Marc L. Smith

Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont

BackgroundThe most diverse marine ecosystems, coral reefs, depend upon a functional symbiosis between cnidarian hosts and unicellular dinoflagellate algae. The molecular mechanisms underlying the establishment, maintenance, and breakdown of the symbiotic partnership are, however, not well understood. Efforts to dissect these questions have been slow, as corals are notoriously difficult to work with. In order to expedite this field of research, we generated and analyzed a collection of expressed sequence tags (ESTs) from the sea anemone Aiptasia pallida and its dinoflagellate symbiont (Symbiodinium sp.), a system that is gaining popularity as a model to study cellular, molecular, and genomic questions related to cnidarian-dinoflagellate symbioses.ResultsA set of 4,925 unique sequences (UniSeqs) comprising 1,427 clusters of 2 or more ESTs (contigs) and 3,498 unclustered ESTs (singletons) was generated by analyzing 10,285 high-quality ESTs from a mixed host/symbiont cDNA library. Using a BLAST-based approach to predict which unique sequences derived from the host versus symbiont genomes, we found that the contribution of the symbiont genome to the transcriptome was surprisingly small (1.6–6.4%). This may reflect low levels of gene expression in the symbionts, low coverage of alveolate genes in the sequence databases, a small number of symbiont cells relative to the total cellular content of the anemones, or failure to adequately lyse symbiont cells. Furthermore, we were able to identify groups of genes that are known or likely to play a role in cnidarian-dinoflagellate symbioses, including oxidative stress pathways that emerged as a prominent biological feature of this transcriptome. All ESTs and UniSeqs along with annotation results and other tools have been made accessible through the implementation of a publicly accessible database named AiptasiaBase.ConclusionWe have established the first large-scale transcriptomic resource for Aiptasia pallida and its dinoflagellate symbiont. These data provide researchers with tools to study questions related to cnidarian-dinoflagellate symbioses on a molecular, cellular, and genomic level. This groundwork represents a crucial step towards the establishment of a tractable model system that can be utilized to better understand cnidarian-dinoflagellate symbioses. With the advent of next-generation sequencing methods, the transcriptomic inventory of A. pallida and its symbiont, and thus the extent of AiptasiaBase, should expand dramatically in the near future.

Integrating Genomics Research throughout the Undergraduate Curriculum: A Collection of Inquiry-Based Genomics Lab Modules

