The rise of computational biology

Systems biology of the cell cycle of Saccharomyces cerevisiae: From network mining to system-level properties

Following a brief description of the operational procedures of systems biology (SB), the cell cycle of budding yeast is discussed as a successful example of a top-down SB analysis. After the reconstruction of the steps that have led to the identification of a sizer plus timer network in the G1 to S transition, it is shown that basic functions of the cell cycle (the setting of the critical cell size and the accuracy of DNA replication) are system-level properties, detected only by integrating molecular analysis with modelling and simulation of their underlying networks. A detailed network structure of a second relevant regulatory step of the cell cycle, the exit from mitosis, derived from extensive data mining, is constructed and discussed. To reach a quantitative understanding of how nutrients control, through signalling, metabolism and transcription, cell growth and cycle is a very relevant aim of SB. Since we know that about 900 gene products are required for cell cycle execution and control in budding yeast, it is quite clear that a purely systematic approach would require too much time. Therefore lines for a modular SB approach, which prioritises molecular and computational investigations for faster cell cycle understanding, are proposed. The relevance of the insight coming from the cell cycle SB studies in developing a new framework for tackling very complex biological processes, such as cancer and aging, is discussed.

Robust simplifications of multiscale biochemical networks

Background

Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.

Results

We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in [1]. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-κB pathway.

Conclusion

Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

Towards a virtual lung: multi-scale, multi-physics modelling of the pulmonary system

The essential function of the lung, gas exchange, is dependent on adequate matching of ventilation and perfusion, where air and blood are delivered through complex branching systems exposed to regionally varying transpulmonary and transmural pressures. Structure and function in the lung are intimately related, yet computational models in pulmonary physiology usually simplify or neglect structure. The geometries of the airway and vascular systems and their interaction with parenchymal tissue have an important bearing on regional distributions of air and blood, and therefore on whole lung gas exchange, but this has not yet been addressed by modelling studies. Models for gas exchange have typically incorporated considerable detail at the level of chemical reactions, with little thought for the influence of structure. To date, relatively little attention has been paid to modelling at the cellular or subcellular level in the lung, or to linking information from the protein structure/interaction and cellular levels to the operation of the whole lung. We review previous work in developing anatomically based models of the lung, airways, parenchyma and pulmonary vasculature, and some functional studies in which these models have been used. Models for gas exchange at several spatial scales are briefly reviewed, and the challenges and benefits from modelling cellular function in the lung are discussed.

Computational models of the heart and their use in assessing the actions of drugs

Models of cardiac cells are sufficiently well developed to answer questions concerning the actions of drugs on repolarization and the initiation of arrhythmias. These models can be used to characterize drug-receptor action profiles that would be expected to avoid arrhythmia and so help to identify drugs that may be safer. Several examples of such action profiles are presented here, including a recently-developed blocker of persistent sodium current, ranolazine. The models have also been incorporated into tissue and organ models that enable arrhythmia to be modelled also at these levels. Work at these levels can reproduce both re-entrant arrhythmia and fibrillation.

Computer modelling of epilepsy

Epilepsy is a complex set of disorders that can involve many areas of the cortex, as well as underlying deep-brain systems. The myriad manifestations of seizures, which can be as varied as déjà vu and olfactory hallucination, can therefore give researchers insights into regional functions and relations. Epilepsy is also complex genetically and pathophysiologically: it involves microscopic (on the scale of ion channels and synaptic proteins), macroscopic (on the scale of brain trauma and rewiring) and intermediate changes in a complex interplay of causality. It has long been recognized that computer modelling will be required to disentangle causality, to better understand seizure spread and to understand and eventually predict treatment efficacy. Over the past few years, substantial progress has been made in modelling epilepsy at levels ranging from the molecular to the socioeconomic. We review these efforts and connect them to the medical goals of understanding and treating the disorder.

