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Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the ‘robust, yet fragile’ nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS) can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.

Background

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

Results

Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).

Conclusions

The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Interaction networks are central elements of ecological systems and have very complex structures. Historically, much effort has focused on niche-mediated processes to explain these structures, while an emerging consensus posits that both niche and neutral mechanisms simultaneously shape many features of ecological communities. However, the study of interaction networks still lacks a comprehensive neutral theory. Here we present a neutral model of predator-prey interactions and analyze the structural characteristics of the simulated networks. We find that connectance values (complexity) and complexity-diversity relationships of neutral networks are close to those observed in empirical bipartite networks. High nestedness and low modularity values observed in neutral networks fall in the range of those from empirical antagonist bipartite networks. Our results suggest that, as an alternative to niche-mediated processes that induce incompatibility between species (“niche forbidden links”), neutral processes create “neutral forbidden links” due to uneven species abundance distributions and the low probability of interaction between rare species. Neutral trophic networks must be seen as the missing endpoint of a continuum from niche to purely stochastic approaches of community organization.

Community ecology and ecosystem ecology provide two perspectives on complex ecological systems that have largely complementary strengths and weaknesses. Merging the two perspectives is necessary both to ensure continued scientific progress and to provide society with the scientific means to face growing environmental challenges. Recent research on biodiversity and ecosystem functioning has contributed to this goal in several ways. By addressing a new question of high relevance for both science and society, by challenging existing paradigms, by tightly linking theory and experiments, by building scientific consensus beyond differences in opinion, by integrating fragmented disciplines and research fields, by connecting itself to other disciplines and management issues, it has helped transform ecology not only in content, but also in form. Creating a genuine evolutionary ecosystem ecology that links the evolution of species traits at the individual level, the dynamics of species interactions, and the overall functioning of ecosystems would give new impetus to this much-needed process of unification across ecological disciplines. Recent community evolution models are a promising step in that direction.

In ecological networks, network robustness should be large enough to confer intrinsic robustness for tolerating intrinsic parameter fluctuations, as well as environmental robustness for resisting environmental disturbances, so that the phenotype stability of ecological networks can be maintained, thus guaranteeing phenotype robustness. However, it is difficult to analyze the network robustness of ecological systems because they are complex nonlinear partial differential stochastic systems. This paper develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance sensitivity in ecological networks. We found that the phenotype robustness criterion for ecological networks is that if intrinsic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations and environmental disturbances. These results in robust ecological networks are similar to that in robust gene regulatory networks and evolutionary networks even they have different spatial-time scales.

The mechanism for maintaining complex food webs has been a central issue in ecology because theory often predicts that complexity (higher the species richness, more the interactions) destabilizes food webs. Although it has been proposed that prey anti-predator defence may affect the stability of prey–predator dynamics, such studies assumed a limited and relatively simpler variation in the food-web structure. Here, using mathematical models, I report that food-web flexibility arising from prey anti-predator defence enhances community-level stability (community persistence and robustness) in more complex systems and even changes the complexity–stability relationship. The model analysis shows that adaptive predator-specific defence enhances community-level stability under a wide range of food-web complexity levels and topologies, while generalized defence does not. Furthermore, while increasing food-web complexity has minor or negative effects on community-level stability in the absence of defence adaptation, or in the presence of generalized defence, in the presence of predator-specific defence, the connectance–stability relationship may become unimodal. Increasing species richness, in contrast, always lowers community-level stability. The emergence of a positive connectance–stability relationship however necessitates food-web compartmentalization, high defence efficiency and low defence cost, suggesting that it only occurs under a restricted condition.

The fossil record presents palaeoecological patterns of rise and fall on multiple scales of time and biological organization. Here, we argue that the rise and fall of species can result from a tragedy of the commons, wherein the pursuit of self-interests by individual agents in a larger interactive system is detrimental to the overall performance or condition of the system. Species evolving within particular communities may conform to this situation, affecting the ecological robustness of their communities. Results from a trophic network model of Permian–Triassic terrestrial communities suggest that community performance on geological timescales may in turn constrain the evolutionary opportunities and histories of the species within them.

