@article{Sander2023b,

title = {Eliminating genes for a two‐component system increases PHB productivity in \textit{Cupriavidus basilensis} 4G11 under PHB suppressing, nonstress conditions},

author = {Kyle Sander and Anthony J. Abel and Skyler Friedline and William Sharpless and Jeffrey Skerker and Adam Deutschbauer and Douglas S. Clark and Adam P. Arkin},

doi = {10.1002/bit.28532},

issn = {1097-0290},

year  = {2024},

date = {2024-01-00},

urldate = {2024-01-00},

journal = {Biotech & Bioengineering},

volume = {121},

number = {1},

pages = {139--156},

publisher = {Wiley},

abstract = {<jats:title>Abstract</jats:title><jats:p>Species of bacteria from the genus <jats:italic>Cupriavidus</jats:italic> are known, in part, for their ability to produce high amounts of poly‐hydroxybutyrate (PHB) making them attractive candidates for bioplastic production. The native synthesis of PHB occurs during periods of metabolic stress, and the process regulating the initiation of PHB accumulation in these organisms is not fully understood. Screening an RB‐TnSeq transposon library of <jats:italic>Cupriavidus basilensis</jats:italic> 4G11 allowed us to identify two genes of an apparent, uncharacterized two‐component system, which when omitted from the genome enable increased PHB productivity in balanced, nonstress growth conditions. We observe average increases in PHB productivity of 56% and 41% relative to the wildtype parent strain upon deleting each gene individually from the genome. The increased PHB phenotype disappears, however, in nitrogen‐free unbalanced growth conditions suggesting the phenotype is specific to fast‐growing, replete, nonstress growth. Bioproduction modeling suggests this phenotype could be due to a decreased reliance on metabolic stress induced by nitrogen limitation to initiate PHB production in the mutant strains. Due to uncertainty in the two‐component system's input signal and regulon, the mechanism by which these genes impart this phenotype remains unclear. Such strains may allow for the use of single‐stage, continuous bioreactor systems, which are far simpler than many PHB bioproduction schemes used previously, given a similar product yield to batch systems in such a configuration. Bioproductivity modeling suggests that omitting this regulation in the cells may increase PHB productivity up to 24% relative to the wildtype organism when using single‐stage continuous systems. This work expands our understanding of the regulation of PHB accumulation in <jats:italic>Cupriavidus</jats:italic>, in particular the initiation of this process upon transition into unbalanced growth regimes.</jats:p>},

keywords = {Applied Microbiology and Biotechnology, Bioengineering, Biotechnology, cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Adams2023,

title = {Engineering osmolysis susceptibility in Cupriavidus necator and Escherichia coli for recovery of intracellular products},

author = {Jeremy David Adams and Kyle B. Sander and Craig S. Criddle and Adam P. Arkin and Douglas S. Clark},

doi = {10.1186/s12934-023-02064-8},

issn = {1475-2859},

year  = {2023},

date = {2023-12-00},

journal = {Microb Cell Fact},

volume = {22},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                Intracellular biomacromolecules, such as industrial enzymes and biopolymers, represent an important class of bio-derived products obtained from bacterial hosts. A common key step in the downstream separation of these biomolecules is lysis of the bacterial cell wall to effect release of cytoplasmic contents. Cell lysis is typically achieved either through mechanical disruption or reagent-based methods, which introduce issues of energy demand, material needs, high costs, and scaling problems. Osmolysis, a cell lysis method that relies on hypoosmotic downshock upon resuspension of cells in distilled water, has been applied for bioseparation of intracellular products from extreme halophiles and mammalian cells. However, most industrial bacterial strains are non-halotolerant and relatively resistant to hypoosmotic cell lysis. 

              

                Results

                To overcome this limitation, we developed two strategies to increase the susceptibility of non-halotolerant hosts to osmolysis using Cupriavidus necator, a strain often used in electromicrobial production, as a prototypical strain. In one strategy, C. necator was evolved to increase its halotolerance from 1.5% to 3.25% (w/v) NaCl through adaptive laboratory evolution, and genes potentially responsible for this phenotypic change were identified by whole genome sequencing. The evolved halotolerant strain experienced an osmolytic efficiency of 47% in distilled water following growth in 3% (w/v) NaCl. In a second strategy, the cells were made susceptible to osmolysis by knocking out the large-conductance mechanosensitive channel (mscL) gene in C. necator. When these strategies were combined by knocking out the mscL gene from the evolved halotolerant strain, greater than 90% osmolytic efficiency was observed upon osmotic downshock. A modified version of this strategy was applied to E. coli BL21 by deleting the mscL and mscS (small-conductance mechanosensitive channel) genes. When grown in medium with 4% NaCl and subsequently resuspended in distilled water, this engineered strain experienced 75% cell lysis, although decreases in cell growth rate due to higher salt concentrations were observed.

              

                Conclusions

                Our strategy is shown to be a simple and effective way to lyse cells for the purification of intracellular biomacromolecules and may be applicable in many bacteria used for bioproduction.

