COPASI--a COmplex PAthway SImulator

Comparison of reverse-engineering methods using an in silico network

The reverse engineering of biochemical networks is a central problem in systems biology. In recent years several methods have been developed for this purpose, using techniques from a variety of fields. A systematic comparison of the different methods is complicated by their widely varying data requirements, making benchmarking difficult. Also, because of the lack of detailed knowledge about most real networks, it is not easy to use experimental data for this purpose. This paper contains a comparison of four reverse-engineering methods using data from a simulated network. The network is sufficiently realistic and complex to include many of the challenges that data from real networks pose. Our results indicate that the two methods based on genetic perturbations of the network outperform the other methods, including dynamic Bayesian networks and a partial correlation method.

DNA sequence- and conformation-directed positioning of nucleosomes by chromatin-remodeling complexes

Chromatin-remodeling complexes can translocate nucleosomes along the DNA in an ATP-coupled reaction. This process is an important regulator of all DNA-dependent processes because it determines whether certain DNA sequences are found in regions between nucleosomes with increased accessibility for other factors or wrapped around the histone octamer complex. In a comparison of seven different chromatin-remodeling machines (ACF, ISWI, Snf2H, Chd1, Mi-2, Brg1, and NURF), it is demonstrated that these complexes can read out DNA sequence features to establish specific nucleosome-positioning patterns. For one of the remodelers, ACF, we identified a 40-bp DNA sequence element that directs nucleosome positioning. Furthermore, we show that nucleosome positioning by the remodelers ACF and Chd1 is determined by a reduced affinity to the end product of the translocation reaction. The results suggest that the linkage of differential remodeling activities with the intrinsic binding preferences of nucleosomes can result in establishing distinct chromatin structures that depend on the DNA sequence and define the DNA accessibility for other protein factors.

Integrated network reconstruction, visualization and analysis using YANAsquare

Background

Modeling of metabolic networks includes tasks such as network assembly, network overview, calculation of metabolic fluxes and testing the robustness of the network.

Results

YANAsquare provides a software framework for rapid network assembly (flexible pathway browser with local or remote operation mode), network overview (visualization routine and YANAsquare editor) and network performance analysis (calculation of flux modes as well as target and robustness tests). YANAsquare comes as an easy-to-setup program package in Java. It is fully compatible and integrates the programs YANA (translation of gene expression values into flux distributions, metabolite network dissection) and Metatool (elementary mode calculation). As application examples we set-up and model the phospholipid network in the phagosome and genome-scale metabolic maps of S.aureus, S.epidermidis and S.saprophyticus as well as test their robustness against enzyme impairment.

Conclusion

YANAsquare is an application software for rapid setup, visualization and analysis of small, larger and genome-scale metabolic networks.

Facile: a command-line network compiler for systems biology

BACKGROUND

A goal of systems biology is the quantitative modelling of biochemical networks. Yet for many biochemical systems, parameter values and even the existence of interactions between some chemical species are unknown. It is therefore important to be able to easily investigate the effects of adding or removing reactions and to easily perform a bifurcation analysis, which shows the qualitative dynamics of a model for a range of parameter values.

RESULTS

We present Facile, a Perl command-line tool for analysing the dynamics of a systems biology model. Facile implements the law of mass action to automatically compile a biochemical network (written as, for example, E + S <-> C) into scripts for analytical analysis (Mathematica and Maple), for simulation (XPP and Matlab), and for bifurcation analysis (AUTO). Facile automatically identifies mass conservations and generates the reduced form of a model with the minimum number of independent variables. This form is essential for bifurcation analysis, and Facile produces a C version of the reduced model for AUTO.

CONCLUSION

Facile is a simple, yet powerful, tool that greatly accelerates analysis of the dynamics of a biochemical network. By acting at the command-line and because of its intuitive, text-based input, Facile is quick to learn and can be incorporated into larger programs or into automated tasks.

Kinetic model of sucrose accumulation in maturing sugarcane culm tissue

Biochemically, it is not completely understood why or how commercial varieties of sugarcane (Saccharum officinarum) are able to accumulate sucrose in high concentrations. Such concentrations are obtained despite the presence of sucrose synthesis/breakdown cycles (futile cycling) in the culm of the storage parenchyma. Given the complexity of the process, kinetic modelling may help to elucidate the factors governing sucrose accumulation or direct the design of experimental optimisation strategies. This paper describes the extension of an existing model of sucrose accumulation (Rohwer, J.M., Botha, F.C., 2001. Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data. Biochem. J. 358, 437-445) to account for isoforms of sucrose synthase and fructokinase, carbon partitioning towards fibre formation, and the glycolytic enzymes phosphofructokinase (PFK), pyrophosphate-dependent PFK and aldolase. Moreover, by including data on the maximal activity of the enzymes as measured in different internodes, a growth model was constructed that describes the metabolic behaviour as sugarcane parenchymal tissue matures from internodes 3-10. While there was some discrepancy between modelled and experimentally determined steady-state sucrose concentrations in the cytoplasm, steady-state fluxes showed a better fit. The model supports a hypothesis of vacuolar sucrose accumulation against a concentration gradient. A detailed metabolic control analysis of sucrose synthase showed that each isoform has a unique control profile. Fructose uptake by the cell and sucrose uptake by the vacuole had a negative control on the futile cycling of sucrose and a positive control on sucrose accumulation, while the control profile for neutral invertase was reversed. When the activities of these three enzymes were changed from their reference values, the effects on futile cycling and sucrose accumulation were amplified. The model can be run online at the JWS Online database (http://jjj.biochem.sun.ac.za/database/uys).

