A case study on qualitative model evaluation using data about river water quality

Qualitative reasoning has been successfully used for ecological modelling, particularly when numerical data are not available. However, in order to further explore the potential of this modelling approach, it is important to discuss how to incorporate numerical data, if available, and to develop means to evaluate conceptual aspects and model outputs. This paper describes a study on qualitative model evaluation, in which numerical data about water quality are used to define different scenarios in a water basin, so that the outputs of simulations with the model can be compared to the actual system. The model was evaluated by independent experts, concerning its conceptual and operational aspects, and with respect to its predictive capability. The model was considered valid for the intended use, which is to increase the understanding of non-expert water managers.     

How plants changed the world: Using qualitative reasoning to explain plant macroevolution's effect on the long-term carbon cycle

We present a qualitative reasoning model of how plant colonization of land during the mid Paleozoic era (450–300 million years ago) altered the long-term carbon cycle resulting in a dramatic decrease in global atmospheric carbon dioxide levels. This model is aimed at facilitating learning and communication about how interactions between biological and geological processes drove system behavior. The model is developed in three submodels of the main system components, namely how competition for limited land habitat drove natural selection for increasing adaptations to life on land; how these adaptations resulted in increased formation of organic-rich sedimentary rocks (coal); and how these adaptations altered weathering of calcium and magnesium silicate rocks, resulting in increased deposition of inorganic carbonates in oceans. These separate submodels are then assembled to derive the full dynamic model of plant macroevolution, colonization of land, and plummeting carbon dioxide levels that occurred during the mid Paleozoic. The qualitative reasoning framework supports explicit representation of causal feedbacks — as with previously developed systems analysis models — but also supports simulation of system dynamics arising from the configuration of entities in the system. The ability of qualitative reasoning to provide causal accounts (explanations) of why certain phenomena occurred and when, is a powerful advantage over numerical simulation such as the complex GEOCARB models, where explanation must be left to interpretation by experts.     

Modular assembly of dynamic models in systems biology

It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.     

Students’ Mental Models of Evolutionary Causation: Natural Selection and Genetic Drift

In an effort to understand how to improve student learning about evolution, a focus of science education research has been to document and address students?? naive ideas. Less research has investigated how students reason about alternative scientific models that attempt to explain the same phenomenon (e.g., which causal model best accounts for evolutionary change?). Within evolutionary biology, research has yet to explore how non-adaptive factors are situated within students?? conceptual ecologies of evolutionary causation. Do students construct evolutionary explanations that include non-adaptive and adaptive factors? If so, how are non-adaptive factors structured within students?? evolutionary explanations? We used clinical interviews and two paper and pencil instruments (one open-response and one multiple-choice) to investigate the use of non-adaptive and adaptive factors in undergraduate students?? patterns of evolutionary reasoning. After instruction that included non-adaptive causal factors (e.g., genetic drift), we found them to be remarkably uncommon in students?? explanatory models of evolutionary change in both written assessments and clinical interviews. However, consistent with many evolutionary biologists?? explanations, when students used non-adaptive factors they were conceptualized as causal alternatives to selection. Interestingly, use of non-adaptive factors was not associated with greater understanding of natural selection in interviews or written assessments, or with fewer naive ideas of natural selection. Thus, reasoning using non-adaptive factors appears to be a distinct facet of evolutionary thinking. We propose a theoretical framework for an expert?Cnovice continuum of evolutionary reasoning that incorporates both adaptive and non-adaptive factors, and can be used to inform instructional efficacy in evolutionary biology.     

Integrating species distribution modelling into decision-making to inform conservation actions

Species distribution models (SDMs) have been widely tagged as valuable tools in a variety of conservation assessments to address pressing conservation problems. However, these solutions could be hampered by difficulties to overcome the knowledge-action boundary between conservation and modelling practice. These difficulties have been well typified in the ecological modelling sphere, but a specific conceptual framework on how to bridge this gap is still lacking. This work reports successful examples on how to use SDMs to identify the most favourable habitats for implementing conservation management actions. We use these examples to discuss about the three main topics that deserve special attention to help enhance information flow between practitioners and modellers: the decision context, the modelling framework and the spatial products. Finally, we suggest some practical solutions to improve applications of effective conservation action on the ground. We emphasize the importance of matching modelling goals and decision targets by a close collaboration of modellers with decision makers and species experts. Moreover, we highlight the key role of clear and useful spatial products to provide relevant and timely feedback to increase understanding and promote utilisation by conservation practitioners, and to inform and involve targeted audiences.     

Design and implementation of a qualitative simulation model of lambda phage infection

MOTIVATION: Molecular biology databases hold a large number of empirical facts about many different aspects of biological entities. That data is static in the sense that one cannot ask a database 'What effect has protein A on gene B?' or 'Do gene A and gene B interact, and if so, how?'. Those questions require an explicit model of the target organism. Traditionally, biochemical systems are modelled using kinetics and differential equations in a quantitative simulator. For many biological processes however, detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions. RESULTS: We designed and implemented a qualitative simulation model of lambda phage growth control in Escherichia coli based on the existing simulation environment QSim. Qualitative reasoning can serve as the basis for automatic transformation of contents of genomic databases into interactive modelling systems that can reason about the relations and interactions of biological entities.      

