Network modelling of gene regulation

Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview of this rapidly expanding subject. In particular, we review some fundamental concepts of systems biology and discuss the role of network modelling in understanding complex biological systems. Several commonly used network modelling paradigms are surveyed with emphasis on their practical use in systems biology research.     

Modelling biological rhythms

With our growing awareness of the complexity underlying biological phenomena, our need for computational models becomes increasingly apparent. Due to their properties, biological clocks have always lent themselves to computational modelling. Their capacity to oscillate without dampening - even when deprived of all rhythmic environmental information - required the hypothesis of an endogenous oscillator. The notion of a 'clock' provided a conceptual model of this system well before the dynamics of circadian oscillators were probed by computational modelling. With growing insight into the molecular basis of circadian rhythmicity, computational models became more concrete and quantitative. Here, we review the history of modelling circadian oscillators and establish a taxonomy of the modelling world to put the large body of circadian modelling literature into context. Finally, we assess the predictive power of circadian modelling and its success in creating new hypotheses.     

Computational Complementation: A Modelling Approach to Study Signalling Mechanisms during Legume Autoregulation of Nodulation

Autoregulation of nodulation (AON) is a long-distance signalling regulatory system maintaining the balance of symbiotic nodulation in legume plants. However, the intricacy of internal signalling and absence of flux and biochemical data, are a bottleneck for investigation of AON. To address this, a new computational modelling approach called “Computational Complementation” has been developed. The main idea is to use functional-structural modelling to complement the deficiency of an empirical model of a loss-of-function (non-AON) mutant with hypothetical AON mechanisms. If computational complementation demonstrates a phenotype similar to the wild-type plant, the signalling hypothesis would be suggested as “reasonable”. Our initial case for application of this approach was to test whether or not wild-type soybean cotyledons provide the shoot-derived inhibitor (SDI) to regulate nodule progression. We predicted by computational complementation that the cotyledon is part of the shoot in terms of AON and that it produces the SDI signal, a result that was confirmed by reciprocal epicotyl-and-hypocotyl grafting in a real-plant experiment. This application demonstrates the feasibility of computational complementation and shows its usefulness for applications where real-plant experimentation is either difficult or impossible.     

New computer architectures as tools for ecological thought

Recent achievements of computer science provide unrivaled power for the advancement of ecology. This power is not merely computational: parallel computers, having hierarchical organization as their architectural principle, also provide metaphors for understanding complex systems. In this sense they might play for a science of ecological complexity a role like equilibrium-based metaphors had in the development of dynamic systems ecology. Parallel computers provide this opportunity through an informational view of ecological reality and multilevel modelling paradigms. Spatial and individual-oriented models allow application and full understanding of the new metaphors in the ecological context.     

Computational wear prediction of artificial knee joints based on a new wear law and formulation

Laboratory joint wear simulator testing has become the standard means for preclinical evaluation of wear resistance of artificial knee joints. Recent simulator designs have been advanced and become successful at reproducing the wear patterns observed in clinical retrievals. However, a single simulator test can be very expensive and take a long time to run. On the other hand computational wear modelling is an alternative attractive solution to these limitations. Computational models have been used extensively for wear prediction and optimisation of artificial knee designs. However, all these models have adopted the classical Archard's wear law, which was developed for metallic materials, and have selected wear factors arbitrarily. It is known that such an approach is not generally true for polymeric bearing materials and is difficult to implement due to the high dependence of the wear factor on the contact pressure. Therefore, these studies are generally not independent and lack general predictability. The objective of the present study was to develop a new computational wear model for the knee implants, based on the contact area and an independent experimentally determined non-dimensional wear coefficient. The effects of cross-shear and creep on wear predictions were also considered. The predicted wear volume was compared with the laboratory simulation measurements. The model was run under two different kinematic inputs and two different insert designs with curved and custom designed flat bearing surfaces. The new wear model was shown to be capable of predicting the difference of the wear volume and wear pattern between the two kinematic inputs and the two tibial insert designs. Conversely, the wear factor based approach did not predict such differences. The good agreement found between the computational and experimental results, on both the wear scar areas and volumetric wear rates, suggests that the computational wear modelling based on the new wear law and the experimentally calculated non-dimensional wear coefficient should be more reliable and therefore provide a more robust virtual modelling platform.     

