Lauren A. Meyers - US grants

This project addresses the evolution of multi-species communities using both bacterial and computational model systems. Biofilms are assemblages of bacteria that adhere to surfaces, both living and nonliving, and to each other. They are often composed of multiple species and cause human diseases, contaminate medical and food production equipment, corrode pipes, purify polluted waters, and protect materials from degradation. In this project, communities of species will be selected both in vitro and in silico to produce biofilms that are stronger and more resistant to parasites, antibiotics and/or caustic chemicals. These experiments will illuminate the effects of species composition and diversity on the evolution of a successful community.

Natural selection not only favors fit organisms but also can act at a higher level, favoring entire populations over others, a process known as group selection. In multi-species bacterial infections, combinations of species that form stable, transmissible infections will successfully proliferate to new hosts, and will thereby be favored as an entire assemblage for further evolution. In cheese production and wastewater decontamination, dairy producers and environmental engineers artificially select among mixed communities of bacteria for those that perform these tasks best. What allows mixed communities to solve some ecological problems better that homogeneous communities? Does community-level evolution follow the same principles as individual-level evolution? Although mixed microbial communities underlie many health and environmental concerns, virtually nothing is known about their evolution. Because biofilms thrive in nature as both monocultures and mixed cultures, they offer an ideal test bed for exploring the fundamentals of community-level selection.

This collaborative project aims to establish a national computational resource to move the research community much closer to the realization of the goal of the Tree of Life initiative, namely, to reconstruct the evolutionary history of all organisms. This goal is the computational Grand Challenge of evolutionary biology. Current methods are limited to problems several orders of magnitude smaller, and they fail to provide sufficient accuracy at the high end of their range.

The planned resource will be designed as an incubator to promote the development of new ideas for this enormously challenging computational task; it will create a forum for experimentalists, computational biologists, and computer scientists to share data, compare methods, and analyze results, thereby speeding up tool development while also sustaining current biological research projects.

The resource will be composed of a large computational platform, a collection of interoperable high-performance software for phylogenetic analysis, and a large database of datasets, both real and simulated, and their analyses; it will be accessible through any Web browser by developers, researchers, and educators. The software, freely available in source form, will be usable on scales varying from laptops to high-performance, Grid-enabled, compute engines such as this project's platform, and will be packaged to be compatible with current popular tools. In order to build this resource, this collaborative project will support research programs in phyloinformatics (databases to store multilevel data with detailed annotations and to support complex, tree-oriented queries), in optimization algorithms, Bayesian inference, and symbolic manipulation for phylogeny reconstruction, and in simulation of branching evolution at the genomic level, all within the context of a virtual collaborative center.

Biology, and phylogeny in particular, have been almost completely redefined by modern information technology, both in terms of data acquisition and in terms of analysis. Phylogeneticists have formulated specific models and questions that can now be addressed using recent advances in database technology and optimization algorithms. The time is thus exactly right for a close collaboration of biologists and computer scientists to address the IT issues in phylogenetics, many of which call for novel approaches, due to a combination of combinatorial difficulty and overall scale. The project research team includes computer scientists working in databases, algorithm design, algorithm engineering, and high-performance computing, evolutionary biologists and systematists, bioinformaticians, and biostatisticians, with a history of successful collaboration and a record of fundamental contributions, to provide the required breadth and depth.

This project will bring together researchers from many areas and foster new types of collaborations and new styles of research in computational biology; moreover, the interaction of algorithms, databases, modeling, and biology will give new impetus and new directions in each area. It will help create the computational infrastructure that the research community will use over the next decades, as more whole genomes are sequenced and enough data are collected to attempt the inference of the Tree of Life. The project will help evolutionary biologists understand the mechanisms of evolution, the relationships among evolution, structure, and function of biomolecules, and a host of other research problems in biology, eventually leading to major progress in ecology, pharmaceutics, forensics, and security.

The project will publicize evolution, genomics, and bioinformatics through informal education programs at museum partners of the collaborating institutions. It also will motivate high-school students and college undergraduates to pursue careers in bioinformatics. The project provides an extraordinary opportunity to train students, both undergraduate and graduate, as well as postdoctoral researchers, in one of the most exciting interdisciplinary areas in science. The collaborating institutions serve a large number of underrepresented groups and are committed to increasing their participation in research.

