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Yurchenko A, Özkul G, van Riel NAW, van Hest JCM, de Greef TFA. Mechanism-based and data-driven modeling in cell-free synthetic biology. Chem Commun (Camb) 2024; 60:6466-6475. [PMID: 38847387 DOI: 10.1039/d4cc01289e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.
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Affiliation(s)
- Angelina Yurchenko
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Gökçe Özkul
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Natal A W van Riel
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Eindhoven MedTech Innovation Center, 5612 AX Eindhoven, The Netherlands
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jan C M van Hest
- Bio-Organic Chemistry, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
- Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
- Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, 3584 CB Utrecht, The Netherlands
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Rolf J, Handke J, Burzinski F, Lütz S, Rosenthal K. Amino acid balancing for the prediction and evaluation of protein concentrations in cell-free protein synthesis systems. Biotechnol Prog 2023; 39:e3373. [PMID: 37408088 DOI: 10.1002/btpr.3373] [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: 03/12/2023] [Revised: 05/31/2023] [Accepted: 06/19/2023] [Indexed: 07/07/2023]
Abstract
Cell-free protein synthesis (CFPS) systems are an attractive method to complement the usual cell-based synthesis of proteins, especially for screening approaches. The literature describes a wide variety of CFPS systems, but their performance is difficult to compare since the reaction components are often used at different concentrations. Therefore, we have developed a calculation tool based on amino acid balancing to evaluate the performance of CFPS by determining the fractional yield as the ratio between theoretically achievable and experimentally achieved protein molar concentration. This tool was applied to a series of experiments from our lab and to various systems described in the literature to identify systems that synthesize proteins very efficiently and those that still have potential for higher yields. The well-established Escherichia coli system showed a high efficiency in the utilization of amino acids, but interestingly, less considered systems, such as those based on Vibrio natriegens or Leishmania tarentolae, also showed exceptional fractional yields of over 70% and 90%, respectively, implying very efficient conversions of amino acids. The methods and tools described here can quickly identify when a system has reached its maximum or has limitations. We believe that this approach will facilitate the evaluation and optimization of existing CFPS systems and provides the basis for the systematic development of new CFPS systems.
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Affiliation(s)
- Jascha Rolf
- Department of Biochemical and Chemical Engineering, Chair for Bioprocess Engineering, TU Dortmund University, Dortmund, Germany
| | - Julian Handke
- Department of Biochemical and Chemical Engineering, Chair for Bioprocess Engineering, TU Dortmund University, Dortmund, Germany
| | - Frank Burzinski
- Department of Biochemical and Chemical Engineering, Chair for Bioprocess Engineering, TU Dortmund University, Dortmund, Germany
| | - Stephan Lütz
- Department of Biochemical and Chemical Engineering, Chair for Bioprocess Engineering, TU Dortmund University, Dortmund, Germany
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Stano P. Chemical Systems for Wetware Artificial Life: Selected Perspectives in Synthetic Cell Research. Int J Mol Sci 2023; 24:14138. [PMID: 37762444 PMCID: PMC10532297 DOI: 10.3390/ijms241814138] [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: 08/11/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
The recent and important advances in bottom-up synthetic biology (SB), in particular in the field of the so-called "synthetic cells" (SCs) (or "artificial cells", or "protocells"), lead us to consider the role of wetware technologies in the "Sciences of Artificial", where they constitute the third pillar, alongside the more well-known pillars hardware (robotics) and software (Artificial Intelligence, AI). In this article, it will be highlighted how wetware approaches can help to model life and cognition from a unique perspective, complementary to robotics and AI. It is suggested that, through SB, it is possible to explore novel forms of bio-inspired technologies and systems, in particular chemical AI. Furthermore, attention is paid to the concept of semantic information and its quantification, following the strategy recently introduced by Kolchinsky and Wolpert. Semantic information, in turn, is linked to the processes of generation of "meaning", interpreted here through the lens of autonomy and cognition in artificial systems, emphasizing its role in chemical ones.
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Affiliation(s)
- Pasquale Stano
- Department of Biological and Environmental Sciences and Technologies (DiSTeBA), University of Salento, 73100 Lecce, Italy
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Müller J, Siemann-Herzberg M, Takors R. Modeling Cell-Free Protein Synthesis Systems-Approaches and Applications. Front Bioeng Biotechnol 2020; 8:584178. [PMID: 33195146 PMCID: PMC7655533 DOI: 10.3389/fbioe.2020.584178] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 09/29/2020] [Indexed: 01/03/2023] Open
Abstract
In vitro systems are ideal setups to investigate the basic principles of biochemical reactions and subsequently the bricks of life. Cell-free protein synthesis (CFPS) systems mimic the transcription and translation processes of whole cells in a controlled environment and allow the detailed study of single components and reaction networks. In silico studies of CFPS systems help us to understand interactions and to identify limitations and bottlenecks in those systems. Black-box models laid the foundation for understanding the production and degradation dynamics of macromolecule components such as mRNA, ribosomes, and proteins. Subsequently, more sophisticated models revealed shortages in steps such as translation initiation and tRNA supply and helped to partially overcome these limitations. Currently, the scope of CFPS modeling has broadened to various applications, ranging from the screening of kinetic parameters to the stochastic analysis of liposome-encapsulated CFPS systems and the assessment of energy supply properties in combination with flux balance analysis (FBA).
