The more the merrier: Multi-species experiments in ecology

A major objective in ecology is to find general patterns, and to establish the rules and underlying mechanisms that generate those patterns. Nevertheless, most of our current insights in ecology are based on case studies of a single or few species, whereas multi-species experimental studies remain rare. We underline the power of the multi-species experimental approach for addressing general ecological questions, e.g. on species environmental responses or on patterns of among- and within-species variation. We present simulations that show that the accuracy of estimates of between-group differences is increased by maximizing the number of species rather than the number of populations or individuals per species. Thus, the more species a multi-species experiment includes, the more powerful it is. In addition, we discuss some inevitable methodological challenges of multi-species experiments. While we acknowledge the value of single- or few-species experiments, we strongly advocate the use of multi-species experiments for addressing ecological questions at a more general level.     

Multi-species SIR models from a dynamical Bayesian perspective

Multi-species compartment epidemic models, such as the multi-species susceptible–infectious–recovered (SIR) model, are extensions of the classic SIR models, which are used to explore the transient dynamics of pathogens that infect multiple hosts in a large population. In this article, we propose a dynamical Bayesian hierarchical SIR (HSIR) model, to capture the stochastic or random nature of an epidemic process in a multi-species SIR (with recovered becoming susceptible again) dynamical setting, under hidden mass balance constraints. We call this a Bayesian hierarchical multi-species SIR (MSIR_B) model. Different from a classic multi-species SIR model (which we call MSIR_C), our approach imposes mass balance on the underlying true counts rather than, improperly, on the noisy observations. Moreover, the MSIR_B model can capture the discrete nature of, as well as uncertainties in, the epidemic process.     

The theoretical basis of vegetation science

There are two distinct research approaches to a theoretical framework for vegetation science. One involves critical analysis of observed patterns of multi-species community composition; the other entails experimental examination, at the population level, of processes in a multi-species context. Each approach is currently incomplete, but has questions in common with the other. Light, nitrogen and disturbance have been identified as critical factors determining vegetation composition by researchers using different theoretical frameworks. The increasing communication between vegetation analysts concerned with community pattern and experimental ecologists concerned with community processes may lead to a convergence in their theoretical concepts.     

Assessing gene significance from cDNA microarray expression data via mixed models.

The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.     

Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach

Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system.     

Mathematical models of the balance between apoptosis and proliferation

As our understanding of cellular behaviour grows, and we identify more and more genes involved in the control of such basic processes as cell division and programmed cell death, it becomes increasingly difficult to integrate such detailed knowledge into a meaningful whole. This is an area where mathematical modelling can complement experimental approaches, and even simple mathematical models can yield useful biological insights. This review presents examples of this in the context of understanding the combined effects of different levels of cell death and cell division in a number of biological systems including tumour growth, the homeostasis of immune memory and pre-implantation embryo development. The models we describe, although simplistic, yield insight into several phenomena that are difficult to understand using a purely experimental approach. This includes the different roles played by the apoptosis of stem cells and differentiated cells in determining whether or not a tumour can grow; the way in which a density dependent rate of apoptosis (for instance mediated by cell-cell contact or cytokine signalling) can lead to homeostasis; and the effect of stochastic fluctuations when the number of cells involved is small. We also highlight how models can maximize the amount of information that can be extracted from limited experimental data. The review concludes by summarizing the various mathematical frameworks that can be used to develop new models and the type of biological information that is required to do this.     

SysFinder:A customized platform for search,comparison and assisted design of appropriate animal models based on systematic similarity

Animal models are increasingly gaining values by cross-comparisons of response or resistance to clinical agents used for patients.However,many disease mechanisms and drug effects generated from animal models are not transferable to human.To address these issues,we developed SysFinder(http://lifecenter.sgst.cn/SysFinder),a platform for scientists to find appropriate animal models for translational research.SysFinder offers a "topic-centered" approach for systematic comparisons of human genes,whose functions are involved in a specific scientific topic,to the corresponding homologous genes of animal models.Scientific topic can be a certain disease,drug,gene function or biological pathway.SysFinder calculates multi-level similarity indexes to evaluate the similarities between human and animal models in specified scientific topics.Meanwhile,SysFinder offers species-specific information to investigate the differences in molecular mechanisms between humans and animal models.Furthermore,SysFinder provides a userfriendly platform for determination of short guide RNAs(sgRNAs) and homology arms to design a new animal model.Case studies illustrate the ability of SysFinder in helping experimental scientists.SysFinder is a useful platform for experimental scientists to carry out their research in the human molecular mechanisms.     