ABSTRACT

Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management.

IMPORTANCE

The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

A detailed analysis of three species-rich ecosystem food webs has shown that they display skewed distributions of connections. Such graphs of interaction are, in fact, shared by a number of biological and technological networks, which have been shown to display a very high homeostasis against random removals of nodes. Here, we analyse the responses of these ecological graphs to both random and selective perturbations (directed against the most-connected species). Our results suggest that ecological networks are very robust against random removals but can be extremely fragile when selective attacks are used. These observations have important consequences for biodiversity dynamics and conservation issues, current estimations of extinction rates and the relevance and definition of keystone species.

Synthesising the relationships between complexity, connectivity, and the stability of large biological systems has been a longstanding fundamental quest in theoretical biology and ecology. With the many exciting developments in modern network theory, interest in these issues has recently come to the forefront in a range of multidisciplinary areas. Here we outline a new theoretical analysis specifically relevant for the study of ecological metapopulations focusing primarily on marine systems, where subpopulations are generally connected via larval dispersal. Our work determines the qualitative and quantitative conditions by which dispersal and network structure control the persistence of a set of age-structured patch populations. Mathematical modelling combined with a graph theoretic analysis demonstrates that persistence depends crucially on the topology of cycles in the dispersal network which tend to enhance the effect of larvae “returning home.” Our method clarifies the impact directly due to network structure, but this almost by definition can only be achieved by examining the simplified case in which patches are identical; an assumption that we later relax. The methodology identifies critical migration routes, whose presence are vital to overall stability, and therefore should have high conservation priority. In contrast, “lonely links,” or links in the network that do not participate in a cyclical component, have no impact on persistence and thus have low conservation priority. A number of other intriguing criteria for persistence are derived. Our modelling framework reveals new insights regarding the determinants of persistence, stability, and thresholds in complex metapopulations. In particular, while theoretical arguments have, in the past, suggested that increasing connectivity is a destabilizing feature in complex systems, this is not evident in metapopulation networks where connectivity, cycles, coherency, and heterogeneity all tend to enhance persistence. The results should be of interest for many other scientific contexts that make use of network theory.

Author Summary

Taking advantage of modern network theory, we present a model formulation for determining those factors that control the stability and persistence of complex biological systems. As a case study, we focus on ecological metapopulations, which may be viewed as a set of distinct subpopulations (/sites) that are connected via a dispersal network of arbitrary complexity. Metapopulation persistence is found to depend critically on the topology of cycles, and cyclical components in the connectivity network, because they allow the offspring of the population to eventually “return home” to the sites from which they originated. The methodology identifies critical migration routes, whose presence are vital to overall stability, and are thus of high conservation priority – information that may be of value when designing networks of marine protected areas. In contrast, links that do not participate in a cyclical component have no impact on persistence and thus have low conservation priority. While network theory is highly fashionable in biology, only few studies go deeper than descriptive statistical applications as attempted here. Moreover, the key results are easily extended to other biological contexts (e.g., disease networks), particularly in situations whereby the network controls the dynamics of a complex system.

Ecological systems are always subjected to various environmental fluctuations. They evolve under these fluctuations and the resulting systems are robust against them. The diversity in ecological systems is also acquired through the evolution. How do the fluctuations affect the evolutionary processes? Do the fluctuations have direct impact on the species diversity in ecological systems? In the present paper, we investigate the relation between the environmental fluctuation and the evolution of species diversity with a mathematical model of evolutionary ecology. In the model, individual organisms compete for a single restricted resource and the temporal fluctuation in the resource supply is introduced as the environmental fluctuation. The evolutionary process is represented by the mutational change of genotypes which determines their resource utilization strategies. We found that when the environmental state is switched form static to fluctuating conditions, the initial closely related population distributed around the genotype adapted for the static environment is destabilized and divided into two groups in the genotype space; i.e., the evolutionary branching is induced by the environmental fluctuation. The consequent multiple species structures is evolutionary stable at the presence of the fluctuation. We perform the evolutionary invasion analysis for the phenomena and illustrate the mechanisms of the branchings. The results indicate a novel process of increasing the species diversity via evolutionary branching, and the analysis reveals the mechanisims of the branching preocess as the response to the environmental fluctuation. The robustness of the evolutionary process is also discussed.