Computational models of epileptic activity: a bridge between observation and pathophysiological interpretation

Epilepsy is a neurological disorder characterized by the recurrence of seizures. It affects 50 million people worldwide. Although a considerable number of new antiepileptic drugs with reduced side effects and toxicity have been introduced since the 1950s, 30% of patients remain pharmacoresistant. Although epilepsy research is making progress, advances in understanding drug resistance have been hampered by the complexity of the underlying neuronal systems responsible for epileptic activity. In such systems where short- or long-term plasticity plays a role, pathophysiological alterations may take place at subcellular (i.e., membrane ion channels and neurotransmitter receptors), cellular (neurons), tissular (networks of neurons) and regional (networks of networks of neurons) scales. In such a context, the demand for integrative approaches is high and neurocomputational models become recognized tools for tackling the complexity of epileptic phenomena. The purpose of this report is to provide an overview on computational modeling as a way of structuring and interpreting multimodal data recorded from the epileptic brain. Some examples are briefly described, which illustrate how computational models closely related with either experimental or clinical data can markedly advance our understanding of essential issues in epilepsy such as the transition from background to seizure activity. A commentary is also made on the potential use of such models in the study of therapeutic strategies such as rational drug design or electrical stimulations.

Simulating invasion with cellular automata: connecting cell-scale and population-scale properties

Interpretive and predictive tools are needed to assist in the understanding of cell invasion processes. Cell invasion involves cell motility and proliferation, and is central to many biological processes including developmental morphogenesis and tumor invasion. Experimental data can be collected across a wide range of scales, from the population scale to the individual cell scale. Standard continuum or discrete models used in isolation are insufficient to capture this wide range of data. We develop a discrete cellular automata model of invasion with experimentally motivated rules. The cellular automata algorithm is applied to a narrow two-dimensional lattice and simulations reveal the formation of invasion waves moving with constant speed. The simulation results are averaged in one dimension-these data are used to identify the time history of the leading edge to characterize the population-scale wave speed. This allows the relationship between the population-scale wave speed and the cell-scale parameters to be determined. This relationship is analogous to well-known continuum results for Fisher's equation. The cellular automata algorithm also produces individual cell trajectories within the invasion wave that are analogous to cell trajectories obtained with new experimental techniques. Our approach allows both the cell-scale and population-scale properties of invasion to be predicted in a way that is consistent with multiscale experimental data. Furthermore we suggest that the cellular automata algorithm can be used in conjunction with individual data to overcome limitations associated with identifying cell motility mechanisms using continuum models alone.

Towards a multiscale model of colorectal cancer

Colorectal cancer (CRC) is one of the best characterised cancers, with extensive data documenting the sequential gene mutations that underlie its development. Complementary datasets are also being generated describing changes in protein and RNA expression, tumour biology and clinical outcome. Both the quantity and the variety of information are inexorably increasing and there is now an accompanying need to integrate these highly disparate datasets. In this article we aim to explain why we believe that mathematical modelling represents a natural tool or language with which to integrate these data and, in so doing, to provide insight into CRC.

Combining protection of different anti-g techniques to +12 gz: a computer simulation study

The aim of this study is to investigate the upper limit of protection afforded by various protective equipment and techniques that used in combination and used individually. A multi-element, nonlinear mathematical model, which was previously used to produce cardiovascular response to +Gz stress and protection afforded by anti-G suits, was modified by incorporating pulmonary-respiration model, as well as detailed vessel models of abdomen and lower extremities into previous model. Using the model, hemodynamic responses of cardiovascular system to +Gz and to different combination of protection measures were simulated. Simulation results indicate that when appropriate counter-pressure is exerted to lower body, the combination of extended coverage anti-G suit with appropriate positive pressure breathing could provide effortless protection around +9Gz. For higher no-staining protection to +12 Gz, tilt-back seat with angles greater than 55 should be used. It also suggests that for anti-G techniques that enhance +Gz tolerance by means of elevating intrathoracic pressure or increasing blood volume, such as ECGS, PBG, and AGSM, their combined protection may be expressed as an additive sum of their individual effects. However, the combined protection of tilt-back seat with other anti-G techniques is higher than the simple addition.

Do nanoparticles present ecotoxicological risks for the health of the aquatic environment?