A critical synthesis of literature on the promoting action on research implementation in health services (PARIHS) framework

Background

The Promoting Action on Research Implementation in Health Services framework, or PARIHS, is a conceptual framework that posits key, interacting elements that influence successful implementation of evidence-based practices. It has been widely cited and used as the basis for empirical work; however, there has not yet been a literature review to examine how the framework has been used in implementation projects and research. The purpose of the present article was to critically review and synthesize the literature on PARIHS to understand how it has been used and operationalized, and to highlight its strengths and limitations.

Methods

We conducted a qualitative, critical synthesis of peer-reviewed PARIHS literature published through March 2009. We synthesized findings through a three-step process using semi-structured data abstraction tools and group consensus.

Results

Twenty-four articles met our inclusion criteria: six core concept articles from original PARIHS authors, and eighteen empirical articles ranging from case reports to quantitative studies. Empirical articles generally used PARIHS as an organizing framework for analyses. No studies used PARIHS prospectively to design implementation strategies, and there was generally a lack of detail about how variables were measured or mapped, or how conclusions were derived. Several studies used findings to comment on the framework in ways that could help refine or validate it. The primary issue identified with the framework was a need for greater conceptual clarity regarding the definition of sub-elements and the nature of dynamic relationships. Strengths identified included its flexibility, intuitive appeal, explicit acknowledgement of the outcome of 'successful implementation,' and a more expansive view of what can and should constitute 'evidence.'

Conclusions

While we found studies reporting empirical support for PARIHS, the single greatest need for this and other implementation models is rigorous, prospective use of the framework to guide implementation projects. There is also need to better explain derived findings and how interventions or measures are mapped to specific PARIHS elements; greater conceptual discrimination among sub-elements may be necessary first. In general, it may be time for the implementation science community to develop consensus guidelines for reporting the use and usefulness of theoretical frameworks within implementation studies.     

On the role of spatial stochastic models in understanding landscape indices in ecology

Spatial stochastic models play an important role in understanding and predicting the behaviour of complex systems. Such models may be implemented with explicit knowledge of only a limited number of parameters relating to spatial relationships among locations. Consequently, they are often used instead of deterministic‐mechanistic models, which may potentially require an unrealistically large number of parameters. Currently, in contrast to spatial stochastic models, the parameterization of the joint spatial distribution of objects in landscape models is more often implicit than explicit. Here, we investigate the similarities and differences between bona fide spatial stochastic models and landscape models by focusing mostly on the relationships between processes, their realizations (patterns), representation and measurement, and their use in exploratory as well as confirmatory data analysis. One of the most important outcomes of recognizing the importance of stochastic processes is the acknowledgement that the spatial pattern observed in a landscape is only one realization of that process. Hence, while ecologists have been using landscape pattern indices (LPIs) to characterize landscape heterogeneity and/or make inferences about processes shaping the landscape, no stochastic modelling framework has been developed for their proper statistical elucidation. Consequently, several (mis)uses of LPIs draw conclusions about landscapes which are suspect. We show that several reports about sensitivities of LPIs to measurements have common roots that can be made explicitly manageable by adopting stochastic models of spatial structure. The key parameters of these stochastic models are composition and configuration, which, in general, cannot be estimated independently from each other. We outline how to develop the stochastic framework to interpret observations and make some recommendations to practitioners about everyday usage. The conceptual linkages between patterns and processes are particularly important in light of recent efforts to bridge the static‐structural and the dynamic‐analytic traditions of ecology.     

Semiotics of Communication: From Semiosis of Nature to Culture

Communication Studies currently undergoes a crisis of paradigms that requires an ontological review that must begin with a debate about the conditions of possibility of every communicational phenomena. In this article we argue that semiosis offers a conceptual framework that allows for the study of communication as qualitative action. Semiosis, or the action of the sign, is here defined as a fundamental process based on perception that models the world of species, creating cognition and culture. At the core of semiosis are dynamic structures that the authors have defined as ‘ontological diagrams’. The first purpose of Semiotics of Communication is to understand how these modeling systems evolve ontologically and phylogenically, producing, in the case of human culture, means of communication ever more varied and technologically advanced.     

Mathematical modelling in the post-genome era: understanding genome expression and regulation--a system theoretic approach

This paper introduces a mathematical framework for modelling genome expression and regulation. Starting with a philosophical foundation, causation is identified as the principle of explanation of change in the realm of matter. Causation is, therefore, a relationship, not between components, but between changes of states of a system. We subsequently view genome expression (formerly known as 'gene expression') as a dynamic process and model aspects of it as dynamic systems using methodologies developed within the areas of systems and control theory. We begin with the possibly most abstract but general formulation in the setting of category theory. The class of models realised are state-space models, input--output models, autoregressive models or automata. We find that a number of proposed 'gene network' models are, therefore, included in the framework presented here. The conceptual framework that integrates all of these models defines a dynamic system as a family of expression profiles. It becomes apparent that the concept of a 'gene' is less appropriate when considering mathematical models of genome expression and regulation. The main claim of this paper is that we should treat (model) the organisation and regulation of genetic pathways as what they are: dynamic systems. Microarray technology allows us to generate large sets of time series data and is, therefore, discussed with regard to its use in mathematical modelling of gene expression and regulation.     