Global change has created a severe biodiversity crisis. Species are driven extinct at an increasing rate, and this has the potential to cause further coextinction cascades. The rate and shape of these coextinction cascades depend very much on the structure of the networks of interactions across species. Understanding network structure and how it relates to network disassembly, therefore, is a priority for system-level conservation biology. This process of network collapse may indeed be related to the process of network build-up, although very little is known about both processes and even less about their relationship. Here we review recent work that provides some preliminary answers to these questions. First, we focus on network assembly by emphasizing temporal processes at the species level, as well as the structural building blocks of complex ecological networks. Second, we focus on network disassembly as a consequence of species extinctions or habitat loss. We conclude by emphasizing some general rules of thumb that can help in building a comprehensive framework to understand the responses of ecological networks to global change.

Ecological communities are structured in part by evolutionary interactions among their members. A number of recent studies incorporating phylogenetics into community ecology have upheld the paradigm that competition drives ecological divergence among species of the same guild. However, the role of other interspecific interactions, in particular positive interactions such as mutualism, remains poorly explored. We characterized the ecological niche and inferred phylogenetic relationships among members of a diverse community of neotropical Müllerian mimetic butterflies. Müllerian mimicry is one of the best studied examples of mutualism, in which unpalatable species converge in wing pattern locally to advertize their toxicity to predators. We provide evidence that mutualistic interactions can drive convergence along multiple ecological axes, outweighing both phylogeny and competition in shaping community structure. Our findings imply that ecological communities are adaptively assembled to a much greater degree than commonly suspected. In addition, our results show that phenotype and ecology are strongly linked and support the idea that mimicry can cause ecological speciation through multiple cascading effects on species' biology.

Author Summary

What governs the composition of communities of species? Competition promotes divergence in behavior and habitat, allowing species to co-exist. But the effects of other interactions, such as mutualism, are less well understood. We examined the interplay between mutualistic interactions, common ancestry and competition in mimetic butterflies, one of the best studied examples of mutualism, in which species converge in wing pattern to advertize their toxicity to predators. We showed that mutualism drives convergence in flight height and forest habitat, and that these effects outweigh common ancestry (which should lead related species to be more similar) and competition (which promotes ecological divergence). Our findings imply that species that benefit from one another might evolve to form more tightly knit local communities, suggesting that adaptation is a more important process affecting community composition than is commonly suspected. Our results also support the idea that mimicry can cause speciation, through its multiple cascading effects on species' biology.

Müllerian mimicry, a classic mutualism, is associated with microhabitat convergence in tropical butterflies, outweighing both common ancestry and competition. Positive interactions may thus be more important in community assembly than commonly assumed.

Synthetic biology encompasses the design of new biological parts and systems as well as the modulation of existing biological networks to generate novel functions. In recent years, increasing emphasis has been placed on the engineering of population-level behaviors using cell-cell communication. From the engineering perspective, cell-cell communication serves as a versatile regulatory module that enables coordination among cells in and between populations and facilitates the generation of reliable dynamics. In addition to exploring biological “design principles” via the construction of increasingly complex dynamics, communication-based synthetic systems can be used as well-defined model systems to study ecological and social interactions such as competition, cooperation and predation. Here we discuss the dynamic properties of cell-cell communication modules, how they can be engineered for synthetic circuit design, and applications of these systems.