Nanotechnology is a major innovative scientific and economic growth area, which may present a variety of hazards for environmental and human health. The surface properties and very small size of nanoparticles and nanotubes provide surfaces that may bind and transport toxic chemical pollutants, as well as possibly being toxic in their own right by generating reactive radicals. There is a wealth of evidence for the harmful effects of nanoscale combustion-derived particulates (ultrafines), which when inhaled can cause a number of pulmonary pathologies in mammals and humans. However, release of manufactured nanoparticles into the aquatic environment is largely an unknown. This review addresses the possible hazards associated with nanomaterials and harmful effects that may result from exposure of aquatic animals to nanoparticles. Possible nanoparticle association with naturally occurring colloids and particles is considered together with how this could affect their bioavailability and uptake into cells and organisms. Uptake by endocytotic routes are identified as probable major mechanisms of entry into cells; potentially leading to various types of toxic cell injury. The higher level consequences for damage to animal health, ecological risk and possible food chain risks for humans are also considered based on known behaviours and toxicities for inhaled and ingested nanoparticles in the terrestrial environment. It is concluded that a precautionary approach is required with individual evaluation of new nanomaterials for risk to the health of the environment. Although current toxicity testing protocols should be generally applicable to identify harmful effects associated with nanoparticles, research into new methods is required to address the special properties of nanomaterials.

Cell proliferation drives neural crest cell invasion of the intestine

A general mathematical model of cell invasion is developed and validated with an experimental system. The model incorporates two basic cell functions: non-directed (diffusive) motility and proliferation to a carrying capacity limit. The model is used here to investigate cell proliferation and motility differences along the axis of an invasion wave. Mathematical simulations yield surprising and counterintuitive predictions. In this general scenario, cells at the invasive front are proliferative and migrate into previously unoccupied tissues while those behind the front are essentially nonproliferative and do not directly migrate into unoccupied tissues. These differences are not innate to the cells, but are a function of proximity to uninvaded tissue. Therefore, proliferation at the invading front is the critical mechanism driving apparently directed invasion. An appropriate system to experimentally validate these predictions is the directional invasion and colonization of the gut by vagal neural crest cells that establish the enteric nervous system. An assay using gut organ culture with chick-quail grafting is used for this purpose. The experimental results are entirely concordant with the mathematical predictions. We conclude that proliferation at the wavefront is a key mechanism driving the invasive process. This has important implications not just for the neural crest, but for other invasion systems such as epidermal wound healing, carcinoma invasion and other developmental cell migrations.

Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach

Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene's mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated.

From the Hodgkin-Huxley axon to the virtual heart

Experimentally based models of the heart have been developed since 1960, starting with the discovery and modelling of potassium channels. The early models were based on extensions of the Hodgkin-Huxley nerve impulse equations. The first models including calcium balance and signalling were made in the 1980s and have now reached a high degree of physiological detail. During the 1990s these cell models have been incorporated into anatomically detailed tissue and organ models to create the first virtual organ, the Virtual Heart. With over 40 years of interaction between simulation and experiment, the models are now sufficiently refined to begin to be of use in drug development.

Post-genomic science: cross-disciplinary and large-scale collaborative research and its organizational and technological challenges for the scientific research process

We examine recent developments in cross-disciplinary science and contend that a 'Big Science' approach is increasingly evident in the life sciences-facilitated by a breakdown of the traditional barriers between academic disciplines and the application of technologies across these disciplines. The first fruits of 'Big Biology' are beginning to be seen in, for example, genomics, (bio)-nanotechnology and systems biology. We suggest that this has profound implications for the research process and presents challenges both in technological design, in the provision of infrastructure and training, in the organization of research groups, and in providing suitable research funding mechanisms and reward systems. These challenges need to be addressed if the promise of this approach is to be fully realized. In this paper, we will draw on the work of social scientists to understand how these developments in science and technology relate to organizational culture, organizational change and the context of scientific work. We seek to learn from previous technological developments that seemed to offer similar potential for organizational and social change.

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.