We wish to let CBE—Life Sciences Education readers know about a portal to a set of curricular lab modules designed to integrate genomics and bioinformatics into commonly taught courses at all levels of the undergraduate curriculum. Through a multi-year, collaborative process, we developed, implemented, and peer-reviewed inquiry-based, integrated instructional units (I3Us) adaptable to a range of teaching settings, with a focus on both model and nonmodel systems. Each of the products is built on vetted design principles: 1) they have clear pedagogical objectives; 2) they are integrated with lessons taught in the lecture; 3) they are designed to integrate the learning of science content with learning about the process of science; and 4) they require student reflection and discussion (Figure 1; National Research Council [NRC], 2005). Eleven I3Us were designed and implemented as multi-week modules within the context of an existing biology course (e.g., microbiology, comparative anatomy, introduction to neurobiology), and three I3Us were incorporated into interdisciplinary biology/computer science classes. Our collection of genomics instructional units, together with extensive supporting material for each module, is accessible on a dedicated website (http://serc.carleton.edu/genomics/activities.html) that also provides links to bioinformatics tools and online assessment and pedagogical resources for teaching genomics. Figure 1. Pedagogical elements of the I3U, which was based on the findings of Americas Lab Report (NRC, 2005 ) and was used as the primary curricular design framework for this project. Rapid advances in genome sequencing and analysis offer unparalleled opportunity and challenge for biology educators. More data are being generated than can be analyzed and contextualized in traditional teaching or research models. Indeed, this explosion of data has spawned rapid growth in the discipline of bioinformatics, which is focused on development of the computational tools and approaches for extracting biologically meaningful insights from genomic data. At the same time, access to vast quantities of genomic data stored in publicly available databases can offer educators ways to engage undergraduates in authentic research and to democratize research that was previously possible only at research-intensive universities with massive instrumentation infrastructures. The integration of genomic and bioinformatic approaches into undergraduate curricula represents one response to the national calls for biology teaching that is more quantitative and promotes deeper understanding of biological systems through interdisciplinary analyses (National Academy of Sciences, 2003 ; Association of American Medical Colleges and Howard Hughes Medical Institute [HHMI], 2009 ; NRC, 2009 ; American Association for the Advancement of Science, 2011 ). Yet relatively few faculty members who teach undergraduate biology have expertise in the fields of genomics or bioinformatics, and they may therefore feel inadequately prepared to develop their own new curricular modules capitalizing on this dispersed abundance of available resources. Our Teagle Foundation–funded genomics education initiative, Bringing Big Science to Small Colleges: A Genomics Collaboration, was designed to address the challenges of helping faculty members integrate genome-scale science into the undergraduate classroom. The goal of the project was to create and disseminate self-contained curricular units that stimulate students and faculty alike to think in new ways and at different scales of biological inquiry. To this end, a series of three workshops over 3 yr brought together a total of 34 faculty participants from 19 institutions and a diverse array of disciplines—including biology, computer science, and science education—to develop a set of lab modules containing a substantial genomics component. We believe that these modules are suitable for integration into existing courses in the biology curriculum and are adaptable to a variety of teaching settings. The project website serves as a portal to activity sheets describing each I3U, complete with learning goals, teaching tips, and links to teaching materials, as well as downloadable resources and assessment tools (Figure 2), that can be customized by any interested educator. Each I3U was peer-reviewed by fellow participants, as well as by a professional project consultant who has extensive experience with Web-based description of teaching materials using this format (Manduca et al., 2006 ). The goals of this review process were to ensure that each I3U met the design criteria articulated above, and to evaluate whether the activity sheet provided both an easily accessible overview of the content and enough detailed information for other instructors to adapt and implement the material and its associated assessment strategies. This peer review was complemented by each participants own explicitly framed evaluation of his/her I3U through a formal reflection form (accessible at http://serc.carleton.edu/genomics/workshop09/index.html). Although these I3Us were designed for courses currently taught by the project participants within the specific institutions’ curricula, we propose that they can be inserted into other courses encompassing similar content (Tables 1 and ​and2)2) and/or learning goals (e.g., Figure 2). We have received many communications from colleagues at other institutions who have adapted our I3Us for their courses. Figure 2. Excerpt from an activity sheet from the Genomics Instructional Units Minicollection describing one of the curricular modules developed within the Bringing Big Science to Small Colleges program (for the complete activity sheet, see http://serc.carleton.edu/genomics/units/19163.html ... Table 1. List of I3Us generated in the Bringing Big Science to Small Colleges collaborative project, grouped by the general level in the curriculum in which they were originally taught Table 2. Pedagogical attributes (scale of biological organization, genomic level of analysis, and bioinformatic skills taught) of I3Us developed in this project and disseminated on the projects website One fundamental characteristic of each I3U in our collection is the focus on guided inquiry. The benefits to an undergraduate of hands-on participation in research are well documented (Nagda et al., 1998 ; Gafney, 2001 ; Hunter et al., 2007 ; Kardash et al., 2008 ; Lopatto, 2009 ). Integrating authentic research experiences into the undergraduate curriculum allows this powerful learning model to be scaled from use with only a few students to use with entire laboratory sections (Lopatto 2009 ; Lopatto et al. 2008 ). Like other national participatory genomic teaching initiatives (Campbell et al., 2006 , 2007 ; Ditty et al., 2010 ; Shaffer et al., 2010 ; HHMI, 2011 ), our model for curriculum development in genomics emphasizes synergies between student-centered research and education. However, in contrast with some of these other projects, our grassroots approach leveraged a wealth of existing expertise by providing opportunities for individual faculty members to develop, implement, modify, evaluate, and share undergraduate teaching modules that stem from their own research and/or teaching interests. In this regard, our project most closely resembles the Genome Consortium for Active Teaching, which provides faculty members and their undergraduates access to microarrays from a variety of organisms, allowing participants to define their own research questions in a model system with which they are already familiar (Campbell et al., 2006 , 2007 ). Our collaborative effort among biologists, computer scientists, and science educators has yielded a collection of pedagogical resources that can be adapted for use in a wide variety of educational settings. We invite other biologists to begin building on our work by using and providing feedback on our I3Us. Faculty who have tested materials that exemplify our design principles are encouraged to add them to our collection. For further information, please contact the corresponding author.

