1
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Ruoff P. Background compensation revisited: Conserved phase response curves in frequency controlled homeostats with coherent feedback. PLoS One 2024; 19:e0305804. [PMID: 39231133 PMCID: PMC11373829 DOI: 10.1371/journal.pone.0305804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 08/16/2024] [Indexed: 09/06/2024] Open
Abstract
Background compensation is the ability of a controlled variable to respond to an applied perturbation in an unchanged manner and independent of different but constant background signals which act in parallel to the perturbation. Background compensation occurs by 'coherent feedback' mechanisms where additional control variables feed directly back to the controlled variable. This paper extends a previous study on background compensation to include phase responses in frequency controlled coherent feedback oscillators. While the frequency resetting amplitude in coherent feedback oscillators is found to be dependent on the inflow/outflow perturbation of the controlled variable and thereby become phase dependent, the frequency resetting itself and the corresponding phase response curves are found to be background compensated. It is speculated that this type of background compensation may be an additional way how ambient noise can be 'ignored' by organisms.
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Affiliation(s)
- Peter Ruoff
- Department of Chemistry, Bioscience, and Environmental Engineering, University of Stavanger, Stavanger, Norway
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2
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Filo M, Gupta A, Khammash M. Anti-windup strategies for biomolecular control systems facilitated by model reduction theory for sequestration networks. SCIENCE ADVANCES 2024; 10:eadl5439. [PMID: 39167660 PMCID: PMC11338268 DOI: 10.1126/sciadv.adl5439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 07/11/2024] [Indexed: 08/23/2024]
Abstract
Robust perfect adaptation, a system property whereby a variable adapts to persistent perturbations at steady state, has been recently realized in living cells using genetic integral controllers. In certain scenarios, such controllers may lead to "integral windup," an adverse condition caused by saturating control elements, which manifests as error accumulation, poor dynamic performance, or instabilities. To mitigate this effect, we here introduce several biomolecular anti-windup topologies and link them to control-theoretic anti-windup strategies. This is achieved using a novel model reduction theory that we develop for reaction networks with fast sequestration reactions. We then show how the anti-windup topologies can be realized as reaction networks and propose intein-based genetic designs for their implementation. We validate our designs through simulations on various biological systems, including models of patients with type I diabetes and advanced biomolecular proportional-integral-derivative (PID) controllers, demonstrating their efficacy in mitigating windup effects and ensuring safety.
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3
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Liao C, Priyanka P, Lai YH, Rao CV, Lu T. How Does Escherichia coli Allocate Proteome? ACS Synth Biol 2024. [PMID: 39120961 DOI: 10.1021/acssynbio.3c00537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2024]
Abstract
Microorganisms are shown to actively partition their intracellular resources, such as proteins, for growth optimization. Recent experiments have begun to reveal molecular components unpinning the partition; however, quantitatively, it remains unclear how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here, we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that the cellular ability of resource partition lies in an ultrasensitive, negative feedback-controlling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism that mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations and is also efficient for biomass production over time. This work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.
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Affiliation(s)
- Chen Liao
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, United States
| | - Priyanka Priyanka
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Yi-Hui Lai
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Christopher V Rao
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Ting Lu
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
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4
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Sun J, Yang R, Li Q, Zhu R, Jiang Y, Zang L, Zhang Z, Tong W, Zhao H, Li T, Li H, Qi D, Li G, Chen X, Dai Z, Liu Z. Living Synthelectronics: A New Era for Bioelectronics Powered by Synthetic Biology. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2400110. [PMID: 38494761 DOI: 10.1002/adma.202400110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/23/2024] [Indexed: 03/19/2024]
Abstract
Bioelectronics, which converges biology and electronics, has attracted great attention due to their vital applications in human-machine interfaces. While traditional bioelectronic devices utilize nonliving organic and/or inorganic materials to achieve flexibility and stretchability, a biological mismatch is often encountered because human tissues are characterized not only by softness and stretchability but also by biodynamic and adaptive properties. Recently, a notable paradigm shift has emerged in bioelectronics, where living cells, and even viruses, modified via gene editing within synthetic biology, are used as core components in a new hybrid electronics paradigm. These devices are defined as "living synthelectronics," and they offer enhanced potential for interfacing with human tissues at informational and substance exchange levels. In this Perspective, the recent advances in living synthelectronics are summarized. First, opportunities brought to electronics by synthetic biology are briefly introduced. Then, strategic approaches to designing and making electronic devices using living cells/viruses as the building blocks, sensing components, or power sources are reviewed. Finally, the challenges faced by living synthelectronics are raised. It is believed that this paradigm shift will significantly contribute to the real integration of bioelectronics with human tissues.
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Affiliation(s)
- Jing Sun
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, P. R. China
| | - Ruofan Yang
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qingsong Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Runtao Zhu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ying Jiang
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Lei Zang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhibo Zhang
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wei Tong
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Hang Zhao
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Tengfei Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Hanfei Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Dianpeng Qi
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, P. R. China
| | - Guanglin Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xiaodong Chen
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhuojun Dai
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhiyuan Liu
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Standard Robots Co.,Ltd,Room 405, Building D, Huafeng International Robot Fusen Industrial Park, Hangcheng Avenue, Guxing Community, Xixiang Street, Baoan District, Shenzhen, 518055, China
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5
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Browning AP, Jenner AL, Baker RE, Maini PK. Smoothing in linear multicompartment biological processes subject to stochastic input. Phys Rev E 2024; 109:054405. [PMID: 38907461 DOI: 10.1103/physreve.109.054405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 04/01/2024] [Indexed: 06/24/2024]
Abstract
Many physical and biological systems rely on the progression of material through multiple independent stages. In viral replication, for example, virions enter a cell to undergo a complex process comprising several disparate stages before the eventual accumulation and release of replicated virions. While such systems may have some control over the internal dynamics that make up this progression, a challenge for many is to regulate behavior under what are often highly variable external environments acting as system inputs. In this work, we study a simple analog of this problem through a linear multicompartment model subject to a stochastic input in the form of a mean-reverting Ornstein-Uhlenbeck process, a type of Gaussian process. By expressing the system as a multidimensional Gaussian process, we derive several closed-form analytical results relating to the covariances and autocorrelations of the system, quantifying the smoothing effect discrete compartments afford multicompartment systems. Semianalytical results demonstrate that feedback and feedforward loops can enhance system robustness, and simulation results probe the intractable problem of the first passage time distribution, which has specific relevance to eventual cell lysis in the viral replication cycle. Finally, we demonstrate that the smoothing seen in the process is a consequence of the discreteness of the system, and does not manifest in systems with continuous transport. While we make progress through analysis of a simple linear problem, many of our insights are applicable more generally, and our work enables future analysis into multicompartment processes subject to stochastic inputs.