Mechanistic study of the cellular interplay of transport and metabolism using the synthetic modeling method

PURPOSE

The aims of this study were 1) to demonstrate a new modeling strategy that uses experimental computational models built by the synthetic method and 2) to study the consequences of spatial alignment, or lack thereof, of P-glycoprotein (Pgp) and CYP3A4 on the transport and metabolism of drug-like compounds and the influence of competitive inhibition by metabolites on the transport and metabolism of those compounds.

METHODS

The synthetic method of modeling and simulation was used to construct discrete-event, discrete-space models. Within a framework designed for experimentation, object-oriented software components were assembled into devices representing the efflux transport and metabolism mechanisms within cell monolayers in Caco-2 transwell systems.

RESULTS

Conditions for transport and metabolism synergism (and lack thereof) were identified. Simulations showed how spatial alignment altered the coordinated influences of Pgp and CYP3A4 on absorption of a series of drug-like compounds. Within those experiments, when the metabolites were also substrates of Pgp, the metabolite levels produced were insufficient to give evidence of a competitive inhibitory effect on either transport or metabolism.

CONCLUSIONS

The results provide evidence of the potential value of using this class of models to improve our understanding of how complex cellular processes influence the transport and absorption of compounds, and the consequences of interventions.

A strategy for integrative computational physiology

Organ function (the heart beat for example) can only be understood through knowledge of molecular and cellular processes within the constraints of structure-function relations at the tissue level. A quantitative modeling framework that can deal with these multiscale issues is described here under the banner of the International Union of Physiological Sciences Physiome Project.

Cellular Automata With Object-Oriented Features for Parallel Molecular Network Modeling

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

Multi-scale modelling and the IUPS physiome project

We review the development of models of cellular and tissue function and in particular address issues of multi-scale modelling, including the transition from stochastic models to continuum models and the incorporation of cell and tissue structure. The heart is used as an example of linking models at the molecular level to cell, tissue and organ level function.

Theoretical investigation of the neuronal Na+ channel SCN1A: abnormal gating and epilepsy

Epilepsy is a paroxysmal neurological disorder resulting from abnormal cellular excitability and is a common cause of disability. Recently, some forms of idiopathic epilepsy have been causally related to genetic mutations in neuronal ion channels. To understand disease mechanisms, it is crucial to understand how a gene defect can disrupt channel gating, which in turn can affect complex cellular dynamic processes. We develop a theoretical Markovian model of the neuronal Na+ channel NaV1.1 to explore and explain gating mechanisms underlying cellular excitability and physiological and pathophysiological mechanisms of abnormal neuronal excitability in the context of epilepsy. Genetic epilepsy has been shown to result from both mutations that give rise to a gain of channel function and from those that reduce the Na+ current. These data may suggest that abnormal excitation can result from both hyperexcitability and hypoexcitability, the mechanisms of which are presumably distinct, and as yet elusive. Revelation of the molecular origins will allow for translation into targeted pharmacological interventions that must be developed to treat syndromes resulting from divergent mechanisms. This work represents a first step in developing a comprehensive theoretical model to investigate the molecular mechanisms underlying runaway excitation that cause epilepsy.

Mathematical and computational techniques to deduce complex biochemical reaction mechanisms

Time series data can now be routinely collected for biochemical reaction pathways, and recently, several methods have been proposed to infer reaction mechanisms for metabolic pathways and networks. In this paper we provide a survey of mathematical techniques for determining reaction mechanisms for time series data on the concentration or abundance of different reacting components, with little prior information about the pathways involved.

Towards computational models of cells for environmental toxicology

This paper outlines an approach to the development of computational models of cells for marine environmental toxicology. Exposure of cells to pollutants can lead to lysosomal damage and dysfunction, augmented autophagy, cellular dysfunction and atrophy and ultimately tissue pathology and organ damage. The application of carbon and nitrogen based models of intra cellular vesicular traffic for simulating the autophagic and lysosomal response of the hepatopancreatic digestive cells of marine molluscs is described. Two numerical models of the vesicular transport of carbon and nitrogen in the cell are presented. These demonstrate the importance of endocytotic uptake as a driver of lysosomal dynamics and the need to recognize and model it as a discrete process. Conceptual and mathematical models of the toxic impact of polycyclic aromatic hydrocarbons on the digestive gland are presented. The role of experimental research and the need to integrate it with modelling is highlighted.

