Richard E. Lenski, Ph.D. - US grants

Pleiotropic trade-offs and compensatory adaptations. Trade offs between components of an organism's fitness are frequently invoked, although their generality is a matter of debate. Of particular significance are trade-offs between competitive ability and resistance to selective agents such as pesticides and parasites. If resistant organisms are competitively inferior to their sensitive counterparts, then the relative abundance of resistant organisms can be reduced by removing the selective agent. Two questions must be addressed to determine whether resistant organisms are, in fact, inferior competitors. Are there antagonistic pleiotropic effects of the genes conferring resistance on other components of fitness? Do these antagonistic effects persist, or are they eventually eliminated by compensatory adaptations? The bacterium Escherichia coli, its virus T4, and the antibiotic chloramphenicol will be used to examine pleiotropic trade-offs and compensatory adaptations. E. coli is ideal for this research because of its rapid evolution and potential for formal genetic analysis. Parallel experiments on chromosomal resistance to T4 and extrachormosomal resistance to chloramphenicol will proceed as follows: (i) select and geneticaly characterize resistant genotypes; (ii) compete sensitive and resistant genotypes to assay antagonistic pleiotropic effects; (iii) allow sensitive and reistant genotypes to evolve for many generations; (iv) compete evolved sensitive and resistant genotypes fo determine if compensatory adaptations have reduced antagonistic pleiotropic effects; and (v) identify the genetic basis of compensatory adaptations. Preliminary studies demonstrate both pleiotropic trade-offs and compensatory adaptations associated with resistance by E. coli to the virus T4.

Funding from the prestigious five-year Presidential Young Investigator award will be used to support research on the population biology of bacteria and their plasmids. Their large populations and short generations permit an experimental, not merely correlative, approach to the study of evolutionary processes. Research efforts will fall into four major areas: genetic basis of fitness in evolving populations of Escherichia coli, fitness consequences associated with alternative modes of regulation of tetracycline resistance genes in E. coli, evolution and genetics of an association between E. coli and plasmid pACYC184, and effects of recombinant functions (particularly chlorobenzoate degradation) on plasmid stability and host fitness in Pseudomonas species. Precise measurements of the effects of particular genetic variants, both chromosomal and extrachromosomal, on bacterial net growth, or relative fitness, are required for these projects.

Body size influences the physiology, behavior and community level interactions of organisms, but little is actually known about the factors controlling it. Small mammalian herbivores, isolated on islands and relieved of predation and competition pressures, offer an ideal situation for the study of selection on size. Island herbivores subsist on a food supply which is not only less diverse, but less abundant than in mainland habitats. Theory predicts that larger animals will have lower specific metabolic rates, yet their gut size increases approximately isometrically. Thus, selection on body size may result from the greater efficiency of larger animals in extracting energy from low-quality foods. A comparative approach will be used to test this hypothesis. Selection on size will be measured on two islands--one where gigantism is exhibited, one where it is not. Dietary composition and quality will be measured and analyzed, then related to biological traits associated with size.

9421237 Lensky Microorganisms perform various ecological functions that may be either beneficial (e.g., decomposition of wastes) or deleterious (e.g., pathogens of humans and crops), depending on the particular species and environmental context. Much is known about the biochemistry and molecular genetics of certain microorganisms, but very little is known about the population dynamics of microorganisms and how various ecological and genetic factors affect microbial evolution. The proposed research seeks to identify and quantify important ecological and genetic processes that may influence the dynamics of microbial populations. There are two major objectives (1) To model mathematically the dynamics of host-pathogen interactions in order to understand the ecological and evolutionary forces that shape virulence, especially in relation to specific modes of pathogen transmission and (2) To quantify experimentally the genetic variation in bacterial populations for such important phenotypic traits as competitiveness and cell size, including the contribution of deleterious mutations, substitution of favorable mutations, and balanced polymorphisms to this variation.

9509034 Jarosz The potential for cytoplasmic hyperparasites to influence the dynamics of plant-pathogen interactions will be the focus of this collaborative research project. The theoretical portion of the research has three objectives: (1) to identify conditions that allow the hyperparasite to invade a pathogen population, (2) to generate predictions on the effects that hyperparasite invasion has on plant-pathogen population dynamics, and (3) to generate predictions on long-term evolutionary dynamics of the plant-pathogen-hyperparasite system. Empirical studies will examine the chestnut blight system involving the American chestnut, the fungal parasite of chestnut, and a double stranded (ds) RNA hyperparasite of the fungus. One experiment will test predictions for the distribution of dsRNAs within chestnut populations, and the diversity of vegetative compatibility groups with and among fungal populations. A second study will evaluate the genetic interaction between the fungus and dsRNA in determining pathogen virulence. The relation between pathogen virulence and disease transmission rates will be evaluated in a third experiment. This work will determine the influence of hyperparasite infection on the relationship between virulence and transmission rate. The last experiment will test model predictions on the relative transmission rates of dsRNA-free and dsRNA-infected lines that allow dsRNAs to invade fungal populations.