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Affiliation(s)
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane 4000, Australia
| | - Ruth E Baker
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Philip K Maini
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
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6
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Andrews SS, Kochen M, Smith L, Feng S, Wiley HS, Sauro HM. Signal integration and integral feedback control with biochemical reaction networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591337. [PMID: 38746178 PMCID: PMC11092504 DOI: 10.1101/2024.04.26.591337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Biochemical reaction networks perform a variety of signal processing functions, one of which is computing the integrals of signal values. This is often used in integral feedback control, where it enables a system's output to respond to changing inputs, but to then return exactly back to some pre-determined setpoint value afterward. To gain a deeper understanding of how biochemical networks are able to both integrate signals and perform integral feedback control, we investigated these abilities for several simple reaction networks. We found imperfect overlap between these categories, with some networks able to perform both tasks, some able to perform integration but not integral feedback control, and some the other way around. Nevertheless, networks that could either integrate or perform integral feedback control shared key elements. In particular, they included a chemical species that was neutrally stable in the open loop system (no feedback), meaning that this species does not have a unique stable steady-state concentration. Neutral stability could arise from zeroth order decay reactions, binding to a partner that was produced at a constant rate (which occurs in antithetic control), or through a long chain of covalent cycles. Mathematically, it arose from rate equations for the reaction network that were underdetermined when evaluated at steady-state.
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7
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Andrews SS, Wiley HS, Sauro HM. Design patterns of biological cells. Bioessays 2024; 46:e2300188. [PMID: 38247191 PMCID: PMC10922931 DOI: 10.1002/bies.202300188] [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: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.
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Affiliation(s)
- Steven S. Andrews
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - H. Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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8
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Öztürk D, Atay FM, Özbay H. Chaos in gene regulatory networks: Effects of time delays and interaction structure. CHAOS (WOODBURY, N.Y.) 2024; 34:033102. [PMID: 38427936 DOI: 10.1063/5.0172767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/07/2024] [Indexed: 03/03/2024]
Abstract
In biological system models, gene expression levels are typically described by regulatory feedback mechanisms. Many studies of gene network models focus on dynamical interactions between components, but often overlook time delays. Here we present an extended model for gene regulatory networks with time delayed negative feedback, which is described by delay differential equations. We analyze nonlinear properties of the model in terms of chaos and compare the conditions with the benchmark homogeneous gene regulatory network model. Chaotic dynamics depend strongly on the inclusion of time delays, but the minimum motifs that show chaos differ when both original and extended models are considered. Our results suggest that, for a particular higher order extension of the gene network, it is possible to observe chaotic dynamics in a two-gene system without adding any self-inhibition. This finding can be explained as a result of modification of the original benchmark model induced by previously unmodeled dynamics. We argue that the inclusion of additional parameters in regulatory gene circuit models substantially enhances the likelihood of observing non-periodic dynamics.
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Affiliation(s)
- Dilan Öztürk
- Department of Electrical and Electronics Engineering, Bilkent University, 06800 Ankara, Turkey
- Control Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Fatihcan M Atay
- Department of Mathematics, Bilkent University, 06800 Ankara, Turkey
| | - Hitay Özbay
- Department of Electrical and Electronics Engineering, Bilkent University, 06800 Ankara, Turkey
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9
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Trofimova E, Logel DY, Jaschke PR. An Improved Method for Eliminating or Creating Intragenic Bacterial Promoters. Methods Mol Biol 2024; 2760:199-207. [PMID: 38468090 DOI: 10.1007/978-1-0716-3658-9_12] [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: 03/13/2024]
Abstract
Recent advances in genomic refactoring have been hindered by the ever-present complication of internal or cryptic transcriptional regulation. Typical approaches to these features have been to randomize or perform mass alterations to the gene sequences thought to contain the regulatory motifs; however, this approach can cause problems by altering translational speeds, introducing long distance DNA-DNA interaction effects, and inducing RNA toxicity. Previously, we developed a rational design approach named COdon Restrained Promoter SilEncing (CORPSE) which takes externally identified promoter sequences and uses position-specific scoring matrices as proxy promoter strengths to make minimal changes to promoter sequences to disable their activity. Additionally, through inverting our system we were also able to modify weak internal promoters to increase their activity. In this chapter, we augment our previous process with the biophysical model Promoter Calculator v1.0 developed by LaFleur et al. to combine promoter identification and activity prediction, with our algorithm to silently modify promoter sequences, to provide more robust promoter elimination and creation.
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Affiliation(s)
- Ellina Trofimova
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW, Australia
| | - Dominic Y Logel
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW, Australia
| | - Paul R Jaschke
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia.
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW, Australia.
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10
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Chew YH, Marucci L. Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology. Methods Mol Biol 2024; 2774:71-84. [PMID: 38441759 DOI: 10.1007/978-1-0716-3718-0_6] [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: 03/07/2024]
Abstract
Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.
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Affiliation(s)
- Yin Hoon Chew
- School of Mathematics, University of Birmingham, Birmingham, UK
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, UK.
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
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11
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Nazerian A, Nathe C, Hart JD, Sorrentino F. Synchronizing chaos using reservoir computing. CHAOS (WOODBURY, N.Y.) 2023; 33:103121. [PMID: 37832520 PMCID: PMC10576628 DOI: 10.1063/5.0161076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/14/2023] [Indexed: 10/15/2023]
Abstract
We attempt to achieve complete synchronization between a drive system unidirectionally coupled with a response system, under the assumption that limited knowledge on the states of the drive is available at the response. Machine-learning techniques have been previously implemented to estimate the states of a dynamical system from limited measurements. We consider situations in which knowledge of the non-measurable states of the drive system is needed in order for the response system to synchronize with the drive. We use a reservoir computer to estimate the non-measurable states of the drive system from its measured states and then employ these measured states to achieve complete synchronization of the response system with the drive.
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Affiliation(s)
- Amirhossein Nazerian
- Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Chad Nathe
- Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | | | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131, USA
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12
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Ohkubo T, Soma Y, Sakumura Y, Hanai T, Kunida K. A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch. Sci Rep 2023; 13:13608. [PMID: 37666852 PMCID: PMC10477343 DOI: 10.1038/s41598-023-40469-y] [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: 05/18/2023] [Accepted: 08/10/2023] [Indexed: 09/06/2023] Open
Abstract
Bioprocess optimization using mathematical models is prevalent, yet the discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a significant challenge. This study proposes a novel hybrid control system called the hybrid in silico/in-cell controller (HISICC) to address PMM by combining model-based optimization (in silico feedforward controller) with feedback controllers utilizing synthetic genetic circuits integrated into cells (in-cell feedback controller). We demonstrated the efficacy of HISICC using two engineered Escherichia coli strains, TA1415 and TA2445, previously developed for isopropanol (IPA) production. TA1415 contains a metabolic toggle switch (MTS) to manage the competition between cell growth and IPA production for intracellular acetyl-CoA by responding to external input of isopropyl β-D-1-thiogalactopyranoside (IPTG). TA2445, in addition to the MTS, has a genetic circuit that detects cell density to autonomously activate MTS. The combination of TA2445 with an in silico controller exemplifies HISICC implementation. We constructed mathematical models to optimize IPTG input values for both strains based on the two-compartment model and validated these models using experimental data of the IPA production process. Using these models, we evaluated the robustness of HISICC against PMM by comparing IPA yields with two strains in simulations assuming various magnitudes of PMM in cell growth rates. The results indicate that the in-cell feedback controller in TA2445 effectively compensates for PMM by modifying MTS activation timing. In conclusion, the HISICC system presents a promising solution to the PMM problem in bioprocess engineering, paving the way for more efficient and reliable optimization of microbial bioprocesses.
