Online ISSN
1751-8857
Print ISSN
1751-8849
IET Systems Biology
Volume 2, Issue 5, September 2008
Volumes & issues:
Volume 2, Issue 5
September 2008
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- Author(s): I. Nemenman ; W.S. Hlavacek ; J.S. Edwards ; J.R. Faeder ; Y. Jiang ; M.E. Wall
- Source: IET Systems Biology, Volume 2, Issue 5, p. 203 –205
- DOI: 10.1049/iet-syb:20089018
- Type: Article
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- Author(s): A. Ma'ayan
- Source: IET Systems Biology, Volume 2, Issue 5, p. 206 –221
- DOI: 10.1049/iet-syb:20070075
- Type: Article
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Abstraction of intracellular biomolecular interactions into networks is useful for data integration and graph analysis. Network analysis tools facilitate predictions of novel functions for proteins, prediction of functional interactions and identification of intracellular modules. These efforts are linked with drug and phenotype data to accelerate drug-target and biomarker discovery. This review highlights the currently available varieties of mammalian biomolecular networks, and surveys methods and tools to construct, compare, integrate, visualise and analyse such networks. - Author(s): R.C. Yu ; O. Resnekov ; A.P. Abola ; S.S. Andrews ; K.R. Benjamin ; J. Bruck ; I.E. Burbulis ; A. Colman-Lerner ; D. Endy ; A. Gordon ; M. Holl ; L. Lok ; C.G. Pesce ; E. Serra ; R.D. Smith ; T.M. Thomson ; A.E. Tsong ; R. Brent
- Source: IET Systems Biology, Volume 2, Issue 5, p. 222 –233
- DOI: 10.1049/iet-syb:20080127
- Type: Article
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One goal of systems biology is to understand how genome-encoded parts interact to produce quantitative phenotypes. The Alpha Project is a medium-scale, interdisciplinary systems biology effort that aims to achieve this goal by understanding fundamental quantitative behaviours of a prototypic signal transduction pathway, the yeast pheromone response system from Saccharomyces cerevisiae. The Alpha Project distinguishes itself from many other systems biology projects by studying a tightly bounded and well-characterised system that is easily modified by genetic means, and by focusing on deep understanding of a discrete number of important and accessible quantitative behaviours. During the project, the authors have developed tools to measure the appropriate data and develop models at appropriate levels of detail to study a number of these quantitative behaviours. The authors have also developed transportable experimental tools and conceptual frameworks for understanding other signalling systems. In particular, the authors have begun to interpret system behaviours and their underlying molecular mechanisms through the lens of information transmission, a principal function of signalling systems. The Alpha Project demonstrates that interdisciplinary studies that identify key quantitative behaviours and measure important quantities, in the context of well-articulated abstractions of system function and appropriate analytical frameworks, can lead to deeper biological understanding. The authors' experience may provide a productive template for systems biology investigations of other cellular systems. - Author(s): K. Kaneko and C. Furusawa
- Source: IET Systems Biology, Volume 2, Issue 5, p. 234 –246
- DOI: 10.1049/iet-syb:20070078
- Type: Article
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Biological processes are inherently noisy, as highlighted in recent measurements of stochasticity in gene expression. Here, the authors show that such phenotypic noise is essential to the adaptation of organisms to a variety of environments and also to the evolution of robustness against mutations. First, the authors show that for any growing cell showing stochastic gene expression, the adaptive cellular state is inevitably selected by noise, without the use of a specific signal transduction network. In general, changes in any protein concentration in a cell are products of its synthesis minus dilution and degradation, both of which are proportional to the rate of cell growth. In an adaptive state, both the synthesis and dilution terms of proteins are large, and so the adaptive state is less affected by stochasticity in gene expression, whereas for a non-adaptive state, both terms are smaller, and so cells are easily knocked out of their original state by noise. This leads to a novel, generic mechanism for the selection of adaptive states. The authors have confirmed this selection by model simulations. Secondly, the authors consider the evolution of gene networks to acquire robustness of the phenotype against noise and mutation. Through simulations using a simple stochastic gene expression network that undergoes mutation and selection, the authors show that a threshold level of noise in gene expression is required for the network to acquire both types of robustness. The results reveal how the noise that cells encounter during growth and development shapes any network's robustness, not only to noise but also to mutations. The authors also establish a relationship between developmental and mutational robustness. - Author(s): L. Saiz and J.M.G. Vilar
- Source: IET Systems Biology, Volume 2, Issue 5, p. 247 –255
- DOI: 10.1049/iet-syb:20080091
- Type: Article
