1
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Biondo M, Singh A, Caselle M, Osella M. Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise. Phys Biol 2023; 20:10.1088/1478-3975/acea4e. [PMID: 37489881 PMCID: PMC10680095 DOI: 10.1088/1478-3975/acea4e] [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: 03/13/2023] [Accepted: 07/25/2023] [Indexed: 07/26/2023]
Abstract
Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments.
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Affiliation(s)
- Marta Biondo
- Department of Physics, University of Turin and INFN, via P. Giuria 1, I-10125 Turin, Italy
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Department of Mathematical Sciences, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19716, United States of America
| | - Michele Caselle
- Department of Physics, University of Turin and INFN, via P. Giuria 1, I-10125 Turin, Italy
| | - Matteo Osella
- Department of Physics, University of Turin and INFN, via P. Giuria 1, I-10125 Turin, Italy
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2
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Lazzardi S, Valle F, Mazzolini A, Scialdone A, Caselle M, Osella M. Emergent statistical laws in single-cell transcriptomic data. Phys Rev E 2023; 107:044403. [PMID: 37198814 DOI: 10.1103/physreve.107.044403] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 03/24/2023] [Indexed: 05/19/2023]
Abstract
Large-scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology, or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique.
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Affiliation(s)
- Silvia Lazzardi
- Department of Physics, University of Turin and INFN, via P. Giuria 1, 10125 Turin, Italy
| | - Filippo Valle
- Department of Physics, University of Turin and INFN, via P. Giuria 1, 10125 Turin, Italy
| | - Andrea Mazzolini
- Laboratoire de Physique de l'École Normale Supérieure (PSL University), CNRS, Sorbonne Université and Université de Paris, 75005 Paris, France
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München, Feodor-Lynen-Straße 21, 81377 München, Germany and Institute of Functional Epigenetics and Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Michele Caselle
- Department of Physics, University of Turin and INFN, via P. Giuria 1, 10125 Turin, Italy
| | - Matteo Osella
- Department of Physics, University of Turin and INFN, via P. Giuria 1, 10125 Turin, Italy
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3
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Borg Y, Alsford S, Pavlika V, Zaikin A, Nesbeth DN. Synthetic biology tools for engineering Goodwin oscillation in Trypanosoma brucei brucei. Heliyon 2022; 8:e08891. [PMID: 35198764 PMCID: PMC8844716 DOI: 10.1016/j.heliyon.2022.e08891] [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] [Received: 07/13/2021] [Revised: 12/10/2021] [Accepted: 01/30/2022] [Indexed: 11/30/2022] Open
Abstract
Kinetoplastid protozoa possess properties that are highly divergent from the mammalian, yeast and bacterial cells more commonly used in synthetic biology and represent a tantalisingly untapped source of bioengineering potential. Trypanosoma brucei brucei (T. b. brucei), an established model organism for studying the Kinetoplastida, is non-pathogenic to humans and provides an interesting test case for establishing synthetic biology in this phylogenetic class. To demonstrate further the tractability of Kinetoplastida to synthetic biology, we sought to construct and demonstrate a Goodwin oscillator, the simplest oscillatory gene network, in T. b. brucei for the first time. We report one completed iteration of the archetypal synthetic biology Design-Build-Test-Learn (DBTL) cycle; firstly, using Ab initio mathematical modelling of the behaviour a theoretical, oscillatory, trypanosomal synthetic gene network (SGN) to inform the design of a plasmid encoding that network. Once assembled, the plasmid was then used to generate a stable transfectant T. b. brucei cell line. To test the performance of the oscillatory SGN, a novel experimental setup was established to capture images of the fluorescent signal from motion-restricted live cells. Data captured were consistent with oscillatory behaviour of the SGN, with cellular fluorescence observed to oscillate with a period of 50 min, with varying amplitude and linear growth trend. This first DBTL cycle establishes a foundation for future cycles in which the SGN design and experimental monitoring setup can be further refined.
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Affiliation(s)
- Yanika Borg
- The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, Bernard Katz Building, Gordon Street, University College London, London, WC1E 6BT, UK.,Department of Mathematics and Institute for Women's Health, University College London, Gower Street, London, WC1E 6BT, UK
| | - Sam Alsford
- Faculty of Infectious and Tropical Diseases & Department of Infection Biology, The London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Vasos Pavlika
- The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, Bernard Katz Building, Gordon Street, University College London, London, WC1E 6BT, UK
| | - Alexei Zaikin
- Department of Mathematics and Institute for Women's Health, University College London, Gower Street, London, WC1E 6BT, UK.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Darren N Nesbeth
- The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, Bernard Katz Building, Gordon Street, University College London, London, WC1E 6BT, UK
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4
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Abstract
Temperature is an important determinant of bacterial growth. While the dependence of bacterial growth on different temperatures has been well studied for many bacterial species, prediction of bacterial growth rate for dynamic temperature changes is relatively unclear. Here, the authors address this issue using a combination of experimental measurements of the growth, at the resolution of 5 min, of Escherichia coli and mathematical models. They measure growth curves at different temperatures and estimate model parameters to predict bacterial growth profiles subject to dynamic temperature changes. They compared these predicted growth profiles for various step‐like temperature changes with experimental measurements using the coefficient of determination and mean square error and based on this comparison, ranked the different growth models, finding that the generalised logistic growth model gave the smallest error. They note that as the maximum specific growth increases the duration of this growth predominantly decreases. These results provide a basis to compute the dependence of the growth rate parameter in biomolecular circuits on dynamic temperatures and may be useful for designing biomolecular circuits that are robust to temperature.