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Affiliation(s)
- Tomoki Ohkubo
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan
| | - Yuki Soma
- Laboratory for Synthetic Biology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, W5-729, 744, Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Yuichi Sakumura
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan
- Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan
| | - Taizo Hanai
- Laboratory for Synthetic Biology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, W5-729, 744, Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Katsuyuki Kunida
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 8916-5, Japan.
- School of Medicine, Fujita Health University, Toyoake, Aichi, 470-1192, Japan.
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13
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Nygård M, Ruoff P. Coherent feedback leads to robust background compensation in oscillatory and non-oscillatory homeostats. PLoS One 2023; 18:e0287083. [PMID: 37639439 PMCID: PMC10461855 DOI: 10.1371/journal.pone.0287083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 08/14/2023] [Indexed: 08/31/2023] Open
Abstract
When in a reaction kinetic integral controller a step perturbation is applied besides a constant background, the concentration of a controlled variable (described as A) will generally respond with decreased response amplitudes ΔA as backgrounds increase. The controller variable E will at the same time provide the necessary compensatory flux to move A back to its set-point. A typical example of decreased response amplitudes at increased backgrounds is found in retinal light adaptation. Due to remarks in the literature that retinal light adaptation would also involve a compensation of backgrounds we became interested in conditions how background compensation could occur. In this paper we describe novel findings how background influences can be robustly eliminated. When such a background compensation is active, oscillatory controllers will respond to a defined perturbation with always the same (damped or undamped) frequency profile, or in the non-oscillatory case, with the same response amplitude ΔA, irrespective of the background level. To achieve background compensation we found that two conditions need to apply: (i) an additional set of integral controllers (here described as I1 and I2) have to be employed to keep the manipulated variable E at a defined set-point, and (ii), I1 and I2 need to feed back to the A-E signaling axis directly through the controlled variable A. In analogy to a similar feedback applied in quantum control theory, we term these feedback conditions as 'coherent feedback'. When analyzing retinal light adaptations in more detail, we find no evidence of the presence of background compensation mechanisms. Although robust background compensation, as described theoretically here, appears to be an interesting regulatory property, relevant biological or biochemical examples still need to be identified.
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Affiliation(s)
- Melissa Nygård
- Department of Chemistry, Bioscience, and Environmental Engineering, University of Stavanger, Stavanger, Norway
| | - Peter Ruoff
- Department of Chemistry, Bioscience, and Environmental Engineering, University of Stavanger, Stavanger, Norway
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14
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Alexis E, Schulte CCM, Cardelli L, Papachristodoulou A. Regulation strategies for two-output biomolecular networks. J R Soc Interface 2023; 20:20230174. [PMID: 37528680 PMCID: PMC10394417 DOI: 10.1098/rsif.2023.0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 07/06/2023] [Indexed: 08/03/2023] Open
Abstract
Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.
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Affiliation(s)
- Emmanouil Alexis
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Carolin C. M. Schulte
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Luca Cardelli
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
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15
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Proverbio D, Skupin A, Gonçalves J. Systematic analysis and optimization of early warning signals for critical transitions using distribution data. iScience 2023; 26:107156. [PMID: 37456849 PMCID: PMC10338236 DOI: 10.1016/j.isci.2023.107156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 04/21/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QL, UK
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- National Center for Microscopy and Imaging Research, University of California San Diego, Gilman Drive, La Jolla, CA 9500, USA
- Department of Physics and Material Science, University of Luxembourg, 162a Avenue de La Faiencerie, 1511 Luxembourg, Luxembourg
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue Du Swing, 4367 Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
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16
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Gyorgy A, Menezes A, Arcak M. A blueprint for a synthetic genetic feedback optimizer. Nat Commun 2023; 14:2554. [PMID: 37137895 PMCID: PMC10156725 DOI: 10.1038/s41467-023-37903-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/05/2023] [Indexed: 05/05/2023] Open
Abstract
Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.
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Affiliation(s)
- Andras Gyorgy
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Amor Menezes
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
| | - Murat Arcak
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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17
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Anderson CE, Ferreira SS, Antunes MS. Integration of multiple stress signals in plants using synthetic Boolean logic gates. PLANT PHYSIOLOGY 2023:kiad254. [PMID: 37119276 PMCID: PMC10400031 DOI: 10.1093/plphys/kiad254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/03/2023] [Accepted: 04/25/2023] [Indexed: 06/19/2023]
Abstract
As photosynthetic organisms, plants have a potential role in the sustainable production of high-value products such as medicines, biofuels, and chemical feedstocks. With effective engineering using synthetic biology approaches, plant-based platforms could conceivably be designed to minimize the costs and waste of production for materials that would otherwise be uneconomical. Additionally, modern agricultural crops could be engineered to be more productive, resilient, or restorative in different or rapidly changing environments and climates. Information-processing genetic devices and circuits containing multiple interacting parts that behave predictably must be developed to achieve these complex goals. A genetic Boolean AND logic gate is a device that computes the presence or absence of two inputs (signals, stimuli) and produces an output (response) only when both inputs are present. We optimized individual genetic components and used synthetic protein heterodimerizing domains to rationally assemble genetic AND logic gates that integrate two hormonal inputs in transgenic Arabidopsis thaliana plants. These AND gates produce an output only in the presence of both abscisic acid and auxin but not when either or neither hormone is present. The AND logic gate can also integrate signals resulting from two plant stresses, cold temperature and bacterial infection, to produce a response. The design principles used here are generalizable, and, therefore, multiple orthogonal AND gates could be assembled and rationally layered to process complex genetic information in plants. These layered logic gates may be used in genetic circuits to probe fundamental questions in plant biology, such as hormonal crosstalk, in addition to plant engineering for bioproduction.
