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Hong H, Cortez MJ, Cheng YY, Kim HJ, Choi B, Josić K, Kim JK. Inferring delays in partially observed gene regulation processes. Bioinformatics 2023; 39:btad670. [PMID: 37935426 PMCID: PMC10660296 DOI: 10.1093/bioinformatics/btad670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 10/25/2023] [Accepted: 11/02/2023] [Indexed: 11/09/2023] Open
Abstract
MOTIVATION Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality. RESULTS We develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components. AVAILABILITY AND IMPLEMENTATION Our code is implemented in R and is freely available with a simple example data at https://github.com/Mathbiomed/SimMCMC.
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Affiliation(s)
- Hyukpyo Hong
- Department of Mathematical Sciences, KAIST, Daejeon 34141, Korea
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Korea
| | - Mark Jayson Cortez
- Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, Laguna 4031, Philippines
| | - Yu-Yu Cheng
- Department of Biochemistry, University of Wisconsin–Madison, Madison, WI 53706, United States
| | - Hang Joon Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Boseung Choi
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Korea
- Division of Big Data Science, Korea University Sejong Campus, Sejong 30019, Korea
- College of Public Health, The Ohio State University, Columbus, OH 43210, United States
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, TX 77204, United States
- Department of Biology and Biochemistry, University of Houston, Houston, TX 77204, United States
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon 34141, Korea
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Korea
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Kim DW, Hong H, Kim JK. Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number. SCIENCE ADVANCES 2022; 8:eabl4598. [PMID: 35302852 PMCID: PMC8932658 DOI: 10.1126/sciadv.abl4598] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Identifying the sources of cell-to-cell variability in signaling dynamics is essential to understand drug response variability and develop effective therapeutics. However, it is challenging because not all signaling intermediate reactions can be experimentally measured simultaneously. This can be overcome by replacing them with a single random time delay, but the resulting process is non-Markovian, making it difficult to infer cell-to-cell heterogeneity in reaction rates and time delays. To address this, we developed an efficient and scalable moment-based Bayesian inference method (MBI) with a user-friendly computational package that infers cell-to-cell heterogeneity in the non-Markovian signaling process. We applied MBI to single-cell expression profiles from promoters responding to antibiotics and discovered a major source of cell-to-cell variability in antibiotic stress response: the number of rate-limiting steps in signaling cascades. This knowledge can help identify effective therapies that destroy all pathogenic or cancer cells, and the approach can be applied to precision medicine.
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Affiliation(s)
- Dae Wook Kim
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Hyukpyo Hong
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Republic of Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Republic of Korea
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3
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Khandibharad S, Nimsarkar P, Singh S. Mechanobiology of immune cells: Messengers, receivers and followers in leishmaniasis aiding synthetic devices. CURRENT RESEARCH IN IMMUNOLOGY 2022; 3:186-198. [PMID: 36051499 PMCID: PMC9424266 DOI: 10.1016/j.crimmu.2022.08.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/04/2022] [Accepted: 08/10/2022] [Indexed: 11/03/2022] Open
Abstract
Cytokines are influential molecules which can direct cells behavior. In this review, cytokines are referred as messengers, immune cells which respond to cytokine stimulus are referred as receivers and the immune cells which gets modulated due to their plasticity induced by infectious pathogen leishmania, are referred as followers. The advantage of plasticity of cells is taken by the parasite to switch them from parasite eliminating form to parasite survival favoring form through a process called as reciprocity which is undergone by cytokines, wherein pro-inflammatory to anti-inflammatory switch occur rendering immune cell population to switch their phenotype. Detailed study of this switch can help in identification of important targets which can help in restoring the phenotype to parasite eliminating form and this can be done through synthetic circuit, finding its wider applicability in therapeutics. Cytokines as messengers for governing reciprocity in infection. Leishmania induces reciprocity modulating the immune cells plasticity. Reciprocity of cytokines identifies important target for therapeutics. Therapeutic targets aiding the design of synthetic devices to combat infection.