Editorial: Computational modelling of cell & tissue processes & function

Computational modelling of whole biological systems from cells to organs is gaining momentum in cell biology and disease studies. This pathway is essential for the derivation of explanatory frameworks that will facilitate the development of a predictive capacity for estimating outcomes or risk associated with particular disease processes and therapeutic or stressful treatments. This article introduces a series of invited papers covering a hierarchy of issues and modelling problems, ranging from crucial conceptual considerations of the validity of cellular modelling through to multi-scale modelling up to organ level. The challenges and approaches in cellular modelling are described, including the potential of 'in silico ' modelling applications for receptor-ligand interactions in cell signalling, simulated organ dysfunction (i.e., heart), human and environmental toxicity and the progress of the IUPS Physiome Project. A major challenge now facing biologists is how to translate the wealth of reductionist detail about cells and tissues into a real understanding of how these systems function and are perturbed in disease processes. In biomedicine, simulation models of biological systems now contain sufficient detail, not only to reconstruct normal functions, but also, to reconstruct major disease states. More widely, simulation modelling will aid the targeting of current 'knowledge gaps' and how to fill them; and also provide a research tool for selecting critical factors from multiple simulated experiments for real experimental design. The envisaged longer-term end- product is the creation of simulation models for predicting drug interactions and harmful side-effects; and their use in therapeutic and environmental health risk management. Finally, we take a speculative look at possible future scenarios in cellular modelling, where it is envisioned that integrative biology will move from being largely qualitative and instead become a highly quantitative, computer-intensive discipline.

An integrated biomarker-based strategy for ecotoxicological evaluation of risk in environmental management

Environmental impacts by both natural events and man-made interventions are a fact of life; and developing the capacity to minimise these impacts and their harmful consequences for biological resources, ecosystems and human health is a daunting task for environmental legislators and regulators. A major challenge in impact and risk assessment, as part of integrated environmental management (IEM), is to link harmful effects of pollution (including toxic chemicals) in individual sentinel animals to their ecological consequences. This obstacle has resulted in a knowledge-gap for those seeking to develop effective policies for sustainable use of resources and environmental protection. Part of the solution to this problem may lie with the use of diagnostic clinical-type laboratory-based ecotoxicological tests or biomarkers, utilising sentinel animals as integrators of pollution, coupled with direct immunochemical tests for contaminants. These rapid and cost-effective ecotoxicological tools can provide information on the health status of individuals and populations based on relatively small samples of individuals. In the context of ecosystem status or health of the environment, biomarkers are also being used to link processes of molecular and cellular damage through to higher levels (i.e., prognostic capability), where they can result in pathology with reduced physiological performance and reproductive success. Complex issues are involved in evaluating environmental risk, such as the effects of the physico-chemical environment on the speciation and uptake of pollutant chemicals and inherent inter-individual and inter-species differences in vulnerability to toxicity; and the toxicity of complex mixtures. Effectively linking the impact of pollutants through the various hierarchical levels of biological organisation to ecosystem and human health requires a pragmatic integrated approach based on existing information that either links or correlates processes of pollutant uptake, detoxication and pathology with each other and higher level effects. It is further proposed here that this process will be facilitated by pursuing a holistic or whole systems approach with the development of computational simulation models of cells, organs and animals in tandem with empirical data (i.e., the middle-out approach). In conclusion, an effective integrated environmental management strategy to secure resource sustainability requires an integrated capability for risk assessment and prediction. Furthermore, if such a strategy is to influence and help in the formulation of environmental policy decisions, then it is crucial to demonstrate scientific robustness of predictions concerning the long-term consequences of pollution to politicians, industrialists and environmental managers; and also increase stakeholder awareness of environmental problems.

Modeling the Heart

Models of the heart have been developed since 1960, starting with the discovery and modeling of potassium channels. The first models of calcium balance were made in the 1980s and have now reached a high degree of physiological detail. During the 1990s, these cell models were incorporated into anatomically detailed tissue and organ models.