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Affiliation(s)
- Charles E Anderson
- Department of Biological Sciences, University of North Texas, Denton, TX, USA
- BioDiscovery Institute, University of North Texas, Denton, TX, USA
| | - Savio S Ferreira
- Department of Biological Sciences, University of North Texas, Denton, TX, USA
- BioDiscovery Institute, University of North Texas, Denton, TX, USA
| | - Mauricio S Antunes
- Department of Biological Sciences, University of North Texas, Denton, TX, USA
- BioDiscovery Institute, University of North Texas, Denton, TX, USA
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18
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Xiao Z, Connor AJ, Worland AM, Tang YJ, Zha RH, Koffas M. Silk fibroin production in Escherichia coli is limited by a positive feedback loop between metabolic burden and toxicity stress. Metab Eng 2023; 77:231-241. [PMID: 37024071 DOI: 10.1016/j.ymben.2023.03.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/11/2023] [Accepted: 03/25/2023] [Indexed: 04/08/2023]
Abstract
To investigate the metabolic elasticity and production bottlenecks for recombinant silk proteins in Escherichia coli, we performed a comprehensive characterization of one elastin-like peptide strain (ELP) and two silk protein strains (A5 4mer, A5 16mer). Our approach included 13C metabolic flux analysis, genome-scale modeling, transcription analysis, and 13C-assisted media optimization experiments. Three engineered strains maintained their central flux network during growth, while measurable metabolic flux redistributions (such as the Entner-Doudoroff pathway) were detected. Under metabolic burdens, the reduced TCA fluxes forced the engineered strain to rely more on substrate-level phosphorylation for ATP production, which increased acetate overflow. Acetate (as low as 10 mM) in the media was highly toxic to silk-producing strains, which reduced 4mer production by 43% and 16mer by 84%, respectively. Due to the high toxicity of large-size silk proteins, 16mer's productivity was limited, particularly in the minimal medium. Therefore, metabolic burden, overflow acetate, and toxicity of silk proteins may form a vicious positive feedback loop that fractures the metabolic network. Three solutions could be applied: 1) addition of building block supplements (i.e., eight key amino acids: His, Ile, Phe, Pro, Tyr, Lys, Met, Glu) to reduce metabolic burden; 2) disengagement of growth and production; and 3) use of non-glucose based substrate to reduce acetate overflow. Other reported strategies were also discussed in light of decoupling this positive feedback loop.
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Affiliation(s)
- Zhengyang Xiao
- Department of Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Alexander J Connor
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Alyssa M Worland
- Department of Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Yinjie J Tang
- Department of Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
| | - R Helen Zha
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Mattheos Koffas
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
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19
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Park JH, Bassalo MC, Lin GM, Chen Y, Doosthosseini H, Schmitz J, Roubos JA, Voigt CA. Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis. ACS Synth Biol 2023; 12:1119-1132. [PMID: 36943773 PMCID: PMC10127285 DOI: 10.1021/acssynbio.2c00607] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.
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Affiliation(s)
- Jong Hyun Park
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Marcelo C Bassalo
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Geng-Min Lin
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Ye Chen
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Hamid Doosthosseini
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Joep Schmitz
- DSM Science & Innovation, Biodata & Translational Sciences, P.O. Box 1, 2600 MA Delft, The Netherlands
| | - Johannes A Roubos
- DSM Science & Innovation, Biodata & Translational Sciences, P.O. Box 1, 2600 MA Delft, The Netherlands
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
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20
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Mohammadian M, Sufi Karimi H. Decentralized PI Controller Design for Robust Perfect Adaptation in Noisy Time-Delayed Genetic Regulatory Networks. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11162-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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21
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Ofir R, Kriecherbauer T, Grüne L, Margaliot M. On the gain of entrainment in the n-dimensional ribosome flow model. J R Soc Interface 2023; 20:20220763. [PMID: 36751928 PMCID: PMC9905980 DOI: 10.1098/rsif.2022.0763] [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: 10/18/2022] [Accepted: 01/11/2023] [Indexed: 02/09/2023] Open
Abstract
The ribosome flow model (RFM) is a phenomenological model for the flow of particles along a one-dimensional chain of n sites. It has been extensively used to study ribosome flow along the mRNA molecule during translation. When the transition rates along the chain are time-varying and jointly T-periodic the RFM entrains, i.e. every trajectory of the RFM converges to a unique T-periodic solution that depends on the transition rates, but not on the initial condition. In general, entrainment to periodic excitations like the 24 h solar day or the 50 Hz frequency of the electric grid is important in numerous natural and artificial systems. An interesting question, called the gain of entrainment (GOE) in the RFM, is whether proper coordination of the periodic translation rates along the mRNA can lead to a larger average protein production rate. Analysing the GOE in the RFM is non-trivial and partial results exist only for the RFM with dimensions n = 1, 2. We use a new approach to derive several results on the GOE in the general n-dimensional RFM. Perhaps surprisingly, we rigorously characterize several cases where there is no GOE, so to maximize the average production rate in these cases, the best choice is to use constant transition rates all along the chain.
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Affiliation(s)
- Ron Ofir
- Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel
| | | | - Lars Grüne
- Mathematical Institute, University of Bayreuth, Bayreuth, Germany
| | - Michael Margaliot
- School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel
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22
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Biomolecular feedback controllers: from theory to applications. Curr Opin Biotechnol 2023; 79:102882. [PMID: 36638743 DOI: 10.1016/j.copbio.2022.102882] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/07/2022] [Indexed: 01/13/2023]
Abstract
Billions of years of evolution have led to the creation of sophisticated genetic regulatory mechanisms that control various biological processes in a timely and precise fashion, despite their uncertain and noisy environments. Understanding such naturally existing mechanisms and even designing novel ones will have direct implications in various fields such as biotechnology, medicine, and synthetic biology. In particular, many studies have revealed that feedback-based control mechanisms inside the living cells endow the overall system with multiple attractive features, including homeostasis, noise reduction, and high dynamic performance. The remarkable interdisciplinary nature of these studies has brought together disparate disciplines such as systems/synthetic biology and control theory in an effort to design and build more powerful and reliable biomolecular control systems. Here, we review various biomolecular feedback controllers, highlight their characteristics, and point out their promising impact.
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23
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Araujo RP, Liotta LA. Design Principles Underlying Robust Adaptation of Complex Biochemical Networks. Methods Mol Biol 2023; 2634:3-32. [PMID: 37074572 DOI: 10.1007/978-1-0716-3008-2_1] [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] [Indexed: 04/20/2023]
Abstract
Biochemical networks are often characterized by tremendous complexity-both in terms of the sheer number of interacting molecules ("nodes") and in terms of the varied and incompletely understood interactions among these molecules ("interconnections" or "edges"). Strikingly, the vast and intricate networks of interacting proteins that exist within each living cell have the capacity to perform remarkably robustly, and reproducibly, despite significant variations in concentrations of the interacting components from one cell to the next and despite mutability over time of biochemical parameters. Here we consider the ubiquitously observed and fundamentally important signalling response known as robust perfect adaptation (RPA). We have recently shown that all RPA-capable networks, even the most complex ones, must satisfy an extremely rigid set of design principles, and are modular, being decomposable into just two types of network building-blocks-opposer modules and balancer modules. Here we present an overview of the design principles that characterize all RPA-capable network topologies through a detailed examination of a collection of simple examples. We also introduce a diagrammatic method for studying the potential of a network to exhibit RPA, which may be applied without a detailed knowledge of the complex mathematical principles governing RPA.