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Cortez MJ, Hong H, Choi B, Kim JK, Josić K. Hierarchical Bayesian models of transcriptional and translational regulation processes with delays. Bioinformatics 2021; 38:187-195. [PMID: 34450624 PMCID: PMC8696106 DOI: 10.1093/bioinformatics/btab618] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/19/2021] [Accepted: 08/25/2021] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION Simultaneous recordings of gene network dynamics across large populations have revealed that cell characteristics vary considerably even in clonal lines. Inferring the variability of parameters that determine gene dynamics is key to understanding cellular behavior. However, this is complicated by the fact that the outcomes and effects of many reactions are not observable directly. Unobserved reactions can be replaced with time delays to reduce model dimensionality and simplify inference. However, the resulting models are non-Markovian, and require the development of new inference techniques. RESULTS We propose a non-Markovian, hierarchical Bayesian inference framework for quantifying the variability of cellular processes within and across cells in a population. We illustrate our approach using a delayed birth-death process. In general, a distributed delay model, rather than a popular fixed delay model, is needed for inference, even if only mean reaction delays are of interest. Using in silico and experimental data we show that the proposed hierarchical framework is robust and leads to improved estimates compared to its non-hierarchical counterpart. We apply our method to data obtained using time-lapse microscopy and infer the parameters that describe the dynamics of protein production at the single cell and population level. The mean delays in protein production are larger than previously reported, have a coefficient of variation of around 0.2 across the population, and are not strongly correlated with protein production or growth rates. AVAILABILITY AND IMPLEMENTATION Accompanying code in Python is available at https://github.com/mvcortez/Bayesian-Inference. CONTACT kresimir.josic@gmail.com or jaekkim@kaist.ac.kr or cbskust@korea.ac.kr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark Jayson Cortez
- Department of Mathematics, University of Houston, Houston, TX 77204, USA,Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, Laguna 4031, Philippines
| | - Hyukpyo Hong
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea,Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Korea
| | - Boseung Choi
- To whom correspondence should be addressed. or or
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Design and Evaluation of Synthetic RNA-Based Incoherent Feed-Forward Loop Circuits. Biomolecules 2021; 11:biom11081182. [PMID: 34439849 PMCID: PMC8391864 DOI: 10.3390/biom11081182] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/31/2021] [Accepted: 08/06/2021] [Indexed: 11/16/2022] Open
Abstract
RNA-based regulators are promising tools for building synthetic biological systems that provide a powerful platform for achieving a complex regulation of transcription and translation. Recently, de novo-designed synthetic RNA regulators, such as the small transcriptional activating RNA (STAR), toehold switch (THS), and three-way junction (3WJ) repressor, have been utilized to construct RNA-based synthetic gene circuits in living cells. In this work, we utilized these regulators to construct type 1 incoherent feed-forward loop (IFFL) circuits in vivo and explored their dynamic behaviors. A combination of a STAR and 3WJ repressor was used to construct an RNA-only IFFL circuit. However, due to the fast kinetics of RNA–RNA interactions, there was no significant timescale difference between the direct activation and the indirect inhibition, that no pulse was observed in the experiments. These findings were confirmed with mechanistic modeling and simulation results for a wider range of conditions. To increase delay in the inhibition pathway, we introduced a protein synthesis process to the circuit and designed an RNA–protein hybrid IFFL circuit using THS and TetR protein. Simulation results indicated that pulse generation could be achieved with this RNA–protein hybrid model, and this was further verified with experimental realization in E. coli. Our findings demonstrate that while RNA-based regulators excel in speed as compared to protein-based regulators, the fast reaction kinetics of RNA-based regulators could also undermine the functionality of a circuit (e.g., lack of significant timescale difference). The agreement between experiments and simulations suggests that the mechanistic modeling can help debug issues and validate the hypothesis in designing a new circuit. Moreover, the applicability of the kinetic parameters extracted from the RNA-only circuit to the RNA–protein hybrid circuit also indicates the modularity of RNA-based regulators when used in a different context. We anticipate the findings of this work to guide the future design of gene circuits that rely heavily on the dynamics of RNA-based regulators, in terms of both modeling and experimental realization.
