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PubMed2004

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.

Cell Physiological Phenomena↗

PubMed2004

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques.

Ammonia↗

PubMed2002

Models and modeling systems in Alzheimer disease drug discovery.

The rapid pace of neurobiology research has increased the prospects of developing drugs to prevent neurodegenerative disorders. Although the goal of delaying the onset of brain disorders may be within the grasp of modern medicine, there are several critical barriers to progress. Among these is the lack of appropriate models and modeling systems for specific neurodegenerative diseases. Traditionally, in drug discovery, testing, and development, a combination of models is used. These include in vitro, in vivo, transgenic, and other animal models. However, each of these models has limitations. In this article, the author advocates the use of "in silico" modeling systems, which could complement currently available models and enable investigators to simulate alternative strategies to modulate neural function in a dynamic interactive mode. Advances in computer technology, including increasing speed and memory, and ready access to parallel processing systems have made it easier for investigators to develop databases for computer abstractions of neural function and dysfunction and to begin to develop prototypes for use in complex systems modeling environments. Multimodeling systems have been widely used in other areas of science to study emergent behavior of complex systems, such as the impact of atmospheric changes on weather, flight patterns of birds in a flock, and the behavior of traders in a commodities market. Adoption of such approaches should increase understanding of the complexities of signal transduction pathways in neural networks and accelerate the drug discovery process.

Alzheimer Disease↗

PubMed2006

Prediction of in vivo embryotoxic effect levels with a combination of in vitro studies and PBPK modelling.

The new EU legislations for chemicals (Registration, Evaluation and Authorization of Chemicals, REACH) and cosmetics (Seventh Amendment) stimulate the acceptance of in vitro and in silico approaches to test chemicals for their potential to cause reproductive effects. In the current study seven compounds with known in vivo developmental effects were tested in the embryonic stem cell test (EST). The EST correctly classified 5-fluorouracil, methotrexate, retinoic acid, 2-ethoxyacetic acid and 2-methoxyacetic acid for their in vivo embryotoxic potential. The toxicity of 2-methoxyethanol and 2-ethoxyethanol was underestimated due to a lack of metabolic capacity in the EST. This study further investigated the possibility to use in silico techniques to extrapolate in vitro effect concentrations determined in the EST to in vivo exposure levels. This approach was evaluated by comparing in silico predicted in vivo effect levels with effect levels measured in rodents. The in vivo effect levels of 2-methoxyethanol, 2-ethoxyethanol, methotrexate and retinoic acid were correctly predicted with in silico modelling. Contrary, in vivo embryotoxicity of 5-fluorouracil was overestimated following this approach. It is concluded that a combination of in vitro and in silico techniques appears to be a promising alternative test method for risk assessment of embryotoxic compounds.

Animal Testing Alternatives↗

PubMedDate not supplied

In silico ADME/Tox: why models fail.

By way of example, we discuss the apparent 'failure' of in silico ADME/Tox models and attempt to understand the causes. Often, the interpretation of the success of models lies in their use and the expectations of the user. Other times, models are, in fact, of little value. Disappointing results can be linked to the key aspects of the model and modeling procedure, many of these related to the original data and its interpretation. We make recommendations to providers of models regarding the development, description, and use of models as well as the data and information that are important to understanding a model's quality and scope of use.

Carrier Proteins↗

PubMed2005

Three-dimensional simulations of airways within human lungs.

Information regarding the deposition patterns of inhaled particles has important implications to the fields of medicine and risk assessment. The former concerns the targeted delivery of inhaled pharmacological drugs (aerosol therapy); the latter concerns the risk assessment of inhaled air pollutants (inhalation toxicology). It is well documented in the literature that the behavior and fate of inhaled particles may be formulated using three families of variables: respiratory system morphologies, aerosol characteristics, and ventilatory parameters. It is straightforward to propose that the seminal role is played by morphology per se because the structures of individual airways and their spatial orientations within lungs affect the motion of air and the trajectories of transported particles. In previous efforts, we have developed original algorithms to describe airway networks within lungs and employed them as templates to interpret the results of single photon emission computed tomography (SPECTs) studies. In this work, we have advanced the process of mathematical modeling and computer simulations to produce three-dimensional (3D) images. We have tested the new in silico model by studying two different branching concepts: an inclusive (all airways present) system and a single "typical" pathway system. When viewed with the glasses supplied with this volume, the 3D nature of airway branching networks within lungs as displayed via our original computer graphics software is clear. We submit that the new technology will have numerous and seminal functions in future medical and toxicological regimens, the most fundamental being the creation of a platform to view natural 3D structures in vivo with related biological processes (e.g., disposition of inhaled pharmaceuticals).