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Affiliation(s)
- Abhishek Dey
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Venkat Bokka
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Shaunak Sen
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
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5
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Yáñez Feliú G, Vidal G, Muñoz Silva M, Rudge TJ. Novel Tunable Spatio-Temporal Patterns From a Simple Genetic Oscillator Circuit. Front Bioeng Biotechnol 2020; 8:893. [PMID: 33014996 PMCID: PMC7509427 DOI: 10.3389/fbioe.2020.00893] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/13/2020] [Indexed: 11/13/2022] Open
Abstract
Multicellularity, the coordinated collective behavior of cell populations, gives rise to the emergence of self-organized phenomena at many different spatio-temporal scales. At the genetic scale, oscillators are ubiquitous in regulation of multicellular systems, including during their development and regeneration. Synthetic biologists have successfully created simple synthetic genetic circuits that produce oscillations in single cells. Studying and engineering synthetic oscillators in a multicellular chassis can therefore give us valuable insights into how simple genetic circuits can encode complex multicellular behaviors at different scales. Here we develop a study of the coupling between the repressilator synthetic genetic ring oscillator and constraints on cell growth in colonies. We show in silico how mechanical constraints generate characteristic patterns of growth rate inhomogeneity in growing cell colonies. Next, we develop a simple one-dimensional model which predicts that coupling the repressilator to this pattern of growth rate via protein dilution generates traveling waves of gene expression. We show that the dynamics of these spatio-temporal patterns are determined by two parameters; the protein degradation and maximum expression rates of the repressors. We derive simple relations between these parameters and the key characteristics of the traveling wave patterns: firstly, wave speed is determined by protein degradation and secondly, wavelength is determined by maximum gene expression rate. Our analytical predictions and numerical results were in close quantitative agreement with detailed individual based simulations of growing cell colonies. Confirming published experimental results we also found that static ring patterns occur when protein stability is high. Our results show that this pattern can be induced simply by growth rate dilution and does not require transition to stationary phase as previously suggested. Our method generalizes easily to other genetic circuit architectures thus providing a framework for multi-scale rational design of spatio-temporal patterns from genetic circuits. We use this method to generate testable predictions for the synthetic biology design-build-test-learn cycle.
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Affiliation(s)
- Guillermo Yáñez Feliú
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Timothy J. Rudge
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
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6
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Wen K, Huang L, Wang Q, Yu J. Modulation of first-passage time for gene expression via asymmetric cell division. INT J BIOMATH 2019. [DOI: 10.1142/s1793524519500529] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
How to balance the size of exponentially growing cells has always been a focus of biologists. Recent experiments have uncovered that the cell is divided into two daughter cells only when the level of time-keeper protein reaches a fixed threshold and cell division in prokaryote is not completely symmetric. The timing of cell division is essentially random because gene expression is stochastic, but cells seen to manage to have precise timing of cell division events. Although the inter-cellular variability of gene expression has attracted much attention, the randomness of event timing has been rarely studied. In our analysis, the timing of cell division is formulated as the first-passage time (denoted by FPT) for time-keeper protein’s level to cross a critical threshold firstly, we derive exact analytical formulae for the mean and noise of FPT based on stochastic gene expression model with asymmetric cell division. The results of numerical simulation show that the regulatory factors (division rate, newborn cell size, exponential growth rate and threshold) have significant influence on the mean and noise of FPT. We also show that both the increase of division rate and newborn cell size could reduce the mean of FPT and increase the noise of FPT, the larger the exponential growth rate is, the smaller the mean and noise of FPT will be; and the larger the threshold value is, the higher the mean of FPT is and the lower the noise is. In addition, compared with symmetric division, asymmetric division can reduce the mean of FPT and improve the noise of FPT. In summary, our results provide insight into the relationship between regulatory factors and FPT and reveal that asymmetric division is an effective mechanism to shorten the mean of FPT.
