Edmund J. Crampin

Computational physiology and the physiome project

Bioengineering analyses of physiological systems use the computational solution of physical conservation laws on anatomically detailed geometric models to understand the physiological function of intact organs in terms of the properties and behaviour of the cells and tissues within the organ. By linking behaviour in a quantitative, mathematically defined sense across multiple scales of biological organization – from proteins to cells, tissues, organs and organ systems – these methods have the potential to link patient‐specific knowledge at the two ends of these spatial scales. A genetic profile linked to cardiac ion channel mutations, for example, can be interpreted in relation to body surface ECG measurements via a mathematical model of the heart and torso, which includes the spatial distribution of cardiac ion channels throughout the myocardium and the individual kinetics for each of the approximately 50 types of ion channel, exchanger or pump known to be present in the heart. Similarly, linking molecular defects such as mutations of chloride ion channels in lung epithelial cells to the integrated function of the intact lung requires models that include the detailed anatomy of the lungs, the physics of air flow, blood flow and gas exchange, together with the large deformation mechanics of breathing. Organizing this large body of knowledge into a coherent framework for modelling requires the development of ontologies, markup languages for encoding models, and web‐accessible distributed databases. In this article we review the state of the field at all the relevant levels, and the tools that are being developed to tackle such complexity. Integrative physiology is central to the interpretation of genomic and proteomic data, and is becoming a highly quantitative, computer‐intensive discipline.

Systems Biology: An Approach

In just over a decade, Systems Biology has moved from being an idea, or rather a disparate set of ideas, to a mainstream feature of research and funding priorities. Institutes, departments, and centers of various flavors of Systems Biology have sprung up all over the world. An Internet search now produces more than 2 million hits. Of the 2,800 entries in PubMed with “Systems Biology” in either the title or the abstract, only two papers were published before 2000, and >90% were published in the past five years. In this article, we interpret Systems Biology as an approach rather than as a field or a destination of research. We illustrate that this approach is productive for the exploration of systems behavior, or “phenotypes,” at all levels of structural and functional complexity, explicitly including the supracellular domain, and suggest how this may be related conceptually to genomes and biochemical networks. We discuss the role of models in Systems Biology and conclude with a consideration of their utility in biomedical research and development.

Cardiac cell modelling: observations from the heart of the cardiac physiome project.

In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field.

Minimum Information About a Simulation Experiment (MIASE).

Reproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment (MIASE, Glossary in Box 1) describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.

Bioinformatics, multiscale modeling and the IUPS Physiome Project

Multiscale modeling is required for linking physiological processes operating at the organ and tissue levels to signal transduction networks and other subcellular processes. Several XML markup languages, including CellML, have been developed to encode models and to facilitate the building of model repositories and general purpose software tools. Progress in this area is described and illustrated with reference to the heart Physiome Project which aims to understand cardiac arrhythmias in terms of structure-function relations from proteins up to cells, tissues and organs.

Multiscale computational modelling of the heart

A computational framework is presented for integrating the electrical, mechanical and biochemical functions of the heart. Finite element techniques are used to solve the large-deformation soft tissue mechanics using orthotropic constitutive laws based in the measured fibre-sheet structure of myocardial (heart muscle) tissue. The reaction-diffusion equations governing electrical current flow in the heart are solved on a grid of deforming material points which access systems of ODEs representing the cellular processes underlying the cardiac action potential. Navier-Stokes equations are solved for coronary blood flow in a system of branching blood vessels embedded in the deforming myocardium and the delivery of oxygen and metabolites is coupled to the energy-dependent cellular processes. The framework presented here for modelling coupled physical conservation laws at the tissue and organ levels is also appropriate for other organ systems in the body and we briefly discuss applications to the lungs and the musculo-skeletal system. The computational framework is also designed to reach down to subcellular processes, including signal transduction cascades and metabolic pathways as well as ion channel electrophysiology, and we discuss the development of ontologies and markup language standards that will help link the tissue and organ level models to the vast array of gene and protein data that are now available in web-accessible databases.

A Thermodynamic Model of the Cardiac Sarcoplasmic/Endoplasmic Ca2+ (SERCA) Pump

We present a biophysically based kinetic model of the cardiac SERCA pump that consolidates a range of experimental data into a consistent and thermodynamically constrained framework. The SERCA model consists of a number of sub-states with partial reactions that are sensitive to Ca(2+) and pH, and to the metabolites MgATP, MgADP, and Pi. Optimization of model parameters to fit experimental data favors a fully cooperative Ca(2+)-binding mechanism and predicts a Ca(2+)/H(+) counter-transport stoichiometry of 2. Moreover, the order of binding of the partial reactions, particularly the binding of MgATP, proves to be a strong determinant of the ability of the model to fit the data. A thermodynamic investigation of the model indicates that the binding of MgATP has a large inhibitory effect on the maximal reverse rate of the pump. The model is suitable for integrating into whole-cell models of cardiac electrophysiology and Ca(2+) dynamics to simulate the effects on the cell of compromised metabolism arising in ischemia and hypoxia.

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

Computational biology of cardiac myocytes: proposed standards for the physiome

SUMMARY Predicting information about human physiology and pathophysiology from genomic data is a compelling, but unfulfilled goal of post-genomic biology. This is the aim of the so-called Physiome Project and is, undeniably, an ambitious goal. Yet if we can exploit even a small proportion of the rich and varied experimental data currently available, significant insights into clinically important aspects of human physiology will follow. To achieve this requires the integration of data from disparate sources into a common framework. Extrapolation of available data across species, laboratory techniques and conditions requires a quantitative approach. Mathematical models allow us to integrate molecular information into cellular, tissue and organ-level, and ultimately clinically relevant scales. In this paper we argue that biophysically detailed computational modelling provides the essential tool for this process and, furthermore, that an appropriate framework for annotating, databasing and critiquing these models will be essential for the development of integrative computational biology.

Acidosis in models of cardiac ventricular myocytes

The effects of acidosis on cardiac electrophysiology and excitation–contraction coupling have been studied extensively. Acidosis decreases the strength of contraction and leads to altered calcium transients as a net result of complex interactions between protons and a variety of intracellular processes. The relative contributions of each of the changes under acidosis are difficult to establish experimentally, however, and significant uncertainties remain about the key mechanisms of impaired cardiac function. In this paper, we review the experimental findings concerning the effects of acidosis on the action potential and calcium handling in the cardiac ventricular myocyte, and we present a modelling study that establishes the contribution of the different effects to altered Ca2+ transients during acidosis. These interactions are incorporated into a dynamical model of pH regulation in the myocyte to simulate respiratory acidosis in the heart.