Online ISSN
1751-8857
Print ISSN
1751-8849
IET Systems Biology
Volume 5, Issue 2, March 2011
Volumes & issues:
Volume 5, Issue 2
March 2011
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- Author(s): D. Seaton and J. Krishnan
- Source: IET Systems Biology, Volume 5, Issue 2, p. 81 –94
- DOI: 10.1049/iet-syb.2009.0061
- Type: Article
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p.
81
–94
(14)
Adaptive signalling is a common and important element in cellular systems. Threshold processes are also ubiquitous in signal transduction. This study takes a modular systems approach to systematically understand the interaction of adaptive modules and threshold modules (both monostable and bistable). The authors employ representative modules of adaptive and threshold elements and use these to examine and analyse various aspects of their interaction including the order of interconnection, the role of relative time scales, the difference between monostable and bistable thresholds in this context and how threshold modules may act as a switch induced by transient signals. Numerical simulations, bifurcation analysis and analytical work are employed to address these questions. Overall, the authors' analysis is a first step towards a detailed systems engineering understanding of the different kinds of interactions between these ubiquitous elements in cellular signal transduction. [Includes supplementary material] - Author(s): X.-D. Wang ; Y.-X. Qi ; Z.-L. Jiang
- Source: IET Systems Biology, Volume 5, Issue 2, p. 95 –102
- DOI: 10.1049/iet-syb.2010.0041
- Type: Article
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p.
95
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(8)
Many methods had been developed on inferring transcriptional network from gene expression. However, it is still necessary to design new method that discloses more detailed and exact network information. Using network-assisted regression, the authors combined the averaged three-way mutual information (AMI3) and non-linear ordinary differential equation (ODE) model to infer the transcriptional network, and to obtain both the topological structure and the regulatory dynamics. Synthetic and experimental data were used to evaluate the performance of the above approach. In comparison with the previous methods based on mutual information, AMI3 obtained higher precision with the same sensitivity. To describe the regulatory dynamics between transcription factors and target genes, network-assisted regression and regression without network, respectively, were applied in the steady-state and time series microarray data. The results revealed that comparing with regression without network, network-assisted regression increased the precision, but decreased the fitting goodness. Then, the authors reconstructed the transcriptional network of Escherichia coli and simulated the regulatory dynamics of genes. Furthermore, the authors' approach identified potential transcription factors regulating yeast cell cycle. In conclusion, network-assisted regression, combined AMI3 and ODE model, was a more precisely to infer the topological structure and the regulatory dynamics of transcriptional network from microarray data. [Includes supplementary material] - Author(s): I.G. Karafyllidis
- Source: IET Systems Biology, Volume 5, Issue 2, p. 103 –109
- DOI: 10.1049/iet-syb.2010.0018
- Type: Article
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p.
103
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(7)
Pathogenic bacteria employ a communication mechanism, known as quorum sensing (QS), to obtain information about their cell density and to synchronise their behaviour. Most bacteria species use QS signalling circuits to optimise the secretion of virulence factors that damage their host. Recently, QS has been recognised as a target for antimicrobial drugs that can control bacterial infections. Here the QS process is modelled as a state transition graph with transitions depending on the diffusion and local concentration of the QS molecules (autoinducers). Based on this model a simulation tool has been developed to simulate the QS process in both open and confined spaces. Using this simulation tool a number of numerical experiments has been carried out with various strategies of QS circuit regulation. The results of these experiments showed that regulation of the QS signalling circuit can lead to significantly reduced bacterial virulence. - Author(s): D.A. Oyarzún
- Source: IET Systems Biology, Volume 5, Issue 2, p. 110 –119
- DOI: 10.1049/iet-syb.2010.0044
- Type: Article
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p.