Like ecological communities, which vary in species composition, eukaryote genomes differ in the amount and diversity of transposable elements (TEs) that they harbor. Because TEs have a considerable impact on the biology of their host species, we need to better understand whether their dynamics reflects some form of organization or is primarily driven by stochastic processes. Here we borrow ecological concepts on species diversity to explore how interactions between TEs can contribute to structure TE communities within their genomic ecosystem. Whereas the niche theory predicts a stable diversity of TEs because of their divergent characteristics, the neutral theory of biodiversity predicts the assembly of TE communities from stochastic processes acting at the level of individual TE. Contrary to ecological communities, however, TE communities are shaped by selection at the level of their ecosystem, i.e., the host individual. Developing ecological models specific to the genome will thus be pre-requisite for modeling the dynamics of TEs.

Summary

The prevalence of overweight among children has doubled within the past two decades. Increases in the rate of childhood overweight are of particular concern due to the negative health and psychological effects noted among overweight children. As shown by previous research, the development of childhood overweight involves a complex set of factors from multiple contexts that interact with each other to place a child at risk of overweight. This multifaceted system can be conceptualized using Ecological Systems Theory (EST). EST highlights the importance of considering the context(s), or ecological niche, in which a person is located in order to understand the emergence of a particular characteristic. In the case of a child, the ecological niche includes the family and the school, which are in turn embedded in larger social contexts including the community and society at large. In this review, EST is used as a framework with which to summarize research assessing predictors of childhood overweight. Specifically, child characteristics that place children at risk of the development of overweight (including dietary intake, physical activity, and sedentary behaviour) will be reviewed while taking into consideration the influence of the familial environment, the school environment, and the community and larger social environments. It is concluded that future research needs to adopt a broader contextual approach in order to understand and intervene against the processes leading to the development of overweight among children and that the use of theories or paradigms such as EST will facilitate developing and testing models of causal processes.

Systematic conservation planning efforts typically focus on protecting current patterns of biodiversity. Climate change is poised to shift species distributions, reshuffle communities, and alter ecosystem functioning. In such a dynamic environment, lands selected to protect today's biodiversity may fail to do so in the future. One proposed approach to designing reserve networks that are robust to climate change involves protecting the diversity of abiotic conditions that in part determine species distributions and ecological processes. A set of abiotically diverse areas will likely support a diversity of ecological systems both today and into the future, although those two sets of systems might be dramatically different. Here, we demonstrate a conservation planning approach based on representing unique combinations of abiotic factors. We prioritize sites that represent the diversity of soils, topographies, and current climates of the Columbia Plateau. We then compare these sites to sites prioritized to protect current biodiversity. This comparison highlights places that are important for protecting both today's biodiversity and the diversity of abiotic factors that will likely determine biodiversity patterns in the future. It also highlights places where a reserve network designed solely to protect today's biodiversity would fail to capture the diversity of abiotic conditions and where such a network could be augmented to be more robust to climate-change impacts.

Microbial ecology is flourishing, and in the process, is making contributions to how the ecology and biology of large organisms is understood. Ongoing advances in sequencing technology and computational methods have enabled the collection and analysis of vast amounts of molecular data from diverse biological communities. While early studies focused on cataloguing microbial biodiversity in environments ranging from simple marine ecosystems to complex soil ecologies, more recent research is concerned with community functions and their dynamics over time. Models and concepts from traditional ecology have been used to generate new insight into microbial communities, and novel system-level models developed to explain and predict microbial interactions. The process of moving from molecular inventories to functional understanding is complex and challenging, and never more so than when many thousands of dynamic interactions are the phenomena of interest. We outline the process of how epistemic transitions are made from producing catalogues of molecules to achieving functional and predictive insight, and show how those insights not only revolutionize what is known about biological systems but also about how to do biology itself. Examples will be drawn primarily from analyses of different human microbiota, which are the microbial consortia found in and on areas of the human body, and their associated microbiomes (the genes of those communities). Molecular knowledge of these microbiomes is transforming microbiological knowledge, as well as broader aspects of human biology, health and disease.