Development of models of active ion transport for whole-cell modelling: cardiac sodium-potassium pump as a case study

This study presents a method for the reduction of biophysically-based kinetic models for the active transport of ions. A lumping scheme is presented which exploits the differences in timescales associated with fast and slow transitions between model states, while maintaining the thermodynamic properties of the model. The goal of this approach is to contribute to modelling of the effects of disturbances to metabolism, associated with ischaemic heart disease, on cardiac cell function. The approach is illustrated for the sodium-potassium pump in the myocyte. The lumping scheme is applied to produce a 4-state representation from the detailed 15-state model of Läuger and Apell, Eur. Biophys. J. 13 (1986) 309, for which the principles of free energy transduction are used to link the free energy released from ATP hydrolysis (deltaGATP) to the transition rates between states of the model. An iterative minimisation algorithm is implemented to determine the transition rate parameters based on the model fit to experimental data. Finally, the relationship between deltaGATP and pump cycling direction is investigated and compared with recent experimental findings.

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.

Evolving strategies for the incorporation of bioinformatics within the undergraduate cell biology curriculum

Recent advances in genomics and structural biology have resulted in an unprecedented increase in biological data available from Internet-accessible databases. In order to help students effectively use this vast repository of information, undergraduate biology students at Drake University were introduced to bioinformatics software and databases in three courses, beginning with an introductory course in cell biology. The exercises and projects that were used to help students develop literacy in bioinformatics are described. In a recently offered course in bioinformatics, students developed their own simple sequence analysis tool using the Perl programming language. These experiences are described from the point of view of the instructor as well as the students. A preliminary assessment has been made of the degree to which students had developed a working knowledge of bioinformatics concepts and methods. Finally, some conclusions have been drawn from these courses that may be helpful to instructors wishing to introduce bioinformatics within the undergraduate biology curriculum.

QT prolongation through hERG K+ channel blockade: Current knowledge and strategies for the early prediction during drug development

Prolongation of the QT interval of the electrocardiogram is a typical effect of Class III antiarrhythmic drugs, achieved through blockade of potassium channels. In the past decade, evidence has accrued that several classes of drugs used for non-cardiovascular indications may prolong the QT interval with the same mechanism (namely, human ether-a-go-go-related gene (hERG) K(+) channel blockade). The great interest in QT prolongation is because of several reasons. First, drug-induced QT prolongation increases the likelihood of a polymorphous ventricular arrhythmia (namely, torsades de pointes, TdP), which may cause syncope and degenerate into ventricular fibrillation and sudden death. Second, the fact that several classes of drugs, such as antihistamines, fluoroquinolones, macrolides, and neuroleptics may cause the long QT syndrome (LQTS) raises the question whether this is a class effect (e.g., shared by all agents of a given pharmacological class) or a specific effect of single agents within a class. There is now consensus that, in most cases, only a few agents within a therapeutic class share the ability to significantly affect hERG K(+) channels. These compounds should be identified as early as possible during drug development. Third, QT prolongation and interaction with hERG K(+) channels have become surrogate markers of cardiotoxicity and have received increasing regulatory attention. This review briefly outlines the mechanisms leading to QT prolongation and the different strategies that can be followed to predict this unwanted effect. In particular, it will focus on the approaches recently proposed for the in silico screening of new compounds.

Will genomics revolutionise pharmaceutical R&D?

The successful identification of drug targets requires an understanding of the high-level functional interactions between the key components of cells, organs and systems, and how these interactions change in disease states. This information does not reside in the genome, or in the individual proteins that genes code for, it is to be found at a higher level. Genomics will succeed in revolutionising pharmaceutical research and development only if these interactions are also understood by determining the logic of healthy and diseased states. The rapid growth in biological databases, models of cells, tissues and organs, and in computing power has made it possible to explore functionality all the way from the level of genes to whole organs and systems. Combined with genomic and proteomic data, in silico simulation technology is set to transform all stages of drug discovery and development. The major obstacle to achieving this will be obtaining the relevant experimental data at levels higher than genomics and proteomics.