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Affiliation(s)
- Robyn P Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- Institute of Health and Biomedical Innovation (IHBI), Kelvin Grove, QLD, Australia.
| | - Lance A Liotta
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
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24
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Barajas C, Del Vecchio D. Synthetic biology by controller design. Curr Opin Biotechnol 2022; 78:102837. [PMID: 36343564 DOI: 10.1016/j.copbio.2022.102837] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/26/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022]
Abstract
Natural biological systems display complex regulation and synthetic biomolecular systems have been used to understand their natural counterparts and to parse sophisticated regulations into core design principles. At the same time, the engineering of biomolecular systems has unarguable potential to transform current and to enable new, yet-to-be-imagined, biotechnology applications. In this review, we discuss the progression of control systems design in synthetic biology, from the purpose of understanding the function of naturally occurring regulatory motifs to that of creating genetic circuits whose function is sufficiently robust for biotechnology applications.
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Affiliation(s)
- Carlos Barajas
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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25
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Fleming GR. Concluding remarks: Directing and controlling materials and molecules. Faraday Discuss 2022; 237:419-427. [PMID: 36062843 DOI: 10.1039/d2fd00133k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Controlling the macroscopic properties of materials, particularly quantum materials, via external inputs such as optical fields is a key goal of modern physical science. The Faraday Discussion presented a cross section of current experimental and theoretical progress with mostly ultrashort pulse excitations with frequencies ranging from the X-ray to the THz regions of the spectrum. This paper offers a perspective on the meaning of control in different scientific and engineering contexts. Despite the enormous challenge of implementing full feedback control on the types of material of interest in this discussion, I sketch such a system taken from a photosynthetic context to provide inspiration for future development in control of materials.
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Affiliation(s)
- Graham R Fleming
- Department of Chemistry, University of California, Berkeley, USA. .,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, USA.,Kavli Energy NanoScience Institute, Berkeley, CA 94720, USA
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26
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Proverbio D, Montanari AN, Skupin A, Gonçalves J. Buffering variability in cell regulation motifs close to criticality. Phys Rev E 2022; 106:L032402. [PMID: 36266798 DOI: 10.1103/physreve.106.l032402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, EX4 4QL, Exeter, United Kingdom
| | - Arthur N Montanari
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- Department of Physics and Material Science, University of Luxembourg, 162a Avenue de la Faiencerie, 1511 Luxembourg, Luxembourg
- Department of Neuroscience, University of California San Diego, 9500 Gilman Drive, La Jolla, California, United States
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, CB2 3EA, Cambridge, United Kingdom
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27
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Versatile tools of synthetic biology applied to drug discovery and production. Future Med Chem 2022; 14:1325-1340. [PMID: 35975897 DOI: 10.4155/fmc-2022-0063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Although synthetic biology is an emerging research field, which has come to prominence within the last decade, it already has many practical applications. Its applications cover the areas of pharmaceutical biotechnology and drug discovery, bringing essential novel methods and strategies such as metabolic engineering, reprogramming the cell fate, drug production in genetically modified organisms, molecular glues, functional nucleic acids and genome editing. This review discusses the main avenues for synthetic biology application in pharmaceutical biotechnology. The authors believe that synthetic biology will reshape drug development and drug production to a similar extent as the advances in organic chemical synthesis in the 20th century. Therefore, synthetic biology already plays an essential role in pharmaceutical, biotechnology, which is the main focus of this review.
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28
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Liu X, Inda ME, Lai Y, Lu TK, Zhao X. Engineered Living Hydrogels. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201326. [PMID: 35243704 PMCID: PMC9250645 DOI: 10.1002/adma.202201326] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/01/2022] [Indexed: 05/31/2023]
Abstract
Living biological systems, ranging from single cells to whole organisms, can sense, process information, and actuate in response to changing environmental conditions. Inspired by living biological systems, engineered living cells and nonliving matrices are brought together, which gives rise to the technology of engineered living materials. By designing the functionalities of living cells and the structures of nonliving matrices, engineered living materials can be created to detect variability in the surrounding environment and to adjust their functions accordingly, thereby enabling applications in health monitoring, disease treatment, and environmental remediation. Hydrogels, a class of soft, wet, and biocompatible materials, have been widely used as matrices for engineered living cells, leading to the nascent field of engineered living hydrogels. Here, the interactions between hydrogel matrices and engineered living cells are described, focusing on how hydrogels influence cell behaviors and how cells affect hydrogel properties. The interactions between engineered living hydrogels and their environments, and how these interactions enable versatile applications, are also discussed. Finally, current challenges facing the field of engineered living hydrogels for their applications in clinical and environmental settings are highlighted.
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Affiliation(s)
- Xinyue Liu
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Maria Eugenia Inda
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yong Lai
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Timothy K Lu
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Xuanhe Zhao
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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29
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de Cesare I, Salzano D, di Bernardo M, Renson L, Marucci L. Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks. ACS Synth Biol 2022; 11:2300-2313. [PMID: 35729740 PMCID: PMC9295158 DOI: 10.1021/acssynbio.1c00632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
![]()
Control-Based Continuation
(CBC) is a general and systematic method
to carry out the bifurcation analysis of physical experiments. CBC
does not rely on a mathematical model and thus overcomes the uncertainty
introduced when identifying bifurcation curves indirectly through
modeling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium
curve of a toggle switch, which includes additive process noise and
exhibits bistability. We compare the results obtained when CBC uses
a model-free and model-based control strategy and show that both can
track stable and unstable solutions, revealing bistability. We then
demonstrate CBC in conditions more representative of an in
vivo experiment using an agent-based simulator describing
cell growth and division, cell-to-cell variability, spatial distribution,
and diffusion of chemicals. We further show how the identified curves
can be used for parameter estimation and discuss how CBC can significantly
accelerate the prototyping of synthetic gene regulatory networks.