9507416 Changes in temperature have significant effects on the rates of biochemical reactions. We know how short term changes in temperature affect the growth rate, respiration and growth efficiency of microbes, but we know relatively little about how these factors change during evolutionary adaptation to change in temperature. This research will contrast the effects of temperature on energetics and growth of ancestral strains of the bacterium Escherichia coli with changes that occur during evolutionary adaptation of the bacteria. Multiple lines of bacteria derived from a common stock will be cultured in new thermal environments, including high, low, and variable temperature. For each line, growth rate, respiration rate, and the efficiency with which the bacteria convert food into new bacterial tissue will be determined. The research will determine how adaptation to a particular thermal regime alters the ability of the bacteria to grow and compete in other thermal environments. Replication of selection experiments with multiple lines of bacteria will reveal the range of variation in genetic responses to new thermal environments. This research will increase our ability to predict the long term response of bacteria to environmental variation, which is important because microbes are important energy consumers and nutrient recyclers in natural ecosystems. It will identify traits that confer adaptive advantages at different temperatures, and demonstrate the impact of those adaptations on bioenergetics. The identification of traits that increase growth efficiency at different temperatures will be of potential value in commercial culture operations.

Lenski
9801538

It is often assumed that evolutionary specialization by an organism must lead to losses of performance and functionality in other environments, but there is little evidence to support this theory. Part of this uncertainty stems from a problem of perception: how does one operationally define a specialist? Also, a paucity of direct experimentation has limited the consideration of alternative population genetic processes that can lead to such losses of function. We describe here a long-term experiment in which twelve populations of the bacterium, Escherichia coli, have evolved in the laboratory for 20,000 generations. During this time, all of the populations became progressively better adapted to growth in a simple glucose-limited environment. We will examine the consequences of this evolution for the bacteria's capacity to utilize a variety of substrates other than glucose, and thereby quantify the extent to which specialization led to losses of function. We will also evaluate the relative contributions of two population processes, tradeoffs (antagonistic pleiotropy) and drift (mutation accumulation), to the observed changes in function.

Lenski et al.
9981397

The principal investigators define biocomplexity as the dynamic web of interactions among genes, organisms, and environments. They will investigate the emergence of biocomplexity and examine its consequences for the performance of living organisms and ecological communities. Parallel experiments will be performed with two very different systems, in order to study general principles. One system employs bacteria, and the other system is digital. The latter consists of special computer programs that self-replicate, mutate, and evolve novel sequences of instructions to solve problems. One set of experiments will monitor the evolution of ecosystem complexity, in which a single progenitor diverges into multiple types that perform distinct functions by exploiting different resources. Follow-up experiments will examine the effects of removing member species on the remainder of the community. Another project will develop the software used for studying digital organisms into an educational tool.
This project, which is being supported by the Directorates for Biological Sciences, Computer Information Science and Engineering, Engineering, and by the division of Mathematical Sciences and the MPS Office of Multidisciplinary Activities, will impact several scientific fields, and the findings may provide basic information useful for both environmental and biotechnological applications. For example, ecologists may find principles useful for improving the performance of beneficial organisms in the environment. Computer scientists may discover computational strategies, evolved by real organisms that can be employed in developing more complex software.

This project examines long-term evolutionary and genetic changes in
populations of bacteria living for thousands of generations in constant
thermal conditions. The PIs will determine the consequences of adaptation to
these constant environments, specifically, the increase in relative fitness
and efficiency of growth. The PIs will also examine whether these changes are
accompanied by loss of function in other thermal environments, that is,
whether the range of temperatures over which the bacteria can grow and
reproduce declines and whether they become less fit and less efficient in
other thermal environments than that in which they are cultured. The PIs will
make these measurements on two sets of bacterial lineages, one that has
been cultured at 37 C for 20,000 generations and another that will be
cultured at 37 C for 2,000 generations, after having adapted to other
thermal environments. The results of these studies will provide important
experimental tests of predictions for the responses of biological systems
to global climate change and for general evolutionary theory concerning
environmental adaptation.