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Affiliation(s)
| | | | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany
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Laohakunakorn N. Cell-Free Systems: A Proving Ground for Rational Biodesign. Front Bioeng Biotechnol 2020; 8:788. [PMID: 32793570 PMCID: PMC7393481 DOI: 10.3389/fbioe.2020.00788] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/22/2020] [Indexed: 11/13/2022] Open
Abstract
Cell-free gene expression systems present an alternative approach to synthetic biology, where biological gene expression is harnessed inside non-living, in vitro biochemical reactions. Taking advantage of a plethora of recent experimental innovations, they easily overcome certain challenges for computer-aided biological design. For instance, their open nature renders all their components directly accessible, greatly facilitating model construction and validation. At the same time, these systems present their own unique difficulties, such as limited reaction lifetimes and lack of homeostasis. In this Perspective, I propose that cell-free systems are an ideal proving ground to test rational biodesign strategies, as demonstrated by a small but growing number of examples of model-guided, forward engineered cell-free biosystems. It is likely that advances gained from this approach will contribute to our efforts to more reliably and systematically engineer both cell-free as well as living cellular systems for useful applications.
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Affiliation(s)
- Nadanai Laohakunakorn
- School of Biological Sciences, Institute of Quantitative Biology, Biochemistry, and Biotechnology, University of Edinburgh, Edinburgh, United Kingdom
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Laohakunakorn N, Grasemann L, Lavickova B, Michielin G, Shahein A, Swank Z, Maerkl SJ. Bottom-Up Construction of Complex Biomolecular Systems With Cell-Free Synthetic Biology. Front Bioeng Biotechnol 2020; 8:213. [PMID: 32266240 PMCID: PMC7105575 DOI: 10.3389/fbioe.2020.00213] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 03/03/2020] [Indexed: 12/16/2022] Open
Abstract
Cell-free systems offer a promising approach to engineer biology since their open nature allows for well-controlled and characterized reaction conditions. In this review, we discuss the history and recent developments in engineering recombinant and crude extract systems, as well as breakthroughs in enabling technologies, that have facilitated increased throughput, compartmentalization, and spatial control of cell-free protein synthesis reactions. Combined with a deeper understanding of the cell-free systems themselves, these advances improve our ability to address a range of scientific questions. By mastering control of the cell-free platform, we will be in a position to construct increasingly complex biomolecular systems, and approach natural biological complexity in a bottom-up manner.
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Affiliation(s)
- Nadanai Laohakunakorn
- School of Biological Sciences, Institute of Quantitative Biology, Biochemistry, and Biotechnology, University of Edinburgh, Edinburgh, United Kingdom
| | - Laura Grasemann
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Barbora Lavickova
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Grégoire Michielin
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Amir Shahein
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Zoe Swank
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Sebastian J. Maerkl
- School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method. INFORMATION 2019. [DOI: 10.3390/info10030100] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Statistical bivariate numerical modeling is a method to infer an empirical relationship between unpaired sets of data based on statistical distributions matching. In the present paper, a novel efficient numerical algorithm is proposed to perform bivariate numerical modeling. The algorithm is then applied to correlate glomerular filtration rate to serum creatinine concentration. Glomerular filtration rate is adopted in clinical nephrology as an indicator of kidney function and is relevant for assessing progression of renal disease. As direct measurement of glomerular filtration rate is highly impractical, there is considerable interest in developing numerical algorithms to estimate glomerular filtration rate from parameters which are easier to obtain, such as demographic and `bedside’ assays data.
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Statistical Modeling of Trivariate Static Systems: Isotonic Models. DATA 2019. [DOI: 10.3390/data4010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This paper presents an improved version of a statistical trivariate modeling algorithm introduced in a short Letter by the first author. This paper recalls the fundamental concepts behind the proposed algorithm, evidences its criticalities and illustrates a number of improvements which lead to a functioning modeling algorithm. The present paper also illustrates the features of the improved statistical modeling algorithm through a comprehensive set of numerical experiments performed on four synthetic and five natural datasets. The obtained results confirm that the proposed algorithm is able to model the considered synthetic and the natural datasets faithfully.
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