110
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(10)
This note addresses the optimal control of non-linear metabolic networks by means of time-dependent enzyme synthesis rates. The author considers networks with general topologies described by a control-affine dynamical system coupled with a linear model for enzyme synthesis and degradation. The problem formulation accounts for transitions between two metabolic equilibria, which typically arise in metabolic adaptations to environmental changes, and the minimisation of a quadratic functional that weights the cost/benefit relation between the transcriptional effort required for enzyme synthesis and the transition to the new phenotype. Using a linear time-variant approximation of the non-linear dynamics, the problem is recast as a sequence of linear-quadratic problems, the solution of which involves a sequence of differential Lyapunov equations. The author provides conditions for convergence to an approximate solution of the original problem, which are naturally satisfied by a wide class of models for saturable enzyme kinetics. As a case study the author uses the method to examine the robustness of an optimal just-in-time gene expression pattern with respect to heterogeneity in the biosynthetic costs of individual proteins. - Author(s): A. Raue ; C. Kreutz ; T. Maiwald ; U. Klingmüller ; J. Timmer
- Source: IET Systems Biology, Volume 5, Issue 2, p. 120 –130
- DOI: 10.1049/iet-syb.2010.0061
- Type: Article
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120
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Mathematical description of biological processes such as gene regulatory networks or signalling pathways by dynamic models utilising ordinary differential equations faces challenges if the model parameters like rate constants are estimated from incomplete and noisy experimental data. Typically, biological networks are only partially observed. Only a fraction of the modelled molecular species is measurable directly. This can result in structurally non-identifiable model parameters. Furthermore, practical non-identifiability can arise from limited amount and quality of experimental data. In the challenge of growing model complexity on one side, and experimental limitations on the other side, both types of non-identifiability arise frequently in systems biological applications often prohibiting reliable prediction of system dynamics. On theoretical grounds this article summarises how and why both types of non-identifiability arise. It exemplifies pitfalls where models do not yield reliable predictions of system dynamics because of non-identifiabilities. Subsequently, several approaches for identifiability analysis proposed in the literature are discussed. The aim is to provide an overview of applicable methods for detecting parameter identifiability issues. Once non-identifiability is detected, it can be resolved either by experimental design, measuring additional data under suitable conditions; or by model reduction, tailoring the size of the model to the information content provided by the experimental data. Both strategies enhance model predictability and will be elucidated by an example application. [Includes supplementary material] - Author(s): R. Alves ; E. Vilaprinyo ; A. Sorribas
- Source: IET Systems Biology, Volume 5, Issue 2, p. 131 –136
- DOI: 10.1049/iet-syb.2010.0032
- Type: Article
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131
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Advances in systems biology are increasingly dependent upon the integration of various types of data and different methodologies to reconstruct how cells work at the systemic level. Thus, teams with a varied array of expertise and people with interdisciplinary training are needed. So far this training was thought to be more productive if aimed at the Masters or PhD level. At this level, multiple specialised and in-depth courses on the different subject matters of systems biology are taught to already well-prepared students. This approach is mostly based on the recognition that systems biology requires a wide background that is hard to find in undergraduate students. Nevertheless, and given the importance of the field, the authors argue that exposition of undergraduate students to the methods and paradigms of systems biology would be advantageous. Here they present and discuss a successful experiment in teaching systems biology to third year undergraduate biotechnology students at the University of Lleida in Spain. The authors' experience, together with that from others, argues for the adequateness of teaching systems biology at the undergraduate level. [Includes supplementary material] - Author(s): C. Damiani ; R. Serra ; M. Villani ; S.A. Kauffman ; A. Colacci
- Source: IET Systems Biology, Volume 5, Issue 2, p. 137 –144
- DOI: 10.1049/iet-syb.2010.0039
- Type: Article
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p.
137
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(8)
Despite myriads of possible gene expression profiles, cells tend to be found in a confined number of expression patterns. The dynamics of Boolean models of gene regulatory networks has proven to be a likely candidate for the description of such self-organisation phenomena. Because cells do not live in isolation, but they constantly shape their functions to adapt to signals from other cells, this raises the question of whether the cooperation among cells entails an expansion or a reduction of their possible steady states. Multi random Boolean networks are introduced here as a model for interaction among cells that might be suitable for the investigation of some generic properties regarding the influence of communication on the diversity of cell behaviours. In spite of its simplicity, the model exhibits a non-obvious phenomenon according to which a moderate exchange of products among adjacent cells fosters the variety of their possible behaviours, which on the other hand are more similar to one another. On the contrary, a more invasive coupling would lead cells towards homogeneity. - Author(s): L.K. Nguyen and D. Kulasiri
- Source: IET Systems Biology, Volume 5, Issue 2, p. 145 –156
- DOI: 10.1049/iet-syb.2010.0020
- Type: Article
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p.
145
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(12)
Molecular fluctuations are known to affect dynamics of cellular systems in important ways. Studies aimed at understanding how molecular systems of certain regulatory architectures control noise therefore become essential. The interplay between feedback regulation and noise has been previously explored for cellular networks governed by a single negative feedback loop. However, similar issues within networks consisting of more complex regulatory structures remain elusive. The authors investigate how negative feedback loops manage noise within a biochemical cascade concurrently governed by multiple negative feedback loops, using the prokaryotic tryptophan (trp) operon system in Escherechia coli as the model system. To the authors knowledge, this is the first study of noise in the trp operon system. They show that the loops in the trp operon system possess distinct, even opposing, noise-controlling effects despite their seemingly analogous feedback structures. The enzyme inhibition loop, although controlling the last reaction of the cascade, was found to suppress noise not only for the tryptophan output but also for other upstream components. In contrast, the Repression (Rep) loop enhances noise for all systems components. Attenuation (Att) poses intermediate effects by attenuating noise for the upstream components but promoting noise for components downstream of its target. Regarding noise at the output tryptophan, Rep and Att can be categorised as noise-enhancing loops whereas Enzyme Inhibition as a noise-reducing loop. These findings suggest novel implications in how cellular systems with multiple feedback mechanisms control noise. [Includes supplementary material]
Modular systems approach to understanding the interaction of adaptive and monostable and bistable threshold processes
Reconstruction of transcriptional network from microarray data using combined mutual information and network-assisted regression
Regulating the quorum sensing signalling circuit to control bacterial virulence: in silico analysis
Optimal control of metabolic networks with saturable enzyme kinetics
Addressing parameter identifiability by model-based experimentation
Teaching systems biology
Cell–cell interaction and diversity of emergent behaviours
Distinct noise-controlling roles of multiple negative feedback mechanisms in a prokaryotic operon system
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