Metabolic division of labor in microbial systems

Tsoi, Ryan; Wu, Feilun; Zhang, Carolyn; Bewick, Sharon; Karig, David; Li, You

doi:10.1073/pnas.1716888115

Cited by 230 publications

(214 citation statements)

References 77 publications

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“…Biophysical and biochemical models are important tools to quantitatively understand genetic circuit dynamics, metabolic network constraints, cell-cell communications (Dai, Lee et al 2019) and microbial consortia interactions (Kong, Meldgin et al 2018, Tsoi, Wu et al 2018). Based on a previously engineered malonyl-CoA switch, nine differential equations were formulated (Table 1) and employed to unravel the design principles underlying a perfect metabolite switch.…”

Section: Discussionmentioning

confidence: 99%

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

2019

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AbstractLiving organism is an intelligent system encoded by hierarchically-organized information to perform precisely-controlled biological functions. Biophysical models are important tools to uncover the design rules underlying complex genetic-metabolic circuit interactions. Based on a previously engineered synthetic malonyl-CoA switch (Xu et al, PNAS 2014), we have formulated nine differential equations to unravel the design principles underlying an ideal metabolic switch to improve fatty acids production in E. coli. By interrogating the physiologically accessible parameter space, we have determined the optimal controller architecture to configure both the metabolic source pathway and metabolic sink pathway. We determined that low protein degradation rate, medium strength of metabolic inhibitory constant, high metabolic source pathway induction rate, strong binding affinity of the transcriptional activator toward the metabolic source pathway, weak binding affinity of the transcriptional repressor toward the metabolic sink pathway, and a strong cooperative interaction of transcriptional repressor toward metabolic sink pathway benefit the accumulation of the target molecule (fatty acids). The target molecule (fatty acid) production is increased from 50% to 10-folds upon application of the autonomous metabolic switch. With strong metabolic inhibitory constant, the system displays multiple steady states. Stable oscillation of metabolic intermediate is the driving force to allow the system deviate from its equilibrium state and permits bidirectional ON-OFF gene expression control, which autonomously compensates enzyme level for both the metabolic source and metabolic sink pathways. The computational framework may facilitate us to design and engineer predictable genetic-metabolic switches, quest for the optimal controller architecture of the metabolic source/sink pathways, as well as leverage autonomous oscillation as a powerful tool to engineer cell function.

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Section: Discussionmentioning

confidence: 99%

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

2019

Preprint

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“…For example, giving up the ability to reproduce momentarily in favor of helping others could be more beneficial in evolutionary terms than a permanent specialization 55 . Transient DoL also allows for the compartmentalization of different processes in a delocalized manner, which can also be more efficient in certain environments [56][57][58] . Depending on the processes that cells would need to complete, sensitivity thresholds and the nature of the coordinating molecule(s) could adapt to provide different proportions of individuals in each task 59 .…”

Section: Discussionmentioning

confidence: 99%

“…Secondly, the conditions favoring DoL, could be more rigorously established. Similar to the work performed by Tsoi et al 58 , where they analyzed different metabolic pathways to derive a general criterion for outperforming DoL, the different degrees of specialization could be measured as a function of their proposed readout: the maximization of the overall productivity. It is worth mentioning that, although initial assumptions and models greatly differ, resulting conclusions offer the same principles.…”

Section: Future Workmentioning

confidence: 99%

Deriving general conditions and mechanisms for division of labor using the cell-based simulator `gro`

Gregorio-Godoy

Rodríguez-Regueira

et al. 2018

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Division of Labor can occur as a consequence of a major evolutionary transition such as multicellularity but is also found in societies of similar individuals like microbes. It has been defined as a process that occurs when cooperating individuals specialize to carry out specific tasks in a distributed manner. This paper analyzes the conditions for division of labor to emerge as a beneficial evolutionary solution and proposes two novel mechanisms for this process to emerge as a consequence of cell communication in an isogenic group of cells. The study is conducted by means of the cell-based model gro that simulates the growth and interaction of cells in a two-dimensional bacterial colony. When the labor is social, like the production of a molecule that is publicly shared, simulation results indicate that division of labor provides higher fitness than individual labor if the benefits of specialization are accelerating. Two genetic networks that 2 generate consensual and reversible specialization are presented and characterized. In the proposed mechanisms, cells self-organize through the exchange of certain molecules and coordinate behaviors at the local level without the requirements of any fitness benefits. In addition, the proposed regulatory mechanisms are able to create de novo patterns unprecedented to this date that can scale with size. MAIN TEXTMulticellularity is considered one of the major transitions in evolution, a concept that encompasses the phenomena in which natural selection transforms autonomously replicative genes/cells/individuals into new higher-level structures 1,2 . In such structures, individuals are no longer independent of each other but rather form a group that coordinates and communicates with a resulting behavior that is determined by the interaction of its fundamental elements 2 . One of this major transitions is multicellularity 3 . It has been identified that multicellularity has evolved independently more than 20 times, at least once in animals, three times in fungi, six times in algae, and multiple times in bacteria 4 .Multicellularity has involved different evolutionary pathways, leading to the belief that there is not a single explanation for its origins. However, some requirements have been identified across many species and are thought to be essential to meet the current definition of what constitutes a multicellular organism 5 . The evolutionary requirements for multicellularity include (i) the formation of clusters of cells via physical contact or adhesion to form a new evolutionary unit 6 , and (ii) communication among the individuals leading to coordinated activity 7 .There are some essential disadvantages to multicellularity. For example, it is much costly to adhere and communicate with your neighbors than to live a solitary life. Employing strategies that assure communication and encourage cooperation to reduce possible conflicts within the group does not 3 come for free 8 . Yet, these pitfalls can be greatly compensated by the benefits that multicellularity can provide. ...

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“…A trade-off was found between robustness to environmental disturbances and robustness to perturbations in available resources for the genetic circuit 41 . Synthetic microbial consortia have been used for engineering multicellular synthetic systems [42][43][44] and metabolic pathways 45 .…”

Section: Resource Competition Is Commonplace At Various Levels Of Regmentioning

confidence: 99%

Winner-Takes-All Resource Competition Redirects Cascading Cell Fate Transitions

Zhang

Goetz

Melendez-Alvarez

et al. 2020

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Failure of modularity remains a significant challenge for synthetic gene circuits assembled with tested modules as they often do not function as expected. Competition over shared limited gene expression resources is a crucial underlying reason. Here, we first built a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve two successive cell fate transitions. Interestingly, we found that the in vivo transition path was redirected as the activation of one switch always prevailed against the other instead of the theoretically expected coactivation. This qualitatively different type of resource competition between the two modules follows a 'winner-takes-all' rule, where the winner is determined by the relative connection strength between the modules. To decouple the resource competition, we constructed a two-strain circuit, which achieved successive activation and stable coactivation of the two switches.We unveiled a nonlinear resource competition within synthetic gene circuits and provided a division of labor strategy to minimize unfavorable effects.

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Metabolic division of labor in microbial systems

Cited by 230 publications

References 77 publications

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

Deriving general conditions and mechanisms for division of labor using the cell-based simulator `gro`

Winner-Takes-All Resource Competition Redirects Cascading Cell Fate Transitions

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Metabolic division of labor in microbial systems

Cited by 230 publications

References 77 publications

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

Deriving general conditions and mechanisms for division of labor using the cell-based simulator gro

Winner-Takes-All Resource Competition Redirects Cascading Cell Fate Transitions

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Deriving general conditions and mechanisms for division of labor using the cell-based simulator `gro`