View-centric reasoning for Linda and Tuple Space computation

In contrast to sequential computation, concurrent computation gives rise to parallel events. Efforts to translate the history of concurrent computations into se- quential event traces result in the potential uncertainty of the observed order of these events. Loosely coupled distributed systems complicate this uncertainty even further by introducing the element of multiple imperfect observers of these parallel events. Properties of such systems are difficult to reason about, and in some cases, attempts to prove safety or liveness lead to ambiguities. We present a survey of challenges of reasoning about properties of concurrent systems. We then propose a new approach, view-centric reasoning, that avoids the problem of translating concurrency into a se- quential representation. Finally. we demonstrate the usefulness of view-centric rea- soning as a framework for disambiguating the meaning of tuple space predicate opera- tions, versions of which exist commercially in IBMs T Spaces and Suns JavaSpaces.

Evolving Robot Morphology Facilitates the Evolution of Neural Modularity and Evolvability

Although recent work has demonstrated that modularity can increase evolvability in non-embodied systems, it remains to be seen how the morphologies of embodied agents influences the ability of an evolutionary algorithm to find useful and modular controllers for them. We hypothesize that a modular control system may enable different parts of a robots body to sense and react to stimuli independently, enabling it to correctly recognize a seemingly novel environment as, in fact, a composition of familiar percepts and thus respond appropriately without need of further evolution. Here we provide evidence that supports this hypothesis: We found that such robots can indeed be evolved if (1) the robots morphology is evolved along with its controller, (2) the fitness function selects for the desired behavior and (3) also selects for conservative and robust behavior. In addition, we show that if constraints (1) and (3) are relaxed, or structural modularity is selected for directly, the robots have too little or too much modularity and lower evolvability. Thus, we demonstrate a previously unknown relationship between modularity and embodied cognition: evolving morphology and control such that robots exhibit conservative behavior indirectly selects for appropriate modularity and, thus, increased evolvability.

Cooperative Rec-I-DCM3: A Population-Based Approach for Reconstructing Phylogenies

In this paper, we study the use of cooperation as a technique for designing faster algorithms for reconstructing phylogenetic trees. Our focus is on the use of cooperation to reconstruct trees based on maximum parsimony. Our baseline algorithm is Rec-I-DCM3, the best-performing MP algorithm known-to-date. Our results demonstrate that cooperation does improve the performance of the baseline algorithm by at least an order of magnitude in terms of running time. The use of cooperation also established a new best known score on one of our datasets.

Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots

While modularity is thought to be central for the evolution of complexity and evolvability, it remains unclear how systems boot-strap themselves into modularity from random or fully integrated starting conditions. Clune et al. (2013) suggested that a positive correlation between sparsity and modularity is the prime cause of this transition. We sought to test the generality of this modularity-sparsity hypothesis by testing it for the first time in physically embodied robots. A population of ten Tadros — autonomous, surface-swimming robots propelled by a flapping tail — was used. Individuals varied only in the structure of their neural net control, a 2 x 6 x 2 network with recurrence in the hidden layer. Each of the 60 possible connections was coded in the genome, and could achieve one of three states: -1, 0, 1. Inputs were two light-dependent resistors and outputs were two motor control variables to the flapping tail, one for the frequency of the flapping and the other for the turning offset. Each Tadro was tested separately in a circular tank lit by a single overhead light source. Fitness was the amount of light gathered by a vertically oriented sensor that was disconnected from the controller net. Reproduction was asexual, with the top performer cloned and then all individuals entered into a roulette wheel selection process, with genomes mutated to create the offspring. The starting population of networks was randomly generated. Over ten generations, the population’s mean fitness increased two-fold. This evolution occurred in spite of an unintentional integer overflow problem in recurrent nodes in the hidden layer that caused outputs to oscillate. Our investigation of the oscillatory behavior showed that the mutual information of inputs and outputs was sufficient for the reactive behaviors observed. While we had predicted that both modularity and sparsity would follow the same trend as fitness, neither did so. Instead, selection gradients within each generation showed that selection directly targeted sparsity of the connections to the motor outputs. Modularity, while not directly targeted, was correlated with sparsity, and hence was an indirect target of selection, its evolution a “by-product” of its correlation with sparsity.

The role of diverse populations in phylogenetic analysis

The most popular approaches for reconstructing phylogenetic trees attempt to solve NP-hard optimization criteria such as maximum parsimony (MP). Currently, the best-performing heuristic for reconstructing MP trees is Recursive-Iterative DCM3 (Rec-I-DCM3), which uses a single tree (or solution) to guide its way through an exponentially-sized tree space. To improve performance further, we designed Cooperative Rec-I-DCM3, a population-based approach for utilizing a population of Rec-I-DCM3 trees.We compare the performance of Cooperative Rec-I-DCM3 to Rec-I-DCM3 on four large biological datasets. Of particular interest is Cooperative Rec-I-DCM3s selection criteria for maintaining a population of solutions. Our experiments reveal that diverse populations outperform Rec-I-DCM3 in terms of average rates of convergence to best-known MP scores. To achieve greater performance, we designed an elitist population strategy, in which each solutions tree score matches that of the best score found in each generation. The elitist strategy was by far the worst overall performer in our experiments. Hence, being greedy is not always the best approach. Instead, a population of diverse solutions allows our cooperative algorithm to achieve the greatest performance improvements.

Phylospaces: reconstructing evolutionary trees in tuple space

Phylospaces is a novel framework for reconstructing evolutionary trees in tuple space, a distributed shared memory that permits processes to communicate and coordinate with each other. Our choice of tuple space as a concurrency model is somewhat unusual, given the prominence and success of pure message passing models, such as MPI. We use phylospaces to devise cooperative Rec-I-DCM3, a population-based strategy for navigating tree space. Cooperative Rec-I-DCM3 is based on Rec-I-DCM3, the fastest sequential algorithm under maximum parsimony. We compare the performance of the algorithms on two datasets consisting of 2,000 and 7,769 taxa, respectively. Our results demonstrate that cooperative Rec-I-DCM3 outperforms its sequential counterpart by at least an order of magnitude

Computer scientists put the informatics into bio, health, and medical informatics education (abstract only)

There has been an explosion of interest in bioinformatics, medical informatics, and healthcare informatics in the past decade. As a result, many computer science departments are developing courses or degree programs in bioinformatics and/or health informatics. This session is aimed at faculty who are teaching, or developing courses that tie together computer science and biology, medicine, or healthcare. The discussion leaders all have experience teaching courses in healthcare informatics and/or bioinformatics within computer science departments. We will share our expertise and experience on such issues as effectively team teaching interdisciplinary courses, developing case studies and projects, and developing links with biologists and clinicians. Some of the questions we might tackle: What types of courses should be included in degree programs? What role do curricular standards, especially in healthcare, play? What topics belong in interdisciplinary bioinformatics courses? How do we cope with students who may have differing backgrounds and prerequisites? The hope is that we can create an informal network for sharing ideas which will persist after the session. To this end, we will also discuss ways for maintaining a community, perhaps as a mailing list, blog, or website.