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Padmakumar JP, Sun JJ, Cho W, Zhou Y, Krenz C, Han WZ, Densmore D, Sontag ED, Voigt CA. Partitioning of a 2-bit hash function across 66 communicating cells. Nat Chem Biol 2024:10.1038/s41589-024-01730-1. [PMID: 39317847 DOI: 10.1038/s41589-024-01730-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 08/14/2024] [Indexed: 09/26/2024]
Abstract
Powerful distributed computing can be achieved by communicating cells that individually perform simple operations. Here, we report design software to divide a large genetic circuit across cells as well as the genetic parts to implement the subcircuits in their genomes. These tools were demonstrated using a 2-bit version of the MD5 hashing algorithm, which is an early predecessor to the cryptographic functions underlying cryptocurrency. One iteration requires 110 logic gates, which were partitioned across 66 Escherichia coli strains, requiring the introduction of a total of 1.1 Mb of recombinant DNA into their genomes. The strains were individually experimentally verified to integrate their assigned input signals, process this information correctly and propagate the result to the cell in the next layer. This work demonstrates the potential to obtain programable control of multicellular biological processes.
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Affiliation(s)
- Jai P Padmakumar
- MIT Microbiology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jessica J Sun
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - William Cho
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Yangruirui Zhou
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Christopher Krenz
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Woo Zhong Han
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Douglas Densmore
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Eduardo D Sontag
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Christopher A Voigt
- MIT Microbiology Program, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Mao J, Zhang H, Chen Y, Wei L, Liu J, Nielsen J, Chen Y, Xu N. Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms. Biotechnol Adv 2024; 74:108401. [PMID: 38944217 DOI: 10.1016/j.biotechadv.2024.108401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 07/01/2024]
Abstract
Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.
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Affiliation(s)
- Jiwei Mao
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Hongyu Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, PR China
| | - Liang Wei
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jun Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen, Denmark.
| | - Yun Chen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kongens Lyngby, Denmark.
| | - Ning Xu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China.
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Shin J, Zielinski DC, Palsson BO. Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules. Nucleic Acids Res 2024:gkae742. [PMID: 39193902 DOI: 10.1093/nar/gkae742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 07/29/2024] [Accepted: 08/19/2024] [Indexed: 08/29/2024] Open
Abstract
Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.
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Affiliation(s)
- Jongoh Shin
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel C Zielinski
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
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Stone A, Youssef A, Rijal S, Zhang R, Tian XJ. Context-dependent redesign of robust synthetic gene circuits. Trends Biotechnol 2024; 42:895-909. [PMID: 38320912 PMCID: PMC11223972 DOI: 10.1016/j.tibtech.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/08/2024]
Abstract
Cells provide dynamic platforms for executing exogenous genetic programs in synthetic biology, resulting in highly context-dependent circuit performance. Recent years have seen an increasing interest in understanding the intricacies of circuit-host relationships, their influence on the synthetic bioengineering workflow, and in devising strategies to alleviate undesired effects. We provide an overview of how emerging circuit-host interactions, such as growth feedback and resource competition, impact both deterministic and stochastic circuit behaviors. We also emphasize control strategies for mitigating these unwanted effects. This review summarizes the latest advances and the current state of host-aware and resource-aware design of synthetic gene circuits.
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Affiliation(s)
- Austin Stone
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Abdelrahaman Youssef
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Sadikshya Rijal
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Rong Zhang
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Xiao-Jun Tian
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA.
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Baig Y, Ma HR, Xu H, You L. Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics. Nat Commun 2023; 14:7937. [PMID: 38049401 PMCID: PMC10696002 DOI: 10.1038/s41467-023-43455-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 11/09/2023] [Indexed: 12/06/2023] Open
Abstract
The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.
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Affiliation(s)
- Yasa Baig
- Department of Physics, Duke University, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Helena R Ma
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA
| | - Helen Xu
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA.
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
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