Systems biology最新文献

Extracellular stimuli are often encoded in the frequency, amplitude and duration of spikes in the intracellular concentration of calcium ([Ca2+]i). However, the timing of individual [Ca2+]i-spikes in relation to the dynamics of an extracellular stimulus is still an open question. To address this question, we use a systems biology approach combining experimental and theoretical methods. Using computer simulations, we predict that more naturalistic pulsed stimuli generate precisely-timed [Ca2+]i-spikes in contrast to the application of constant stimuli of the same dose. These computational results are confirmed experimentally in single primary rat hepatocytes upon alpha1-adrenergic stimulation. Hormonal signalling in analogy to neuronal signalling thus has the potential to make use of temporal coding on the level of single cells. The [Ca2+]i-signalling cascade provides a first example for increasing the information capacity of an intracellular regulatory signal beyond the known coding mechanisms of amplitude (AM) and frequency modulation (FM).

Advances in molecular biology provide an opportunity to develop detailed models of biological processes that can be used to obtain an integrated understanding of the system. However, development of useful models from the available knowledge of the system and experimental observations still remains a daunting task. In this work, a model identification strategy for complex biological networks is proposed. The approach includes a state regulator problem (SRP) that provides estimates of all the component concentrations and the reaction rates of the network using the available measurements. The full set of the estimates is utilised for model parameter identification for the network of known topology. An a priori model complexity test that indicates the feasibility of performance of the proposed algorithm is developed. Fisher information matrix (FIM) theory is used to address model identifiability issues. Two signalling pathway case studies, the caspase function in apoptosis and the MAP kinase cascade system, are considered. The MAP kinase cascade, with measurements restricted to protein complex concentrations, fails the a priori test and the SRP estimates are poor as expected. The apoptosis network structure used in this work has moderate complexity and is suitable for application of the proposed tools. Using a measurement set of seven protein concentrations, accurate estimates for all unknowns are obtained. Furthermore, the effects of measurement sampling frequency and quality of information in the measurement set on the performance of the identified model are described.

The activities and interactions of proteins that govern the cellular response to a signal generate a multitude of protein phosphorylation states and heterogeneous protein complexes. Here, using a computational model that accounts for 307 molecular species implied by specified interactions of four proteins involved in signalling by the immunoreceptor FcepsilonRI, we determine the relative importance of molecular species that can be generated during signalling, chemical transitions among these species, and reaction paths that lead to activation of the protein tyrosine kinase (PTK) Syk. By all of these measures and over two- and ten-fold ranges of model parameters--rate constants and initial concentrations--only a small portion of the biochemical network is active. The spectrum of active complexes, however, can be shifted dramatically, even by a change in the concentration of a single protein, which suggests that the network can produce qualitatively different responses under different cellular conditions and in response to different inputs. Reduced models that reproduce predictions of the full model for a particular set of parameters lose their predictive capacity when parameters are varied over two-fold ranges.

Signal transducer and actuator of transcription (STATs) are a family of transcription factors activated by various cytokines, growth factors and hormones. They are important mediators of immune responses and growth and differentiation of various cell types. The STAT signalling system represents a defined functional module with a pattern of signalling that is conserved from flies to mammals. In order to probe and gain insights into the signalling properties of the STAT module by computational means, we developed a simple non-linear ordinary differential equations model within the 'Virtual Cell' framework. Our results demonstrate that the STAT module can operate as a 'biphasic amplitude filter' with an ability to amplify input signals within a specific intermediate range. We show that dimerisation of phosphorylated STAT is crucial for signal amplification and the amplitude filtering function. We also demonstrate that maximal amplification at intermediate levels of STAT activation is a moderately robust property of STAT module. We propose that these observations can be extrapolated to the analogous SMAD signalling module.

