1
|
Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. NPJ Syst Biol Appl 2024; 10:35. [PMID: 38565850 PMCID: PMC10987498 DOI: 10.1038/s41540-024-00361-5] [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: 11/21/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.
Collapse
Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, Baltimore, Baltimore, MD, USA.
| |
Collapse
|
2
|
van Sluijs B, Zhou T, Helwig B, Baltussen MG, Nelissen FHT, Heus HA, Huck WTS. Iterative design of training data to control intricate enzymatic reaction networks. Nat Commun 2024; 15:1602. [PMID: 38383500 PMCID: PMC10881569 DOI: 10.1038/s41467-024-45886-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/06/2024] [Indexed: 02/23/2024] Open
Abstract
Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.
Collapse
Affiliation(s)
- Bob van Sluijs
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Tao Zhou
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands.
| | - Britta Helwig
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Mathieu G Baltussen
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Frank H T Nelissen
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Hans A Heus
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands
| | - Wilhelm T S Huck
- Institute for Molecules and Materials, Radboud University, Nijmegen, AJ, The Netherlands.
| |
Collapse
|
3
|
Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550573. [PMID: 37546866 PMCID: PMC10402059 DOI: 10.1101/2023.07.26.550573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.
Collapse
Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
| |
Collapse
|
4
|
A microfluidic optimal experimental design platform for forward design of cell-free genetic networks. Nat Commun 2022; 13:3626. [PMID: 35750678 PMCID: PMC9232554 DOI: 10.1038/s41467-022-31306-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 06/14/2022] [Indexed: 12/20/2022] Open
Abstract
Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks. Characterization of cell-free genetic networks is inherently difficult. Here the authors use optimal experimental design and microfluidics to improve characterization, demonstrating modularity and predictability of parts in applied test cases.
Collapse
|
5
|
Chakraborty D, Rengaswamy R, Raman K. Designing Biological Circuits: From Principles to Applications. ACS Synth Biol 2022; 11:1377-1388. [PMID: 35320676 DOI: 10.1021/acssynbio.1c00557] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.
Collapse
Affiliation(s)
- Debomita Chakraborty
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Raghunathan Rengaswamy
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| |
Collapse
|
6
|
Deneer A, Fleck C. Mathematical Modelling in Plant Synthetic Biology. Methods Mol Biol 2022; 2379:209-251. [PMID: 35188665 DOI: 10.1007/978-1-0716-1791-5_13] [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] [Indexed: 06/14/2023]
Abstract
Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.
Collapse
Affiliation(s)
- Anna Deneer
- Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Wageningen, The Netherlands
| | - Christian Fleck
- ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland.
- Freiburg Institute for Data Analysis and Mathematical Modelling, University of Freiburg, Freiburg im Breisgau, Germany.
| |
Collapse
|
7
|
Bowyer JE, Ding C, Weinberg BH, Wong WW, Bates DG. A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits. PLoS Comput Biol 2020; 16:e1007849. [PMID: 33338034 PMCID: PMC7781486 DOI: 10.1371/journal.pcbi.1007849] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 01/04/2021] [Accepted: 11/03/2020] [Indexed: 11/26/2022] Open
Abstract
Boolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision. After calibrating the model on experimental data from two circuits, we demonstrate close agreement between model outputs and data on the other 111 circuits that have so far been experimentally constructed using the 2-input BLADE platform. Model simulations of the remaining 143 circuits that have yet to be tested experimentally predict excellent performance of the 2-input BLADE platform across the range of possible circuits. Circuits from both the tested and untested subsets that perform less well consist of a disproportionally high number of STOP sequences. Model predictions suggested that circuit performance declines with a decrease in recombinase expression and new experimental data was generated that confirms this relationship.
Collapse
Affiliation(s)
- Jack E. Bowyer
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry, United Kingdom
| | - Chloe Ding
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Benjamin H. Weinberg
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Wilson W. Wong
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Declan G. Bates
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
8
|
Volk MJ, Lourentzou I, Mishra S, Vo LT, Zhai C, Zhao H. Biosystems Design by Machine Learning. ACS Synth Biol 2020; 9:1514-1533. [PMID: 32485108 DOI: 10.1021/acssynbio.0c00129] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
Collapse
|
9
|
Bowyer JE, Chakravarti D, Wong WW, Bates DG. Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit. ENGINEERING BIOLOGY 2020; 4:10-19. [PMID: 36970230 PMCID: PMC9996713 DOI: 10.1049/enb.2019.0020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/30/2020] [Accepted: 02/10/2020] [Indexed: 12/30/2022] Open
Abstract
Inducible genetic switches based on tyrosine recombinase-based DNA excision are a promising platform for the regulation and control of chimeric antigen receptor (CAR) T cell activity in cancer immunotherapy. These switches exploit the increased stability of DNA excision in tyrosine recombinases through an inversion-excision circuit design. Here, the authors develop the first mechanistic mathematical model of switching dynamics in tyrosine recombinases and validate it against experimental data through both global optimisation and statistical approximation approaches. Analysis of this model provides guidelines regarding which system parameters are best suited to experimental tuning in order to establish optimal switch performance in vivo. In particular, they find that the switching response can be made significantly faster by increasing the concentration of the inducer drug 4-OHT and/or by using promoters generating higher expression levels of the FlpO recombinase.