Computational approaches to the topology, stability and dynamics of metabolic networks

Cellular metabolism is characterized by an intricate network of interactions between biochemical fluxes, metabolic compounds and regulatory interactions. To investigate and eventually understand the emergent global behavior arising from such networks of interaction is not possible by intuitive reasoning alone. This contribution seeks to describe recent computational approaches that aim to asses the topological and functional properties of metabolic networks. In particular, based on a recently proposed method, it is shown that it is possible to acquire a quantitative picture of the possible dynamics of metabolic systems, without assuming detailed knowledge of the underlying enzyme-kinetic rate equations and parameters. Rather, the method builds upon a statistical exploration of the comprehensive parameter space to evaluate the dynamic capabilities of a metabolic system, thus providing a first step towards the transition from topology to function of metabolic pathways. Utilizing this approach, the role of feedback mechanisms in the maintenance of stability is discussed using minimal models of cellular pathways.

Experimental and mathematical approaches to modeling plant metabolic networks

To support their sessile and autotrophic lifestyle higher plants have evolved elaborate networks of metabolic pathways. Dynamic changes in these metabolic networks are among the developmental forces underlying the functional differentiation of organs, tissues and specialized cell types. They are also important in the various interactions of a plant with its environment. Further complexity is added by the extensive compartmentation of the various interconnected metabolic pathways in plants. Thus, although being used widely for assessing the control of metabolic flux in microbes, mathematical modeling approaches that require steady-state approximations are of limited utility for understanding complex plant metabolic networks. However, considerable progress has been made when manageable metabolic subsystems were studied. In this article, we will explain in general terms and using simple examples the concepts underlying stoichiometric modeling (metabolic flux analysis and metabolic pathway analysis) and kinetic approaches to modeling (including metabolic control analysis as a special case). Selected studies demonstrating the prospects of these approaches, or combinations of them, for understanding the control of flux through particular plant pathways are discussed. We argue that iterative cycles of (dry) mathematical modeling and (wet) laboratory testing will become increasingly important for simulating the distribution of flux in plant metabolic networks and deriving rational experimental designs for metabolic engineering efforts.

Understanding diseases by mouse click: the promise and potential of computational approaches in Systems Biology

Computational modelling approaches can nowadays build large-scale simulations of cellular behaviour based on data describing detailed molecular level interactions, thus performing the space- and time-scale integrations that would be impossible just by intuition. Recent progress in the development of both experimental methods and computational tools has provided the means to generate the necessary quantitative data and has made computational methods accessible even to non-theorists, thereby removing a major hurdle that has in the past made many experimentalists hesitate to invest serious effort in formulating quantitative models. We describe how computational biology differs from classical bioinformatics, how it emerged from mathematical biology and elucidate the role it plays for the integration of traditionally separated areas of biomedical research within the larger framework of Systems Biology.

Stochastic simulation GUI for biochemical networks

MOTIVATION

This article describes the development of a useful graphical user interface for stochastic simulation of biochemical networks which allows model builders to run stochastic simulations of their models and perform statistical analysis on the results. These include the construction of correlations, power-spectral densities and transfer functions between selected inputs and outputs.

AVAILABILITY

The software is licensed under the BSD open source license and is available at http://sourceforge.net/projects/jdesigner. In addition, a more detailed account of the algorithms employed in the tool can be found at the Wiki at http://www.sys-bio.org/sbwWiki.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioengineering novel in vitro metabolic pathways using synthetic biology

Huge numbers of enzymes have evolved in nature to function in aqueous environments at moderate temperatures and neutral pH. This gives us, in principle, the unique opportunity to construct multistep reaction systems of considerable catalytic complexity in vitro. However, this opportunity is rarely exploited beyond research scale, because such systems are difficult to assemble and to operate productively. Recent advances in DNA synthesis, genome engineering, high-throughput analytics, model-based analysis of biochemical systems and (semi-)rational protein engineering suggest that we have all the tools available to rationally design and efficiently operate such systems of enzymes, and finally harvest their potential for preparative syntheses.

RMBNToolbox: random models for biochemical networks

BACKGROUND

There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.

RESULTS

We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.

CONCLUSION

While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Modeling biochemical transformation processes and information processing with Narrator

Background

Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing.

Results

Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development.

Conclusion

Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from .