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Affiliation(s)
- Irene de Cesare
- Engineering Mathematics Department, University of Bristol, Bristol BS8 1TW, U.K.,Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80125 Naples, Italy
| | - Davide Salzano
- Engineering Mathematics Department, University of Bristol, Bristol BS8 1TW, U.K.,Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80125 Naples, Italy
| | - Mario di Bernardo
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80125 Naples, Italy
| | - Ludovic Renson
- Department of Mechanical Engineering, Imperial College London, London SW7 2BX, U.K
| | - Lucia Marucci
- Engineering Mathematics Department, University of Bristol, Bristol BS8 1TW, U.K.,BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K.,School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1UB, U.K
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30
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Hsu KL, Yen HCS, Yeang CH. Cooperative stability renders protein complex formation more robust and controllable. Sci Rep 2022; 12:10490. [PMID: 35729235 PMCID: PMC9213465 DOI: 10.1038/s41598-022-14362-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/06/2022] [Indexed: 11/19/2022] Open
Abstract
Protein complexes are the fundamental units of many biological functions. Despite their many advantages, one major adverse impact of protein complexes is accumulations of unassembled subunits that may disrupt other processes or exert cytotoxic effects. Synthesis of excess subunits can be inhibited via negative feedback control or they can be degraded more efficiently than assembled subunits, with this latter being termed cooperative stability. Whereas controlled synthesis of complex subunits has been investigated extensively, how cooperative stability acts in complex formation remains largely unexplored. To fill this knowledge gap, we have built quantitative models of heteromeric complexes with or without cooperative stability and compared their behaviours in the presence of synthesis rate variations. A system displaying cooperative stability is robust against synthesis rate variations as it retains high dimer/monomer ratios across a broad range of parameter configurations. Moreover, cooperative stability can alleviate the constraint of limited supply of a given subunit and makes complex abundance more responsive to unilateral upregulation of another subunit. We also conducted an in silico experiment to comprehensively characterize and compare four types of circuits that incorporate combinations of negative feedback control and cooperative stability in terms of eight systems characteristics pertaining to optimality, robustness and controllability. Intriguingly, though individual circuits prevailed for distinct characteristics, the system with cooperative stability alone achieved the most balanced performance across all characteristics. Our study provides theoretical justification for the contribution of cooperative stability to natural biological systems and represents a guideline for designing synthetic complex formation systems with desirable characteristics.
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Affiliation(s)
- Kuan-Lun Hsu
- Institute of Molecular Biology, Academia Sinica, 128 Academia Road, Section 2, Taipei, Taiwan
| | - Hsueh-Chi S Yen
- Institute of Molecular Biology, Academia Sinica, 128 Academia Road, Section 2, Taipei, Taiwan
| | - Chen-Hsiang Yeang
- Institute of Statistical Science, Academia Sinica, 128 Academia Road, Section 2, Taipei, Taiwan.
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31
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Wu J, Chen B, Liu Y, Ma L, Huang W, Lin Y. Modulating gene regulation function by chemically controlled transcription factor clustering. Nat Commun 2022; 13:2663. [PMID: 35562359 PMCID: PMC9106659 DOI: 10.1038/s41467-022-30397-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 04/29/2022] [Indexed: 12/21/2022] Open
Abstract
Recent studies have suggested that transcriptional protein condensates (or clusters) may play key roles in gene regulation and cell fate determination. However, it remains largely unclear how the gene regulation function is quantitatively tuned by transcription factor (TF) clustering and whether TF clustering may confer emergent behaviors as in cell fate control systems. Here, to address this, we construct synthetic TFs whose clustering behavior can be chemically controlled. Through single-parameter tuning of the system (i.e., TF clustering propensity), we provide lines of evidence supporting the direct transcriptional activation and amplification of target genes by TF clustering. Single-gene imaging suggests that such amplification results from the modulation of transcriptional dynamics. Importantly, TF clustering propensity modulates the gene regulation function by significantly tuning the effective TF binding affinity and to a lesser extent the ultrasensitivity, contributing to bimodality and sustained response behavior that are reminiscent of canonical cell fate control systems. Collectively, these results demonstrate that TF clustering can modulate the gene regulation function to enable emergent behaviors, and highlight the potential applications of chemically controlled protein clustering. Transcription factor (TF) condensates appear to be pervasive, yet their roles remain debated. Here, the authors use a synthetic biology approach to show that TF clusters causally amplify transcription and can confer bimodality and “memory”.
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Affiliation(s)
- Jiegen Wu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.,The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.,School of Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Baoqiang Chen
- School of Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Yadi Liu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.,The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China
| | - Liang Ma
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.,The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China
| | - Wen Huang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.,The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, 100871, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China
| | - Yihan Lin
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China. .,The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, 100871, Beijing, China. .,Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.
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32
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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.
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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
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33
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Walker RSK, Pretorius IS. Synthetic biology for the engineering of complex wine yeast communities. NATURE FOOD 2022; 3:249-254. [PMID: 37118192 DOI: 10.1038/s43016-022-00487-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 03/11/2022] [Indexed: 04/30/2023]
Abstract
Wine fermentation is a representation of complex higher-order microbial interactions. Despite the beneficial properties that these communities bring to wine, their complexity poses challenges in predicting the nature and outcome of fermentation. Technological developments in synthetic biology enable the potential to engineer synthetic microbial communities for new purposes. Here we present the challenges and applications of engineered yeast communities in the context of a wine fermentation vessel, how this represents a model system to enable novel solutions for winemaking and introduce the concept of a 'synthetic' terroir. Furthermore, we introduce our vision for the application of control engineering.
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Affiliation(s)
- Roy S K Walker
- School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia.
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia.
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia.
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34
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Liu B, Cuba Samaniego C, Bennett M, Chappell J, Franco E. RNA Compensation: A Positive Feedback Insulation Strategy for RNA-Based Transcription Networks. ACS Synth Biol 2022; 11:1240-1250. [PMID: 35244392 DOI: 10.1021/acssynbio.1c00540] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The lack of signaling modularity of biomolecular systems poses major challenges toward engineering complex networks. Directional signaling between an upstream and a downstream circuit requires the presence of binding events, which result in the consumption of regulatory molecules and can compromise the operation of the upstream circuit. This issue has been previously addressed by introducing insulation strategies that include high-gain negative feedback and activation-deactivation reaction cycles. In this paper, we focus on RNA-based circuits and propose a new positive-feedback strategy to mitigate signal consumption that we propose occurs for each regulatory event due to irreversible binding of the RNA input to the RNA target. To mitigate this, an extra RNA input is added in tandem with transcription output to compensate the RNA consumption, leading to concentration robustness of the input RNA molecule regardless of the amount of downstream modules. We term this strategy RNA compensation, and it can be applied to systems that have a stringent input-output gain, such as Small Transcription Activating RNAs (STARs). Our theoretical analysis shows that RNA compensation not only eliminates the signaling consumption in individual STAR-based regulators, but also improves the composability of STAR cascades and the modularity of RNA bistable systems.
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Affiliation(s)
- Baiyang Liu
- Graduate Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States
| | - Christian Cuba Samaniego
- Department of Mechanical and Aerospace Engineering, Bioengineering, and Molecular Biology Institute, University of California at Los Angeles, Los Angeles, California 90095, United States
| | - Matthew Bennett
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
| | - James Chappell
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
| | - Elisa Franco
- Department of Mechanical and Aerospace Engineering, Bioengineering, and Molecular Biology Institute, University of California at Los Angeles, Los Angeles, California 90095, United States
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35
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Suen JY, Navlakha S. A feedback control principle common to several biological and engineered systems. J R Soc Interface 2022; 19:20210711. [PMID: 35232277 PMCID: PMC8889180 DOI: 10.1098/rsif.2021.0711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/02/2022] [Indexed: 11/12/2022] Open
Abstract
Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules-additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease-are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.
