[Predictive toxicology: in silico modelling and expert systems].
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Models, Theoretical↗
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Integrative biological modelling in silico.
In silico models of biological systems provide a powerful tool for integrative analysis of physiological function. Using the computational models of the heart as examples, we discuss three types of integration: structural integration implies integration across physical scales of biological organization from protein molecule to whole organ; functional integration of interacting physiological processes such as signalling, metabolism, excitation and contraction; and the synthesis of experimental observation with physicochemical and mathematical principles.
Animals↗
Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model.
Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of alpha-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37 degrees C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.
Adaptation, Physiological↗
In silico model-driven assessment of the effects of single nucleotide polymorphisms (SNPs) on human red blood cell metabolism.
The completion of the human genome project and the construction of single nucleotide polymorphism (SNP) maps have lead to significant efforts to find SNPs that can be linked to pathophysiology. In silico models of complete biochemical reaction networks relate a cell's individual reactions to the function of the entire network. Sequence variations can in turn be related to kinetic properties of individual enzymes, thus allowing an in silico model-driven assessment of the effects of defined SNPs on overall cellular functions. This process is applied to defined SNPs in two key enzymes of human red blood cell metabolism: glucose-6-phosphate dehydrogenase and pyruvate kinase. The results demonstrate the utility of in silico models in providing insight into differences between red cell function in patients with chronic and nonchronic anemia. In silico models of complex cellular processes are thus likely to aid in defining and understanding key SNPs in human pathophysiology.
Computational Biology↗
In Silico Metabolic Model and Protein Expression of Haemophilus influenzae Strain Rd KW20 in Rich Medium.
The intermediary metabolism of Haemophilus influenzae strain Rd KW20 was studied by a combination of protein expression analysis using a recently developed direct proteomics approach, mutational analysis, and mathematical modeling. Special emphasis was placed on carbon utilization, sugar fermentation, TCA cycle, and electron transport of H. influenzae cells grown microaerobically and anaerobically in a rich medium. The data indicate that several H. influenzae metabolic proteins similar to Escherichia coli proteins, known to be regulated by low concentrations of oxygen, were well expressed in both growth conditions in H. influenzae. An in silico model of the H. influenzae metabolic network was used to study the effects of selective deletion of certain enzymatic steps. This allowed us to define proteins predicted to be essential or non-essential for cell growth and to address numerous unresolved questions about intermediary metabolism of H. influenzae. Comparison of data from in vivo protein expression with the protein list associated with a genome-scale metabolic model showed significant coverage of the known metabolic proteome. This study demonstrates the significance of an integrated approach to the characterization of H. influenzae metabolism.
Biochemistry↗
Proteomic and computational methods in systems modeling of cellular signaling.
Cellular signaling lies at the core of cellular behavior, and is central for the understanding of many pathologic conditions. To comprehend how signal transduction is orchestrated at the molecular level remains the ultimate challenge for cell biology. In the last years there has been a revolution in the development of high-throughput methodologies in proteomics and genomics, which have provided extensive knowledge about expression profiles and molecular interaction-networks. However, these methods have typically provided qualitative and static information. This is about to turn, and several high-throughput methods are now available that provide quantitative and temporal information. These data are well suited for analysis by computational methods and bioinformatics, which are becoming increasingly valuable tools to grasp the complexity of cellular networks. At present, several cellular pathways have been modeled in silico and the analysis provides new understanding of the underlying properties that contribute to their dynamic features. Here, we review methodologies that are used for in silico modeling as well as methods to obtain large-scale quantitative data, and discuss how they can be integrated to generate powerful and predictive models of cellular processes. We argue that the generation of such models provide powerful tools to understand how systems properties emerges in healthy and pathologic states, and to generate efficient strategies for pharmacological intervention.
Computational Biology↗
Genome-scale metabolic model of Helicobacter pylori 26695.
A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.
Amino Acids↗
Transcriptional regulation in constraints-based metabolic models of Escherichia coli.
