Modelling biological modularity with CellML

In recent years advances in the construction of mathematical models of biological systems have yielded an array of valuable constructs. The authors seek to provide a 'leading practice' method for implementing modularised kinetic mass-action models in order to obtain a number of advantages in model construction, validation and derived insights. The authors advocate the consideration of 'accounting cycles' or 'chains' to define 'functional' components and the separate consideration of 'messenger' components for mobile or diffusive molecular species. From a conceptual modularisation the authors illustrate, with an example drawn from signal transduction, a component-based formulation in the model exchange format cellular modelling markup language (CellML) 1.1 - demonstrating loose coupling between functionally-focused reusable components. Finally, the authors discuss the dilemmas associated with modelling protein-to-protein interactions, and the vision for using future CellML enhancements to resolve potential duplications when combining independently developed models.     

Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting

Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy datasets. Over the years, a variety of heuristics have been proposed to solve this complex optimization problem, with good results in some cases yet with limitations in the biological setting. In this work, we develop an algorithm for model parameter fitting that combines ideas from evolutionary algorithms, sequential Monte Carlo and direct search optimization. Our method performs well even when the order of magnitude and/or the range of the parameters is unknown. The method refines iteratively a sequence of parameter distributions through local optimization combined with partial resampling from a historical prior defined over the support of all previous iterations. We exemplify our method with biological models using both simulated and real experimental data and estimate the parameters efficiently even in the absence of a priori knowledge about the parameters.     

Validation and invalidation of systems biology models using robustness analysis

Robustness, the ability of a system to function correctly in the presence of both internal and external uncertainty, has emerged as a key organising principle in many biological systems. Biological robustness has thus become a major focus of research in Systems Biology, particularly on the engineering-biology interface, since the concept of robustness was first rigorously defined in the context of engineering control systems. This review focuses on one particularly important aspect of robustness in Systems Biology, that is, the use of robustness analysis methods for the validation or invalidation of models of biological systems. With the explosive growth in quantitative modelling brought about by Systems Biology, the problem of validating, invalidating and discriminating between competing models of a biological system has become an increasingly important one. In this review, the authors provide a comprehensive overview of the tools and methods that are available for this task, and illustrate the wide range of biological systems to which this approach has been successfully applied.     

Mathematical models in physiology

Computational modelling of biological processes and systems has witnessed a remarkable development in recent years. The search-term (modelling OR modeling) yields over 58000 entries in PubMed, with more than 34000 since the year 2000: thus, almost two-thirds of papers appeared in the last 5-6 years, compared to only about one-third in the preceding 5-6 decades. The development is fuelled both by the continuously improving tools and techniques available for bio-mathematical modelling and by the increasing demand in quantitative assessment of element inter-relations in complex biological systems. This has given rise to a worldwide public domain effort to build a computational framework that provides a comprehensive theoretical representation of integrated biological function-the Physiome. The current and next issues of this journal are devoted to a small sub-set of this initiative and address biocomputation and modelling in physiology, illustrating the breadth and depth of experimental data-based model development in biological research from sub-cellular events to whole organ simulations.     

Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict

This paper provides statistical guidance on the development and application of model-based geostatistical methods for disease prevalence mapping. We illustrate the different stages of the analysis, from exploratory analysis to spatial prediction of prevalence, through a case study on malaria mapping in Tanzania. Throughout the paper, we distinguish between predictive modelling, whose main focus is on maximizing the predictive accuracy of the model, and explanatory modelling, where greater emphasis is placed on understanding the relationships between the health outcome and risk factors. We demonstrate that these two paradigms can result in different modelling choices. We also propose a simple approach for detecting over-fitting based on inspection of the correlation matrix of the estimators of the regression coefficients. To enhance the interpretability of geostatistical models, we introduce the concept of domain effects in order to assist variable selection and model validation. The statistical ideas and principles illustrated here in the specific context of disease prevalence mapping are more widely applicable to any regression model for the analysis of epidemiological outcomes but are particularly relevant to geostatistical models, for which the separation between fixed and random effects can be ambiguous.     

WebML modelling in UML

In recent years, we have witnessed how the Web Engineering community has started using the standard unified modelling language (UML) notation, techniques and supporting tools for modelling Web systems, which has led to the adaptation to UML of several existing modelling languages, notations and development processes. This interest for being MOF and UML-compliant arises from the increasing need to interoperate with other notations and tools, and to exchange data and models, thus facilitating reuse. WebML, like any other domain-specific language, allows one to express in a precise and natural way the concepts and mechanisms of its domain of reference. However, it cannot fully interoperate with other notations, nor be integrated with other model-based tools. As a solution to these requirements, a UML 2.0 profile for WebML which allows WebML models to be used in conjunction with other notations and modelling tools has been described. The paper also evaluates UML 2.0 as a platform for Web modelling and identifies some key requirements for making this version of the standard more usable.     

