Systems Biology Toolbox Research Articles

Assembling a plug-and-play production line for combinatorial biosynthesis of aromatic polyketides in Escherichia coli.

Polyketides are a class of specialised metabolites synthesised by both eukaryotes and prokaryotes. These chemically and structurally diverse molecules are heavily used in the clinic and include frontline antimicrobial and anticancer drugs such as erythromycin and doxorubicin. To replenish the clinicians’ diminishing arsenal of bioactive molecules, a promising strategy aims at transferring polyketide biosynthetic pathways from their native producers into the biotechnologically desirable host Escherichia coli. This approach has been successful for type I modular polyketide synthases (PKSs); however, despite more than 3 decades of research, the large and important group of type II PKSs has until now been elusive in E. coli. Here, we report on a versatile polyketide biosynthesis pipeline, based on identification of E. coli–compatible type II PKSs. We successfully express 5 ketosynthase (KS) and chain length factor (CLF) pairs—e.g., from Photorhabdus luminescens TT01, Streptomyces resistomycificus, Streptoccocus sp. GMD2S, Pseudoalteromonas luteoviolacea, and Ktedonobacter racemifer—as soluble heterodimeric recombinant proteins in E. coli for the first time. We define the anthraquinone minimal PKS components and utilise this biosynthetic system to synthesise anthraquinones, dianthrones, and benzoisochromanequinones (BIQs). Furthermore, we demonstrate the tolerance and promiscuity of the anthraquinone heterologous biosynthetic pathway in E. coli to act as genetically applicable plug-and-play scaffold, showing it to function successfully when combined with enzymes from phylogenetically distant species, endophytic fungi and plants, which resulted in 2 new-to-nature compounds, neomedicamycin and neochaetomycin. This work enables plug-and-play combinatorial biosynthesis of aromatic polyketides using bacterial type II PKSs in E. coli, providing full access to its many advantages in terms of easy and fast genetic manipulation, accessibility for high-throughput robotics, and convenient biotechnological scale-up. Using the synthetic and systems biology toolbox, this plug-and-play biosynthetic platform can serve as an engine for the production of new and diversified bioactive polyketides in an automated, rapid, and versatile fashion.

Metabolome-Wide Analysis of Stable Isotope Labeling-Is It Worth the Effort?

Metabolome-Wide Analysis of Stable Isotope Labeling-Is It Worth the Effort?

SBEToolbox: A Matlab Toolbox for Biological Network Analysis

We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis

Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB

The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB. The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user's models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2. The software is open source under the GPL v3 and available at http://www.maths.ox.ac.uk/cmb/STOCHSIMGPU. The web site also contains supplementary information. klingbeil@maths.ox.ac.uk Supplementary data are available at Bioinformatics online.

Thermodynamically consistent model calibration in chemical kinetics

BackgroundThe dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function.ResultsWe introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from http://www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database.ConclusionsTCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting.

Zooming of states and parameters using a lumping approach including back-translation

BackgroundSystems biology models tend to become large since biological systems often consist of complex networks of interacting components, and since the models usually are developed to reflect various mechanistic assumptions of those networks. Nevertheless, not all aspects of the model are equally interesting in a given setting, and normally there are parts that can be reduced without affecting the relevant model performance. There are many methods for model reduction, but few or none of them allow for a restoration of the details of the original model after the simplified model has been simulated.ResultsWe present a reduction method that allows for such a back-translation from the reduced to the original model. The method is based on lumping of states, and includes a general and formal algorithm for both determining appropriate lumps, and for calculating the analytical back-translation formulas. The lumping makes use of efficient methods from graph-theory and ϵ-decomposition and is derived and exemplified on two published models for fluorescence emission in photosynthesis. The bigger of these models is reduced from 26 to 6 states, with a negligible deviation from the reduced model simulations, both when comparing simulations in the states of the reduced model and when comparing back-translated simulations in the states of the original model. The method is developed in a linear setting, but we exemplify how the same concepts and approaches can be applied to non-linear problems. Importantly, the method automatically provides a reduced model with back-translations. Also, the method is implemented as a part of the systems biology toolbox for matlab, and the matlab scripts for the examples in this paper are available in the supplementary material.ConclusionsOur novel lumping methodology allows for both automatic reduction of states using lumping, and for analytical retrieval of the original states and parameters without performing a new simulation. The two models can thus be considered as two degrees of zooming of the same model. This is a conceptually new development of model reduction approaches, which we think will stimulate much further research and will prove to be very useful in future modelling projects.