Finite element modelling approaches for well-ordered porous metallic materials for orthopaedic applications: cost effectiveness and geometrical considerations

The mechanical properties of well-ordered porous materials are related to their geometrical parameters at the mesoscale. Finite element (FE) analysis is a powerful tool to design well-ordered porous materials by analysing the mechanical behaviour. However, FE models are often computationally expensive. This article aims to develop a cost-effective FE model to simulate well-ordered porous metallic materials for orthopaedic applications. Solid and beam FE modelling approaches are compared, using finite size and infinite media models considering cubic unit cell geometry. The model is then applied to compare two unit cell geometries: cubic and diamond. Models having finite size provide similar results than the infinite media model approach for large sample sizes. In addition, these finite size models also capture the influence of the boundary conditions on the mechanical response for small sample sizes. The beam FE modelling approach showed little computational cost and similar results to the solid FE modelling approach. Diamond unit cell geometry appeared to be more suitable for orthopaedic applications than the cubic unit cell geometry.     

Self-expanding stent modelling and radial force accuracy

Computational simulations using finite element analysis are a tool commonly used to analyse stent designs, deployment geometries and interactions between stent struts and arterial tissue. Such studies require large computational models and efforts are often made to simplify models in order to reduce computational time while maintaining reasonable accuracy. The objective of the study is focused on computational modelling and specifically aims to investigate how different methods of modelling stent–artery interactions can affect the results, computational time taken and computational size of the model. Various different models, each with increasing levels of complexity, are used to simulate this analysis, representing the many assumptions and simplifications used in other similar studies in order to determine what level of simplification will still allow for an accurate representation of stent radial force and resulting stress concentrations on the inner lining of the vessel during self-expanding stent deployment. The main conclusions of the study are that methods used in stent crimping impact on the resulting predicted radial force of the stent; that accurate representation of stent–artery interactions can only be made when modelling the full length of the stent due to the incorporation of end effects; and that modelling self-contact of the stent struts greatly impacts on the resulting stress concentrations within the stent, but that the effect of this on the unloading behaviour and resulting radial force of the stent is negligible.     

On the effects of different strategies in modelling balloon-expandable stenting by means of finite element method

In recent years, computational structural analyses have emerged as important tools to investigate the mechanical response of stent placement into arterial walls. Although most coronary stents are expanded by inflating a polymeric balloon, realistic computational balloon models have been introduced only recently. In the present study, the finite element method is applied to simulate three different approaches to evaluate stent-free expansion and stent expansion inside an artery. Three different stent expansion modelling techniques were analysed by: (i) imposing a uniform pressure on the stent internal surface, (ii) a rigid cylindrical surface expanded with displacement control and (iii) modelling a polymeric deformable balloon. The computational results showed differences in the free and confined-stent expansions due to different expansion techniques. The modelling technique of the balloon seems essential to estimate the level of injury caused on arterial walls during stent expansion.     

N-Myristoylethanolamine-cholesterol (1:1) complex: first evidence from differential scanning calorimetry,fast-atom-bombardment mass spectrometry and computational modelling

The interaction of N-myristoylethanolamine (NMEA) with cholesterol is investigated by differential scanning calorimetry (DSC), fast-atom-bombardment mass spectrometry (FAB-MS) and computational modelling. Addition of cholesterol to NMEA leads to a new phase transition at 55 degrees C besides the chain-melting transition of NMEA at 72.5 degrees C. The enthalpy of the new transition increases with cholesterol content up to 50 mol%, but decreases thereafter, vanishing at 80 mol%. The enthalpy of the chain-melting transition of NMEA decreases with an increase in cholesterol; the transition disappears at 50 mol%. FAB-MS spectra of mixtures of NMEA and cholesterol provide clear signatures of the formation of ([NMEA+cholesterol]+) ([NMEA+cholesterol+Na]+). These results are consistent with the formation of a 1:1 complex between NMEA and cholesterol. Molecular modelling studies support this experimental finding and provide a plausible structural model for the complex, which highlights multiple H-bond interactions between the hydroxy group of cholesterol and the hydroxy and carbonyl groups of NMEA besides appreciable dispersion interaction between the hydrocarbon domains of the two molecules.     

Inside the brain of a neuron

For many decades, neurons were considered to be the elementary computational units of the brain and were assumed to summate incoming signals and elicit action potentials only in response to suprathreshold stimuli. Although modelling studies predicted that single neurons constitute a much more powerful computational entity, able to perform an array of nonlinear calculations, this possibility was not explored experimentally until the discovery of active mechanisms in the dendrites of most neuron types. Here, we review several modelling studies that have addressed information processing in single neurons, starting with those characterizing the arithmetic of different dendritic components, to those tackling neuronal integration at the cell body and, finally, those analysing the computational abilities of the axon. We present modelling predictions along with supporting experimental data in an effort to highlight the significant contribution of modelling work to enhancing our understanding of single-neuron arithmetic.     

The role of molecular modelling in biomedical research

The synergy between experimental and computational biology has greatly benefited both fields, providing invaluable information in many different areas of the life sciences. This minireview will focus on one specific aspect of computational biology, molecular modelling, and describe a few examples highlighting the effectiveness of protein structural analysis and modelling in providing relevant information about systems of biomedical interest.     