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The assembly of macromolecular structures consisting of proteins and DNA lies at the core of many fundamental cellular processes, such as transcription, recombination and replication. A common theme to all these processes is DNA looping, which provides the backbone for the required long-range interactions on DNA and results in further complexity that is exceptionally difficult to tackle with traditional quantitative approaches. Here, recent advances in mathematical and computational methods to study the assembly of protein–protein/DNA complexes with loops and their effects in the cellular behaviour through gene regulation are reviewed. The interplay between multisite DNA looping and DNA bending regulatory proteins, such as the catabolite activator protein (CAP), and on its physiological consequences is focused on. It has become clear in the last few years that the complexity that looping brings about can actively control transcriptional noise and cell-to-cell variability. Here, it is shown that the DNA looping, through the effects of CAP, can also control the balance between robustness and sensitivity of the induction of gene expression. - Author(s): M.-Y. Hsieh ; S. Yang ; M.A. Raymond-Stinz ; S. Steinberg ; D.G. Vlachos ; W. Shu ; B. Wilson ; J.S. Edwards
- Source: IET Systems Biology, Volume 2, Issue 5, p. 256 –272
- DOI: 10.1049/iet-syb:20070073
- Type: Article
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ErbB overexpression is linked to carcinogenesis. It is hypothesised that this is due to increased receptor density and receptor clustering, leading to increased receptor dimerisation and activation. Herein, spatial stochastic simulations have been performed to shed light receptor dimerisation processes. First, ligand-independent homodimerisation, is considered, based upon constitutive oligomerisation estimates (14%) in A431 cells that overexpress epidermal growth factor receptor (EGFR). When autocrine stimulation is blocked, ligand-independent EGFR activation is demonstrated by persistent, low levels of phosphorylation. The possibility that ligand-independent signalling is due to the fluctuation of EGFR conformation is considered. The agent-based model predicts the frequency (expressed as a probability) that uniformly distributed receptors would need to flux to the open conformation to reach 14% EGFR dimers at high receptor density. Simulations suggest that ligand-independent EGFR homodimerisation is highly density dependent, since collisions between ‘open’, dimerisation-competent receptors are a rare event at low receptor levels. Simulations that incorporate receptor clustering lower the threshold for homodimerisation of unoccupied receptors as well as the estimate of the probability for fluxing to the dimer-competent conformation. The impact of ErbB receptor clustering patterns on hetero and homodimerisation rates is also considered, using immunoelectron microscopy data derived from SKBR3 breast cancer cells that express ErbB2≫EGFR>ErbB3. Partial spatial segregation of ErbB receptors has a profound effect on simulated heterodimerisation rates. Despite the general assumption that ErbB2 is a preferred heterodimerising partner for other ErbBs, it is predicted that most ErbB2 will form homodimers. Overall, it is proposed that both receptor density and membrane spatial organisation contribute to the carcinogenesis process. [Includes supplementary material] - Author(s): Y. Zhang ; H. Shankaran ; L. Opresko ; H. Resat
- Source: IET Systems Biology, Volume 2, Issue 5, p. 273 –284
- DOI: 10.1049/iet-syb:20080116
- Type: Article
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The partitioning of biological networks into coupled-functional modules is being increasingly applied for developing predictive models of biological systems. This approach has the advantage that predicting a system-level response does not require a mechanistic description of the internal dynamics of each module. Identification of the input–output characteristics of the network modules and the connectivity between the modules provide the necessary quantitative representation of system dynamics. However, the determination of the input–output relationships of the modules is not trivial; it requires the controlled perturbation of module inputs and systematic analysis of experimental data. In this report, the authors apply a system theoretical analysis approach to derive the time-dependent input–output relationships of the functional module for the human epidermal growth factor receptor (HER) mediated Erk and Akt signalling pathways. Using a library of cell lines expressing endogenous levels of epidermal growth factor receptor (EGFR) and varying levels of HER2, the authors show that a transfer function-based representation can be successfully applied to quantitatively characterise information transfer in this system. [Includes supplementary material] - Author(s): X. Xu and R.V. Kulkarni
- Source: IET Systems Biology, Volume 2, Issue 5, p. 285 –292
- DOI: 10.1049/iet-syb:20070083
- Type: Article
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Recent research has highlighted several examples wherein bacterial cell fate is determined by precise subcellular localisation of proteins. A prominent example is the polar localisation and oscillation of the Min proteins which is necessary for accurate cell division in Escherichia coli. Several computational models have been proposed which reproduce the oscillatory behaviour and observed phenotypes. However, these models use varying assumptions to do so leading to different mechanisms for precise polar localisation of MinD zones. To gain further insight, the authors extend a simplified model which focused on some key processes to explain the observed length scale for MinD zone formation. Using analytical approaches and numerical simulations, the authors explore cellular MinD distributions produced by these processes and propose a mechanism for precise polar localisation of MinD. - Author(s): D.W. Dreisigmeyer ; J. Stajic ; I. Nemenman ; W.S. Hlavacek ; M.E. Wall