Systems biology requires mathematical tools not only to analyse large genomic datasets, but also to explore large experimental spaces in a systematic yet economical way. We demonstrate that two-factor combinatorial design (CD), shown to be useful in software testing, can be used to design a small set of experiments that would allow biologists to explore larger experimental spaces. Further, the results of an initial set of experiments can be used to seed further 'Adaptive' CD experimental designs. As a proof of principle, we demonstrate the usefulness of this Adaptive CD approach by analysing data from the effects of six binary inputs on the regulation of genes in the N-assimilation pathway of Arabidopsis. This CD approach identified the more important regulatory signals previously discovered by traditional experiments using far fewer experiments, and also identified examples of input interactions previously unknown. Tests using simulated data show that Adaptive CD suffers from fewer false positives than traditional experimental designs in determining decisive inputs, and succeeds far more often than traditional or random experimental designs in determining when genes are regulated by input interactions. We conclude that Adaptive CD offers an economical framework for discovering dominant inputs and interactions that affect different aspects of genomic outputs and organismal responses.

The epidermal growth factor receptor (EGFR) signalling pathway is a complex signalling process with a wide network of interactions. The activation of the mitogen-activated protein kinases (MAPKs) cascade by this activated EGFR has been well studied. MAPKs form a highly integrated network, which is essential for certain specialised cell functions. This paper presents a kinetic model for the MAPK pathway downstream of the EGFR using a biochemical simulator. The model includes 30 signalling events and 29 signalling molecules. The time course data were examined for the activation of each signalling component. The simulation provides a large volume of data, by monitoring the kinetics of the signalling components, which were compared experimentally using the PC12 cell line. The kinetic model corresponded well with the experimental results observed in the EGFR induced activation of proteins. An examination of the kinetic analysis of the multiple signalling events provides a quantitative framework for representing the EGFR signalling network.

Detailed quantitative understanding and specific external control of cellular behaviour are general long-term goals of modem bioscience research activities in systems biology. Pattern formation and self-organisation processes both in single cells and in distributed cell populations are phenomena which are highly significant for the functionality of life, because life requires to maintain a highly organised spatiotemporal system structure. In particular chemotaxis is crucial for various biological aspects of intercellular signalling and cell aggregation. As an example for model based control of self-organising biological systems, we describe numerical optimal control of E. coli bacterial chemotaxis based on a 1-D two-component partial differential equation (PDE) model of reaction diffusion type. We present a numerical scheme to force cell aggregation patterns to particular desired results by applying a boundary influx control of chemoattractant without interfering with the system itself. Optimal controls are numerically computed by using a specially tailored interior point optimisation technique applied to a direct collocation discretisation of the control function and the PDE constraint. The objective to be minimised is the deviation of a desired cell distribution from the cell density, which results from the dynamics of the controlled system.

Bi-stable chemical systems are the basic building blocks for intracellular memory and cell fate decision circuits. These circuits are built from molecules, which are present at low copy numbers and are slowly diffusing in complex intracellular geometries. The stochastic reaction-diffusion kinetics of a double-negative feedback system and a MAPK phosphorylation-dephosphorylation system is analysed with Monte-Carlo simulations of the reaction-diffusion master equation. The results show the geometry of intracellular reaction compartments to be important both for the duration and the locality of biochemical memory. Rules for when the systems lose global hysteresis by spontaneous separation into spatial domains in opposite phases are formulated in terms of geometrical constraints, diffusion rates and attractor escape times. The analysis is facilitated by a new efficient algorithm for exact sampling of the Markov process corresponding to the reaction-diffusion master equation.

In this paper, we aim to develop a new methodology to model and design periodic oscillators of biological networks, in particular gene regulatory networks with multiple genes, proteins and time delays, by using multiple timescale networks (MTN). Fast reactions constitute a positive feedback-loop network (PFN), while slow reactions consist of a cyclic feedback-loop network (CFN), in MTN. Multiple timescales are exploited to simplify models according to singular perturbation theory. We show that a MTN has no stable equilibrium but stable periodic orbits when certain conditions are satisfied. Specifically, we first prove the basic properties of MTNs with only one PFN, and then generalise the result to MTNs with multiple PFNs. Finally, we design a biologically plausible gene regulatory network by the cI and Lac genes, to demonstrate the theoretical results. Since there is less restriction on the network structure of a MTN, it can be expected to apply to a wide variety of areas on the modelling, analysing and designing of biological systems.