Full genome sequences enable the construction of genome-scale in silico models of complex cellular functions. Genome-scale constraints-based models of Escherichia coli metabolism have been constructed and used to successfully interpret and predict cellular behavior under a range of conditions. These previous models do not account for regulation of gene transcription and thus cannot accurately predict some organism functions. Here we present an in silico model of the central E. coli metabolism that accounts for regulation of gene expression. This model accounts for 149 genes, the products of which include 16 regulatory proteins and 73 enzymes. These enzymes catalyze 113 reactions, 45 of which are controlled by transcriptional regulation. The combined metabolic/regulatory model can predict the ability of mutant E. coli strains to grow on defined media as well as time courses of cell growth, substrate uptake, metabolic by-product secretion, and qualitative gene expression under various conditions, as indicated by comparison with experimental data under a variety of environmental conditions. The in silico model may also be used to interpret dynamic behaviors observed in cell cultures. This combined metabolic/regulatory model is thus an important step toward the goal of synthesizing genome-scale models that accurately represent E. coli behavior.
Aerobiosis↗
Recent uses of topological indices in the development of in silico ADMET models.
Topological indices are employed in an ever-widening family of quantitative models to describe the structural attributes of compounds as these relate to experimental endpoints in a host of physicochemical and biological processes. This is especially true where attention to ADMET (absorption, distribution, metabolism, excretion and toxicity) properties is a priority, using various training or learning algorithms to construct quantitative structure-activity or -property relationship ADMET models. This review discusses the in silico ADMET approaches used over the past two years, where the majority of descriptors are topological, including comparisons between models and their important descriptors where applicable. ADMET models for aqueous solubility involving several large datasets are reviewed, as are a number of models for human intestinal absorption. Also included is the use of topological indices extended to modeling metabolic stability for the cytochrome P450 cassette of enzymes in an interesting study with compounds measured in a uniform bioassay using human liver S9 homogenate. Finally, in the area of genotoxicity, two recent ADMET models are discussed, one for chromosomal aberrations and another using a large compound dataset for Ames mutagenicity. The latter study involved several thousand compounds, with comparisons of validation results for a number of well-known predictors of mutagenicity.
Animals↗
In silico prediction of blood-brain barrier permeation.
This review examines the progress that is being made towards the in silico prediction of brain permeation. Following a brief introduction to the blood-brain barrier, the datasets currently available for in silico modeling are discussed. Recent developments in in silico models of brain permeation are summarized in the context of the current state of the art in prediction accuracy. An analysis of recent models is presented, focusing on what such models reveal about the molecular properties that determine brain permeation. The review concludes by presenting the current key issues in this area of research, noting in particular, the paucity of brain permeation data available for modeling. Finally, possible future directions are suggested.
Algorithms↗
Modeling and simulation of cancer immunoprevention vaccine.
UNLABELLED: We present an in silico model that simulates the immune system responses to tumor cells in naive and vaccinated mice. We have demonstrated the ability of this model to accurately reproduce the experimental results. MOTIVATION: In vivo experiments on HER-2/neu mice have shown the effectiveness of Triplex vaccine in the protection of mice from mammary carcinoma. Full protection was conferred using chronic (prophylactic) vaccination protocol while therapeutic vaccination was less efficient. Our in silico model was able to closely reproduce the effects of various vaccination protocols. This model is the first step towards the development of in silico experiments searching for optimal vaccination protocols. RESULTS: In silico experiments carried out on two large statistical samples of virtual mice showed very good agreements with in vivo experiments for all experimental vaccination protocols. They also show, as supported by in vivo experiments, that the humoral response is fundamental in controlling the tumor growth and therefore suggest the selection and timing of experiments for measuring the activity of T cells. CONTACT: francesco@dmi.unict.it SUPPLEMENTARY INFORMATION: http://www.dmi.unict.it/CIG/suppdata_bioinf.html.
Animals↗
Role of predictive metabolism and toxicity modeling in drug discovery--a summary of some recent advancements.
In this article, we discuss recent advances and selected publications in the area of computer-based (in silico) metabolism and toxicity prediction, and comment on the opportunities for prediction of metabolism-based toxicity. The article also discusses the growing interest and importance of in silico prediction of metabolism and toxicity as tools to assist in library design and lead optimization. In addition, we consider the status of commercial metabolism and toxicity databases, and the need for future improvement, since data is often from older studies and from structures not typically considered as 'drug-like'. The article highlights the current status and potential of in silico models for property prediction, and the potential for linkage with vivo models to improve the integration of metabolism and toxicity into the drug discovery process and extrapolation to clinical studies. The article underscores that the future development, integration and application of in silico models will require a balance of local and global model approaches. The article also indicates that implementation and integration of models into drug discovery processes needs to be carried out in a rational and systematic manner, if we are to fully capitalize on the opportunity presented by in silico predictive modeling.