Collapse
Affiliation(s)
- Jack E. Bowyer
- School of Engineering University of Warwick Coventry CV4 7AL UK
- Warwick Integrative Synthetic Biology Centre Coventry CV4 7AL UK
| | - Deboki Chakravarti
- Department of Biomedical Engineering Boston University Boston MA 02215 USA
- Biological Design Center Boston University Boston MA 02215 USA
| | - Wilson W. Wong
- Department of Biomedical Engineering Boston University Boston MA 02215 USA
- Biological Design Center Boston University Boston MA 02215 USA
| | - Declan G. Bates
- School of Engineering University of Warwick Coventry CV4 7AL UK
- Warwick Integrative Synthetic Biology Centre Coventry CV4 7AL UK
| |
Collapse
|
10
|
On the importance of reaction networks for synthetic living systems. Emerg Top Life Sci 2019; 3:517-527. [DOI: 10.1042/etls20190016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/14/2019] [Accepted: 08/15/2019] [Indexed: 12/11/2022]
Abstract
The goal of creating a synthetic cell necessitates the development of reaction networks which will underlie all of its behaviours. Recent developments in in vitro systems, based upon both DNA and enzymes, have created networks capable of a range of behaviours e.g. information processing, adaptation and diffusive signalling. These networks are based upon reaction motifs that when combined together produce more complex behaviour. We highlight why it is inevitable that networks, based on enzymes or enzyme-like catalysts, will be required for the construction of a synthetic cell. We outline several of the challenges, including (a) timing, (b) regulation and (c) energy distribution, that must be overcome in order to transition from the simple networks we have today to much more complex networks capable of a variety of behaviours and which could find application one day within a synthetic cell.
Collapse
|
11
|
Hiscock TW. Adapting machine-learning algorithms to design gene circuits. BMC Bioinformatics 2019; 20:214. [PMID: 31029103 PMCID: PMC6487017 DOI: 10.1186/s12859-019-2788-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 04/02/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Gene circuits are important in many aspects of biology, and perform a wide variety of different functions. For example, some circuits oscillate (e.g. the cell cycle), some are bistable (e.g. as cells differentiate), some respond sharply to environmental signals (e.g. ultrasensitivity), and some pattern multicellular tissues (e.g. Turing's model). Often, one starts from a given circuit, and using simulations, asks what functions it can perform. Here we want to do the opposite: starting from a prescribed function, can we find a circuit that executes this function? Whilst simple in principle, this task is challenging from a computational perspective, since gene circuit models are complex systems with many parameters. In this work, we adapted machine-learning algorithms to significantly accelerate gene circuit discovery. RESULTS We use gradient-descent optimization algorithms from machine learning to rapidly screen and design gene circuits. With this approach, we found that we could rapidly design circuits capable of executing a range of different functions, including those that: (1) recapitulate important in vivo phenomena, such as oscillators, and (2) perform complex tasks for synthetic biology, such as counting noisy biological events. CONCLUSIONS Our computational pipeline will facilitate the systematic study of natural circuits in a range of contexts, and allow the automatic design of circuits for synthetic biology. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.
Collapse
Affiliation(s)
- Tom W Hiscock
- Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
- Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK.
| |
Collapse
|
12
|
Yelleswarapu M, van der Linden AJ, van Sluijs B, Pieters PA, Dubuc E, de Greef TFA, Huck WTS. Sigma Factor-Mediated Tuning of Bacterial Cell-Free Synthetic Genetic Oscillators. ACS Synth Biol 2018; 7:2879-2887. [PMID: 30408412 PMCID: PMC6305555 DOI: 10.1021/acssynbio.8b00300] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
![]()
Cell-free
transcription–translation provides a simplified
prototyping environment to rapidly design and study synthetic networks.
Despite the presence of a well characterized toolbox of genetic elements,
examples of genetic networks that exhibit complex temporal behavior
are scarce. Here, we present a genetic oscillator implemented in an E. coli-based cell-free system under steady-state conditions
using microfluidic flow reactors. The oscillator has an activator–repressor
motif that utilizes the native transcriptional machinery of E. coli: the RNAP and its associated sigma factors.
We optimized a kinetic model with experimental data using an evolutionary
algorithm to quantify the key regulatory model parameters. The functional
modulation of the RNAP was investigated by coupling two oscillators
driven by competing sigma factors, allowing the modification of network
properties by means of passive transcriptional regulation.
Collapse
Affiliation(s)
- Maaruthy Yelleswarapu
- Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Ardjan J. van der Linden
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Computational Biology Group, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Bob van Sluijs
- Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Pascal A. Pieters
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Computational Biology Group, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Emilien Dubuc
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Computational Biology Group, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Tom F. A. de Greef
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Computational Biology Group, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Wilhelm T. S. Huck
- Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| |
Collapse
|
13
|
Helwig B, van Sluijs B, Pogodaev AA, Postma SGJ, Huck WTS. Bottom-Up Construction of an Adaptive Enzymatic Reaction Network. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201806944] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Britta Helwig
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Bob van Sluijs
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Aleksandr A. Pogodaev
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Sjoerd G. J. Postma
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Wilhelm T. S. Huck
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| |
Collapse
|
14
|
Helwig B, van Sluijs B, Pogodaev AA, Postma SGJ, Huck WTS. Bottom-Up Construction of an Adaptive Enzymatic Reaction Network. Angew Chem Int Ed Engl 2018; 57:14065-14069. [DOI: 10.1002/anie.201806944] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 08/13/2018] [Indexed: 01/23/2023]
Affiliation(s)
- Britta Helwig
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Bob van Sluijs
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Aleksandr A. Pogodaev
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Sjoerd G. J. Postma
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| | - Wilhelm T. S. Huck
- Radboud University; Institute for Molecules and Materials; Heyendaalseweg 135 6525 AJ Nijmegen The Netherlands
| |
Collapse
|