Rob Kulathinal - US grants

Economic competitiveness relies upon innovation and digitized tools, services and data representation. These innovations are required for organizations to remain effective. Designers and design managers guide the evolution of digitally enabled design capabilities by integrating different types of digital process capabilities and resources. Such capabilities can also help optimize complex systems (e.g., smart grid, pervasive healthcare). Building on organizational and evolutionary theory, this project studies changes in organizational processes as they incorporate innovative virtual elements. It applies a process modeling framework to explore underlying mechanisms that generate patterns of change, and uses computational tools in conjunction with theories of evolutionary genetics to analyze longitudinal changes in organizational processes for integrating virtualized innovations. Generative structural elements of design processes (e.g., genotypes) give birth to surface-level design routines and variations (e.g., phenotypes) over time. Processes are represented as sequences akin to biological genes and their translated protein products. while combinations of elements akin to DNA base pairs and their corresponding amino acids capture essential traits of design activity. This new vocabulary helps us delineate structurally the fundamental design task elements and their variation across design task instances.

The study advances theoretical understanding of how digital capabilities alter organizational processes. It shows how mutations emerge and how processes change over time. It identifies strategies for embedding digital capabilities into processes, and explores the impact of complexity. It advances instrumentation, methodology and analytical techniques by describing digitally-enabled processes and performing comparative, hierarchical, structural-analytical analyses of event-sequence-based process data. It provides longitudinal data on the micro- and meso-level changes in design processes from systematic studies of design for cars, chips and buildings. Genetics research is used to evaluate design in light of evolutionary models and agent-based simulations and to identify patterns of integration of digital capabilities into design processes over time.

The Gulf of Mexico deep water ecosystems are threatened by the persistent threat of ocean acidification. Deep-water corals will be among the first to feel the effects of this process, in particular the deep-water scleractinians that form their skeleton from aragonite. The continued shoaling of the aragonite saturation horizon (the depth below which aragonite is undersaturated) will place many of the known, and as yet undiscovered, deep-water corals at risk in the very near future. The most common deep-water framework-forming scleractinian in the world's oceans is Lophelia pertusa. This coral is most abundant in the North Atlantic, where aragonite saturation states are relatively high, but it also creates extensive reef structures between 300 and 600 m depth in the Gulf of Mexico where aragonite saturation states were previously unknown. Preliminary data indicate that pH at this depth range is between 7.85 and 8.03, and the aragonite saturation state is typically between 1.28 and 1.69. These are the first measurements of aragonite saturation state for the deep Gulf of Mexico, and are among the lowest Aragonite saturation state yet recorded for framework-forming corals in any body of water, at any depth. This project will examine the effects of ocean acidification on L. pertusa, combining laboratory experiments, rigorous oceanographic measurements, the latest genome and transcriptome sequencing platforms, and quantitative PCR and enzyme assays to examine changes in coral gene expression and enzyme activity related to differences in carbonate chemistry. Short-term and long-term laboratory experiments will be performed at Aragonite saturation state of 1.45 and 0.75 and the organismal (e.g., survivorship and calcification rate) and genetic (e.g., transcript abundance) responses of the coral will be monitored. Genomic DNA and RNA will be extracted, total mRNA purified, and comprehensive and quantitative profiles of the transcriptome generated using a combination of 454 and Illumina sequencing technologies. Key genes in the calcification pathways as well as other differentially expressed genes will be targeted for specific qPCR assays to verify the Illumina sequencing results. On a research cruise, L. pertusa will be sampled (preserved at depth) along a natural gradient in carbonate chemistry, and included in the Illumina sequencing and qPCR assays. Water samples will be obtained by submersible-deployed niskin bottles adjacent to the coral collections as well as CTD casts of the water column overlying the sites. Water samples will be analyzed for pH, alkalinity, nitrates and soluble reactive phosphorus. These will be used in combination with historical data in a model to hindcast Aragonite saturation state.

Intellectual Merit: This project will provide new physiological and genetic data on an ecologically-significant and anthropogenically-threatened deepwater coral in the Gulf of Mexico. An experimental system, already developed by the PIs, offers controlled conditions to test the effect of Aragonite saturation state on calcification rates in scleractinians and, subsequently, to identify candidate genes and pathways involved in the response to reduced pH and Aragonite saturation state. Both long-term and population sampling experiments will provide additional transcriptomic data and specifically investigate the expression of the candidate genes. These results will contribute to our understanding of the means by which scleractinians may acclimate and acclimatize to low pH, alkalinity, and Aragonite saturation state. Furthermore, the investigators will continue a time series of oceanographic measurements of the carbonate system in the Gulf of Mexico, which will allow the inclusion of this significant body of water in models of past and future ocean acidification scenarios.