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Cetnar DP, Salis HM. Systematic Quantification of Sequence and Structural Determinants Controlling mRNA stability in Bacterial Operons. ACS Synth Biol 2021; 10:318-332. [PMID: 33464822 DOI: 10.1021/acssynbio.0c00471] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
mRNA degradation is a central process that affects all gene expression levels, and yet, the determinants that control mRNA decay rates remain poorly characterized. Here, we applied a synthetic biology, learn-by-design approach to elucidate the sequence and structural determinants that control mRNA stability in bacterial operons. We designed, constructed, and characterized 82 operons in Escherichia coli, systematically varying RNase binding site characteristics, translation initiation rates, and transcriptional terminator efficiencies in the 5' untranslated region (UTR), intergenic, and 3' UTR regions, followed by measuring their mRNA levels using reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays during exponential growth. We show that introducing long single-stranded RNA into 5' UTRs reduced mRNA levels by up to 9.4-fold and that lowering translation rates reduced mRNA levels by up to 11.8-fold. We also found that RNase binding sites in intergenic regions had much lower effects on mRNA levels. Surprisingly, changing the transcriptional termination efficiency or introducing long single-stranded RNA into 3' UTRs had no effect on upstream mRNA levels. From these measurements, we developed and validated biophysical models of ribosome protection and RNase activity with excellent quantitative agreement. We also formulated design rules to rationally control a mRNA's stability, facilitating the automated design of engineered genetic systems with desired functionalities.
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7
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Toward a systematic design of smart probiotics. Curr Opin Biotechnol 2020; 64:199-209. [DOI: 10.1016/j.copbio.2020.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 04/24/2020] [Accepted: 05/07/2020] [Indexed: 12/13/2022]
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8
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Qiu B, Zhou T, Zhang J. Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing. ROYAL SOCIETY OPEN SCIENCE 2020; 7:190462. [PMID: 32257298 PMCID: PMC7062090 DOI: 10.1098/rsos.190462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 01/20/2020] [Indexed: 06/11/2023]
Abstract
Fractional killing, which is a significant impediment to successful chemotherapy, is observed even in a population of genetically identical cancer cells exposed to apoptosis-inducing agents. This phenomenon arises not from genetic mutation but from cell-to-cell variation in the activation timing and level of the proteins that regulates apoptosis. To understand the mechanism behind the phenomenon, we formulate complex fractional killing processes as a first-passage time (FPT) problem with a stochastically fluctuating boundary. Analytical calculations are performed for the FPT distribution in a toy model of stochastic p53 gene expression, where the cancer cell is killed only when the p53 expression level crosses an active apoptotic threshold. Counterintuitively, we find that threshold fluctuations can effectively enhance cellular killing by significantly decreasing the mean time that the p53 protein reaches the threshold level for the first time. Moreover, faster fluctuations lead to the killing of more cells. These qualitative results imply that fluctuations in threshold are a non-negligible stochastic source, and can be taken as a strategy for combating fractional killing of cancer cells.
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Affiliation(s)
- Baohua Qiu
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Key Laboratory of Computational Mathematics, Guangzhou, Guangdong Province, People's Republic of China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Key Laboratory of Computational Mathematics, Guangzhou, Guangdong Province, People's Republic of China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Key Laboratory of Computational Mathematics, Guangzhou, Guangdong Province, People's Republic of China
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9
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Cao M, Qiu B, Zhou T, Zhang J. Control strategies for the timing of intracellular events. Phys Rev E 2020; 100:062401. [PMID: 31962487 DOI: 10.1103/physreve.100.062401] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Indexed: 11/07/2022]
Abstract
While the timing of intracellular events is essential for many cellular processes, gene expression inside a single cell can exhibit substantial cell-to-cell variability, raising the question of how cells ensure precision in event timing despite such stochasticity. We address this question by analyzing a biologically reasonable model of gene expression in the context of first passage time (FPT), focusing on two experimentally measurable statistics: mean FPT (MFPT) and timing variability (TV). We show that (1) transcriptional burst size (BS) and burst frequency (BF) can minimize the TV; (2) translational BS monotonically reduces the MFPT to a nonzero low bound; (3) the timescale of promoter kinetics can minimize both the MFPT and the TV, depending on the ratio of the on-switching rate over the off-switching rate; and (4) positive feedback regulation of any form can all minimize the TV, whereas negative feedback regulation of transcriptional BF or BS always enhances the TV. These control strategies can have broad implications for diverse cellular processes relying on precise temporal triggering of events.