ATP Binding Cassette Transporter, Subfamily B, Mem↗
Strategies for more effective and reliable ADME. 30-31 March 2004, London, UK.
This conference was organised by Vision in Business, in order to address the issue that 'more than 40% of drug preclinical failure is due to ADME-related issues and many drugs are later withdrawn because of unanticipated drug-drug interactions (C Masimirembwa, AstraZeneca Mölndal, Sweden). Academic and industrial scientists were brought together to discuss and present current knowledge/theories on subjects of importance in preclinical and clinical drug development. These included in vivo/in vitro correlations, approaches and experimental models used, in silico models and methods, metabolic databases and their application, as well as safety and financial outcomes. Participants were challenged by the issues covered and encouraged to share their own experiences and opinions during extensive discussions with other scientists.
Drug Interactions↗
Investigation of metabolic objectives in cultured hepatocytes.
Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.
Cell Culture Techniques↗
A comparative analysis of multi-conductance neuronal models in silico.
We demonstrate that a previously presented flexible silicon-neuron architecture can implement three disparate conductance-based neuron models with both fast and slow dynamics. By exploiting the real-time nature of this physical implementation, we mapped the model dynamics across a large region of parameter space. We also found that two of these dynamically different models represent points in a contiguous bursting space that spans between the two models. By systematically varying the model parameters, we also found that multiple, diverse trajectories in parameter space connected the two canonical bursting points. In addition, we found that the combination of parameter values keeps the neuron in the bursting region. These findings demonstrate the usefulness of the silicon-neuron architecture as a neural-modeling tool and illustrate its versatility as a platform for a multi-behavioral neuron that resembles its living analog.
Animals↗
Systems biotechnology for strain improvement.
Various high-throughput experimental techniques are routinely used for generating large amounts of omics data. In parallel, in silico modelling and simulation approaches are being developed for quantitatively analyzing cellular metabolism at the systems level. Thus informative high-throughput analysis and predictive computational modelling or simulation can be combined to generate new knowledge through iterative modification of an in silico model and experimental design. On the basis of such global cellular information we can design cells that have improved metabolic properties for industrial applications. This article highlights the recent developments in these systems approaches, which we call systems biotechnology, and discusses future prospects.
Genome↗
Integrating high-throughput and computational data elucidates bacterial networks.
The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.
Aerobiosis↗
Strategies for absorption screening in drug discovery and development.
This review gives an overview of the current approaches to evaluate drug absorption potential in the different phases of drug discovery and development. Methods discussed include in silico models, artificial membranes as absorption models, in vitro models such as the Ussing chamber and Caco-2 monolayers, in situ rat intestinal perfusion and in vivo absorption studies. In silico models such as iDEA can help optimizing chemical synthesis since the fraction absorbed (Fa) can be predicted based on structural characteristics only. A more accurate prediction of Fa can be obtained by feeding the iDEA model with Caco-2 permeability data and solubility data at various pH's. Permeability experiments with artificial membranes such as the filter-IAM technology are high-throughput and offer the possibility to group compounds according to a low and a high permeability. Highly permeable compounds, however, need to be further evaluated in Caco-2 cells, since artificial membranes lack active transport systems and efflux mechanisms such as P-glycoprotein (PgP). Caco-2 and other "intestinal-like" cell lines (MDCK, TC-7, HT29-MTX, 2/4/A1) permit to perform mechanistic studies and identify drug-drug interactions at the level of PgP. The everted sac and Ussing chamber techniques are more advanced models in the sense that they can provide additional information with respect to intestinal metabolism. In situ rat intestinal perfusion is a reliable technique to investigate drug absorption potential in combination with intestinal metabolism, however, it is time consuming, and therefore not suited for screening purposes. Finally, in vivo absorption in animals can be estimated from bioavailability studies (ratio of the plasma AUC after oral and i.v. administration). The role of the liver in affecting bioavailability can be evaluated by portal vein sampling experiments in dogs.
Animals↗