Broader Impacts: Results that combine the study of ongoing ocean acidification in the Gulf of Mexico with the physiological and genetic responses of the corals to low saturation state will be presented at conferences, seminars, and published in high-impact and open-access publications. Raw and processed data will be made available in existing databases including NCBI (genetic and genomic data), and through the Biological and Chemical Oceanography Data Management Office (BCO-DMO). All project data will also be made available via a local ftp server linked to each of the PIs websites. The PIs are committed to the inclusion of under-represented minorities in their research, and have a proven record of mentoring undergraduates at Temple University, one of the most diverse institutions in the U.S. A two-day workshop for 20 Gulf Coast high school teachers, and also including students from Temple, will be led by the PIs and coordinated by Dr. Shelia Brown at the Gulf Coast Research Lab in Ocean Springs, MS to provide teachers with the background and materials needed to bring curricula based on these results directly into their classrooms. A high school teacher will also participate in the cruise. Through these efforts, the investigators hope to raise public consciousness of the issue of ocean acidification, increase the level of awareness of the presence of deep-water coral communities in U.S. waters, and to inspire the next generation of scientists.

This project puts forth organizational genetics as a novel theoretical and empirical approach to understand the structure and dynamics of generative innovations. Generativity refers to an overall capacity to produce unprompted change driven by a large, varied, and uncoordinated audience, as seen in digital ecosystems such as Apple, Youtube, Android and Wordpress. In such ecosystems, most innovations are accomplished by third-party individuals who often go beyond the design intent of the original innovators. Using data from highly generative innovation contexts, the researchers seek to understand how combinations of existing technological components that act as genetic elements (i.e., genotypes) can give birth to the incredibly rich and dynamic varieties at the product level (i.e., phenotypes). Each individual innovation is characterized as a co-expression of a certain set of existing components (referred to as "technology genes") in the similar way system biologists characterize the structure and dynamics of cell behaviors though a co-expression network of genes. Furthermore, the ways in which mutations in technology genes can cause changes in the co-expression network, which in turn causes changes in the behavior of the digital product is explored.

Such evolutionary analysis of generative innovations ultimately allows an understanding of how distributed organizations can produce generative innovations without a traditional organizational structure that centrally governs and coordinates innovation activities. More specifically, the research contributes to the organization literature by developing new evolutionary model of organization design for innovations and to the open innovation literature by showing the structural and dynamic patterns of open innovations through interactions among third-party developers. Additional important methodological contributions are in the advancement of an analytical approach to analyze big data to understand complex and multi-level, socio-technical phenomenon.

Although digital artifacts from open ecosystems have become an important part of the economy, very little is known about the generativity of such artifacts. Understanding the mechanisms of generativity can empower organizations in several ways: first, they can become more innovative by using existing artifacts to design new digital artifacts; second, it can help organizations create innovation opportunities based on effective exploitation of digital platform that prompts uncoordinated interactions among heterogeneous and distributed innovators; and finally, understanding generativity can not only help organizations in product design and innovation, but also in related contexts such as viral marketing. These findings will be of interest not only to firms and innovators, but also to policy makers and the media as well.

Behavioral isolation rapidly evolves between species, yet relatively little is known about the genetic basis of pre-mating reproductive isolation, particularly the role of behavioral genes involved in courtship. This knowledge gap is important to fill since behavioral cues are generally species-specific and thought to evolve under strong selective pressures. Using a combination of computational, genetic, and behavioral approaches, the researchers propose to address three main objectives: 1) to identify positively selected genes in the fruit fly, Drosophila melanogaster, that are not specifically expressed in the gonads, 2) to characterize the effects of reducing gene expression in adaptive genes on mating and aggression behavior, and 3) to characterize the adaptive genes involved in behavioral interactions by tissue and molecular function.

The proposed work will extend our understanding of the genetic bases of pre-mating reproductive barriers and their role in early species divergence. Data produced from this proposal will advance the training of students in the areas of genetics, bioinformatics, computational biology, and genomic analysis. Second, this project will implement high school curricula designed to expose students to new high-throughput methods in biology, along with applied hands-on experience using modern genetic and behavioral techniques. Third, the development of a portable high-throughput behavioral lab will be used to increase awareness and engagement in STEM fields to a broader non-scientific audience.

Our lives are becoming increasingly connected with Big Data. Massive amounts of digital trace data are being generated from the activities and events of complex socio-technical systems consisting of human actors and man-made artifacts (which refer to as social objects). Such complexity comes from the massively interconnected and computed nature of the contemporary digital world. These data sets are different from traditional data as they are typically massive, unstructured, granular, heterogeneous, dynamic, and performative. Using Big Data, the researchers are able to understand and predict behaviors of complex socio-technical systems. To support such efforts, the researchers will build a new methodological framework based on an evolutionary ontology that treats variation as real and as the fuel of evolution. Specifically, the researchers analyze data set from Twitter (one of the largest social media sites) and Github (the largest open source community) to test and validate their framework. As the role of Big Data continues to increase in our society, the researchers plan to develop online curricula to help students learn how to access, manage, analyze, and visualize big data sets via a variety of approaches.