Evolution, conflict and cooperation in mixed-species bacterial communities
Lauren A. Meyers
University of Texas at Austin

Evolutionary theory has much to say about the evolution of single species and about the evolution of two species living in conflict or harmony with each other. Ecosystems, however, consist of many species that compete and cooperate with each other in diverse ways. These interactions may result from a number of different causes, such as protection from the elements or other species, or the use or production of limited resources. The interactions may be irregular - dependent on the environment, asymmetric, or non-transitive. For example one species may out-compete another for resources only when it is the first to colonize a particular environment; or one species may facilitate the growth of another, the second may facilitate the growth of a third, but the third have an antagonistic relationship with the first.

This project brings together two complementary approaches to evolutionary research - experimental evolution of bacteria and mathematical modeling of ecological and evolutionary dynamics - to investigate the evolution of multi-species communities. Using four bacterial species that can coexist in the laboratory, we will characterize their ecological interactions; build mathematical models of these interactions with which we will predict their evolutionary dynamics; and finally test our predictions by evolving communities consisting of one, two or four species in the laboratory. This work represents a novel direction in experimental evolution, one that will provide insight into the evolution of ecological dynamics within a community, and extend our purview beyond the interactions of just two species. An important broader impact of this project will be the training of graduate students at the interface of the mathematical and biological sciences.

Contact network epidemiology is a new and powerful mathematical approach to study the ecology of infectious diseases. It involves building realistic models of the complex host contact patterns that underlie disease transmission, and then applying methods from statistical physics to predict outbreak dynamics. This project will extend these methods to study disease spread through modular host populations - those consisting of highly intra-connected subgroups - and then address specific questions about viral diseases in Serengeti carnivore populations. This project will also train students from kindergarten through graduate school through participation in the research, new courses, and the development of web-distributed teaching materials on infectious disease ecology and evolution.

As the field of mathematical biology becomes increasingly critical to scientific progress, there is demand for a new generation of computational tools and researchers who can use them effectively. This project will yield a more versatile mathematical framework for predicting disease spread and a better understanding of multi-host disease transmission, a topic of great importance to the health of humans, domestic animals, and wildlife populations of conservation concern. It will provide young men and women from diverse backgrounds the opportunity to experience the utility, accessibility and excitement of mathematical biology.

Many decisions are made in the face of risk and uncertainty and under circumstances where each person's individual decision affects other people's outcomes. The 2009 outbreak of swine flu (influenza A H1N1) provides a chance to study how lay people's perceptions of risks change over time and how those perceptions drive willingness to engage in precautionary behaviors (such as anti-viral medication use or self quarantine) that not only have consequences for the person who engages in the behaviors but that also affect the risks for others in the population. The investigators use an internet survey regarding people?s risk perception and willingness to take precautionary measures to query cohorts of US adults starting a few days after the first news of the outbreak and continuing at regular intervals throughout the epidemic. The research examines the relationships over time among information from the media about the influenza outbreak, perceptions of risk, and interest in taking precautionary measures. This outbreak of a new infectious disease represents a rare opportunity to study how risk perceptions and precautionary behaviors change over time in response to information about how the hazard unfolds. The results could have implications not only for public health responses to natural disasters but also for an understanding of the basic mechanisms underlying risk perception and decision making under uncertainty.

DESCRIPTION (provided by applicant): Influenza transmission and its resulting morbidity and mortality are of great concern to society. Strategic intervention may greatly reduce these factors, but the effectiveness of an intervention depends on public adherence, and, more generally, on individual decision making in response to actual and perceived health risks. This project aims to define optimal intervention strategies and policies that significantly improve intervention adherence for both epidemic and pandemic influenza outbreaks. To meet this objective, we will integrate knowledge and methods from epidemiology, mathematical modeling, economics, game theory, and experimental psychology. Contact patterns, which are often dynamic and highly variable, fundamentally influence the spread of disease. These patterns change as individuals make decisions to be vaccinated, accept treatment, take hygienic precautions, or avoid work, school, or public spaces. We will develop new epidemiological models that explicitly consider individual-level perceptions and decisions and their impacts on the contact networks underlying influenza transmission. These models will capture the evolutionary dynamics of influenza, including antigenic drift and the emergence of antiviral resistance. We will apply game-theoretical methods to these models to evaluate different influenza intervention strategies, including vaccination, antiviral-based interventions, and non-pharmaceutical interventions, and to identify strategic opportunities for improving adherence through informational and incentive programs that change individual perceptions and decisions. The contact patterns and psychological components of the models will be based on Bayesian analysis of census data, workflow and recreational mobility data, and real-time influenza surveillance data, as well as on survey studies that evaluate public knowledge and perceptions about the disease. The latter will also provide information on adherence behavior, contact patterns, and the impact of real-time influenza-related decisions on these patterns. Integrating realistic, perception-driven individual-level decision making into epidemiological models will facilitate the evaluation of interventions and the development of strategies to improve adherence both in epidemic and pandemic outbreaks of influenza. Public Health Relevance: By integrating realistic, perception-driven individual-level decision making into epidemiological models, this project will advance methods for predicting the success of interventions and for developing effective strategies for improving intervention adherence.