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Affiliation(s)
- Jonathan Y. Suen
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA
| | - Saket Navlakha
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY, USA
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36
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Logel DY, Trofimova E, Jaschke PR. Codon-Restrained Method for Both Eliminating and Creating Intragenic Bacterial Promoters. ACS Synth Biol 2022; 11:689-699. [PMID: 35043622 DOI: 10.1021/acssynbio.1c00359] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Future applications of synthetic biology will require refactored genetic sequences devoid of internal regulatory elements within coding sequences. These regulatory elements include cryptic and intragenic promoters, which may constitute up to a third of the predicted Escherichia coli promoters. The promoter activity is dependent on the structural interaction of core bases with a σ factor. Rational engineering can be used to alter key promoter element nucleotides interacting with σ factors and eliminate downstream transcriptional activity. In this paper, we present codon-restrained promoter silencing (CORPSE), a system for removing intragenic promoters. CORPSE exploits the DNA-σ factor structural relationship to disrupt σ70 promoters embedded within gene coding sequences with a minimum of synonymous codon changes. Additionally, we present an inverted CORPSE system, iCORPSE, which can create highly active promoters within a gene sequence while not perturbing the function of the modified gene.
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Affiliation(s)
- Dominic Y. Logel
- School of Natural Sciences, ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney 2109, New South Wales, Australia
| | - Ellina Trofimova
- School of Natural Sciences, ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney 2109, New South Wales, Australia
| | - Paul R. Jaschke
- School of Natural Sciences, ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney 2109, New South Wales, Australia
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37
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Dray KE, Muldoon JJ, Mangan NM, Bagheri N, Leonard JN. GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems. ACS Synth Biol 2022; 11:1009-1029. [PMID: 35023730 PMCID: PMC9097825 DOI: 10.1021/acssynbio.1c00528] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing the potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce the Generation and Analysis of Models for Exploring Synthetic Systems (GAMES) workflow, which includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications.
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Affiliation(s)
- Kate E. Dray
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Joseph J. Muldoon
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA
| | - Niall M. Mangan
- Engineering Sciences and Applied Mathematics Program, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
| | - Neda Bagheri
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
- Departments of Biology and Chemical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Joshua N. Leonard
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, and Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, IL 60208, USA
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38
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Foo M, Akman OE, Bates DG. Restoring circadian gene profiles in clock networks using synthetic feedback control. NPJ Syst Biol Appl 2022; 8:7. [PMID: 35169147 PMCID: PMC8847486 DOI: 10.1038/s41540-022-00216-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 01/24/2022] [Indexed: 11/29/2022] Open
Abstract
The circadian system-an organism's built-in biological clock-is responsible for orchestrating biological processes to adapt to diurnal and seasonal variations. Perturbations to the circadian system (e.g., pathogen attack, sudden environmental change) often result in pathophysiological responses (e.g., jetlag in humans, stunted growth in plants, etc.) In view of this, synthetic biologists are progressively adapting the idea of employing synthetic feedback control circuits to alleviate the effects of perturbations on circadian systems. To facilitate the design of such controllers, suitable models are required. Here, we extend our recently developed model for the plant circadian clock-termed the extended S-System model-to model circadian systems across different kingdoms of life. We then use this modeling strategy to develop a design framework, based on an antithetic integral feedback (AIF) controller, to restore a gene's circadian profile when it is subject to loss-of-function due to external perturbations. The use of the AIF controller is motivated by its recent successful experimental implementation. Our findings provide circadian biologists with a systematic and general modeling and design approach for implementing synthetic feedback control of circadian systems.
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Affiliation(s)
- Mathias Foo
- School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry, CV1 5FB, UK
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - Ozgur E Akman
- College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, EX4 4QF, UK
| | - Declan G Bates
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
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39
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Verma BK, Mannan AA, Zhang F, Oyarzún DA. Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering. ACS Synth Biol 2022; 11:228-240. [PMID: 34968029 DOI: 10.1021/acssynbio.1c00391] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.
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Affiliation(s)
- Babita K. Verma
- School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, U.K
| | - Ahmad A. Mannan
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, U.K
| | - Fuzhong Zhang
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Diego A. Oyarzún
- School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, U.K
- School of Informatics, The University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London, NW1 2DB, U.K
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40
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Espinel‐Ríos S, Bettenbrock K, Klamt S, Findeisen R. Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover. AIChE J 2022. [DOI: 10.1002/aic.17555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Sebastián Espinel‐Ríos
- Laboratory for Systems Theory and Automatic Control Otto von Guericke University Magdeburg Germany
- Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
| | - Katja Bettenbrock
- Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
| | - Steffen Klamt
- Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
- Technische Universität Darmstadt Darmstadt Germany
| | - Rolf Findeisen
- Laboratory for Systems Theory and Automatic Control Otto von Guericke University Magdeburg Germany
- Control and Cyber‐Physical Systems Laboratory Technical University of Darmstadt Darmstadt Germany
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Abstract
Auxin biology as a field has been at the forefront of advances in delineating the structures, dynamics, and control of plant growth networks. Advances have been enabled by combining the complementary fields of top-down, holistic systems biology and bottom-up, build-to-understand synthetic biology. Continued collaboration between these approaches will facilitate our understanding of and ability to engineer auxin's control of plant growth, development, and physiology. There is a need for the application of similar complementary approaches to improving equity and justice through analysis and redesign of the human systems in which this research is undertaken.
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Affiliation(s)
- R Clay Wright
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia 24061, USA
| | - Britney L Moss
- Department of Biology, Whitman College, Walla Walla, Washington 99362, USA
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42
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Biomolecular mechanisms for signal differentiation. iScience 2021; 24:103462. [PMID: 34927021 PMCID: PMC8649740 DOI: 10.1016/j.isci.2021.103462] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/24/2021] [Accepted: 11/12/2021] [Indexed: 01/05/2023] Open
Abstract
Cells can sense temporal changes of molecular signals, allowing them to predict environmental variations and modulate their behavior. This paper elucidates biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of applications, ultimately expanding our understanding of cell behavior. In particular, we describe and analyze three alternative biomolecular topologies that are able to work as signal differentiators to input signals around their nominal operation. We propose strategies to preserve their performance even in the presence of high-frequency input signal components which are detrimental to the performance of most differentiators. We find that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance. The simple structure of our designs makes them promising tools for realizing derivative control action in synthetic biology.
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43
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Synthetic Protein Circuits and Devices Based on Reversible Protein-Protein Interactions: An Overview. Life (Basel) 2021; 11:life11111171. [PMID: 34833047 PMCID: PMC8623019 DOI: 10.3390/life11111171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 12/30/2022] Open
Abstract
Protein-protein interactions (PPIs) contribute to regulate many aspects of cell physiology and metabolism. Protein domains involved in PPIs are important building blocks for engineering genetic circuits through synthetic biology. These domains can be obtained from known proteins and rationally engineered to produce orthogonal scaffolds, or computationally designed de novo thanks to recent advances in structural biology and molecular dynamics prediction. Such circuits based on PPIs (or protein circuits) appear of particular interest, as they can directly affect transcriptional outputs, as well as induce behavioral/adaptational changes in cell metabolism, without the need for further protein synthesis. This last example was highlighted in recent works to enable the production of fast-responding circuits which can be exploited for biosensing and diagnostics. Notably, PPIs can also be engineered to develop new drugs able to bind specific intra- and extra-cellular targets. In this review, we summarize recent findings in the field of protein circuit design, with particular focus on the use of peptides as scaffolds to engineer these circuits.
