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Pathway Dynamics (pathway + dynamics)
Selected AbstractsStimulation, Monitoring, and Analysis of Pathway Dynamics by Metabolic Profiling in the Aromatic Amino Acid PathwayBIOTECHNOLOGY PROGRESS, Issue 6 2004M. Oldiges Using a concerted approach of biochemical standard preparation, analytical access via LC-MS/MS, glucose pulse, metabolic profiling, and statistical data analysis, the metabolism dynamics in the aromatic amino acid pathway has been stimulated, monitored, and analyzed in different tyrosine-auxotrophic l -phenylalanine-producing Escherichiacoli strains. During the observation window from ,4 s (before) up to 27 s after the glucose pulse, the dynamics of the first five enzymatic reactions in the aromatic amino acid pathway was observed by measuring intracellular concentrations of 3-deoxy- d -arabino-heptulosonate 7-phosphate DAH(P), 3-dehydroquinate (3-DHQ), 3-dehydroshikimate (3-DHS), shikimate 3-phosphate (S3P), and shikimate (SHI), together with the pathway precursors phosphoenolpyruvate (PEP) and P5P, the lumped pentose phosphate pool as an alternative to the nondetectable erythrose 4-phosphate (E4P). Provided that a sufficient fortification of the carbon flux into the pathway of interest is ensured, respective metabolism dynamics can be observed. On the basis of the intracellular pool measurements, the standardized pool velocities were calculated, and a simple, data-driven criterion-called "pool efflux capacity" (PEC)-is derived. Despite its simplifying system description, the criterion managed to identify the well-known AroB limitation in the E. coli strain A (genotype ,( pheA tyrA aroF)/pJF119EH aroFfbrpheAfbramp) and it also succeeded to identify AroL and AroA (in strain B, genotype ,( pheA tyrA aroF)/pJF119EH aroFfbrpheAfbraroB amp) as promising metabolic engineering targets to alleviate respective flux control in subsequent l -Phe producing strains. Furthermore, using of a simple correlation analysis, the reconstruction of the metabolite sequence of the observed pathway was enabled. The results underline the necessity to extend the focus of glucose pulse experiments by studying not only the central metabolism but also anabolic pathways. [source] Sex chromosomes and sex determination pathway dynamics in plant and animal modelsBIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY, Issue 4 2010BOHUSLAV JANOUSEK In this review, we discuss and compare data obtained from animal and plant models, focusing our attention on the mechanisms that affect sex linkage and changes in sex-determining pathways. Patterns in data across taxa suggest that sex bias and the dynamics that occurs within hybrid zones can play an important role in these processes that enable the spread of some otherwise handicapped genotypes. We discuss the data obtained from several main plant model species in the light of the patterns demonstrated in animal models. In several plant models, we discuss possible differences in the age of their sex-determining pathways and the age of their current sex chromosomes. We also address an open question: how can an X/A ratio based sex-determining system evolve from a sex-determining system based on two genes on the Y chromosome that control two separate sex-determining pathways (for the control of gynoecium suppression and anther promotion)? Taking inspiration from the well described mechanisms involved in sex determination dynamics in animals, we suggest a hypothetical stepwise scenario of change of the plant sex-determining system based on two separate sex-determining pathways (for the control of gynoecium suppression and anther promotion) into the other sex-determining systems. We suppose that an intermediate step occurs before shift to X/A based sex determination. At that phase, sex determination in plants is still based on an active Y chromosome, although there exists already a connected control of both sex-determining pathways. We suggest that this connection is enabled by the existence of the genes that control sexual dimorphism in the vegetative state of plant development, and that, in some circumstances, these genes can become sex-determining genes. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 737,752. [source] A Computational Study of Feedback Effects on Signal Dynamics in a Mitogen-Activated Protein Kinase (MAPK) Pathway ModelBIOTECHNOLOGY PROGRESS, Issue 2 2001Anand R. Asthagiri Exploiting signaling pathways for the purpose of controlling cell function entails identifying and manipulating the information content of intracellular signals. As in the case of the ubiquitously expressed, eukaryotic mitogen-activated protein kinase (MAPK) signaling pathway, this information content partly resides in the signals' dynamical properties. Here, we utilize a mathematical model to examine mechanisms that govern MAPK pathway dynamics, particularly the role of putative negative feedback mechanisms in generating complete signal adaptation, a term referring to the reset of a signal to prestimulation levels. In addition to yielding adaptation of its direct target, feedback mechanisms implemented in our model also indirectly assist in the adaptation of signaling components downstream of the target under certain conditions. In fact, model predictions identify conditions yielding ultra-desensitization of signals in which complete adaptation of target and downstream signals culminates even while stimulus recognition (i.e., receptor-ligand binding) continues to increase. Moreover, the rate at which signal decays can follow first-order kinetics with respect to signal intensity, so that signal adaptation is achieved in the same amount of time regardless of signal intensity or ligand dose. All of these features are consistent with experimental findings recently obtained for the Chinese hamster ovary (CHO) cell lines (Asthagiri et al., J. Biol. Chem.1999, 274, 27119,27127). Our model further predicts that although downstream effects are independent of whether an enzyme or adaptor protein is targeted by negative feedback, adaptor-targeted feedback can "back-propagate" effects upstream of the target, specifically resulting in increased steady-state upstream signal. Consequently, where these upstream components serve as nodes within a signaling network, feedback can transfer signaling through these nodes into alternate pathways, thereby promoting the sort of signaling cross-talk that is becoming more widely appreciated. [source] Pathway sensitivity analysis for detecting pro-proliferation activities of oncogenes and tumor suppressors of epidermal growth factor receptor-extracellular signal-regulated protein kinase pathway at altered protein levelsCANCER, Issue 18 2009Hu Li PhD Abstract BACKGROUND: Mathematic models and sensitivity analyses of biologic pathways have been used for exploring the dynamics and for detecting the key components of signaling pathways. METHODS: The authors previously developed a mathematic model of the epidermal growth factor receptor-extracellular signal-regulated protein kinase (EGFR-ERK) pathway using ordinary differential equations from existing EGFR-ERK pathway models. By using prolonged ERK activation as an indicator that may lead to cell proliferation under certain circumstances, in the current study, a pathway sensitivity analysis was performed to test its capability of detecting pro-proliferative activities through altered protein levels to examine the effects on ERK activation. RESULTS: The analysis revealed that 12 of 20 oncoproteins and 4 of 5 tumor suppressors were detected, consistent with reported experimental works. Because pathway dynamics depend on many factors, some of which were not included in the current models, failure to detect all known oncogenes and tumor suppressors can be because of the failure to include relevant crosstalk to other pathway components. CONCLUSIONS: Overall, the current results indicated that pathway sensitivity analysis is a useful approach for detecting and distinguishing pro-proliferation activities of oncoproteins and suppressed proliferative activities of tumor suppressors at altered protein levels at least in the EGFR-ERK model. Cancer 2009. © 2009 American Cancer Society. [source] | ||||||||||