Engineered genetic information processing circuits

Qi, Hao; Blanchard, Andrew E.; Lu, Ting

doi:10.1002/wsbm.1216

Cited by 26 publications

(21 citation statements)

References 135 publications

(72 reference statements)

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“…At a molecular level, information processing (Ausländer et al, 2012 ; Daniel et al, 2013 ) has been observed in numerous processes, such as the reversible phosphorylation of proteins (Thomson and Gunawardena, 2009 ); microtubule dynamics (Faber et al, 2006 ); enzymatic processes (Baron et al, 2006 ; Katz and Privman, 2010 ); redox regulation (Dwivedi and Kemp, 2012 ); transcription (Mooney et al, 1998 ); genetic regulatory networks (Qi et al, 2013 ); input signal transduction (Roper, 2007 ); biochemical networks (Bowsher, 2011 ); NF-kappaB dynamics (Tay et al, 2010 ); intracellular signaling reactions (Kamimura and Kobayashi, 2012 ; Purvis and Lahav, 2013 ); metabolic switches (Ramakrishnan and Bhalla, 2008 ); the chemotaxis pathway (Shimizu et al, 2010 ); network motifs (Alon, 2007 ), and other cellular processes (Ben-Jacob, 2009 ).…”

Section: Metabolic Network and Information Propertiesmentioning

confidence: 99%

Elements of the cellular metabolic structure

Fuente

2015

Front. Mol. Biosci.

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A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell.

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Section: Metabolic Network and Information Propertiesmentioning

confidence: 99%

Elements of the cellular metabolic structure

Fuente

2015

Front. Mol. Biosci.

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“…Activation of each phase is dependent on the proper progression and completion of the previous one, which can be monitored by cell cycle checkpoints. It is now believed that all proliferation, differentiation, and cell death processes are controlled by the underlying gene regulatory networks (5), which often involve many complex feedback loops (6). The complexity of the large regulatory networks for the cell cycle makes it difficult to understand the global natures and connections between the underlying network and the cell cycle process.…”

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confidence: 99%

Landscape and flux reveal a new global view and physical quantification of mammalian cell cycle

Wang

2014

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

133

202

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Cell cycles, essential for biological function, have been investigated extensively. However, enabling a global understanding and defining a physical quantification of the stability and function of the cell cycle remains challenging. Based upon a mammalian cell cycle gene network, we uncovered the underlying Mexican hat landscape of the cell cycle. We found the emergence of three local basins of attraction and two major potential barriers along the cell cycle trajectory. The three local basins of attraction characterize the G1, S/G2, and M phases. The barriers characterize the G1 and S/G2 checkpoints, respectively, of the cell cycle, thus providing an explanation of the checkpoint mechanism for the cell cycle from the physical perspective. We found that the progression of a cell cycle is determined by two driving forces: curl flux for acceleration and potential barriers for deceleration along the cycle path. Therefore, the cell cycle can be promoted (suppressed), either by enhancing (suppressing) the flux (representing the energy input) or by lowering (increasing) the barrier along the cell cycle path. We found that both the entropy production rate and energy per cell cycle increase as the growth factor increases. This reflects that cell growth and division are driven by energy or nutrition supply. More energy input increases flux and decreases barrier along the cell cycle path, leading to faster oscillations. We also identified certain key genes and regulations for stability and progression of the cell cycle. Some of these findings were evidenced from experiments whereas others lead to predictions and potential anticancer strategies.cell cycle phases | cell cycle checkpoints | landscape | flux T he cell cycle is a series of events that take place in a cell leading to its replication and division. Studying the cell cycle process is essential for understanding cell growth, proliferation, development, and death (1-4). Cell cycle comprises several distinct phases: G1 phase (resting), S phase (synthesis), G2 phase (interphase), and M phase (mitosis). Activation of each phase is dependent on the proper progression and completion of the previous one, which can be monitored by cell cycle checkpoints. It is now believed that all proliferation, differentiation, and cell death processes are controlled by the underlying gene regulatory networks (5), which often involve many complex feedback loops (6). The complexity of the large regulatory networks for the cell cycle makes it difficult to understand the global natures and connections between the underlying network and the cell cycle process. Furthermore, in the cell, the intrinsic fluctuations from the finite number of molecules and extrinsic fluctuations from dynamical and inhomogeneous environments (7, 8) coexist. Therefore, stochastic approaches are often required to explore the nature of the underlying network of chemical reaction soups (9-14). The challenge is how to understand the global picture and physical principles for the stability and function of the cell cycle ...

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“…For example, extended from the programming of cellular dynamics, synthetic bacterial systems have been applied to understand ecological and evolutionary questions that are difficult to address with natural communities [108]. Towards real-world applications, bacterial consortia synthesized with designer communication modules have been used for information processing [109,110], bio-computation [111], and therapeutics [112][113][114], as well as material and chemical productions [115][116][117]. There are a variety of research fields where synthetic bacterial consortia have started to play an important role: In metabolic engineering, cellular communication can be used to implement self-regulated control between cellular growth and product manufacturing in bioreactors for autonomous bioproduction.…”

Section: Discussionmentioning

confidence: 99%

Programming the group behaviors of bacterial communities with synthetic cellular communication

Kong

Celik

Liao

et al. 2014

Bioresour. Bioprocess.

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Synthetic biology is a newly emerged research discipline that focuses on the engineering of novel cellular behaviors and functionalities through the creation of artificial gene circuits. One important class of synthetic circuits currently under active development concerns the programming of bacterial cellular communication and collective population-scale behaviors. Because of the ubiquity of cell-cell interactions within bacterial communities, having an ability of engineering these circuits is vital to programming robust cellular behaviors. Here, we highlight recent advances in communication-based synthetic gene circuits by first discussing natural communication systems and then surveying various functional engineered circuits, including those for population density control, temporal synchronization, spatial organization, and ecosystem formation. We conclude by summarizing recent advances, outlining existing challenges, and discussing potential applications and future opportunities.

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Engineered genetic information processing circuits

Cited by 26 publications

References 135 publications

Elements of the cellular metabolic structure

Elements of the cellular metabolic structure

Landscape and flux reveal a new global view and physical quantification of mammalian cell cycle

Programming the group behaviors of bacterial communities with synthetic cellular communication

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