Mechanism responsible for glucose–lactose diauxie in <i>Escherichia coli</i>: challenge to the cAMP model

Inada, Toshifumi; Kimata, Keiko; Aiba, Hirofumi

doi:10.1046/j.1365-2443.1996.24025.x

Cited by 194 publications

(182 citation statements)

References 29 publications

(36 reference statements)

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“…the LacY permease activity occurs when the PTS component and signal-transduction protein EIIA glc is present in its unphosphorylated state and binds to LacY (20). This occurs when the cell is grown in the presence of PTS sugars, such as glucose, and, to lesser extents, when the cell is grown in the presence of other substrates that exert catabolite repression (21).…”

mentioning

confidence: 99%

Building biological memory by linking positive feedback loops

Chang

Leung

Atkinson

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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A common topology found in many bistable genetic systems is two interacting positive feedback loops. Here we explore how this relatively simple topology can allow bistability over a large range of cellular conditions. On the basis of theoretical arguments, we predict that nonlinear interactions between two positive feedback loops can produce an ultrasensitive response that increases the range of cellular conditions at which bistability is observed. This prediction was experimentally tested by constructing a synthetic genetic circuit in Escherichia coli containing two well-characterized positive feedback loops, linked in a coherent fashion. The concerted action of both positive feedback loops resulted in bistable behavior over a broad range of inducer concentrations; when either of the feedback loops was removed, the range of inducer concentrations at which the system exhibited bistability was decreased by an order of magnitude. Furthermore, bistability of the system could be tuned by altering growth conditions that regulate the contribution of one of the feedback loops. Our theoretical and experimental work shows how linked positive feedback loops may produce the robust bistable responses required in cellular networks that regulate development, the cell cycle, and many other cellular responses.bistability | genetic network | synthetic biology | ultrasensitivity | hysteresis B istable genetic systems display a discontinuity of expression states, where two distinct stable steady states are obtained without the presence of stable intermediate steady states. The previous history of the system determines which stable steady state is occupied. One of the important problems in systems biology is to understand how genetic bistability is established and regulated. This is because bistable genetic switches play an important role in a variety of cellular processes, such as cellular oscillators, progression through the eukaryotic cell cycle, and the development of differentiated cell and tissue types in organisms ranging from the temperate bacteriophage to the human (1-7). Many previous studies have focused on whether a given circuit topology has the capacity to display bistability for some range of environmental conditions (e.g., refs. 8-10). Although the possibility of bistable behavior is important, it is also important that the range of environmental conditions at which it occurs be large enough to achieve practical control of biological processes. Here, we focus upon identification and manipulation of the parameters that control the range of environmental conditions at which bistablity is obtained for systems known to be capable of bistability. We use the methods of synthetic biology to create model experimental systems to address the functions of multiple positive feedback loops in bistability.Theoretical studies have argued that the minimal requirements for genetic bistability are twofold. First, there must be some type of positive feedback controlling gene expression. Examples of positive feedback are when an activa...

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mentioning

confidence: 99%

Building biological memory by linking positive feedback loops

Chang

Leung

Atkinson

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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“…This control mechanism is redundant and does not seem to add any significant effect with regards to the question asked here (Chu and Barnes, 2016). Moreover, there is experimental evidence (Inada et al, 1996;Bettenbrock et al, 2006;Görke and Stülke, 2008) suggesting that the repression of the metabolic genes in E.coli is of secondary importance for diauxic growth. Therefore and for reasons of model parsimony we will concentrate exclusively on direct inducer exclusion here.…”

Section: Mathematical Modelmentioning

confidence: 82%

“…Diauxic growth and the network that controls it has since been subject to intense experimental (Stülke and Hillen, 1999;Brückner and Titgemeyer, 2002;Boulineau et al, 2013;Boianelli et al, 2012;Inada et al, 1996;Kompala et al, 1986;New et al, 2014) and theoretical (Boianelli et al, 2012;Narang, 2006;Narang and Pilyugin, 2007;Kremling et al, 2009) investigation. There are two main mechanisms responsible for two phase growth in bacteria, both of which depend on the phosphotransferase (PTS) system (Deutscher, 2008): (i Regulation of metabolic genes via global transcription regulators, especially cAMP.…”

Section: Introductionmentioning

confidence: 99%

Limited by sensing - A minimal stochastic model of the lag-phase during diauxic growth

Chu

2017

Journal of Theoretical Biology

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Many microbes when grown on a mixture of two carbon sources utilise first and exclusively the preferred sugar, before switching to the less preferred carbon source. This results in two distinct exponential growth phases, often interrupted by a lag-phase of reduced growth termed the lag-phase. While the lag-phase appears to be an evolved feature, it is not clear what drives its evolution, as it comes with a substantial up-front fitness penalty due to lost growth. In this article a minimal mathematical model based on a master-equation approach is proposed. This model can explain many empirically observed phenomena. It suggests that the lag-phase can be understood as a manifestation of the trade-off between switching speed and switching efficiency. Moreover, the model predicts heterogeneity of the population during the lag-phase. Finally, it is shown that the switch from one carbon source to another one is a sensing problem and the lag-phase is a manifestation of known fundamental limitations of biological sensors.

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“…For example, xylose metabolism was completely inhibited when glucose concentration exceeds 40% of the total sugars (49). Further, there is a strong debate on which of the cAMP-CRP complex or the inducer exclusion by glucose PTS is the major determinant of CCR in E. coli (6,12,62). Despite these complexities and disputes, several catabolite derepressed strains of E. coli have been constructed.…”

Section: Escherichia Colimentioning

confidence: 99%

Rewiring carbon catabolite repression for microbial cell factory

Parisutham¹,

Kim²,

Lee³

et al. 2012

BMB Reports

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Carbon catabolite repression (CCR) is a key regulatory system found in most microorganisms that ensures preferential utilization of energy-efficient carbon sources. CCR helps microorganisms obtain a proper balance between their metabolic capacity and the maximum sugar uptake capability. It also constrains the deregulated utilization of a preferred cognate substrate, enabling microorganisms to survive and dominate in natural environments. On the other side of the same coin lies the tenacious bottleneck in microbial production of bioproducts that employs a combination of carbon sources in varied proportion, such as lignocellulose-derived sugar mixtures. Preferential sugar uptake combined with the transcriptional and/or enzymatic exclusion of less preferred sugars turns out one of the major barriers in increasing the yield and productivity of fermentation process. Accumulation of the unused substrate also complicates the downstream processes used to extract the desired product. To overcome this difficulty and to develop tailor-made strains for specific metabolic engineering goals, quantitative and systemic understanding of the molecular interaction map behind CCR is a prerequisite. Here we comparatively review the universal and strain-specific features of CCR circuitry and discuss the recent efforts in developing synthetic cell factories devoid of CCR particularly for lignocellulose-based biorefinery. [BMB reports 2012; 45(2): 59-70]

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Mechanism responsible for glucose–lactose diauxie in Escherichia coli: challenge to the cAMP model

Cited by 194 publications

References 29 publications

Building biological memory by linking positive feedback loops

Building biological memory by linking positive feedback loops

Limited by sensing - A minimal stochastic model of the lag-phase during diauxic growth

Rewiring carbon catabolite repression for microbial cell factory

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