Interdisciplinary (99) This project is developing Avida-ED, an educational simulation based on a proven artificial life research platform. In the digital environment of Avida-ED, one can observe the evolution of self-replicating, autonomous digital organisms, and perform experiments to test evolutionary hypotheses in ways that no other tool allows. This unprecedented ability to introduce experimental evolution in biology classrooms lets students learn about basic principles of scientific method and see for themselves how evolutionary theory is confirmed. We are developing and testing the Avida-ED environment, formally accessing its pedagogic efficacy in the classroom for undergraduate science and non-science majors, and distributing the software and associated materials to biology educators. The project is addressing significant science education challenges identified in national standards in a novel, rigorous, and promising manner. Our initial results show that by being able to observe the evolutionary mechanism in action, students come to understand the key biological insight of how evolution produces complex information. The intellectual merit of this project is the novel use of information technology - artificial life - that was inspired by evolutionary principles, and has become a revolutionary research tool for biologists. The broader impacts of this project lie in the application of this simulation and the developed materials in a wide range of undergraduate science curricula.

Evolution has a remarkable ability to design organisms possessing novel features that enable them to survive, and even thrive, in challenging environments. To a human observer, the biochemical and physiological complexity of living organisms is amazing, leading some to disbelieve that such biological designs could have been accomplished by evolution alone. Considering the levels of biocomplexity found throughout nature - the development of an embryo from a single-cell; the delicate balance of an ecosystem in a rainforest; all the way to the reasoning power of the human brain, complex and elegant adaptive designs abound. As we slowly understand more details about how evolution produces such complex traits, it becomes clear that this is a powerful constructive force that we need to fully understand in order to harness its potential in solving difficult computational design problems.
The twin goals of this project are to learn more about the mechanisms by which evolution produces innovative complex features, and then to apply this newfound understanding to develop a new generation of evolution-based algorithms for solving computational problems. In the process, we will use a mathematical framework for the study of complexity in biological systems to better understand the natural design process. We will start this work by answering fundamental questions in evolutionary biology and ecology including: Why do complex designs arise faster in multi-species ecosystems? If organisms have the capacity to modify their environment and communicate with one another, will this spur open-ended complexity growth? and How can we harness these forces to solve design problems?
The evolving system that we use in this study is an artificial one, based on self-replicating computer programs, that nonetheless exhibit the key characteristics of natural evolving systems - most notably the ability to produce novel complex traits and innovative solutions to problems. These "digital organisms" exist in a user-defined computational environment, and their genomes are composed can theoretically perform any computable mathematical function. Indeed, we have witnessed a wide variety of unexpected and innovative adaptations arise through evolution in Avida. This system allows us to explore fundamental questions in evolutionary biology with speed, detail and precision that would not be possible in any natural system. Ideally it is a tool that will allow us to fully understand the origins of biocomplexity, and constructively make use of these same forces that were able to produce human intelligence in the natural world.

Bacteria undergo many thousands of generations during a human lifetime, and thus they can evolve rapidly with potentially important consequences. In this long-term experimental research, the dynamics of bacterial evolution are being monitored, measured, and preserved for thousands of generations in twelve populations of Escherichia coli in order to examine the dynamics of evolutionary change, the predictability of outcomes, and the coupling of phenotypic and genetic changes. Specific objectives include daily propagation of the experimental populations, periodic storage of samples, and competitions between the evolved and ancestral strains. A key feature of this system is that the ancestral bacteria can be frozen and revived for later study.

Owing to the important roles of bacteria in nature, it is imperative that society understands the principles that govern their evolution. This project will provide fundamental knowledge about these principles that may inform policies on managing infectious diseases and environmental practices. For example, the principal investigator has consulted with federal agencies about the relevance of this research for predicting the rate of genetic change in pathogens, including those used in bioterrorism. This project also addresses a subject of great interest to many members of the public, specifically how organisms adapt to their environments.

