend forecasting. This topic seeks innovative research to develop automated tools that can (1) learn models of the dynamics of inter- as well as intra- group interaction in social media and (2) track the evolution of such dynamics and derive causal factors from online interaction data. The algorithms must be able to operate on large datasets of millions of nodes, generate robust and reliable group behavior and interaction models, and provide the users with factors and their relative contribution to changes in online behaviors. This technology will be used by analysts in forecasting online behaviors and identifying competition and possible cyber terrorism events. PHASE I: * Task 1: Design and prove the feasibility of a system that can track groups and their state changes in social media. * Task 2: Research key indicators of group interactions, including competition, recruitment activities, and effects of events and topics on group structure changes. PHASE II: * Task 1: Design and develop a system that learns dynamics of group behavior and inter- and intra-group interactions in an unsupervised manner based upon design and innovation developed in Phase I. * Task 2: Demonstrate the system on a social media dataset containing>1K groups,>100K postings/day, and>1M members. Achieve high accuracy (90%) of detecting group state changes, activities, conflicts, and competitions. PHASE III DUAL USE APPLICATIONS: Successful development of the prototype capability would be of great interest to industrial espionage prevention specialists, law enforcement, market analysts, and polling organizations. This capability would be applicable to a broad range of tactical as well as strategic military operations. REFERENCES: 1) M. A. Porter, J.-P. Onnela and P. J. Mucha (2009)."Communities in Networks"Not. Amer. Math. Soc. 56: 10821097, 11641166 2) Mihalkova, L., W.-E. Moustafa, and Lise Getoor (2011)"Learning to Predict Web Collaborations", Workshop on User Modeling for Web Applications 3) Saha, B., and L. Getoor (2008)"Group Proximity Measure for Recommending Groups in Online Social Networks", 2nd ACM SIGKDD Workshop on SNA-KDD 4) J. Leskovec, L. Backstrom, R. Kumar, and A. Tomkins (2008)"Microscopic evolution of social networks", KDD

ST12B-003: Automated Approaches to Cellular Engineering and Biomanufacturing

Description: OBJECTIVE: Develop an automated, software-controlled platform that enhances cutting edge methodologies for genome-scale cellular engineering to enable rapid engineering and optimization of new biomanufacturing systems. DESCRIPTION: Current approaches to engineering biology rely on an ad hoc, laborious, trial-and-error process, wherein one successful project often does not translate to enabling subsequent new designs. As a result, the state of the art development cycle for engineering new biological products often takes several years and costs tens to hundreds of millions of dollars (e.g. microbial production of artemisinic acid for the treatment of malaria and the non-petroleum-based production 1,3-propanediol). The impact from these current approaches is that the number of new entrants and innovators into both the commercial and research space is immediately limited few have the expertise, capital and/or time necessary to develop and engineer a new product. Consequently, while progress has been made, we are constrained to producing only a tiny fraction of the vast number of possible chemicals, materials, diagnostics, therapeutics, and fuels that would be enabled by the ability to truly engineer biology. A new approach is needed. To address bottlenecks plaguing the biological design-build-test cycle and to enable more complex design and engineering, DARPA seeks technologies that enhance automation for genome-scale, cellular engineering. These include automated, programmable, affordable, and compact systems capable of running complex bio-engineering processes (e.g. genome engineering at multiple sites across the genome, cell transfection, combinatorial genome assembly, library design, continuous evolution, etc.). Successful approaches will leverage automation software to enable more complex and robust experimental design (e.g. real-time feedback and control) resulting in outcomes and a scale of experimentation that would be difficult to achieve otherwise. Current platforms developed for complex, genome-scale, cellular engineering protocols are often custom-designed and tailored to a specific lab"s expertise and needs. Few of these techniques are automated; the expectation being that others can implement the protocols in their own labs using their own, custom means. Consequently, transformative techniques are limited to the hands of a relative few. This underscores the inherent challenges to engineering biology replicability and reproducibility. There is significant opportunity for the automation of complex cellular engineering, reducing variability between experiments, and increasing the throughput and capabilities of constructing new biological designs. These innovations will introduce new architectures and tools that will form the foundational technology for engineering biology. This solicitation focuses on the development of automated platforms for enhanced, genome-scale, cellular engineering that enable rapid engineering and optimization of biotechnology, including new biologically-based manufacturing systems. Automated platforms should address several or all of the following challenges