Metabolic flux analysis as a tool in metabolic engineering of plants

Methods of metabolic flux analysis (MFA) provide insights into the theoretical capabilities of metabolic networks and allow probing the in vivo performance of cellular metabolism. In recent years, an increasing awareness has developed that network analysis methods within the systems biology toolbox are serving to improve our understanding and ability to manipulate metabolism. In this minireview the potential of MFA to increase the chances of success in metabolic engineering of plants is presented, recent progress related to engineering and flux analysis in central metabolism of plants is discussed, and some recent advances in flux analysis methodology are highlighted.

Complexity reduction of biochemical rate expressions

The current trend in dynamical modelling of biochemical systems is to construct more and more mechanistically detailed and thus complex models. The complexity is reflected in the number of dynamic state variables and parameters, as well as in the complexity of the kinetic rate expressions. However, a greater level of complexity, or level of detail, does not necessarily imply better models, or a better understanding of the underlying processes. Data often does not contain enough information to discriminate between different model hypotheses, and such overparameterization makes it hard to establish the validity of the various parts of the model. Consequently, there is an increasing demand for model reduction methods. We present a new reduction method that reduces complex rational rate expressions, such as those often used to describe enzymatic reactions. The method is a novel term-based identifiability analysis, which is easy to use and allows for user-specified reductions of individual rate expressions in complete models. The method is one of the first methods to meet the classical engineering objective of improved parameter identifiability without losing the systems biology demand of preserved biochemical interpretation. The method has been implemented in the Systems Biology Toolbox 2 for MATLAB, which is freely available from http://www.sbtoolbox2.org. The Supplementary Material contains scripts that show how to use it by applying the method to the example models, discussed in this article.

SBML export interface for the systems biology toolbox for MATLAB

In this application note, we present an Systems biology markup language (SBML) export interface for the Systems Biology Toolbox for MATLAB. This interface allows modelers to automatically convert models, represented in the toolbox's own format (SBmodels) to SBML files. Since SBmodels do not explicitly contain all the information that is required to generate SBML, the necessary information is gathered by parsing SBmodels. The export can be done in two different ways. First, it is possible to call the export from the command line, thereby directly converting a model to an SBML file. The second option is to inspect and edit the conversion results with the help of a graphical user interface and to subsequently export the model to SBML. The SBML export interface has been integrated into the Systems Biology Toolbox for MATLAB, which is open source and freely available from http://www.sbtoolbox.org. The website also contains a tutorial, extensive documentation and examples.

SBaddon: high performance simulation for the Systems Biology Toolbox for MATLAB

We present the SBaddon package as an extension to the Systems Biology Toolbox for MATLAB (SBtoolbox). The goal of this extension is to provide the users of the SBtoolbox with important functionality that is needed for parameter estimation applications. While simulation in the SBtoolbox relies on the MATLAB ODE solvers, the SBaddon package provides considerably increased simulation performance through automatic generation of compiled simulation functions. Furthermore, the package contains improved optimization algorithms, forward parameter sensitivity analysis and basic numeric parameter identifiability analysis. The SBaddon package is open source and freely available for non-commercial use. Commercial use of SBaddon is only possible through a specific licensing agreement (contact sbaddon@sbtoolbox.org). SBaddon can be obtained from http://www.sbtoolbox.org/SBaddon. The website also contains extensive documentation, and examples.

Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology

We present a Systems Biology Toolbox for the widely used general purpose mathematical software MATLAB. The toolbox offers systems biologists an open and extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological and biochemical systems. Additionally it is well suited for educational purposes. The toolbox supports the Systems Biology Markup Language (SBML) by providing an interface for import and export of SBML models. In this way the toolbox connects nicely to other SBML-enabled modelling packages. Models are represented in an internal model format and can be described either by entering ordinary differential equations or, more intuitively, by entering biochemical reaction equations. The toolbox contains a large number of analysis methods, such as deterministic and stochastic simulation, parameter estimation, network identification, parameter sensitivity analysis and bifurcation analysis.