In ecosystems, species interact with other species directly and through abiotic factors in multiple ways, often forming complex networks of various types of ecological interaction. Out of this suite of interactions, predator–prey interactions have received most attention. The resulting food webs, however, will always operate simultaneously with networks based on other types of ecological interaction, such as through the activities of ecosystem engineers or mutualistic interactions. Little is known about how to classify, organize and quantify these other ecological networks and their mutual interplay. The aim of this paper is to provide new and testable ideas on how to understand and model ecosystems in which many different types of ecological interaction operate simultaneously. We approach this problem by first identifying six main types of interaction that operate within ecosystems, of which food web interactions are one. Then, we propose that food webs are structured among two main axes of organization: a vertical (classic) axis representing trophic position and a new horizontal ‘ecological stoichiometry’ axis representing decreasing palatability of plant parts and detritus for herbivores and detrivores and slower turnover times. The usefulness of these new ideas is then explored with three very different ecosystems as test cases: temperate intertidal mudflats; temperate short grass prairie; and tropical savannah.

Graph theoretical analysis has played a key role in characterizing global features of the topology of complex networks, describing diverse systems such as protein interactions, food webs, social relations and brain connectivity. How system elements communicate with each other depends not only on the structure of the network, but also on the nature of the system's dynamics which are constrained by the amount of knowledge and resources available for communication processes. Complementing widely used measures that capture efficiency under the assumption that communication preferentially follows shortest paths across the network (“routing”), we define analytic measures directed at characterizing network communication when signals flow in a random walk process (“diffusion”). The two dimensions of routing and diffusion efficiency define a morphospace for complex networks, with different network topologies characterized by different combinations of efficiency measures and thus occupying different regions of this space. We explore the relation of network topologies and efficiency measures by examining canonical network models, by evolving networks using a multi-objective optimization strategy, and by investigating real-world network data sets. Within the efficiency morphospace, specific aspects of network topology that differentially favor efficient communication for routing and diffusion processes are identified. Charting regions of the morphospace that are occupied by canonical, evolved or real networks allows inferences about the limits of communication efficiency imposed by connectivity and dynamics, as well as the underlying selection pressures that have shaped network topology.

The robustness of ecosystems to species losses is a central question in ecology, given the current pace of extinctions and the many species threatened by human impacts, including habitat destruction and climate change. Robustness from the perspective of secondary extinctions has been addressed in the context of food webs to consider the complex network of species interactions that underlie responses to perturbations. In-silico removal experiments have examined the structural properties of food webs that enhance or hamper the robustness of ecosystems to species losses, with a focus on the role of hubs, the most connected species. Here we take a different approach and focus on the role of the connections themselves. We show that trophic links can be divided into functional and redundant based on their contribution to robustness. The analysis of empirical webs shows that hubs are not necessarily the most important species as they may hold many redundant links. Furthermore, the fraction of functional connections is high and constant across systems regardless of size and interconnectedness. The main consequence of this scaling pattern is that ecosystem robustness can be considerably reduced by species extinctions even when these do not result in any secondary extinctions. This introduces the possibility of tipping points in the collapse of ecosystems.

Deterministic theories in community ecology suggest that local, niche-based processes, such as environmental filtering, biotic interactions and interspecific trade-offs largely determine patterns of species diversity and composition. In contrast, more stochastic theories emphasize the importance of chance colonization, random extinction and ecological drift. The schisms between deterministic and stochastic perspectives, which date back to the earliest days of ecology, continue to fuel contemporary debates (e.g. niches versus neutrality). As illustrated by the pioneering studies of Robert H. MacArthur and co-workers, resolution to these debates requires consideration of how the importance of local processes changes across scales. Here, we develop a framework for disentangling the relative importance of deterministic and stochastic processes in generating site-to-site variation in species composition (β-diversity) along ecological gradients (disturbance, productivity and biotic interactions) and among biogeographic regions that differ in the size of the regional species pool. We illustrate how to discern the importance of deterministic processes using null-model approaches that explicitly account for local and regional factors that inherently create stochastic turnover. By embracing processes across scales, we can build a more synthetic framework for understanding how niches structure patterns of biodiversity in the face of stochastic processes that emerge from local and biogeographic factors.