Ticks feed on many different host species, including humans. While doing so, they can contract and spread infectious diseases but it remains unclear which hosts and which ticks drive the prevalence of tick-borne diseases. This research focuses on the bacterium, Ehrlichia chaffeensis, which is carried by the Lone Star tick and is known to cause disease in humans and to infect white-tailed deer. Lone Star ticks also feed on a variety of other species, including raccoons and Virginia opossums, whose role in E. chaffeensis outbreaks is unclear. The aim of this project is to advance our understanding of the multi-host transmission of E. chaffeensis, with a particular focus on how ticks choose their hosts. Using a combination of experiments on tick behavior, studies of tick genetic variation, and mathematical modeling of disease transmission, this project will identify how tick feeding preferences and the variety of host species contribute to the spread of E. chaffeensis.

This work is relevant to public health and wildlife management, and may lead to the development of effective tick-borne disease prevention and control strategies. Some of the field work will be conducted on state- and federally-owned land, and the findings will be shared with the managers of these properties. Several undergraduate students will participate in the research, and will be trained in cutting-edge molecular laboratory techniques that will both enhance their studies and advance the goals of this project.

DESCRIPTION (provided by applicant): We aim to improve infectious disease surveillance and control through mathematical modeling, optimization, and translational collaborations with public health decision makers. Methodologically, we will advance the application of mathematical modeling to inform public heath policy decisions by (i) integrating large-scale optimization, economic analyses, and uncertainty quantification into mathematical models of disease transmission in complex and dynamic populations, and by (ii) developing goal-oriented optimization methods for integrating diverse data sources to improve infectious disease surveillance systems. We will apply these approaches using data on influenza, respiratory syncytial virus (RSV), pertussis, West Nile virus (WNV), and dengue from around the world to elucidate the complex drivers of outbreaks and control and to identify highly effective, economical, and feasible control policies. We will disseminate our models and results to public health authorities and develop user-friendly modeling tools to facilitate preparedness and real-time decision- making regarding the optimal distribution of limited disease control resources. Thus, our interdisciplinary research will expand the methodological toolkit for modeling infectious disease dynamics, provide better strategies for tracking and mitigating epidemics, and make science, data, and models more broadly accessible to public health agencies engaged in the global fight against infectious diseases.

? DESCRIPTION (provided by applicant): The ever-increasing accumulation of data continues to outstrip the graduate training needed to meaningfully mine the data collected. This issue is further complicated by the fact that holistic training in biomedical big data analysis requires PhD level expertise in not one, but three core research areas: (1) biology (2) statistics and (3) computer science, yet the majority of traditional PhD training programs demand that students choose just one of these areas as their focus. A growing number of biomedical PhD students are recognizing the need to develop data analysis and computational biology skills, at the same time that a growing number of computer science and statistics PhD students are realizing that their marketability could be substantially expanded if they knew how to apply their skills to solve outstanding problems in the health arena. The purpose of this pre-doctoral training program we are proposing to introduce at The University of Texas at Austin is for the trainee to become an expert in one of the following areas: 1. Statistics (STAT); 2. Computer Science (CS); 3. Computational science, engineering, and mathematics (CSEM); or 4. Biology (via a PhD in one of a. neuroscience [NS]; b. ecology, evolution, and behavior [EEB]; c. cell and molecular biology [CMB]; or d. Biomedical Engineering [BME]) while also obtaining essential training in all three core areas (statistics, computer science, and biology). This will ideally equip the graduates from this program to make important scientist c discoveries using big data. The challenge is in developing a program that trains these multidisciplinary skills without sacrificing strength in ther core PhD area. This is an exciting opportunity for the new PhD program in statistics and the already established PhD programs involved, and it is consistent with the interdisciplinary emphasis of all the faculty involved with this application. This training program will differ from he standard training programs at UT- Austin by incorporating new courses, a new seminar/workshop, and program-specific rotations during year 3. These rotations will provide opportunities for trainees to work in research labs in the new University of Texas at Austin Dell Medical School and the Dell Pediatric Research Institute. Research at the interface of these three areas requires excellent collaborative skills. In addition to subject matter training, we wil help trainees develop strong oral and written communication skills. This combination of knowledge and communication will equip the trainees to make major contributions to big data biomedical science. We anticipate funding five trainees per year. Trainees will formally start the training program during year 2 of their PhD programs.

