Machine learning for metabolic engineering: A review

Lawson, Christopher E.; Martí, Jose Manuel; Radivojević, Tijana; Jonnalagadda, Sai Vamshi R.; Gentz, Reinhard; Hillson, Nathan J.; Peisert, Sean; Kim, Joonhoon; Simmons, Blake A.; Kim, Young-Mo; Singer, Steven W.; Mukhopadhyay, Aindrila; Tanjore, Deepti; Dunn, Joshua G.; Martín, Héctor García

doi:10.1016/j.ymben.2020.10.005

Cited by 186 publications

(119 citation statements)

References 207 publications

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“…Such approaches to construct genetic tools with predictable functions are highly relevant for the design of more complex expression systems as required for synthetic gene networks [ 321 ] or for orchestrating complex metabolic fluxes [ 329 , 330 ]. Accordingly, AI approaches are expected to foster innovation in metabolic engineering by simplifying the reconstruction and design of metabolic pathways, optimizing pathways, building and testing cellular factories, as well as scaling up cellular factories (as recently reviewed elsewhere [ 331 ]). Such metabolic engineering widens the opportunity for the bio-based production of monomers or precursors of materials or to incorporate desired metabolic reactions (for example, drug production) into (therapeutic) ELMs.…”

Section: Discussionmentioning

confidence: 99%

Synthetic biology as driver for the biologization of materials sciences

Burgos-Morales

Gueye

Lacombe

et al. 2021

Materials Today Bio

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Materials in nature have fascinating properties that serve as a continuous source of inspiration for materials scientists. Accordingly, bio-mimetic and bio-inspired approaches have yielded remarkable structural and functional materials for a plethora of applications. Despite these advances, many properties of natural materials remain challenging or yet impossible to incorporate into synthetic materials. Natural materials are produced by living cells, which sense and process environmental cues and conditions by means of signaling and genetic programs, thereby controlling the biosynthesis, remodeling, functionalization, or degradation of the natural material. In this context, synthetic biology offers unique opportunities in materials sciences by providing direct access to the rational engineering of how a cell senses and processes environmental information and translates them into the properties and functions of materials. Here, we identify and review two main directions by which synthetic biology can be harnessed to provide new impulses for the biologization of the materials sciences: first, the engineering of cells to produce precursors for the subsequent synthesis of materials. This includes materials that are otherwise produced from petrochemical resources, but also materials where the bio-produced substances contribute unique properties and functions not existing in traditional materials. Second, engineered living materials that are formed or assembled by cells or in which cells contribute specific functions while remaining an integral part of the living composite material. We finally provide a perspective of future scientific directions of this promising area of research and discuss science policy that would be required to support research and development in this field.

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Section: Discussionmentioning

confidence: 99%

Synthetic biology as driver for the biologization of materials sciences

Burgos-Morales

Gueye

Lacombe

et al. 2021

Materials Today Bio

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“…promoters, ribosome binding sites) [287][288][289] to identify improved pathways and target compound production, 41,256,290 with an increased emphasis on the use of data-driven predictive engineering. 289,291,292 Establishing high-throughput screening protocols for terpenoids beyond available low sensitivity and non-specic methods will be important. 293 Currently, GC-MS is widely used in the eld but suffers from low throughput.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Alternative metabolic pathways and strategies to high-titre terpenoid production inEscherichia coli

2022

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“…However, engineering enzymes in the data-assisted synthetic biology landscape could accelerate the hunt of the “super-enzyme” in environmental perspectives. However, as this is a new frontier to the scientific literature body, only a handful of the kindest efforts are available at present (Ajjolli Nagaraja et al, 2020 ; Lawson et al, 2020 ; Mou et al, 2020 ; Robinson et al, 2020 ; Siedhoff et al, 2020 ; Wittmann et al, 2020 ). Herein, we have summarized current state-of-the-art knowledge of the data-assisted enzyme redesigning ( Figure 1 ) to promote new studies on enzyme redesigning from an environmental perspective.…”

Section: Introductionmentioning

confidence: 99%