Determination and validation of the elastic moduli of small and complex biological samples: bone and keratin in bird beaks

In recent years, there has been a surge in the development of finite-element (FE) models aimed at testing biological hypotheses. For example, recent modelling efforts suggested that the beak in Darwin''s finches probably evolved in response to fracture avoidance. However, knowledge of the material properties of the structures involved is crucial for any model. For many biological structures, these data are not available and may be difficult to obtain experimentally given the complex nature of biological structures. Beaks are interesting as they appear to be highly optimized in some cases. In order to understand the biomechanics of this small and complex structure, we have been developing FE models that take into account the bilayered structure of the beak consisting of bone and keratin. Here, we present the results of efforts related to the determination and validation of the elastic modulus of bone and keratin in bird beaks. The elastic moduli of fresh and dried samples were obtained using a novel double-indentation technique and through an inverse analysis. A bending experiment is used for the inverse analysis and the validation of the measurements. The out-of-plane displacements during loading are measured using digital speckle pattern interferometry.     

Predictive constitutive modelling of arteries by deep learning

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R² = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.     

Modelling biological behaviours with the unified modelling language: an immunological case study and critique

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML''s ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML''s lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML''s activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.     

A composite computational model of liver glucose homeostasis. I. Building the composite model

A computational model of the glucagon/insulin-driven liver glucohomeostasis function, focusing on the buffering of glucose into glycogen, has been developed. The model exemplifies an ‘engineering’ approach to modelling in systems biology, and was produced by linking together seven component models of separate aspects of the physiology. The component models use a variety of modelling paradigms and degrees of simplification. Model parameters were determined by an iterative hybrid of fitting to high-scale physiological data, and determination from small-scale in vitro experiments or molecular biological techniques. The component models were not originally designed for inclusion within such a composite model, but were integrated, with modification, using our published modelling software and computational frameworks. This approach facilitates the development of large and complex composite models, although, inevitably, some compromises must be made when composing the individual models. Composite models of this form have not previously been demonstrated.     

Process models in design and development

Many models of the design and development process have been published over the years, representing it for different purposes and from different points of view. This article contributes an organising framework that clarifies the topology of the literature on these models and thereby relates the main perspectives that have been developed. The main categories of model are introduced. Their contexts, advantages, and limitations are considered through discussion of selected examples. It is demonstrated that the framework integrates coverage of earlier reviews and as such provides a new perspective on the literature. Finally, key characteristics of design and development process models are discussed considering their applications in practice, and opportunities for further research are suggested. Overall, the article should aid researchers in positioning new models and new modelling approaches in relation to state-of-the-art. It may also be of interest to practitioners and educators seeking an overview of developments in this area.     

Surface-seeking radionuclides in the skeleton: current approach and recent developments in biokinetic modelling for humans and beagles

In the last decade, the biokinetics of surface-seeking radionuclides in the skeleton has been the object of several studies. Investigations were carried out to determine the kinetics of plutonium and americium in the skeleton of humans and beagles. As a result of these investigations, in recent years the models presented by ICRP in Publication 67 for humans were partially revised, particularly the skeletal part. The aim of the present work is to present recent developments in the biokinetic modelling of surface-seeking radionuclides (plutonium and americium) in beagles and humans. Various assumptions and physiological interpretations of the different approaches to the biokinetic modelling of the skeleton are discussed. Current ICRP concepts and skeleton modelling of plutonium and americium in humans are compared to the latest developments in biokinetic modelling in beagles.     

Structure-Preserving Dynamics of Stochastic Epidemic Model with the Saturated Incidence Rate

The structure-preserving features of the nonlinear stochastic models are positivity, dynamical consistency and boundedness. These features have a significant role in different fields of computational biology and many more. Unfortunately, the existing stochastic approaches in literature do not restore aforesaid structure-preserving features, particularly for the stochastic models. Therefore, these gaps should be occupied up in literature, by constructing the structure-preserving features preserving numerical approach. This writing aims to describe the structure-preserving dynamics of the stochastic model. We have analysed the effect of reproduction number in stochastic modelling the same as described in the literature for deterministic modelling. The usual explicit stochastic numerical approaches are time-dependent. We have developed the implicitly driven explicit approach for the stochastic epidemic model. We have proved that the newly developed approach is preserving the structural, dynamical properties as positivity, boundedness and dynamical consistency. Finally, convergence analysis of a newly developed approach and graphically illustration is also presented.     