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Affiliation(s)
- Mengfang Cao
- Key Laboratory of Computational Mathematics, Guangdong Province, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Baohua Qiu
- Key Laboratory of Computational Mathematics, Guangdong Province, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Tianshou Zhou
- Key Laboratory of Computational Mathematics, Guangdong Province, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Jiajun Zhang
- Key Laboratory of Computational Mathematics, Guangdong Province, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
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10
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Lehr FX, Hanst M, Vogel M, Kremer J, Göringer HU, Suess B, Koeppl H. Cell-Free Prototyping of AND-Logic Gates Based on Heterogeneous RNA Activators. ACS Synth Biol 2019; 8:2163-2173. [PMID: 31393707 DOI: 10.1021/acssynbio.9b00238] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages such as short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multilevel regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterize the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analyzed a prototype gate in vitro as well as in silico, employing parametrized ordinary differential equations (ODEs), for which parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. On the basis of this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.
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Affiliation(s)
- François-Xavier Lehr
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Maleen Hanst
- Department of Electrical Engineering, Technische Universität Darmstadt, 64283 Darmstadt, Germany
| | - Marc Vogel
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Jennifer Kremer
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - H. Ulrich Göringer
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Beatrix Suess
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Heinz Koeppl
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
- Department of Electrical Engineering, Technische Universität Darmstadt, 64283 Darmstadt, Germany
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11
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Pandi A, Koch M, Voyvodic PL, Soudier P, Bonnet J, Kushwaha M, Faulon JL. Metabolic perceptrons for neural computing in biological systems. Nat Commun 2019; 10:3880. [PMID: 31462649 PMCID: PMC6713752 DOI: 10.1038/s41467-019-11889-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/08/2019] [Indexed: 12/30/2022] Open
Abstract
Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing.
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Affiliation(s)
- Amir Pandi
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Peter L Voyvodic
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, Montpellier, France
| | - Paul Soudier
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
- iSSB Laboratory, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Jerome Bonnet
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, Montpellier, France
| | - Manish Kushwaha
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
- iSSB Laboratory, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.
- SYNBIOCHEM Center, School of Chemistry, University of Manchester, Manchester, UK.
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12
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Bervoets I, Charlier D. Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology. FEMS Microbiol Rev 2019; 43:304-339. [PMID: 30721976 PMCID: PMC6524683 DOI: 10.1093/femsre/fuz001] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 01/21/2019] [Indexed: 12/15/2022] Open
Abstract
Gene expression occurs in two essential steps: transcription and translation. In bacteria, the two processes are tightly coupled in time and space, and highly regulated. Tight regulation of gene expression is crucial. It limits wasteful consumption of resources and energy, prevents accumulation of potentially growth inhibiting reaction intermediates, and sustains the fitness and potential virulence of the organism in a fluctuating, competitive and frequently stressful environment. Since the onset of studies on regulation of enzyme synthesis, numerous distinct regulatory mechanisms modulating transcription and/or translation have been discovered. Mostly, various regulatory mechanisms operating at different levels in the flow of genetic information are used in combination to control and modulate the expression of a single gene or operon. Here, we provide an extensive overview of the very diverse and versatile bacterial gene regulatory mechanisms with major emphasis on their combined occurrence, intricate intertwinement and versatility. Furthermore, we discuss the potential of well-characterized basal expression and regulatory elements in synthetic biology applications, where they may ensure orthogonal, predictable and tunable expression of (heterologous) target genes and pathways, aiming at a minimal burden for the host.