Computer Graphics↗

PubMed2002

Mathematical modelling for the new millenium: medicine by numbers.

Physicists, engineers and mathematicians are accustomed to the combination of elegance, rigour and utility that characterise mathematical models. They are familiar with the need to dip into their mathematical toolbox to select the technique of choice. However, medicine and biology have not been characterised, in general, by a mathematical formalism. The relative paucity of mathematical models in biology and medicine reflects in part the difficulty in making accurate and appropriate experimental measurements in the field. Signal noise, the lack of appropriate sensors, and uncertainty as to what constitutes the significant measurements are largely to blame for this. The purpose of this paper is to characterise a 'good' model, encourage the development and application of such models to new areas, and outline future developments in the field. It is proposed that a good model will be accurate, predictive, economical, unique and elegant. These principles will be illustrated with reference to four models: radiosensitisation of tumours, modelling solute clearance in haemodialysis, the myogenic response in reactive hyperaemia and cardiac electrical activity. It is suggested that, in the immediate future, the mathematical model will become a useful adjunct to laboratory experiment (and possibly clinical trial), and the provision of 'in silico' models will become routine.

Arrhythmias, Cardiac↗

PubMed2002

Pharmacophore and quantitative structure activity relationship modelling of UDP-glucuronosyltransferase 1A1 (UGT1A1) substrates.

UDP-glucuronosyltransferase 1A1 (UGT1A1) is a polymorphic enzyme responsible for the glucuronidation of structurally diverse drugs, non-drug xenobiotics and endogenous compounds (e.g. bilirubin). Thus, definition of UGT1A1 substrate and inhibitor selectivities and binding affinities assumes importance for the identification of compounds whose elimination may be impaired in subjects with variant genotypes, and for the prediction of potentially inhibitory interactions involving xenobiotics and endogenous compounds metabolized by UGT1A1. We report the generation of two- and three-dimensional (2D and 3D) quantitative structure activity relationships (QSAR) and pharmacophore models for 23 known UGT1A1 substrates with diverse structure and binding affinity. Initially, a simple procedure was developed to determine apparent inhibition constants (Ki,app) for these compounds. Eighteen substrates were subsequently used to construct models and the remaining five to validate the predictive ability of the models. Three different models were constructed: (i) three feature pharmacophore model able to predict the Ki,app on the basis of the degree to which a substrate can fit to the arrangement of 3D features (r2 = 0.87, Ki,app for all five test substrates predicted within log unit); (ii) 3D-QSAR using a 'common features' pharmacophore to align the substrates (r2 = 0.71, Ki,app for four out of five test substrates predicted within one log unit); (iii) 2D-QSAR constructed with six chemical descriptors (r2 = 0.92, Ki,app of all five test substrates predicted within one log unit). The common features pharmacophore demonstrated the importance of two hydrophobic domains separated from the glucuronidation site by 4 A and 7 A, respectively. These models, which represent the first generalized predictive models for a UGT isoform, complement each other and are an important first step towards computer based (in silico) models of UGT1A1 for high throughput prediction of metabolism.

Glucuronosyltransferase↗

PubMed2002

The evolving role of information technology in the drug discovery process.

Information technologies for chemical structure prediction, heterogeneous database access, pattern discovery, and systems and molecular modeling have evolved to become core components of the modern drug discovery process. As this evolution continues, the balance between in silico modeling and 'wet' chemistry will continue to shift and it might eventually be possible to step through the discovery pipeline without the aid of traditional laboratory techniques. Rapid advances in the industrialization of gene sequencing combined with databases of protein sequence and structure have created a target-rich but lead-poor environment. During the next decade, newer information technologies that facilitate the molecular modeling of drug-target interactions are likely to shift this balance towards molecular-based personalized medicine -- the ultimate goal of the drug discovery process.

Animals↗

PubMed2001

Integrating metabolism and toxicity in multi-organ systems.