Zika virus (ZIKV), a flavivirus, has recently spread to the Americas and is currently associated with a major pandemic in human populations in South, Central, and North America. ZIKV infections in pregnant women has been associated with severe birth defects, notably microcephaly; thus, it is critical to understand the epidemiology and spread of this viral infection. ZIKV, which can be spread by mosquitos, was first isolated in Africa from non-human primates, suggesting that these animals may serve as a reservoir for the virus. However, much less is known about the reservoirs or spread of the virus in the Americas. Countries where outbreaks are occurring, including Brazil, Columbia, Ecuador and Mexico, are home to a number of species of Neotropical primates, and the human and non-human primate populations occupy overlapping areas. This suggests that there is the potential for these animals to be a reservoir for maintenance and spread of the virus in human populations. Our first aim is to determine whether Neotropical primates are a potential reservoir for ZIKV. We have assembled an expert team of primatologists, virologists, enteric infectious disease researchers, and epidemiologist who will work collaboratively to answer this question. We will sample 12 species of Neotropical primates at 8 field sites in 4 countries. We will determine the presence of active infection or carriage of ZIKV by quantitative RT-PCR, and we will test for antibodies against the virus as an indicator of prior exposure. Additionally we will obtain blood samples from human volunteers in proximal geographic areas to determine if human infection positively correlates with ZIKV presence in Neotropical primates. Molecular phylogenetic analysis of ZIKV gene sequences from human and non-human primates will help determine the epidemiology and spread of the virus in these populations. Because obtaining blood samples from the animals is a difficult and invasive procedure, our second aim is to develop a rapid, noninvasive screen for the virus. Some flaviviruses are shed in the feces of primates, therefore we will analyze fecal samples from primates that have ZIKV-positive blood cultures or serology to determine whether the virus is present. Urine, which has been used to detect ZIKV, will be an alternative method. We will modify the protocol to optimize ZIKV detection from this non-invasive sampling method. The results of this study will provide critical information on the role of Neotropical primates in the spread of Zika in the Americas.

Project Abstract/Summary Our interdisciplinary research team will develop algorithms to accelerate the detection of respiratory virus outbreaks at an unprecedented local scale in US cities. We propose to advance outbreak detection by combining machine learning data integration methods and spatial models of disease transmission. The dynamic models that will be developed will provide mechanistic engines for distinguishing typical from atypical disease trends and the optimization methods evaluate the informativeness of data sources to achieve specified public health goals through the rapid evaluation of diverse input data sources. Working with local healthcare and public health leaders, we will translate the algorithms into user-friendly online tools to support preparedness plans and decision-making. Our proposed research is organized around three major aims. In Aim 1, we will apply machine learning and signal processing methods to build systems that track the earliest indicators of emerging outbreaks within seven US cities. We will evaluate non-clinical data reflecting early and mild symptoms as well as clinical data covering underserved communities and geographic and demographic hotspots for viral emergence. In Aim 2, we will develop sub-city scale models reflecting the syndemics of co-circulating respiratory viruses and chronic respiratory diseases (CRD) that can exacerbate viral infections. We will infer viral transmission rates and socio-environmental risk cofactors by fitting the model to respiratory disease data extracted from millions of electronic health records (EHRs) for the last nine years. We will then partner with clinical and EHR experts to translate our models into the first outbreak detection system for severe respiratory viruses that incorporates EHR data on CRDs. Using machine learning techniques, we will further integrate other surveillance, environmental, behavioral and internet predictor data sources to maximize the accuracy, sensitivity, speed and population coverage of our algorithms. In Aim 3, we will develop an open-access Python toolkit to facilitate the integration of next generation data into outbreak surveillance models. This project will produce practical early warning algorithms for detecting emerging viral threats at high spatiotemporal resolution in several US cities, elucidate socio-geographic gaps in current surveillance systems and hotspots for viral emergence, and provide a robust design framework for extrapolating these algorithms to other US cities.