- Source: IET Systems Biology, Volume 2, Issue 5, p. 293 –303
- DOI: 10.1049/iet-syb:20080095
- Type: Article
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The authors have developed a mathematical model of regulation of expression of the Escherichia coli lac operon, and have investigated bistability in its steady-state induction behaviour in the absence of external glucose. Numerical analysis of equations describing regulation by artificial inducers revealed two natural bistability parameters that can be used to control the range of inducer concentrations over which the model exhibits bistability. By tuning these bistability parameters, the authors found a family of biophysically reasonable systems that are consistent with an experimentally determined bistable region for induction by thio-methylgalactoside (TMG) (in Ozbudak et al.Nature, 2004, 427; p. 737). To model regulation by lactose, the authors developed similar equations in which allolactose, a metabolic intermediate in lactose metabolism and a natural inducer of lac, is the inducer. For biophysically reasonable parameter values, these equations yield no bistability in response to induction by lactose – only systems with an unphysically small permease-dependent export effect can exhibit small amounts of bistability for limited ranges of parameter values. These results cast doubt on the relevance of bistability in the lac operon within the natural context of E. coli, and help shed light on the controversy among existing theoretical studies that address this issue. The results also motivate a deeper experimental characterisation of permease-independent transport of lac inducers, and suggest an experimental approach to address the relevance of bistability in the lac operon within the natural context of E. coli. The sensitivity of lac bistability to the type of inducer emphasises the importance of metabolism in determining the functions of genetic regulatory networks. - Author(s): C.R. Myers
- Source: IET Systems Biology, Volume 2, Issue 5, p. 304 –312
- DOI: 10.1049/iet-syb:20080076
- Type: Article
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Biological information processing as implemented by regulatory and signalling networks in living cells requires sufficient specificity of molecular interaction to distinguish signals from one another, but much of regulation and signalling involves somewhat fuzzy and promiscuous recognition of molecular sequences and structures, which can leave systems vulnerable to crosstalk. A simple model of biomolecular interactions that reveals both a sharp onset of crosstalk and a fragmentation of the neutral network of viable solutions is examined as more proteins compete for regions of sequence space, revealing intrinsic limits to reliable signalling in the face of promiscuity. These results suggest connections to both phase transitions in constraint satisfaction problems and coding theory bounds on the size of communication codes. [Includes supplementary material] - Author(s): A. Mugler ; E. Ziv ; I. Nemenman ; C.H. Wiggins
- Source: IET Systems Biology, Volume 2, Issue 5, p. 313 –322
- DOI: 10.1049/iet-syb:20080097
- Type: Article
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It is shown that biological networks with serial regulation (each node regulated by at most one other node) are constrained to direct functionality, in which the sign of the effect of an environmental input on a target species depends only on the direct path from the input to the target, even when there is a feedback loop allowing for multiple interaction pathways. Using a stochastic model for a set of small transcriptional regulatory networks that have been studied experimentally, it is further found that all networks can achieve all functions permitted by this constraint under reasonable settings of biochemical parameters. This underscores the functional versatility of the networks. - Author(s): B. Munsky and M. Khammash
- Source: IET Systems Biology, Volume 2, Issue 5, p. 323 –333
- DOI: 10.1049/iet-syb:20070082
- Type: Article
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Many gene regulatory networks are modelled at the mesoscopic scale, where chemical populations change according to a discrete state (jump) Markov process. The chemical master equation (CME) for such a process is typically infinite dimensional and unlikely to be computationally tractable without reduction. The recently proposed finite state projection (FSP) technique allows for a bulk reduction of the CME while explicitly keeping track of its own approximation error. In previous work, this error has been reduced in order to obtain more accurate CME solutions for many biological examples. Here, it is shown that this ‘error’ has far more significance than simply the distance between the approximate and exact solutions of the CME. In particular, the original FSP error term serves as an exact measure of the rate of first transition from one system region to another. As such, this term enables one to (i) directly determine the statistical distributions for stochastic switch rates, escape times, trajectory periods and trajectory bifurcations, and (ii) evaluate how likely it is that a system will express certain behaviours during certain intervals of time. This article also presents two systems-theory based FSP model reduction approaches that are particularly useful in such studies. The benefits of these approaches are illustrated in the analysis of the stochastic switching behaviour of Gardner's genetic toggle switch. [Includes supplementary material] - Author(s): Z. Liu and Y. Cao