A wide range of experimental and predictive approaches are currently employed by the pharmaceutical industry in an attempt to avoid developing molecules with likely toxicities. Experimental systems may be adapted to handle the increasing number of molecules that require screening. The cost implication of such extensive screening is, however, immense and in silico models must therefore become the first lines of investigation to assess the metabolic and toxicological potential of new compound libraries. In silico systems have been based on the relationship between the chemical structure of novel molecules and toxicological outcomes in terms of types, but not always, locations of lesions. There should be sufficient published and in-house data available on the distribution of diverse molecules to identify the rules that govern organ, cellular and subcellular distribution of molecules (and their metabolites) as the basis of tissue and cell targeting and, more importantly, the possibility of toxicological processes. In addition, the extensive cell biology knowledge needs to be linked to current in silico systems to enhance our understanding of metabolic activation and other processes that affect likely toxicological consequences in different tissues and specific cell types. There is a need to provide a single knowledge platform that can access all current in silico products that use different datasets. The closer integration of these diverse tools will help predict other approaches to help identify the most likely toxicological potential of novel molecules, and ensure the 'least' toxic molecules are developed.

Animals↗

PubMed2004

Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model.

A fully compartmentalized genome-scale metabolic model of Saccharomyces cerevisiae that accounts for 750 genes and their associated transcripts, proteins, and reactions has been reconstructed and validated. All of the 1149 reactions included in this in silico model are both elementally and charge balanced and have been assigned to one of eight cellular locations (extracellular space, cytosol, mitochondrion, peroxisome, nucleus, endoplasmic reticulum, Golgi apparatus, or vacuole). When in silico predictions of 4154 growth phenotypes were compared to two published large-scale gene deletion studies, an 83% agreement was found between iND750's predictions and the experimental studies. Analysis of the failure modes showed that false predictions were primarily caused by iND750's limited inclusion of cellular processes outside of metabolism. This study systematically identified inconsistencies in our knowledge of yeast metabolism that require specific further experimental investigation.

Cell Compartmentation↗

PubMed2005

Are hERG channel inhibition and QT interval prolongation all there is in drug-induced torsadogenesis? A review of emerging trends.

Contemporary preclinical in vitro and in vivo methods have been imperfect in predicting drug-induced Torsades de Pointes (TdP) in humans. A better understanding of additional relevant factors in the genesis of drug-induced TdP is necessary. New sophisticated in vitro techniques, such as arterially perfused ventricular wedge preparations or isolated perfused hearts, potentially offer a better understanding of torsadogenic mechanisms and a refinement of drug testing. Of particular interest are the dispersion of repolarization and the refractoriness of different cell types across the ventricular wall, triangulation of the action potential, reverse use dependence and instability of the action potential duration. In vivo models are currently refined by establishing parameters such as beat-to-beat variability and T-wave morphology as derived from the in vitro proarrhythmia indices. Animal models of proarrhythmia are to date not recommended for routine evaluation. A pharmacodynamic interaction with combinations of torsadogenic compounds is another area to be considered. Little is known about channel/receptor cross talk, although considerable evidence exists that cardiac G protein-coupled receptors can modulate hERG channel function. More investigations are necessary to further evaluate the role of altered gene expression, mutations, and polymorphisms in drug-induced TdP. A novel mechanism of drug-induced torsadogenesis is the reduced expression of hERG channel protein on the plasma membrane due to a trafficking defect. Pharmacokinetic and metabolism data are crucial for calculating the risk of a torsadogenic potential in man. Consideration of intracardiac accumulation can help in delineating pharmacokinetic-pharmacodyamic relationships. In silico virtual screening procedures with new chemical entities to predict hERG block may develop as a promising tool. The role of in silico modeling of TdP arrhythmia is likely to become increasingly important for organizing and integrating the vast amount of generated data. At present, however, in silico methods cannot replace existing preclinical in vitro and in vivo models.

Animals↗

PubMed2003

An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR).

BACKGROUND: Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions. RESULTS: An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth. CONCLUSIONS: E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously.

Citric Acid Cycle↗

PubMed2005

Probing a model of a GPCR/ligand complex in an explicit membrane environment: the human cholecystokinin-1 receptor.

