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Here we present a modular agent-based mathematical model of the human cardiovascular and renal systems. It integrates the previous models primarily developed by A. C. Guyton, F. Karaaslan, K. M. Hallow, and Y. V. Solodyannikov. We performed the model calibration to find an equilibrium state within the normal vital sign ranges for a healthy adult. We verified the model’s abilities to reproduce equilibrium states with abnormal physiological values related to different combinations of cardiovascular diseases (such as systemic hypertension, chronic heart failure, pulmonary hypertension, etc.). For the model creation and validation, we involved over 200 scientific studies covering known models of the human cardiovascular and renal functions, biosimulation platforms, and clinical measurements of physiological quantities in normal and pathological conditions. We compiled detailed documentation describing all equations, parameters and variables of the model with justification of all formulas and values. The model is implemented in BioUML and available in the web-version of the software.

GEO database contains a lot of different experiments about transcription regulation. Most part of such experiments (ChIP-seq, DNase-seq and Histone-ChIP-seq) for 9 species (Homo sapiens, Mus musculus, Rattus norvegicus, Danio rerio, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Arabidopsis thaliana) were annotated and uniformly processed in GTRD database. Here we are describing the latest advances in these data annotations for GTRD database to formalize experimental data: hierarchical dictionaries of cell types and experimental factors. This approach helps us to integrate experimental data by cell lines (cell types and tissues) and experimental conditions as well as simplifies searching for relevant information for a user.

Using a modular principle of computer hardware as a metaphor, we defined and implemented in the BioUML platform a module concept for biological pathways. BioUML provides a user interface to create modular models and convert them automatically into plain models for further simulations. Using this approach, we created the apoptosis model including 13 modules: death stimuli (TRAIL, CD95L, and TNF-α)-induced activation of caspase-8; survival stimuli (p53, EGF, and NF-κB) regulation; the mitochondria level; cytochrome C- and Smac-induced activation of caspase-3; direct activation of effector caspases by caspase-8 and − 12; PARP and apoptosis execution phase modules. Each module is based on earlier published models and extended by data from the Reactome and TRANSPATH databases. The model ability to simulate the apoptosis-related processes was checked; the modules were validated using experimental data. Availability: http://www.biouml.org/apoptosis.shtml.

Despite wide application of the powerful ChIP-Seq technology for experimental identification of transcription factor (TF) binding sites, the computational prediction of the TF-binding sites is also relevant. Many methods for the prediction of the TF-binding sites have been developed over the last decades. Some of them represent position weight matrix (PWM) approach that is the most common and widely used. However, there exists little guidance in the choice among these methods because of a comprehensive comparison of existing methods is still challenging in practice. Thus, the direct use of the ChIP-Seq data for assessing predictive ability of the methods does not seem advisable because of such reasons as the tethered binding or false positive rates of peak detection algorithms. We have developed computational toolkit for reliable comparison of prediction methods under condition that unknown fraction of the ChIP-Seq data do not contain genuine TF-binding sites. On the base of developed toolkit, we have performed comparative analysis of three existing methods that represent PWM approach. The analysis has revealed that MATCH performed significantly worse than two other methods while common additive method outperformed others.

Motivation and Aim: Non-linear mixed effects (NLME) model is an efficient tool for analyzing of population data and estimating of model parameters. It is widely used in pharmacological modeling, but may be applied to a variety of fields. NLME model contains core structural model which is the mathematical model of studying process (e.g. drug dynamics in organism) and suggests distribution of model parameters across population. nlme is a library written in R language implementing log-likelihood approach for NLME models creation and analysis. Core structural model should be supplied as R function which complicates usage of sophisticated mathematical models. Library nlmeODE allows usage of ODEs inside nlme, but it has certain restrictions on possible models and textual format complicates using of complex ODE systems. Results: We have implemented plugin for BioUML platform for population-based modeling. Plugin includes graphical notation facilitating work with detailed and large-scaled models, parameter estimation is performed by R function, supplied (using rJava) with Java simulator from BioUML. It grants possibility to use any model created in BioUML as a structural model. Executing R script is automatically generated on the basis of the diagram. Availability: Plugin is freely available as a part of BioUML software at www.biouml.org .

Motivation: Modeling of complex biological systems such as cells, organs or even whole organisms is not a trivial task because of their intricate structure. Although modern computers allow simulation for quite complex models, such models are difficult to support and work with. Modular approach facilitates the creation of complex models by representing them as combinations of submodels. On the other hand, there are a large number of models describing particular subsystems created by different authors using different formalisms and scales. These models may be reused as “bricks” in the creation of comprehensive overall models. Results: We have developed a modular approach to the modeling of complex biological systems. It includes a formal definition of graphical notation for modular models, an algorithm for the transformation of a modular model into a non-modular model which is appropriate for simulation using standard methods for solving ordinary differential equations (ODE), and an algorithm for simulating modular model based on the agent-model principles. The approach was implemented in software plug-in for BioUML platform. Availability: The developed software and the source code are freely available as a part of BioUML in both standalone and web versions at www.biouml.org .

Nat. Biotechnol. 27 735–741 (2009); published online 7 August 2009; corrected after print 11 August 2009 In the version of this article initially published, the wrong versions of Figures 1, 2 and 3 were used. The error has been corrected in the HTML and PDF versions of the article.