ABSTRACT
0750787
Cheng
Michigan State

The Problem. Computing technology now affects nearly every dimension of modern society: managing
critical infrastructure such as power grids and telecommunication networks; supporting electronic commerce
and medical information systems; and controlling the operation of aircraft and automobiles. This pervasiveness of computing technology, coupled with its rapidly increasing complexity, gives rise to the need for computer systems that are able to adapt to changing conditions. In the last decade, extensive research has been conducted on many aspects of self-adaptive software systems. Examples include adaptive software mechanisms [1 20]; software-architecture-based techniques for supporting dynamic adaptation [21 38]; adaptable and extensible operating systems [39 42]; and requirements-level and formal methods-based techniques [43 52]. This research has greatly improved our understanding of adaptive software and several key supporting concepts, including computational reflection [53 55], separation of concerns [12, 56], component-based design [57, 58], and transparent interception of program flow [59 61].
Despite these advances, designing an adaptive software system remains a very challenging task. We speculate
that much of the difficulty is due to the fact that adaptive software is being designed and implemented using
traditional tools and environments intended for the development of non-adaptive software. We contend that the
full potential of dynamically adaptive software systems cannot be realized without fundamental advances in
the corresponding development environments. Such environments must enable developers to explicitly address
those aspects of the design problem that distinguish adaptive systems from non-adaptive systems. These issues
include anticipating how the software may need to adapt in the future, constructing decision-making software
to govern the adaptation, and ensuring that system integrity is not compromised by adaptation.

Even a rather small population of bacteria may contain millions of cells that undergo several generations every day. As a result, bacteria can quickly evolve and adapt to their environments. In this renewal project, the investigators will propagate, monitor, preserve, and study populations of Escherichia coli while they evolve in a defined laboratory environment for five years, bringing the duration of this long-term experiment to over 60,000 cell generations. To measure the adaptation of the bacteria, the researchers allow the evolved bacteria to compete against their ancestral strain. The researchers also compare these adaptive dynamics with changes in genome sequences in order to elucidate the rates, mechanisms, and predictability of bacterial evolution.

This project offers a unique opportunity to observe and quantify the process of evolution owing to the speed with which the bacteria evolve and the careful design of this long-term experiment. At the same time, bacteria are extremely important in several respects: as essential players in ecosystem processes, as agents of disease in humans and many other species, and as workhorses in biotechnology. Therefore, in-depth knowledge about the rates, mechanisms, and outcomes of bacterial evolution has many important implications and applications for science and society. The scientific aims and findings of this on-going research project have been and will be presented to the public via outreach activities including interviews with journalists, seminars for non-specialists, and web-based presentations hosted by the NSF and other outlets. Biological samples and datasets generated by this study will continue to be shared with the scientific community, including genome sequences placed in public repositories.

The Bio/computational Evolution in Action CONsortium (BEACON) is a Science and Technology Center (STC) that enables research on evolutionary dynamics in natural and artificial systems and training of multi-disciplinary scientists in bio/computation, with a unique focus on the intersection of evolutionary biology, computer science, and engineering. The Center will enhance the development of applications of computational methods in biology, the use of artificial intelligence in computer science, and the enhancement of genetic algorithms in engineering design. Evolution by natural selection defines an algorithmic approach to finding solutions for complex problems; computer scientists and engineers have harnessed similar algorithms to a diversity of challenges that require optimization over multiple competing dimensions. Likewise, biologists have begun to employ digital modeling of the evolutionary process to examine evolution of complex biological structures and patterns in areas such as paleontology and the gene networks, which defy experimental manipulation in vivo. The Center will promote these interdisciplinary efforts by coordinating activities through three thrust groups: (1) Evolution of Genomes, Networks, and Evolvability, (2) Evolution of Behavior and Intelligence, and (3) Evolution of Communities and Collective Dynamics.

This center has the potential to transform both biology and computational sciences by developing digital experiments to test and apply fundamental principles of evolutionary biology. The possible impacts will be far reaching: from cyber-security to everyday computing applications, from the evolution of disease resistance to the self-organization of social behavior. The BEACON center will train the next generation of interdisciplinary scientists and educate the public about evolution and its role in solving real-world problems through significant educational outreach for K12 students and science museums.