PROJECT SUMMARY We will develop a data-driven model of seasonal and pandemic influenza transmission throughout the US to accelerate robust assessments of multifaceted influenza intervention strategies. We will work closely with the CDC Modeling Network to advance the fidelity, transparency and translation of models as an evidence base for influenza policy making, prevention and control. This project extends a metapopulation model of influenza transmission within and between 217 major metropolitan areas in the US that we are developing in collaboration with the CDC Modeling Network. The model includes travel between cities, age- and risk-group specific susceptibility, probability of clinical outcomes, intervention efficacies and uptake rates, as well as the impacts of local climate and school calendars on transmission rates. Using a range of public health, epidemiological, societal and economic metrics, the model can flexibly evaluate thousands of candidate intervention strategies, including time- and location-based combinations of vaccines, antivirals, and social distancing measures with potential subgroup-specific prioritization. Our proposal includes four major aims. In Aim 1, we will extend our US Influenza Model to include the co- circulation of multiple viruses competing via transient heterosubtypic immunity. We will derive new estimates for the duration and magnitude of heterosubtypic immunity and design strain-specific strategies for effectively controlling co-circulating seasonal and pandemic influenza viruses. In Aim 2, we will evaluate intervention strategies that leverage newly approved and combined antiviral drugs. We will fit within-host viral dynamic models to clinical data on new antivirals to estimate the efficacy of various drug regimens in different subpopulations with respect to disease severity, infectiousness, and the risk of antiviral resistance. In Aim 3, we will build a granular within-city model of influenza transmission based on abundant data and local collaborations with public health and healthcare leaders in the Austin-Round Rock Metropolitan Area. We will apply the model to elucidate socioeconomic and geographic disparities in influenza risk and design interventions that ameliorate such gaps. In Aim 4, we will build an interactive visualization platform that allows users to specify epidemic scenarios, implement layered interventions as simulations unfold, and view the model dynamics through the lens of a surveillance module based on the CDC?s FluView Interactive portal. We will work extensively with the CDC Modeling Network to build a diverse portfolio of validated models and best practices for collaborative decision support. Our projects will contribute flexible models for the evaluation of multifaceted influenza interventions, elucidate competition among influenza viruses and the efficacies of novel antivirals, and provide insights into socioeconomic disparities in influenza burden. Furthermore, our innovative visualization tool will broadly support the translation of science to public policy.

Despite decades of pandemic preparedness efforts, COVID-19 took the world by surprise. The national and global health community did not foresee the extend of challenges associated with charting ecosystems of potential threats, elucidating interdependent behavioral and political dynamics, and equipping decision makers with nimble science, strategies, and training. This project imagines a better prepared future for responding to pathogen threats and aims to build the basis for a Center for Pandemic Decision Science that will break down the persistent silos separating the academic, government, and industry institutions that have collectively, but not always collaboratively, guided pandemic preparedness and response efforts. Over the next 18 months, a team of 35 natural scientists, social scientists, computer scientists, engineers, physicians, and public health officials from 10 institutions will host a series of interdisciplinary workshops and undertake pilot studies that will lay the intellectual and organizational groundwork for tackling three fundamental research questions - How can we anticipate the vast universe of potential pathogen threats and detect them at their source? How will people, communities, and leaders behave and respond to emerging threats? How can we integrate science into decision making across the preparedness, containment, and response spectrum? For each of these questions, the team will identify immediate and long-term goals for basic research, training of scientists and decision makers, and development of predictive intelligence capabilities. These activities will establish a new research paradigm that is grounded in complex systems modeling, integrate perspectives and methods across diverse disciplines, and engage extensively with decision makers to ensure that the science is both relevant and practical. The project will broadly engage the research and public health communities through workshops and colloquia, train a diverse group of students, develop an undergraduate teaching module in pandemic decision science, and disseminate resulting insights and products through online platforms, media, and peer-reviewed publications. <br/><br/>Throughout the COVID-19 pandemic, this interdisciplinary team of scientists, engineers, social scientists, and clinicians has been developing mathematical models to provide situational awareness, actionable forecasts, and time-sensitive policy analyses for decision makers on all scales, from local to global. The team has partnered closely with government agencies, healthcare systems, and schools to provide predictive intelligence as the virus, human behavioral responses, and the arsenal of effective countermeasures continually shifted. This work has elucidated three interlinked grand challenges. The first is the global failure of imagination in anticipating novel pathogen threats, despite decades of concerted preparedness efforts. The second is the fundamental inability to anticipate individual, collective, and governmental behavioral responses during the threats. The third is the persistent gap between science and the decisions made by individuals, agencies, and policymakers. This project will launch a Center for Pandemic Decision Science that tackles these grand challenges by advancing the integration of complex systems science into pandemic decision making. As a first step, the Center will conduct a series of inclusive, multidisciplinary workshops and pilot studies that will spur innovative interdisciplinary research into the emergence and detection of novel threats, the dynamics of people’s behavior, and the design and adoption of adaptive decision paradigms for preventing, tracking and mitigating pathogen threats under uncertainty. These activities will hone the Center’s vision, identify key research priorities, and embark on a diverse portfolio of educational and community building activities to advance the science and practice of pathogen preparedness. <br/><br/>This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG). This project was also funded in collaboration with the CDC to support research projects to further advance federal infectious disease modeling, prevention and response capabilities.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.