intrinsic to the dissemination of complex, cellular engineering protocols: reproducibility, replicability, robustness, efficiency of processes, throughput of experiments, and others. In addition, these automated platforms should enable new experimental protocols and designs that would be difficult or impossible to achieve otherwise (e.g. real-time feedback and control). PHASE I: Develop an initial concept design for an automated platform for enhanced genome-scale cellular engineering that enables rapid engineering and optimization of new biomanufacturing systems. Develop detailed analysis of the automated platform"s predicted performance, including detailed performance analyses of each of the component technologies and processes to be integrated. Include analysis of the performance compared to the standard, non-automated protocol and anticipated improvement on speed or complexity of process. Define key component technological milestones and metrics and establish the minimum performance goals necessary to achieve successful execution of the automated platform. Phase I deliverable will include both a technical analysis of the proposed platform and a commercialization assessment. The technical analysis will include: a technical report of experiments supporting the feasibility of this approach; defined milestones and metrics (including minimum performance metrics) for the development and performance of component processes; a detailed design of the proposed automation platform system; and a description of new experimental protocols and designs that would be difficult or impossible to achieve without automation and software control (e.g. real-time feedback and control). The commercialization assessment will include a Phase II proposal that outlines plans for the development, fabrication, and validation of an automated platform for genome-scale, cellular engineering. This proposal should also include a detailed assessment of the potential path to commercialization, barriers to market entry, competitive landscape (if it exists), and collaborators or partners identified as early adopters for the new system. PHASE II: Finalize the design from Phase I and initiate construction and demonstration of a prototype of the automation platform. Demonstrate that each of the components and processes necessary for implementing the genome-scale, cellular engineering protocol are capable of being performed on an automated platform under software control. Establish baseline performance metrics that improve on comparable non-automated and automated competing processes. Provide an experimentally validated performance comparison of the new, automated process to competing SOA processes. Key metrics include (but are not limited to): reproducibility of experiments, efficiency of component processes, throughput of experiments, total cost and total time to reach end goal, performance of final design/product, and amount of human intervention required. Demonstrate new experimental protocols and designs that would be difficult or impossible to achieve otherwise (e.g. real-time feedback and control) and include attendant, relevant metrics of performance. Deliverables of a prototype device and valid test data appropriate for a commercial production path are expected. PHASE III DUAL USE APPLICATIONS: The ability to rapidly engineer and optimize new biologically-based production systems will have widespread utility and applications across the entire biotechnology and pharmaceutical industries including rapid, optimized production of high value chemicals, industrial enzymes, fuels, diagnostics, and therapeutics. These automated platforms would be impactful for industrial biotechnology firms as well as academic, research-scale operations. These platforms could enable the DoD to leverage the unique and powerful attributes of biology to solve challenges associated with production of new materials, novel capabilities, fuels, and medicines while providing novel solutions and enhancements to military needs and capabilities. For example, automated genome-scale cellular engineering platforms will facilitate the design of systems to rapidly and dynamically prevent, seek out, identify, and repair corrosion/materials degradationa challenge that costs the DoD $23B/yr and has no near term solution in sight. REFERENCES: 1) Wang HH, Isaacs FJ, Carr PA, Sun ZZ, Xu G, Forest CR, Church GM. Programming cells by multiplex genome engineering and accelerated evolution. Nature, vol. 460, p. 894-898 (2009). 2) Isaacs FJ, Carr PA, Wang HH, Lajoie MJ, Sterling B, Kraal L, Tolonen AC, Gianoulis TA, Goodman DB, Reppas NB, Emig CJ, Bang D, Hwang SJ, Jewett MC, Jacobson JM, Church GM. Precise manipulation of chromosomes in vivo enables genome-wide codon replacement. Science, vol. 333, p. 348-353 (2011). 3) Esvelt EM, Carlson JC, Liu DR. A system for the continuous directed evolution of biomolecules. Nature, vol, 472, p. 499503 (2011). 4) RB Fair et al, Chemical and Biological Applications of Digital- Microfluidic Devices, Design & Test of Computers, IEEE (2007). 5) Gibson DG, Glass JI, Lartigue C, Noskov VN, Chuang RY, Algire MA, Benders GA, Montague MG, Ma L, Moodie MM, Merryman C, Vashee S, Krishnakumar R, Assad-Garcia N, Andrews-Pfannkoch C, Denisova EA, Young L, Qi ZQ, Segall-Shapiro TH, Calvey CH, Parmar PP, Hutchison CA, Smith HO, Venter JC. Creation of a bacterial cell controlled by a chemically synthesized genome. Science, vol. 329, p. 52-56 (2010).