Unravelling gene networks from noisy under-determined experimental perturbation data

Systems biology aims to study the properties of biological systems in terms of the properties of their molecular constituents. This occurs frequently by a process of mathematical modelling. The first step in this modelling process is to unravel the interaction structure of biological systems from experimental data. Previously, an algorithm for gene network inference from gene expression perturbation data was proposed. Here, the algorithm is extended by using regression with subset selection. The performance of the algorithm is extensively evaluated on a set of data produced with gene network models at different levels of simulated experimental noise. Regression with subset selection outperforms the previously stated matrix inverse approach in the presence of experimental noise. Furthermore, this regression approach enables us to deal with under-determination, that is, when not all genes are perturbed. The results on incomplete data sets show that the new method performs well at higher number of perturbations, even when noise levels are high. At lower number of perturbations, although still being able to recover the majority of the connections, less confidence can be placed in the recovered edges.     

Spontaneous and engineered mutant mice as models for experimental and comparative pathology: history, comparison, and developmental technology

During the last half-century pathologists have explored the biologic mechanisms associated with inherited human and veterinary diseases by using inbred and inbred mutant (spontaneous) strains of mice. The first successful gene transfer to mice by pronuclear injection of the herpes simplex virus thymidine kinase gene and rabbit and human beta-globulin genes was achieved in the early 1980s. This accomplishment was followed a few years later with the creation of a mouse bearing a disrupted hypoxanthine phosphoribosyl transferase (hrpt) gene (targeted mutation based on ES cell blastocyst injection). Since then, hundreds of genetically engineered models of biomedical importance have been created. The unprecedented scale and scope of development of engineered models present great opportunities as well as experimental challenges to the investigator. The aim of the present review is to provide a framework of information on engineered mouse models from the perspective of experimental and comparative pathology research. Sections include: 1) a brief historical account of the development of mouse models of disease, with increasing progression of genetic refinement as represented by inbred (spontaneous) and congenic (targeted) mutant strains of mice; 2) a synopsis of spontaneous and targeted mutations, with anecdotal examples of expression of individual genes and interactions between multiple mutant genes; 3) selected examples of targeted mutations of interest to developmental and cancer biologists and immunologists; 4) an overview of the technology of development of transgenic mice; and 5) an introduction to on-line database resources of current multi-species genomic information.     

Distinct evolutionary trajectories of MHC class I and class II genes in Old World finches and buntings

Major histocompatibility complex (MHC) genes code for key proteins of the adaptive immune system, which present antigens from intra-cellular (MHC class I) and extra-cellular (MHC class II) pathogens. Because of their unprecedented diversity, MHC genes have long been an object of scientific interest, but due to methodological difficulties in genotyping of duplicated loci, our knowledge on the evolution of the MHC across different vertebrate lineages is still limited. Here, we compared the evolution of MHC class I and class II genes in three sister clades of common passerine birds, finches (Fringillinae and Carduelinae) and buntings (Emberizidae) using a uniform methodological (genotyping and data processing) approach and uniform sample sizes. Our analyses revealed contrasting evolutionary trajectories of the two MHC classes. We found a stronger signature of pervasive positive selection and higher allele diversity (allele numbers) at the MHC class I than class II. In contrast, MHC class II genes showed greater allele divergence (in terms of nucleotide diversity) and a much stronger recombination (gene conversion) signal. Gene copy numbers at both MHC class I and class II evolved via fluctuating selection and drift (Brownian Motion evolution), but the evolutionary rate was higher at class I. Our study constitutes one of few existing examples, where evolution of MHC class I and class II genes was directly compared using a multi-species approach. We recommend that re-focusing MHC research from single-species and single-class approaches towards multi-species analyses of both MHC classes can substantially increase our understanding MHC evolution in a broad phylogenetic context.Subject terms: Molecular evolution, Immunogenetics     

Ecological community integration increases with added trophic complexity

The existence of functional biological organization at the level of multi-species communities has long been contested in ecology and evolutionary biology. I found that adding a trophic level to simulated ecological communities enhanced their ability to compete at the community level, increasing the likelihood of one community forcing all or most species in a second community to extinction. Community-level identity emerged within systems of interacting ecological networks, while competitive ability at the community level was enhanced by intense within-community selection pressure. These results suggest a reassessment of the nature of biological organization above the level of species, indicating that the drive toward biological integration, so prominent throughout the history of life, might extend to multi-species communities.     

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

Background  

The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.     