- Source: IET Systems Biology, Volume 2, Issue 5, p. 334 –341
- DOI: 10.1049/iet-syb:20070074
- Type: Article
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Morton-Firth and Bray's stochastic simulator (StochSim) and Gillespie's stochastic simulation algorithm (SSA) are two important methods for stochastic modelling and simulation of biochemical systems. They have been widely applied to different biological problems. A key question is discussed here: Are these two methods equivalent? These two methods are compared using fundamental probability analysis. The analysis clearly shows that, when the time step in the StochSim is chosen very small, the StochSim can be viewed as a first-order approximation to the SSA. The analysis also explains why the SSA is usually much more efficient than the StochSim for biochemical systems. However, when multistate species present in a system, the StochSim clearly shows its advantage. The Complexity analysis is used to explain this advantage. The hybrid SSA (HSSA) is proposed to combine the advantages of both the StochSim and SSA. When the populations of the multistate species are small, the HSSA is very efficient. Numerical experiments are presented to verify the analysis. - Author(s): N.M. Borisov ; A.S. Chistopolsky ; J.R. Faeder ; B.N. Kholodenko
- Source: IET Systems Biology, Volume 2, Issue 5, p. 342 –351
- DOI: 10.1049/iet-syb:20070081
- Type: Article
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The coupling of membrane-bound receptors to transcriptional regulators and other effector functions is mediated by multi-domain proteins that form complex assemblies. The modularity of protein interactions lends itself to a rule-based description, in which species and reactions are generated by rules that encode the necessary context for an interaction to occur, but also can produce a combinatorial explosion in the number of chemical species that make up the signalling network. The authors have shown previously that exact network reduction can be achieved using hierarchical control relationships between sites/domains on proteins to dissect multi-domain proteins into sets of non-interacting sites, allowing the replacement of each ‘full’ (progenitor) protein with a set of derived auxiliary (offspring) proteins. The description of a network in terms of auxiliary proteins that have fewer sites than progenitor proteins often greatly reduces network size. The authors describe here a method for automating domain-oriented model reduction and its implementation as a module in the BioNetGen modelling package. It takes as input a standard BioNetGen model and automatically performs the following steps: 1) detecting the hierarchical control relationships between sites; 2) building up the auxiliary proteins; 3) generating a raw reduced model and 4) cleaning up the raw model to provide the correct mass balance for each chemical species in the reduced network. The authors tested the performance of this module on models representing portions of growth factor receptor and immunoreceptor-mediated signalling networks and confirmed its ability to reduce the model size and simulation cost by at least one or two orders of magnitude. Limitations of the current algorithm include the inability to reduce models based on implicit site dependencies or heterodimerisation and loss of accuracy when dynamics are computed stochastically. [Includes supplementary material] - Author(s): I.I. Moraru ; J.C. Schaff ; B.M. Slepchenko ; M.L. Blinov ; F. Morgan ; A. Lakshminarayana ; F. Gao ; Y. Li ; L.M. Loew
- Source: IET Systems Biology, Volume 2, Issue 5, p. 352 –362
- DOI: 10.1049/iet-syb:20080102
- Type: Article
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The Virtual Cell (VCell; http://vcell.org/) is a problem solving environment, built on a central database, for analysis, modelling and simulation of cell biological processes. VCell integrates a growing range of molecular mechanisms, including reaction kinetics, diffusion, flow, membrane transport, lateral membrane diffusion and electrophysiology, and can associate these with geometries derived from experimental microscope images. It has been developed and deployed as a web-based, distributed, client–server system, with more than a thousand world-wide users. VCell provides a separation of layers (core technologies and abstractions) representing biological models, physical mechanisms, geometry, mathematical models and numerical methods. This separation clarifies the impact of modelling decisions, assumptions and approximations. The result is a physically consistent, mathematically rigorous, spatial modelling and simulation framework. Users create biological models and VCell will automatically (i) generate the appropriate mathematical encoding for running a simulation and (ii) generate and compile the appropriate computer code. Both deterministic and stochastic algorithms are supported for describing and running non-spatial simulations; a full partial differential equation solver using the finite volume numerical algorithm is available for reaction–diffusion–advection simulations in complex cell geometries including 3D geometries derived from microscope images. Using the VCell database, models and model components can be reused and updated, as well as privately shared among collaborating groups, or published. Exchange of models with other tools is possible via import/export of SBML, CellML and MatLab formats. Furthermore, curation of models is facilitated by external database binding mechanisms for unique identification of components and by standardised annotations compliant with the MIRIAM standard. VCell is now open source, with its native model encoding language (VCML) being a public specification, which stands as the basis for a new generation of more customised, experiment-centric modelling tools using a new plug-in based platform. - Author(s): M.L. Blinov ; O. Ruebenacker ; I.I. Moraru