The researchers are developing a method to predict the emergence of system-level behaviors by analyzing large volumes of digital trace data using evolutionary social ontology to build a multi-level model of complex socio-technical systems. They use analytical techniques developed in evolutionary biology and systems biology: (1) to characterize a stream of digital trace data from a complex socio-technical system with finite genetic elements; (2) to predict the behavior of socio-technical systems based on the pattern of "behavioral gene" interactions; and (3) to explore the impact of mutational input, gene flow, and recombination in "behavioral genes" on the evolution of socio-technical systems. The researchers test their model in GitHub, one of the largest open source communities that includes over 5 million open source software development projects and Twitter, one of the largest social media site, that has over 500 million messages per day. The model generated from this research can be used for other types of massive digital trace data including sensor data from Internet of the Things and mobile data from smartphones.

NRT-IGE: Innovating Graduate STEM Education Through Bio-Social Partnerships

Distinct barriers of communication exist between the social sciences and the biological or life sciences in the academy. Scholars from each are trained to produce knowledge from a discipline-specific perspective, and seldom have the opportunity, as students, to engage in cross-disciplinary inquiry. This National Science Foundation Research Traineeship (NRT) award in the Innovations in Graduate Education (IGE) Track to Temple University brings together scientists from the departments of Geography and Urban Studies (Social Sciences) and Biology (Life Sciences) to pilot a model for cross-disciplinary learning and communication that will use studios as a collaborative learning space and a science museum as a venue for public communication. The project will contribute to wider academic reflection about the production of body-centered knowledge and the role of university partnerships to share this knowledge.

The goal of the project is to prepare graduate students in the social and life sciences to communicate with each other and with the public by focusing on one broad commonality - the drive to produce knowledge about the human body. The body is a complex phenomenon that is simultaneously biological and social, and thus represents a strategic thematic focus for interdisciplinary collaboration. The model of interdisciplinary graduate education to be developed and tested will use the design studio approach, which asks students to structure inquiry around an ill-defined problem. Graduate student learning will be organized around two sequential design studios and an interactive exhibit. This series is sequential in that each stage demands a broader audience around which to focus design problems. Students in the first studio will design for intra-classroom communication; while in the second studio, they will design for outside audiences and receive feedback from students in middle school, high school, and undergraduate classrooms. Prototypes developed in the design studios will be transformed into exhibits to be presented to public audiences at the Franklin Institute, a science museum in Philadelphia, PA. Student learning will be tracked and evaluated, as will the impacts of outreach activities on students and families. As such, the project seeks to collect data on the effectiveness of this model for interdisciplinary graduate education and university-museum partnerships that could be more broadly scaled and adapted.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative, and scalable models for STEM graduate education training. The Innovations in Graduate Education Track is dedicated solely to piloting, testing, and evaluating novel, innovative, and potentially transformative approaches to graduate education.

Comparative analyses of DNA and amino acid sequences is a powerful paradigm in biology, including evolution, ecology, development, and medicine. The Molecular Evolutionary Genetics Analysis (MEGA) software is a widely used tool to apply state-of-the-art methods for sequence analysis in these biological disciplines. MEGA provides an intuitive graphical user interface (GUI) to enable researchers to explore their primary data and results interactively, along with an integrated analytical engine that makes a large repertoire of evolutionary analysis methods easily accessible. MEGA is used by thousands of researchers in biology and has become a major teaching tool in universities worldwide. However, MEGA is primarily for use on Windows operating systems, which limits its utility and impact. The goal of this project is to (a) make MEGA cross-platform; (b) ready MEGA for community contributions and third-party expansion of functionalities; and (c) add a workflow GUI in MEGA to enable researchers to assemble pipelines and workflows that are becoming essential for rigorous, reproducible, and automated analytics in this era of big data. These developments will lead to a major enhancement of software infrastructure in biology and related interdisciplinary areas, all of which are critical in modern biological research. They will advance education through the development of tutorials, and benefit underserved populations through workshops and teaching.

Comparative analyses of DNA and amino acid sequences, a powerful paradigm in computational biology, have grown and adapted to the changing needs of biological sciences. Due to its increasing analytical complexity, successful application of the comparative paradigm requires efficient, sophisticated, and intuitive software. The Molecular Evolutionary Genetics Analysis (MEGA, now in version 7) software is among the most widely used tools for sequence analysis, as it provides a powerful and intuitive graphical user interface (GUI), giving researchers tools for exploring their data and visualizing results interactively, and placing a large repertoire of state-of-the-art evolutionary analysis methods at their fingertips. This "Development" proposal is to modernize and re-engineer MEGA using cutting edge software libraries and development philosophies. It will ensure that this vital community resource not only remains useful for the next decade, but also becomes cross-platform and extensible for community integration of new methods and tools. The project will leverage modern open source application development stacks and libraries, drawing upon web technologies (JavaScript, HTML5, and CSS) for a platform agnostic application, which will lead to an open source, cross-platform MEGA GUI that is ready for community contributions and third-party expansion of functionalities. It will also add a new workflow system (AppFlows) to enable researchers to assemble pipelines and workflows that are becoming essential for rigorous, reproducible, and automated analytics in this era of big data. The new AppDepot in MEGA will host add-ins and extensions (small and large) developed by community programmers by reusing MEGA-X components and core libraries. AppDepot will bridge the gap between computational/statistical method developers and biological users, as the large installed base of MEGA will immediately make new methods available to many to test and use. All of these results will be available from www.megasoftware.net .