Non-equilibrium gating in cardiac Na+ channels: an original mechanism of arrhythmia

BACKGROUND

Many long-QT syndrome (LQTS) mutations in the cardiac Na+ channel result in a gain of function due to a fraction of channels that fail to inactivate (burst), leading to sustained current (Isus) during depolarization. However, some Na+ channel mutations that are causally linked to cardiac arrhythmia do not result in an obvious gain of function as measured using standard patch-clamp techniques. An example presented here, the SCN5A LQTS mutant I1768V, does not act to increase Isus (<0.1% of peak) compared with wild-type (WT) channels. In fact, it is difficult to reconcile the seemingly innocuous kinetic alterations in I1768V as measured during standard protocols under steady-state conditions with the disease phenotype.

METHODS AND RESULTS

We developed new experimental approaches based on theoretical analyses to investigate Na+ channel gating under non-equilibrium conditions, which more closely approximate physiological changes in membrane potential that occur during the course of a cardiac action potential. We used this new approach to investigate channel-gating transitions that occur subsequent to channel activation.

CONCLUSIONS

Our data suggest an original mechanism for development of LQT-3 arrhythmias. This work demonstrates that a combination of computational and experimental analysis of mutations provides a framework to understand complex mechanisms underlying a range of disorders, from molecular defect to cellular and systems function.

Integration from proteins to organs: the Physiome Project

The Physiome Project will provide a framework for modelling the human body, using computational methods that incorporate biochemical, biophysical and anatomical information on cells, tissues and organs. The main project goals are to use computational modelling to analyse integrative biological function and to provide a system for hypothesis testing.

Modelling the heart: insights, failures and progress

Mathematical models of the heart have developed over a period of about 40 years. Cell types in all regions of the heart have been modelled and they are now being incorporated into anatomically detailed models of the whole organ. This combination is leading to the creation of the first 'virtual organ,' which is being used in drug discovery and testing, and in simulating the action of devices, such as cardiac defibrillators. Simulation is a necessary tool of analysis in attempting to understand biological complexity. We often learn as much from the failures as from the successes of mathematical models. It is the iterative interaction between experiment and simulation that is important. Examples are given where this process has been instrumental in some of the major advances in the field.

The community of the self

Good health, which reflects the harmonious integration of molecules, cells, tissues and organs, is dynamically stable: when displaced by disease, compensation and correction are common, even without medical care. Physiology and computational biology now suggest that healthy dynamic stability arises through the combination of specific feedback mechanisms and spontaneous properties of interconnected networks. Today's physicians are already testing to 'see if the network is right'; tomorrow's physicians may well use therapies to 'make the network right'.

Insights into the molecular mechanisms of bradycardia-triggered arrhythmias in long QT-3 syndrome

Congenital long QT syndrome is a rare disease in which the electrocardiogram QT interval is prolonged due to dysfunctional ventricular repolarization. Variant 3 (LQT-3) is associated with mutations in SCN5A, the gene coding for the heart Na(+) channel alpha subunit. Arrhythmias in LQT-3 mutation carriers are more likely to occur at rest, when heart rate is slow. Several LQT-3 Na(+) channel mutations exert their deleterious effects by promoting a mode of Na(+) channel gating wherein a fraction of channels fails to inactivate. This gating mode, termed "bursting, " results in sustained macroscopic inward Na(+) channel current (I(sus)), which can delay repolarization and prolong the QT interval. However, the mechanism of heart-rate dependence of I(sus) has been unresolved at the single-channel level. We investigate an LQT-3 mutant (Y1795C) using experimental and theoretical frameworks to elucidate the molecular mechanism of I(sus) rate dependence. Our results indicate that mutation-induced changes in the length of time mutant channels spend bursting, rather than how readily they burst, determines I(sus) inverse heart-rate dependence. Our results indicate that mutation-induced changes in the length of time mutant channels spend bursting, rather than how readily they burst, determines I(sus) inverse heart-rate dependence. These results link mutation-induced changes in Na+ channel gating mode transitions to heart rate-dependent changes in cellular electrical activity underlying a key LQT-3 clinical phenotype.