Principles behind the multifarious control of signal transduction

Hornberg, Jorrit J.; Bruggeman, Frank J.; Binder, Bernd R.; Geest, Christian R.; Vaate, A. J. Marjolein Bij de; Lankelma, J.; Heinrich, Reinhart; Westerhoff, Hans V.

doi:10.1111/j.1432-1033.2004.04404.x

Cited by 147 publications

(121 citation statements)

References 43 publications

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“…Taken together, enhanced expression of Ptp2 altered the amplitude, but not the period of HOG pathway activity after osmotic shock. This conclusion is different from that reached in a recent report suggesting that phosphatases are more important for signal duration than kinases 30 .…”

Section: Time Course Experimentscontrasting

confidence: 99%

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Integrative model of the response of yeast to osmotic shock

et al. 2005

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Integration of experimental studies with mathematical modeling allows insight into systems properties, prediction of perturbation effects and generation of hypotheses for further research. We present a comprehensive mathematical description of the cellular response of yeast to hyperosmotic shock. The model integrates a biochemical reaction network comprising receptor stimulation, mitogen-activated protein kinase cascade dynamics, activation of gene expression and adaptation of cellular metabolism with a thermodynamic description of volume regulation and osmotic pressure. Simulations agree well with experimental results obtained under different stress conditions or with specific mutants. The model is predictive since it suggests previously unrecognized features of the system with respect to osmolyte accumulation and feedback control, as confirmed with experiments. The mathematical description presented is a valuable tool for future studies on osmoregulation in yeast and-with appropriate modifications-other organisms. It also serves as a starting point for a comprehensive description of cellular signaling.Osmoregulation encompasses active processes with which cells monitor and adjust osmotic pressure and control shape, turgor and relative water content. Even individual cells in multicellular organisms respond to osmotic changes, and strategies of cellular adaptation are conserved from bacteria to human 1 . The yeast Saccharomyces cerevisiae is a suitable model system to study osmoregulation and a substantial amount of information is available on osmotic shockinduced signal transduction, control of gene expression and accumulation of osmolytes 2 .Osmoregulation is a homeostatic process, though commonly studied as a response to osmotic shock. Central to yeast osmotic adaptation is the high osmolarity glycerol (HOG) signaling system 2,3 (Fig. 1). S. cerevisiae monitors osmotic changes through the plasma membrane-localized sensor histidine kinase Sln1. Under ambient conditions, Sln1 is active and inhibits signaling. Upon loss of turgor pressure, Sln1 is inactivated 4 resulting in activation of a mitogen-activated protein (MAP) kinase cascade and phosphorylation of the MAP kinase Hog1. Active Hog1 accumulates in the nucleus where it affects gene expression. Two HOG target genes encode enzymes in glycerol production. Hence, activation of Hog1 stimulates the production of glycerol, which serves as an osmolyte to increase intracellular osmotic pressure. Glycerol accumulation is also controlled by rapid closing of the aquaglyceroporin Fps1, which is an osmolarity-regulated glycerol channel. Hog1 activation and Hog1-dependent transcriptional stimulation are transient processes, indicating rigorous feedback control. Several protein phosphatases are known as negative regulators of the pathway. Although the overall organization of the systems is well characterized, open questions concern the mechanisms underlying activation and deactivation of the system, feedback control and the causal relationship between different events in...

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Section: Time Course Experimentscontrasting

confidence: 99%

“…A critical test of the model was simulation of two subsequent osmotic shocks addressing the questions whether a second shock can reactivate the pathway. This was simulated by treating cells twice at different time intervals (15,30 and 60 min) with 0.5 M NaCl (Fig. 5 and Supplementary Fig.…”

Section: Time Course Experimentsmentioning

confidence: 99%

Integrative model of the response of yeast to osmotic shock

et al. 2005

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“…However, if the pathway map is small, and the values for the rate constants are available, it is possible to perform pathway map-based simulations. For example, mitogen-activated protein kinase (MAPK) mediated signal transduction [50,51], Fas-induced caspase activation [52], protein kinase A (PKA) activation [53], signal transduction via the mTOR pathway [54] and CAMKII activation [55] have all been successfully simulated.…”

Section: Pathway Map-based Simulationsmentioning

confidence: 99%

Systems‐based understanding of pharmacological responses with combinations of multidisciplinary methodologies

Kariya

Honma

Suzuki

2013

Biopharm & Drug Disp

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The importance of systems-based pharmacological approaches to drug discovery and development has increasingly been recognized. This reviews summarizes recent advances in the development of systems pharmacology and introduces the methods used for analysis. To understand the cellular response at the molecular level, pathway maps must be prepared to show how the function of the constituent molecules within cells are linked and integrated to form molecular networks. First, the methods used to prepare these pathway maps, such as databases, knowledge bases and software platforms, are introduced. Then the mathematical theories used to analyse the behavior of molecular networks are summarized. To quantitatively predict cellular responses, simulations are performed that are based on the rate equations for each reaction within the pathway map. If the number of reactions described in the pathway map is small, and if the parameter values for the rate constants are available, it is possible accurately to simulate the behavior of the molecular networks. However, to analyse complex maps, mathematical abstraction is required. Such abstraction methods are important to integrate cellular responses and to understand tissue/organ and in vivo pharmacological/toxicological responses. The scope and limitations of the methods are also discussed.

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“…They are a standard quantitative measure in sensitivity analysis [1]. MCCs can also be calculated for finite times as described in [18,19]. They are defined by…”

Section: Quantitative Measures To Characterize Transient Dynamicsmentioning

confidence: 99%

“…They should be designed such that an inspection of trajectories is not necessary, i. e. they should deliver initial conditions (concentrations) of proteins and respective time intervals where a response shows a threshold. Recent examples of quantitative methods to characterize transient signals include transient metabolic control coefficients (MCC) [18,19] and finite time Lyapunov exponents (FTL) [2]. We here focus on system structures that generate thresholds in transient dynamics and investigate quantitative measures to identify such thresholds.…”

mentioning

confidence: 99%

Thresholds in transient dynamics of signal transduction pathways

Rateitschak¹

2010

Journal of Theoretical Biology

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Principles behind the multifarious control of signal transduction

Cited by 147 publications

References 43 publications

Integrative model of the response of yeast to osmotic shock

Integrative model of the response of yeast to osmotic shock

Systems‐based understanding of pharmacological responses with combinations of multidisciplinary methodologies

Thresholds in transient dynamics of signal transduction pathways

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