Parasitism is the most common consumer strategy among organisms, yet only recently has there been a call for the inclusion of infectious disease agents in food webs. The value of this effort hinges on whether parasites affect food-web properties. Increasing evidence suggests that parasites have the potential to uniquely alter food-web topology in terms of chain length, connectance and robustness. In addition, parasites might affect food-web stability, interaction strength and energy flow. Food-web structure also affects infectious disease dynamics because parasites depend on the ecological networks in which they live. Empirically, incorporating parasites into food webs is straightforward. We may start with existing food webs and add parasites as nodes, or we may try to build food webs around systems for which we already have a good understanding of infectious processes. In the future, perhaps researchers will add parasites while they construct food webs. Less clear is how food-web theory can accommodate parasites. This is a deep and central problem in theoretical biology and applied mathematics. For instance, is representing parasites with complex life cycles as a single node equivalent to representing other species with ontogenetic niche shifts as a single node? Can parasitism fit into fundamental frameworks such as the niche model? Can we integrate infectious disease models into the emerging field of dynamic food-web modelling? Future progress will benefit from interdisciplinary collaborations between ecologists and infectious disease biologists.

Background

The immune system behaves like a complex, dynamic network with interacting elements including leukocytes, cytokines, and chemokines. While the immune system is broadly distributed, leukocytes must communicate effectively to respond to a pathological challenge. The Basic Immune Simulator 2010 contains agents representing leukocytes and tissue cells, signals representing cytokines, chemokines, and pathogens, and virtual spaces representing organ tissue, lymphoid tissue, and blood. Agents interact dynamically in the compartments in response to infection of the virtual tissue. Agent behavior is imposed by logical rules derived from the scientific literature. The model captured the agent-to-agent contact history, and from this the network topology and the interactions resulting in successful versus failed viral clearance were identified. This model served to integrate existing knowledge and allowed us to examine the immune response from a novel perspective directed at exploiting complex dynamics, ultimately for the design of therapeutic interventions.

Results

Analyzing the evolution of agent-agent interactions at incremental time points from identical initial conditions revealed novel features of immune communication associated with successful and failed outcomes. There were fewer contacts between agents for simulations ending in viral elimination (win) versus persistent infection (loss), due to the removal of infected agents. However, early cellular interactions preceded successful clearance of infection. Specifically, more Dendritic Agent interactions with TCell and BCell Agents, and more BCell Agent interactions with TCell Agents early in the simulation were associated with the immune win outcome. The Dendritic Agents greatly influenced the outcome, confirming them as hub agents of the immune network. In addition, unexpectedly high frequencies of Dendritic Agent-self interactions occurred in the lymphoid compartment late in the loss outcomes.

Conclusions

An agent-based model capturing several key aspects of complex system dynamics was used to study the emergent properties of the immune response to viral infection. Specific patterns of interactions between leukocyte agents occurring early in the response significantly improved outcome. More interactions at later stages correlated with persistent inflammation and infection. These simulation experiments highlight the importance of commonly overlooked aspects of the immune response and provide insight into these processes at a resolution level exceeding the capabilities of current laboratory technologies.

Research in community genetics seeks to understand how the dynamic interplay between ecology and evolution shapes simple and complex communities and ecosystems. A community genetics perspective, however, may not be necessary or informative for all studies and systems. To better understand when and how intraspecific genetic variation and microevolution are important in community and ecosystem ecology, we suggest future research should focus on three areas: (i) determining the relative importance of intraspecific genetic variation compared with other ecological factors in mediating community and ecosystem properties; (ii) understanding the importance of microevolution in shaping ecological dynamics in multi-trophic communities; and (iii) deciphering the phenotypic and associated genetic mechanisms that drive community and ecosystem processes. Here, we identify key areas of research that will increase our understanding of the ecology and evolution of complex communities but that are currently missing in community genetics. We then suggest experiments designed to meet these current gaps.