Towards mechanistic models of plant organ growth

Modelling and simulation are increasingly used as tools in the study of plant growth and developmental processes. By formulating experimentally obtained knowledge as a system of interacting mathematical equations, it becomes feasible for biologists to gain a mechanistic understanding of the complex behaviour of biological systems. In this review, the modelling tools that are currently available and the progress that has been made to model plant development, based on experimental knowledge, are described. In terms of implementation, it is argued that, for the modelling of plant organ growth, the cellular level should form the cornerstone. It integrates the output of molecular regulatory networks to two processes, cell division and cell expansion, that drive growth and development of the organ. In turn, these cellular processes are controlled at the molecular level by hormone signalling. Therefore, combining a cellular modelling framework with regulatory modules for the regulation of cell division, expansion, and hormone signalling could form the basis of a functional organ growth simulation model. The current state of progress towards this aim is that the regulation of the cell cycle and hormone transport have been modelled extensively and these modules could be integrated. However, much less progress has been made on the modelling of cell expansion, which urgently needs to be addressed. A limitation of the current generation models is that they are largely qualitative. The possibilities to characterize existing and future models more quantitatively will be discussed. Together with experimental methods to measure crucial model parameters, these modelling techniques provide a basis to develop a Systems Biology approach to gain a fundamental insight into the relationship between gene function and whole organ behaviour.     

Ants and agents: a process algebra approach to modelling ant colony behaviour

Process algebras are widely used in the analysis of distributed computer systems. They allow formal reasoning about how the various components of a system contribute to its overall behaviour. In this paper we show how process algebras can be usefully applied to understanding social insect biology, in particular to studying the relationship between algorithmic behaviour of individual insects and the dynamical behaviour of their colony. We argue that process algebras provide a useful formalism for understanding this relationship, since they combine computer simulation, Markov chain analysis and mean-field methods of analysis. Indeed, process algebras can provide a framework for relating these three methods of analysis to each other and to experiments. We illustrate our approach with a series of graded examples of modelling activity in ant colonies.     

Nutritional ecology beyond the individual: a conceptual framework for integrating nutrition and social interactions

Over recent years, modelling approaches from nutritional ecology (known as Nutritional Geometry) have been increasingly used to describe how animals and some other organisms select foods and eat them in appropriate amounts in order to maintain a balanced nutritional state maximising fitness. These nutritional strategies profoundly affect the physiology, behaviour and performance of individuals, which in turn impact their social interactions within groups and societies. Here, we present a conceptual framework to study the role of nutrition as a major ecological factor influencing the development and maintenance of social life. We first illustrate some of the mechanisms by which nutritional differences among individuals mediate social interactions in a broad range of species and ecological contexts. We then explain how studying individual‐ and collective‐level nutrition in a common conceptual framework derived from Nutritional Geometry can bring new fundamental insights into the mechanisms and evolution of social interactions, using a combination of simulation models and manipulative experiments.     

From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.     

Scales of association: hierarchical linear models and the measurement of ecological systems

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance–covariance parameters in hierarchically structured data. Although hierarchical models have occasionally been used in the analysis of ecological data, their full potential to describe scales of association, diagnose variance explained, and to partition uncertainty has not been employed. In this paper we argue that the use of the HLM framework can enable significantly improved inference about ecological processes across levels of organization. After briefly describing the principals behind HLM, we give two examples that demonstrate a protocol for building hierarchical models and answering questions about the relationships between variables at multiple scales. The first example employs maximum likelihood methods to construct a two-level linear model predicting herbivore damage to a perennial plant at the individual- and patch-scale; the second example uses Bayesian estimation techniques to develop a three-level logistic model of plant flowering probability across individual plants, microsites and populations. HLM model development and diagnostics illustrate the importance of incorporating scale when modelling associations in ecological systems and offer a sophisticated yet accessible method for studies of populations, communities and ecosystems. We suggest that a greater coupling of hierarchical study designs and hierarchical analysis will yield significant insights on how ecological processes operate across scales.     

Conceptual data modelling for bioinformatics

Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.     

An approach to multiscale modelling with graph grammars

Background and Aims

Functional–structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs.

Methods

A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL.

Key Results

Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas.

Conclusions

The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.     

A patch-dynamic framework for food web metacommunities

The metacommunity concept has proved to be a valuable tool for studying how space can affect the properties and assembly of competitive communities. However, the concept has not been as extensively applied to the study of food webs or trophically structured communities. Here, we demonstrate how to develop a modelling framework that permits food webs to be considered from a spatial perspective. We do this by broadening the classic metapopulation patch-dynamic framework so that it can also account for trophic interactions between many species and patches. Unlike previous metacommunity models, we argue that this requires a system of equations to track the changing patch occupancy of the various species interactions, not the patch occupancy of individual species. We then suggest how this general theoretical framework can be used to study complex and spatially extended food web metacommunities.