- Source: IET Systems Biology, Volume 2, Issue 5, p. 363 –368
- DOI: 10.1049/iet-syb:20080092
- Type: Article
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Assembly of quantitative models of large complex networks brings about several challenges. One of them is the combinatorial complexity, where relatively few signalling molecules can combine to form thousands or millions of distinct chemical species. A receptor that has several separate phosphorylation sites can exist in hundreds of different states, many of which must be accounted for individually when simulating the time course of signalling. When assembly of protein complexes is being included, the number of distinct molecular species can easily increase by a few orders of magnitude. Validation, visualisation and understanding the network can become intractable. Another challenge appears when the modeller needs to recast or grow a model. Keeping track of changes and adding new elements present a significant difficulty. An approach to solve these challenges within the virtual cell (VCell) is described. Using (i) automatic extraction from pathway databases of model components (http://vcell.org/biopax) and (ii) rules of interactions that serve as reaction network generators (http://vcell.org/bionetgen), a way is provided for semi-automatic generation of quantitative mathematical models that also facilitates the reuse of model elements. In this approach, kinetic models of large, complex networks can be assembled from separately constructed modules, either directly or via rules. To implement this approach, the strength of several related technologies is combined: the BioPAX ontology, the BioNetGen rule-based description of molecular interactions and the VCell modelling and simulation framework. - Author(s): J.C. Atlas ; E.V. Nikolaev ; S.T. Browning ; M.L. Shuler
- Source: IET Systems Biology, Volume 2, Issue 5, p. 369 –382
- DOI: 10.1049/iet-syb:20070079
- Type: Article
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The advent of thousands of annotated genomes, detailed metabolic reconstructions and databases within the flourishing field of systems biology necessitates the development of functionally complete computer models of whole cells and cellular systems. Such models would realistically describe fundamental properties of living systems such as growth, division and chromosome replication. This will inevitably bridge bioinformatic technologies with ongoing mathematical modelling efforts and would allow for in silico prediction of important dynamic physiological events. To demonstrate a potential for the anticipated merger of bioinformatic genome-wide data with a whole-cell computer model, the authors present here an updated version of a dynamic model of Escherichia coli, including a module that correctly describes the initiation and control of DNA replication by nucleoprotein DnaA-ATP molecules. Specifically, a rigorous mathematical approach used to explicitly include the genome-wide distribution of DnaA-binding sites on the replicating chromosome into a computer model of a bacterial cell is discussed. A new simple deterministic approximation of the complex stochastic process of DNA replication initiation is also provided. It is shown for the first time that reasonable assumptions about the mechanism of DNA replication initiation can be implemented in a deterministic whole-cell model to make predictions about the timing of chromosome replication. Furthermore, it is proposed that a large increase in the concentration of DnaA-binding boxes will result in a decreased steady-state growth rate in E. coli. [Includes supplementary material]
Editorial: Selected papers from the First q-bio Conference on Cellular Information Processing
Network integration and graph analysis in mammalian molecular systems biology
The Alpha Project: a model system for systems biology research
Relevance of phenotypic noise to adaptation and evolution
Protein–protein/DNA interaction networks: versatile macromolecular structures for the control of gene expression
Stochastic simulations of ErbB homo and heterodimerisation: potential impacts of receptor conformational state and spatial segregation
System theoretical investigation of human epidermal growth factor receptor-mediated signalling
Modelling of processes governing subcellular localisation of MinD in Escherichia coli
Determinants of bistability in induction of the Escherichia coli lac operon
Satisfiability, sequence niches and molecular codes in cellular signalling
Serially regulated biological networks fully realise a constrained set of functions
Transient analysis of stochastic switches and trajectories with applications to gene regulatory networks
Detailed comparison between StochSim and SSA
Domain-oriented reduction of rule-based network models
Virtual Cell modelling and simulation software environment
Complexity and modularity of intracellular networks: a systematic approach for modelling and simulation
Incorporating genome-wide DNA sequence information into a dynamic whole-cell model of Escherichia coli: application to DNA replication
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