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Affiliation(s)
- Indra Bervoets
- Research Group of Microbiology, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
| | - Daniel Charlier
- Research Group of Microbiology, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
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13
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Choi B, Cheng YY, Cinar S, Ott W, Bennett MR, Josić K, Kim JK. Bayesian inference of distributed time delay in transcriptional and translational regulation. Bioinformatics 2019; 36:586-593. [PMID: 31347688 PMCID: PMC7868000 DOI: 10.1093/bioinformatics/btz574] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 06/07/2019] [Accepted: 07/17/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks. RESULTS We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. AVAILABILITY AND IMPLEMENTATION Accompanying code in R is available at https://github.com/cbskust/DDE_BD. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Boseung Choi
- Department of National Statistics, Korea University Sejong Campus, Sejong 30019, Korea
| | - Yu-Yu Cheng
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Selahattin Cinar
- Department of Mathematics, University of Houston, Houston, TX 77204, USA
| | - William Ott
- Department of Mathematics, University of Houston, Houston, TX 77204, USA
| | - Matthew R Bennett
- Department of Biosciences, Rice University, Houston, TX 77005, USA,Department of Bioengineering, Rice University, Houston, TX 77005, USA
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Menn D, Sochor P, Goetz H, Tian XJ, Wang X. Intracellular Noise Level Determines Ratio Control Strategy Confined by Speed-Accuracy Trade-off. ACS Synth Biol 2019; 8:1352-1360. [PMID: 31083890 DOI: 10.1021/acssynbio.9b00030] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Robust and precise ratio control of heterogeneous phenotypes within an isogenic population is an essential task, especially in the development and differentiation of a large number of cells such as bacteria, sensory receptors, and blood cells. However, the mechanisms of such ratio control are poorly understood. Here, we employ experimental and mathematical techniques to understand the combined effects of signal induction and gene expression stochasticity on phenotypic multimodality. We identify two strategies to control phenotypic ratios from an initially homogeneous population, suitable roughly to high-noise and low-noise intracellular environments, and we show that both can be used to generate precise fractional differentiation. In noisy gene expression contexts, such as those found in bacteria, induction within the circuit's bistable region is enough to cause noise-induced bimodality within a feasible time frame. However, in less noisy contexts, such as tightly controlled eukaryotic systems, spontaneous state transitions are rare and hence bimodality needs to be induced with a controlled pulse of induction that falls outside the bistable region. Finally, we show that noise levels, system response time, and ratio tuning accuracy impose trade-offs and limitations on both ratio control strategies, which guide the selection of strategy alternatives.
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Affiliation(s)
- David Menn
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
| | - Patrick Sochor
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
| | - Hanah Goetz
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
| | - Xiao-Jun Tian
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
| | - Xiao Wang
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
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Westbrook A, Tang X, Marshall R, Maxwell CS, Chappell J, Agrawal DK, Dunlop MJ, Noireaux V, Beisel CL, Lucks J, Franco E. Distinct timescales of RNA regulators enable the construction of a genetic pulse generator. Biotechnol Bioeng 2019; 116:1139-1151. [DOI: 10.1002/bit.26918] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/25/2018] [Accepted: 01/06/2019] [Indexed: 01/01/2023]
Affiliation(s)
- Alexandra Westbrook
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University Ithaca New York
| | - Xun Tang
- Department of Mechanical Engineering University of California at Riverside Riverside California
| | - Ryan Marshall
- School of Physics and Astronomy, University of Minnesota Minneapolis Minnesota
| | - Colin S. Maxwell
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh North Carolina
| | | | - Deepak K. Agrawal
- Biomedical Engineering Department Boston University Boston Massachusetts
| | - Mary J. Dunlop
- Biomedical Engineering Department Boston University Boston Massachusetts
| | - Vincent Noireaux
- School of Physics and Astronomy, University of Minnesota Minneapolis Minnesota
| | - Chase L. Beisel
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh North Carolina
- Helmholtz Institute for RNA‐based Infection Research (HIRI) Helmholtz‐Centre for Infection Research (HZI), Würzburg Germany
- Faculty of Medicine, University of Würzburg Würzburg Germany
| | - Julius Lucks
- Department of Chemical and Biological Engineering Northwestern University Evanston Illinois
| | - Elisa Franco
- Department of Mechanical Engineering University of California at Riverside Riverside California
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