An enterprise modelling approach for better optimisation modelling: application to the humanitarian relief chain coordination problem

Humanitarian supply chains (HSC) can be considered a new research area. The number of applied scientific publications has considerably increased over the past 15 years. About half of this research work uses quantitative techniques as optimisation decision-support systems. But due to the recentness of this academic area, researchers are finding it difficult to develop accurate, and above all, reliable mathematical models to support their steps towards improvement. This is particularly true concerning the crucial problems of coordination in HSCs. This paper tackles the issue by developing an original quantitative modelling support method. Based on enterprise modelling methodologies, we propose a business process modelling approach that helps in understanding, analysing, evaluating and then developing the formal expression of an HSC. Such a model, therefore, clearly has an added value for practitioners and should enable relevant quantitative models to be produced. Finally, an application on the emergency response processes of the International Federation of Red Cross is detailed in order to validate the relevance and the applicability of our proposal. This experiment allows all the variables and parameters that should be useful for improving the efficiency of the network to be identified.     

Simplification and its consequences in biological modelling: conclusions from a study of calcium oscillations in hepatocytes.

Systems Biology requires that biological modelling is scaled up from small components to system level. This can produce exceedingly complex models, which obscure understanding rather than facilitate it. The successful use of highly simplified models would resolve many of the current problems faced in Systems Biology. This paper questions whether the conclusions of simple mathematical models of biological systems are trustworthy. The simplification of a specific model of calcium oscillations in hepatocytes is examined in detail, and the conclusions drawn from this scrutiny generalized. We formalize our choice of simplification approach through the use of functional 'building blocks'. A collection of models is constructed, each a progressively more simplified version of a well-understood model. The limiting model is a piecewise linear model that can be solved analytically.We find that, as expected, in many cases the simpler models produce incorrect results. However, when we make a sensitivity analysis, examining which aspects of the behaviour of the system are controlled by which parameters, the conclusions of the simple model often agree with those of the richer model. The hypothesis that the simplified model retains no information about the real sensitivities of the unsimplified model can be very strongly ruled out by treating the simplification process as a pseudo-random perturbation on the true sensitivity data. We conclude that sensitivity analysis is, therefore, of great importance to the analysis of simple mathematical models in biology. Our comparisons reveal which results of the sensitivity analysis regarding calcium oscillations in hepatocytes are robust to the simplifications necessarily involved in mathematical modelling. For example, we find that if a treatment is observed to strongly decrease the period of the oscillations while increasing the proportion of the cycle during which cellular calcium concentrations are rising, without affecting the inter-spike or maximum calcium concentrations, then it is likely that the treatment is acting on the plasma membrane calcium pump.     

Biocharts: a visual formalism for complex biological systems

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis.     

Simplified mechanistic models of gene regulation for analysis and design

Simplified mechanistic models of gene regulation are fundamental to systems biology and essential for synthetic biology. However, conventional simplified models typically have outputs that are not directly measurable and are based on assumptions that do not often hold under experimental conditions. To resolve these issues, we propose a ‘model reduction’ methodology and simplified kinetic models of total mRNA and total protein concentration, which link measurements, models and biochemical mechanisms. The proposed approach is based on assumptions that hold generally and include typical cases in systems and synthetic biology where conventional models do not hold. We use novel assumptions regarding the ‘speed of reactions’, which are required for the methodology to be consistent with experimental data. We also apply the methodology to propose simplified models of gene regulation in the presence of multiple protein binding sites, providing both biological insights and an illustration of the generality of the methodology. Lastly, we show that modelling total protein concentration allows us to address key questions on gene regulation, such as efficiency, burden, competition and modularity.     

Underestimating the bullwhip effect: a simulation study of the decomposability assumption

We investigate the assumption of decomposability as it pertains to modelling the bullwhip effect in multi-stage supply chains. Decomposing a multi-stage supply chain into a set of node pairs, each of which can be efficiently represented with a two-stage model, is a common modelling technique when analysing the bullwhip effect in supply chains. This approach depends on the validity of the decomposability assumption since most supply chains are coupled systems that are a logical fit for singular, or ‘monolithic’, multi-stage models. We utilise a simulation study to compare decomposition-based supply-chain models with monolithic models and determine if decomposition modelling significantly alters the predicted severity of the bullwhip effect. We find decomposition-based models exhibit a significantly lower level of bullwhip effect than monolithic models of the same supply chain. The systematic underestimation of the bullwhip effect by decomposition-based models indicates that the assumption of decomposability is flawed. Our study also confirms previous work showing the significant benefit of using actual, instead of approximate, lead-time demand information. We discuss implications for supply-chain modelling, supply-chain design, and data collection.