Modeling how soluble microbial products (SMP) support heterotrophic bacteria in autotroph-based biofilms

Multi-species biofilm modeling has been used for many years to understand the interactions between species in different biofilm systems, but the complex symbiotic relationship between species is sometimes overlooked, because models do not always include all relevant species and components. In this paper, we develop and use a mathematical model to describe a model biofilm system that includes autotrophic and heterotrophic bacteria and the key products produced by the bacteria. The model combines the methods of earlier multi-species models with a multi-component biofilm model in order to explore the interaction between species via exchange of soluble microbial products (SMP). We show that multiple parameter sets are able to describe the findings of experimental studies, and that heterotrophs growing on autotrophically produced SMP may pursue either r- or K-strategies to sustain themselves when SMP is their only substrate. We also show that heterotrophs can colonize some distance from the autotrophs and still be sustained by autotrophically produced SMP. This work defines the feasible range of parameters for utilization of SMP by heterotrophs and the nature of the interactions between autotrophs and heterotrophs in multi-species, multi-component biofilms.     

Nematode species abundance patterns and their use in the detection of environmental perturbations

If the concepts of biological indices and biomonitoring at the multi-species level are to prove viable, then meiofauna and marine nematodes in particular should be an ideal group with which to test the hypothesis. Many attempts to assess the structure of species assemblages, such as the use of diversity indices, nematode: copepod ratios and the graphical method of log normal plots, have been shown to be theoretically unsound and/or impractical, especially for routine use by extension workers. A method of assessing shifts in dominance patterns which involves all the proportional species abundances is suggested as a better means of comparing diversity. A modified method of rapidly assessing Simpson's dominance-weighted diversity index is also advocated as being of practical use. In combination, they should enable the diversity aspect of the multi-species approach to biomonitoring to be rigorously and exhaustively evaluated.     

Evolution in metacommunities

A metacommunity can be defined as a set of communities that are linked by migration, and extinction and recolonization. In metacommunities, evolution can occur not only by processes that occur within communities such as drift and individual selection, but also by among-community processes, such as divergent selection owing to random differences among communities in species composition, and group and community-level selection. The effect of these among-community-level processes depends on the pattern of migration among communities. Migrating units may be individuals (migrant pool model), groups of individuals (single-species propagule pool model) or multi-species associations (multi-species propagule pool model). The most interesting case is the multi-species propagule pool model. Although this pattern of migration may a priori seem rare, it becomes more plausible in small well-defined 'communities' such as symbiotic associations between two or a few species. Theoretical models and experimental studies show that community selection is potentially an effective evolutionary force. Such evolution can occur either through genetic changes within species or through changes in the species composition of the communities. Although laboratory studies show that community selection can be important, little is known about how important it is in natural populations.     

Formal agent-based modelling of intracellular chemical interactions

Individual-based or agent-based models have proved useful in a variety of different biological contexts. This paper presents an agent-based model using a formal computational modelling approach to model a crucial biological system--the intracellular NF-kappaB signalling pathway. The pathway is vital to immune response regulation, and is fundamental to basic survival in a range of species. Alterations in pathway regulation underlie many diseases, including atherosclerosis and arthritis. Our modelling of individual molecules, receptors and genes provides a more comprehensive outline of regulatory network mechanisms than previously possible with equation-based approaches. The model has been validated with data obtained from single cell experimental analysis.     

GIRAE: a generalised approach for linking the total impact of invasion to species' range,abundance and per-unit effects

The total impact of an alien species was conceptualised as the product of its range size, local abundance and per-unit effect in a seminal paper by Parker et al. (Biol Invasions 1:3–19, 1999). However, a practical approach for estimating the three components has been lacking. Here, we generalise the impact formula and, through use of regression models, estimate the relationship between the three components of impact, an approach we term GIRAE (Generalised Impact?=?Range size × Abundance × per-unit Effect). We discuss how GIRAE can be applied to multiple types of impact, including environmental impacts, damage and management costs. We propose two methods for applying GIRAE. The species-specific method computes the relationship between impact, range size, abundance and per-unit effect for a given species across multiple invaded sites or regions of different sizes. The multi-species method combines data from multiple species across multiple sites or regions to calculate a per-unit effect for each species and is computed using a single regression model. The species-specific method is more accurate, but it requires a large amount of data for each species and assumes a constant per-unit effect for a species across the invaded area. The multi-species method is more easily applicable and data-parsimonious, but assumes the same relationship between impact, range size and abundance for all considered species. We illustrate these methods using data about money spent managing plant invasions in different biomes of South Africa. We found clear differences between species in terms of money spent per unit area invaded, with per-unit expenditure varying substantially between biomes for some species—insights that are useful for monitoring and evaluating management. GIRAE offers a versatile and practical method that can be applied to many different types of data to better understand and manage the impacts of biological invasions.