A three-dimensional model structure of a complex formed by a G-protein-coupled receptor (GPCR) and an agonist ligand is probed and refined using molecular-dynamics simulations and free energy calculations in a realistic environment. The model of the human receptor of cholecystokinin associated to agonist ligand CCK9 was obtained from a synergistic procedure combining site-directed mutagenesis experiments and in silico modeling. The 31-ns molecular-dynamics simulation in an explicit membrane environment indicates that both the structure of the receptor and its interactions with the ligand are robust. Whereas the secondary structure of the alpha-helix bundle is well preserved, the region of the intracellular loops exhibits a significant flexibility likely to be ascribed to the absence of G-protein subunits in the model. New insight into the structural features of the binding pocket is gained, in particular, the interplay of the ligand with both the receptor and internal water molecules. Water-mediated interactions are shown to participate in the binding, hence, suggesting additional site-directed mutagenesis experiments. Accurate free energy calculations on mutated ligands provide differences in the receptor-ligand binding affinity, thus offering a direct, quantitative comparison to experiment. We propose that this detailed consistency-checking procedure be used as a routine refinement step of in vacuo GPCR models, before further investigation and application to structure-based drug design.

Cell Membrane↗

PubMedDate not supplied

Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach.

P-glycoprotein (P-gp), a drug efflux pump, affects the bioavailability of therapeutic drugs and plays a potentially important role in clinical drug-drug interactions. Classification of candidate drugs as substrates or inhibitors of the carrier protein is of crucial importance in drug development. Accurate classification is difficult to achieve due to two major factors: i. The extreme diversity of substrates and the presence of multiple binding sites complicate the understanding of the mechanisms behind and hinder the development of a true, conclusive quantitative structure-activity relationship (QSAR) for P-gp substrates. ii. Both inhibitors and substrates interact with the same binding site of P-gp, as a result, it is not surprising that both share many common structural features. In this work, an unsupervised machine learning approach based on the Kohonen self-organizing maps (SOM) was explored, which incorporated a predefined set of physicochemical descriptors encoding the key molecular properties capable of discerning a substrate from an inhibitor. The SOM model can discriminate between substrates and inhibitors with an average accuracy of 82.3%. The current results show that the SOM-based method provides a potential in silico model for virtual screening.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2006

Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data.

BACKGROUND: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. RESULTS: Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE), this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model) performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re-sampling techniques were studied. A random over-sampling method supported the implementation of the most powerful prediction models. The KStar model was also able to achieve higher predictive sensitivities and specificities using random over-sampling techniques. CONCLUSION: The approaches assessed in this paper represent an efficient and relatively inexpensive in silico methodology for supporting large-scale analysis of photoreceptor gene expression by SAGE. They may be applied as complementary methodologies to support functional predictions before implementing more comprehensive, experimental prediction and validation methods. They may also be combined with other large-scale, data-driven methods to facilitate the inference of transcriptional regulatory networks in the developing retina. Furthermore, the methodology assessed may be applied to other data domains.

Animals↗

PubMed2006

A flexible approach for optimising in silico ADME/Tox characterisation of lead candidates.

Over the years, multiple in silico solutions have been developed for the early characterisation of lead candidates at early stages of the drug development process. Despite the nascent promise this technology holds for the pharmaceutical and biotech industries, in many cases, inherent limitations in many of these computational technologies still hinders the prediction performance of absorption, distribution, metabolism and excretion (ADME), and toxicological (Tox) properties. However, as the result of recent developments in this arena and key technology collaborations, Bio-Rad Laboratories, Inc. has made some breakthroughs with their in silico ADME/Tox prediction and lead optimisation solutions. The company's KnowItA11 ADME/Tox system, when used in conjunction with Equbits' Foresight support vector machine platform and other best-of-breed partnering technologies, provides an intelligent and flexible approach to in silico modelling that helps to overcome these difficulties. The system ultimately does this by offering various approaches and technologies that can lead researchers toward improvement in results and overall greater confidence in the in silico approach as a whole. In this technology evaluation, several examples and case studies on mutagenicity and hERG-channel blocking illustrate how researchers can take advantage of this system from compound characterisation to knowledge extraction to achieve better and faster results in their research process.

Animals↗

PubMed2006

Structure modeling of all identified G protein-coupled receptors in the human genome.

G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR).

Algorithms↗