Intellectual Merit: Human activities are driving up atmospheric carbon dioxide concentrations at an unprecedented rate, perturbing the ocean's carbonate buffering system, lowering oceanic pH, and changing the concentration and composition of dissolved inorganic carbon. Recent studies have shown that this ocean acidification has many short-term effects on phytoplankton, including changes in carbon fixation among others. These physiological changes could have profound effects on phytoplankton metabolism and community structure, with concomitant effects on Earth's carbon cycle and, hence, global climate. However, extrapolation of present understanding to the field are complicated by the possibility that natural populations might evolve in response to their changing environments, leading to different outcomes than those predicted from short-term studies. Indeed, evolution experiments demonstrate that microbes are often able to rapidly adapt to changes in the environment, and that beneficial mutations are capable of sweeping large populations on time scales relevant to predictions of environmental dynamics in the coming decades. This project addresses two major areas of uncertainty for phytoplankton populations with the following questions: 1) What adaptive mutations to elevated CO2 are easily accessible to extant species, how often do they arise, and how large are their effects on fitness? 2) How will physical and ecological interactions affect the expansion of those mutations into standing populations? This study will address these questions by coupling experimental evolution with computational modeling of ocean biogeochemical cycles. First, cultured unicellular phytoplankton, representative of major functional groups (e.g. cyanobacteria, diatoms, coccolithophores), will be evolved under simulated year 2100 CO2 concentrations. From these experiments, estimates will be made of a) the rate of beneficial mutations, b) the magnitude of fitness gains conferred by these mutations, and c) secondary phenotypes (i.e., trade-offs) associated with these mutations, assayed using both physiological and genetic approaches. Second, an existing numerical model of the global ocean system will be modified to a) simulate the effects of changing atmospheric CO2 concentrations on ocean chemistry, and b) allow the introduction of CO2-specific adaptive mutants into the extant populations of virtual phytoplankton. The model will be used to explore the ecological and biogeochemical impacts of beneficial mutations in realistic environmental situations (e.g. resource availability, predation, etc.). Initially, the model will be applied to idealized sensitivity studies; then, as experimental results become available, the implications of the specific beneficial mutations observed in our experiments will be explored.

Broader Impacts: This interdisciplinary study will provide novel, transformative understanding of the extent to which evolutionary processes influence phytoplankton diversity, physiological ecology, and carbon cycling in the near-future ocean. One of many important outcomes will be the development and testing of nearly-neutral genetic markers useful for competition studies in major phytoplankton functional groups, which has applications well beyond the current proposal. An inherent component of the proposed work is the integration of education and outreach to provide advanced interdisciplinary training to undergraduate students, while involving both them and the PIs in community outreach and education related to ocean acidification. At MSU, undergraduate students will participate in bench work as well as computer modeling, and will therefore gain interdisciplinary research experience. Among other projects, these students will produce a simplified version of the ocean modeling software that may be implemented as an "app" for use with education and outreach programs. At least two additional undergraduates will be recruited to work on the project at Columbia and MIT, with a focus on broadening participation in STEM through hands-on training. Additionally, visits with secondary education institutes will be arranged to talk about ocean acidification and microbial "evolution in action." These visits will be facilitated by instructional resources focused on ocean acidification, microbiology, and evolution, which are available from two NSF STCs, BEACON (MSU) and CMORE (MIT/Columbia).

Recent science education reform efforts (Vision and Change in Undergraduate Education: A Call to Action, the AP Biology Frameworks and the Next Generation Science Standards [http://www.nextgenscience.org/]) emphasize the importance of evolution as a major unifying theme in the science curriculum and in understanding life processes. They further identify inquiry-based laboratory experiences as key to helping students gain a better understanding of evolution and highlight the need for controlled studies to test such interventions. A major impediment in involving students in the experimental study of evolutionary processes is that the evolutionary process is difficult or impossible to demonstrate in the classroom. Current efforts rely on simulations with limited opportunities to test hypotheses and to learn how science is practiced. This project specifically and directly addresses these challenges through ongoing development and testing of Avida-ED, an educational software tool derived from an experimental digital tool used by scientists to study bio/computational evolution. It was first developed under an NSF CCLI award (031484). Activities planned will provide in-depth faculty development through a national series of workshops, and an expansion of an ongoing national study of the effectiveness of this approach for learning about evolution and the nature of science.

Avida-ED engages students in observation and manipulation of populations of self-replicating, autonomous digital organisms while they are actively evolving. It is built on top of a proven experimental evolution research platform, so students can perform experiments to test evolutionary hypotheses that they generate on their own in the same modeling system used by scientists. Evidence from on-going national classroom studies indicates the unique value of being able to directly observe evolution in action in this digital environment. Pilot studies show that Avida-ED is effective in (i) overcoming common misconceptions about evolutionary mechanisms and (ii) increasing acceptance of evolutionary science. This study examines these issues in more detail, including cognitive and non-cognitive elements of active learning when using digital evolution models. These constitute some of the first quasi-experimental classroom studies in evolution education. Other activities planned include a series of national workshops to provide profession development in the use of Avida-ED to over 150 faculty

This project is funded jointly by the Directorate for Biological Sciences and the Directorate of Education and Human Resources, Division of Undergraduate Education in support of efforts to address the challenges posed in Vision and Change in Undergraduate Education: A Call to Action http://visionandchange.org/finalreport/