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44
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Abstract
Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering-which is used to confer and balance properties such as stability and efficiency-with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.
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45
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Sharp JA, Burrage K, Simpson MJ. Implementation and acceleration of optimal control for systems biology. J R Soc Interface 2021; 18:20210241. [PMID: 34428951 PMCID: PMC8385371 DOI: 10.1098/rsif.2021.0241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Optimal control theory provides insight into complex resource allocation decisions. The forward–backward sweep method (FBSM) is an iterative technique commonly implemented to solve two-point boundary value problems arising from the application of Pontryagin’s maximum principle (PMP) in optimal control. The FBSM is popular in systems biology as it scales well with system size and is straightforward to implement. In this review, we discuss the PMP approach to optimal control and the implementation of the FBSM. By conceptualizing the FBSM as a fixed point iteration process, we leverage and adapt existing acceleration techniques to improve its rate of convergence. We show that convergence improvement is attainable without prohibitively costly tuning of the acceleration techniques. Furthermore, we demonstrate that these methods can induce convergence where the underlying FBSM fails to converge. All code used in this work to implement the FBSM and acceleration techniques is available on GitHub at https://github.com/Jesse-Sharp/Sharp2021.
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Affiliation(s)
- Jesse A Sharp
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Kevin Burrage
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia.,Department of Computer Science, University of Oxford, Oxford OX2 6GG, UK
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
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46
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Ellery A. Are There Biomimetic Lessons from Genetic Regulatory Networks for Developing a Lunar Industrial Ecology? Biomimetics (Basel) 2021; 6:biomimetics6030050. [PMID: 34449537 PMCID: PMC8395472 DOI: 10.3390/biomimetics6030050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 11/21/2022] Open
Abstract
We examine the prospect for employing a bio-inspired architecture for a lunar industrial ecology based on genetic regulatory networks. The lunar industrial ecology resembles a metabolic system in that it comprises multiple chemical processes interlinked through waste recycling. Initially, we examine lessons from factory organisation which have evolved into a bio-inspired concept, the reconfigurable holonic architecture. We then examine genetic regulatory networks and their application in the biological cell cycle. There are numerous subtleties that would be challenging to implement in a lunar industrial ecology but much of the essence of biological circuitry (as implemented in synthetic biology, for example) is captured by traditional electrical engineering design with emphasis on feedforward and feedback loops to implement robustness.
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Affiliation(s)
- Alex Ellery
- Department of Mechanical & Aerospace Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
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Mishra D, Bepler T, Teague B, Berger B, Broach J, Weiss R. An engineered protein-phosphorylation toggle network with implications for endogenous network discovery. Science 2021; 373:eaav0780. [PMID: 34210851 PMCID: PMC11203391 DOI: 10.1126/science.aav0780] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 04/29/2021] [Indexed: 12/23/2022]
Abstract
Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle's architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.
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Affiliation(s)
- Deepak Mishra
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tristan Bepler
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Simons Machine Learning Center, New York Structural Biology Center, New York, NY, USA
| | - Brian Teague
- Department of Biology, University of Wisconsin, Stout, WI, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jim Broach
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computational and Systems Biology Graduate Program, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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48
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Shakiba N, Jones RD, Weiss R, Del Vecchio D. Context-aware synthetic biology by controller design: Engineering the mammalian cell. Cell Syst 2021; 12:561-592. [PMID: 34139166 PMCID: PMC8261833 DOI: 10.1016/j.cels.2021.05.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/19/2021] [Accepted: 05/14/2021] [Indexed: 12/25/2022]
Abstract
The rise of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner. Within mammalian cells, context effects impact device performance at multiple scales, including the genetic, cellular, and extracellular levels. In order for synthetic genetic devices to achieve predictable behaviors, approaches to overcome context dependence are necessary. Here, we describe control systems approaches for achieving context-aware devices that are robust to context effects. We then consider cell fate programing as a case study to explore the potential impact of context-aware devices for regenerative medicine applications.
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Affiliation(s)
- Nika Shakiba
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Ross D Jones
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Domitilla Del Vecchio
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Pandelakis M, Delgado E, Ebrahimkhani MR. CRISPR-Based Synthetic Transcription Factors In Vivo: The Future of Therapeutic Cellular Programming. Cell Syst 2021; 10:1-14. [PMID: 31972154 DOI: 10.1016/j.cels.2019.10.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 08/14/2019] [Accepted: 10/09/2019] [Indexed: 01/04/2023]
Abstract
Pinpoint control over endogenous gene expression in vivo has long been a fevered dream for clinicians and researchers alike. With the recent repurposing of programmable, RNA-guided DNA endonucleases from the CRISPR bacterial immune system, this dream is becoming a powerful reality. Engineered CRISPR/Cas9-based transcriptional regulators and epigenome editors have enabled researchers to perturb endogenous gene expression in vivo, allowing for the therapeutic reprogramming of cell and tissue behavior. For this technology to be of maximal use, a variety of technological hurdles still need to be addressed. Better understanding of the design principle controlling gene expression together with technologies that enable spatiotemporal control of transcriptional engineering are fundamental for rational design, improved efficacy, and ultimately safe translation to humans. In this review, we will discuss recent advances and integrative strategies that can help pave the path toward a new class of transcriptional therapeutics.
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Affiliation(s)
- Matthew Pandelakis
- School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
| | - Elizabeth Delgado
- School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
| | - Mo R Ebrahimkhani
- School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA; Department of Pathology, Division of Experimental Pathology, University of Pittsburgh, Pittsburgh, PA, USA; Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA.
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50
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Perrino G, Hadjimitsis A, Ledesma-Amaro R, Stan GB. Control engineering and synthetic biology: working in synergy for the analysis and control of microbial systems. Curr Opin Microbiol 2021; 62:68-75. [PMID: 34062481 DOI: 10.1016/j.mib.2021.05.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/30/2021] [Accepted: 05/06/2021] [Indexed: 01/12/2023]
Abstract
The implementation of novel functionalities in living cells is a key aspect of synthetic biology. In the last decade, the field of synthetic biology has made progress working in synergy with control engineering, whose solid framework has provided concepts and tools to analyse biological systems and guide their design. In this review, we briefly highlight recent work focused on the application of control theoretical concepts and tools for the analysis and design of synthetic biology systems in microbial cells.
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Affiliation(s)
- Giansimone Perrino
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK
| | - Andreas Hadjimitsis
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK
| | - Guy-Bart Stan
- Department of Bioengineering & Imperial College Centre for Synthetic Biology, Imperial College London, UK.
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