              },

keywords = {Applied Microbiology and Biotechnology, Bioengineering, Biotechnology, cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Averesch2023,

title = {Microbial biomanufacturing for space-exploration—what to take and when to make},

author = {Nils J. H. Averesch and Aaron J. Berliner and Shannon N. Nangle and Spencer Zezulka and Gretchen L. Vengerova and Davian Ho and Cameran A. Casale and Benjamin A. E. Lehner and Jessica E. Snyder and Kevin B. Clark and Lewis R. Dartnell and Craig S. Criddle and Adam P. Arkin},

doi = {10.1038/s41467-023-37910-1},

issn = {2041-1723},

year  = {2023},

date = {2023-12-00},

journal = {Nat Commun},

volume = {14},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractAs renewed interest in human space-exploration intensifies, a coherent and modernized strategy for mission design and planning has become increasingly crucial. Biotechnology has emerged as a promising approach to increase resilience, flexibility, and efficiency of missions, by virtue of its ability to effectively utilize in situ resources and reclaim resources from waste streams. Here we outline four primary mission-classes on Moon and Mars that drive a staged and accretive biomanufacturing strategy. Each class requires a unique approach to integrate biomanufacturing into the existing mission-architecture and so faces unique challenges in technology development. These challenges stem directly from the resources available in a given mission-class—the degree to which feedstocks are derived from cargo and in situ resources—and the degree to which loop-closure is necessary. As mission duration and distance from Earth increase, the benefits of specialized, sustainable biomanufacturing processes also increase. Consequentially, we define specific design-scenarios and quantify the usefulness of in-space biomanufacturing, to guide techno-economics of space-missions. Especially materials emerged as a potentially pivotal target for biomanufacturing with large impact on up-mass cost. Subsequently, we outline the processes needed for development, testing, and deployment of requisite technologies. As space-related technology development often does, these advancements are likely to have profound implications for the creation of a resilient circular bioeconomy on Earth.},

keywords = {cubes, General Biochemistry, General Chemistry, General Physics and Astronomy, Genetics and Molecular Biology, Multidisciplinary},

pubstate = {published},

tppubtype = {article}

}

@article{Makrygiorgos2023,

title = {Data‐driven flow‐map models for data‐efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems},

author = {Georgios Makrygiorgos and Aaron J. Berliner and Fengzhe Shi and Douglas S. Clark and Adam P. Arkin and Ali Mesbah},

doi = {10.1002/bit.28295},

issn = {1097-0290},

year  = {2023},

date = {2023-03-00},

journal = {Biotech & Bioengineering},

volume = {120},

number = {3},

pages = {803--818},

publisher = {Wiley},

abstract = {AbstractComputational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow‐map (de)compositions to present a framework that can address both of these challenges via learning data‐driven models useful for capturing the dynamical behavior of biochemical systems. Data‐driven flow‐map models seek to directly learn the integration operators of the governing differential equations in a black‐box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast‐to‐evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long‐term system dynamics via experimental observations. We present a data‐efficient approach to data‐driven flow‐map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data‐driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.},

keywords = {Applied Microbiology and Biotechnology, Bioengineering, Biotechnology, cubes},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems},

author = {Makrygiorgos, G.

Berliner, A. J.

Shi, F.

Clark, D. S.

Arkin, A. P.

Mesbah, A.},

doi = {10.1002/bit.28295},

issn = {1097-0290 },

year  = {2022},

date = {2022-12-02},

urldate = {2022-12-02},

journal = {Biotechnol Bioeng},

volume = {120},

issue = {3},

pages = {803-818},

abstract = {Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.},

keywords = {cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Berliner2021,

title = {Extension of Equivalent System Mass for Human Exploration Missions on Mars},

author = {Aaron Berliner and George Makrygiorgos and Avery Hill},

doi = {doi: 10.20944/preprints202101.0363.v1},

year  = {2021},

date = {2021-01-01},

journal = {preprints.org},

publisher = {Preprints},

keywords = {cubes},

pubstate = {published},

tppubtype = {article}

}

@article{McNulty2020,

title = {Molecular Pharming to Support Human Life on the Moon, Mars, and Beyond},

author = {Matthew J. McNulty , Yongao Xiong , Kevin Yates , Kalimuthu Karuppanan , Jacob M. Hilzinger , Aaron J. Berliner , Jesse Delzio , Adam P. Arkin , Nancy E. Lane , Somen Nandi , Karen A. McDonald },

doi = {10.20944/preprints202009.0086.v1},

year  = {2020},

date = {2020-09-03},

journal = {Preprints},

keywords = {cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Berliner2020,

title = {Towards a Biomanufactory on Mars},

author = {Aaron Berliner and Jacob M Hilzinger and Anthony J Abel and Matthew McNulty and George Makrygiorgos and Nils J H Averesch and Soumyajit Sen Gupta and Alexander Benvenuti and Daniel Caddell and Stefano Cestellos-Blanco},

doi = {doi: 10.20944/preprints202012.0714.v1},

year  = {2020},

date = {2020-01-01},

journal = {preprints.org},

publisher = {Preprints},

keywords = {cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Abel2020,

title = {Systems-informed genome mining for electroautotrophic microbial production},

author = {Anthony J Abel and Jacob M Hilzinger and Adam P Arkin and Douglas S Clark},

year  = {2020},

date = {2020-01-01},

journal = {bioRxiv},

publisher = {Cold Spring Harbor Laboratory},

keywords = {cubes},

pubstate = {published},

tppubtype = {article}

}