CFD simulation of flow through heart: a perspective review

The heart is an organ which pumps blood around the body by contraction of muscular wall. There is a coupled system in the heart containing the motion of wall and the motion of blood fluid; both motions must be computed simultaneously, which make biological computational fluid dynamics (CFD) difficult. The wall of the heart is not rigid and hence proper boundary conditions are essential for CFD modelling. Fluid-wall interaction is very important for real CFD modelling. There are many assumptions for CFD simulation of the heart that make it far from a real model. A realistic fluid-structure interaction modelling the structure by the finite element method and the fluid flow by CFD use more realistic coupling algorithms. This type of method is very powerful to solve the complex properties of the cardiac structure and the sensitive interaction of fluid and structure. The final goal of heart modelling is to simulate the total heart function by integrating cardiac anatomy, electrical activation, mechanics, metabolism and fluid mechanics together, as in the computational framework.     

Computational immunology: The coming of age

The explosive growth in biotechnology combined with major advances in information technology has the potential to radically transform immunology in the postgenomics era. Not only do we now have ready access to vast quantities of existing data, but new data with relevance to immunology are being accumulated at an exponential rate. Resources for computational immunology include biological databases and methods for data extraction, comparison, analysis and interpretation. Publicly accessible biological databases of relevance to immunologists number in the hundreds and are growing daily. The ability to efficiently extract and analyse information from these databases is vital for efficient immunology research. Most importantly, a new generation of computational immunology tools enables modelling of peptide transport by the transporter associated with antigen processing (TAP), modelling of antibody binding sites, identification of allergenic motifs and modelling of T-cell receptor serial triggering.     

Evaluation of accuracy and applicability of protein models: retrospective analysis of biological and biomedical predictions

In order to study protein function and activity structural data is required. Since experimental structures are available for just a small fraction of all known protein sequences, computational methods such as protein modelling can provide useful information. Over the last few decades we have predicted, with homology modelling methods, the structures for numerous proteins. In this study we assess the structural quality and validity of the biological and medical interpretations and predictions made based on the models. All the models had correct scaffolding and were ranked at least as correct or good by numerical evaluators even though the sequence identity with the template was as low as 8%. The biological explanations made based on models were well in line with experimental structures and other experimental studies. Retrospective analysis of homology models indicates the power of protein modelling when made carefully from sequence alignment to model building and refinement. Modelling can be applied to studying and predicting different kinds of biological phenomena and according to our results it can be done so with success.     

The IUPS Physiome Project: a framework for computational physiology

The IUPS Physiome Project is an internationally collaborative open-source project to provide a public domain framework for computational physiology, including the development of modelling standards, computational tools and web-accessible databases of models of structure and function at all spatial scales. A number of papers in this volume deal with the development of specific mathematical models of physiological processes. This paper stands back from the detail of individual models and reviews the current state of the IUPS Physiome Project including organ and organ system continuum models, the interpretation of constitutive law parameters in terms of micro-structural models, and markup languages for standardizing cellular processes. Some current practical applications of the physiome models are given and some of the challenges for the next 5 years of the Physiome Project at the level of organs, cells and proteins are proposed.     

Integrating modelling of biodiversity composition and ecosystem function

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macroecological relationships and hybrid correlative‐mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management. Synthesis There is increasing attention from researchers and policy makers around the world on both assessing and projecting the state of the planet's biodiversity, its ecosystems and the essential services they provide to society. However, existing modelling approaches largely ignore the interactions between biodiversity composition and ecosystem function. We highlight the key challenges and potential solutions to developing integrated models of composition and function. Such models will require a new effort and focus from ecologists, yet the benefits are likely to be substantial, including better informing the management of natural systems at regional, national and international scales.     

Knowledge management of the neuroscientific literature: the data model and underlying strategy of the NeuroScholar system.

This paper describes the underlying strategy and system's design of a knowledge management system for the neuroscientific literature called 'NeuroScholar'. The problem that the system is designed to address is to delineate fully the neural circuitry involved in a specific behaviour. The use of this system provides experimental neuroscientists with a new method of building computational models ('knowledge models') of the contents of the published literature. These models may provide input for analysis (conceptual or computational), or be used as constraint sets for conventional neural modelling work. The underlying problems inherent in this approach, the general framework for the proposed solution, the practical issues concerning usage of the system and a detailed, technical account of the system are described. The author uses a widely used software specification language (the Universal Modelling Language) to describe the design of the system and present examples from published work concerned with classical eyeblink conditioning in the rabbit.     

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions

Background  

Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design.