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Affiliation(s)
- Kunwen Wen
- School of Mathematics, Jiaying University, Meizhou 514015, P. R. China
| | - Lifang Huang
- School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou 510320, P. R. China
| | - Qi Wang
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, P. R. China
| | - Jianshe Yu
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, P. R. China
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7
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Abstract
Many proteins assemble into homomultimeric structures, with a number of subunits that can vary substantially among phylogenetic lineages. As protein-protein interactions require productive encounters among subunits, such variation might partially be explained by variation in cellular protein abundance. Protein abundance in turn depends on the intrinsic rates of production and decay of mRNA and protein molecules, as well as rates of cell growth and division. Using a stochastic framework for prediction of the multimeric state of a protein as a function of these processes and the free energy associated with interface-interface binding, we demonstrate agreement with a wide class of proteins using E. coli proteome data. As such, this platform, which links protein quaternary structure with biochemical rates governing gene expression, protein association and dissociation, and cell growth and division, can be extended to evolutionary models for the emergence and diversification of multimers. While it is tempting to think of multimerization as adaptive, the diversity of multimeric states raises the question of its functional role and impact on fitness. As a force driving selection, we consider the possible increase in enzymatic activity of proteins arising strictly as a consequence of interface-interface binding-namely, enhanced stability to degradation, substrate binding affinity, or catalytic rate of multimers with respect to monomers without invoking further conformational changes, as in allostery. For fixed cost of protein production, we find a benefit conferred by multimers that is dependent on context and can therefore become different in diverging lineages.
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Affiliation(s)
- Kyle Hagner
- Department of Physics, Indiana University, Bloomington, Indiana 47405, USA
| | - Sima Setayeshgar
- Department of Physics, Indiana University, Bloomington, Indiana 47405, USA
| | - Michael Lynch
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona 85287, USA
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8
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Li Q, Huang L, Yu J. Modulation of first-passage time for bursty gene expression via random signals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2018; 14:1261-1277. [PMID: 29161860 DOI: 10.3934/mbe.2017065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The stochastic nature of cell-specific signal molecules (such as transcription factor, ribosome, etc.) and the intrinsic stochastic nature of gene expression process result in cell-to-cell variations at protein levels. Increasing experimental evidences suggest that cell phenotypic variations often depend on the accumulation of some special proteins. Hence, a natural and fundamental question is: How does input signal affect the timing of protein count up to a given threshold? To this end, we study effects of input signal on the first-passage time (FPT), the time at which the number of proteins crosses a given threshold. Input signal is distinguished into two types: constant input signal and random input signal, regulating only burst frequency (or burst size) of gene expression. Firstly, we derive analytical formulae for FPT moments in each case of constant signal regulation and random signal regulation. Then, we find that random input signal tends to increases the mean and noise of FPT compared with constant input signal. Finally, we observe that different regulation ways of random signal have different effects on FPT, that is, burst size modulation tends to decrease the mean of FPT and increase the noise of FPT compared with burst frequency modulation. Our findings imply a fundamental mechanism that random fluctuating environment may prolong FPT. This can provide theoretical guidance for studies of some cellular key events such as latency of HIV and lysis time of bacteriophage λ. In conclusion, our results reveal impacts of external signal on FPT and aid understanding the regulation mechanism of gene expression.
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Affiliation(s)
- Qiuying Li
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
| | - Lifang Huang
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
| | - Jianshe Yu
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
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9
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Bertaux F, Marguerat S, Shahrezaei V. Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits. ROYAL SOCIETY OPEN SCIENCE 2018; 5:172234. [PMID: 29657814 PMCID: PMC5882738 DOI: 10.1098/rsos.172234] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 02/15/2018] [Indexed: 05/12/2023]
Abstract
The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.
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Affiliation(s)
- François Bertaux
- Department of Mathematics, Imperial College London, London SW7 2AZ,UK
- MRC London Institute of Medical Sciences (LMS), London W12 0NN, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London W12 0NN, UK
| | - Samuel Marguerat
- MRC London Institute of Medical Sciences (LMS), London W12 0NN, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London W12 0NN, UK
- Authors for correspondence: Samuel Marguerat e-mail:
| | - Vahid Shahrezaei
- Department of Mathematics, Imperial College London, London SW7 2AZ,UK
- Authors for correspondence: Vahid Shahrezaei e-mail:
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10
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Bertaux F, Marguerat S, Shahrezaei V. Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits. ROYAL SOCIETY OPEN SCIENCE 2018; 5:172234. [PMID: 29657814 DOI: 10.1101/209593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 02/15/2018] [Indexed: 05/25/2023]
Abstract
The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.
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Affiliation(s)
- François Bertaux
- Department of Mathematics, Imperial College London, London SW7 2AZ,UK
- MRC London Institute of Medical Sciences (LMS), London W12 0NN, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London W12 0NN, UK
| | - Samuel Marguerat
- MRC London Institute of Medical Sciences (LMS), London W12 0NN, UK
- Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London W12 0NN, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Imperial College London, London SW7 2AZ,UK
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11
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Co AD, Lagomarsino MC, Caselle M, Osella M. Stochastic timing in gene expression for simple regulatory strategies. Nucleic Acids Res 2017; 45:1069-1078. [PMID: 28180313 PMCID: PMC5388427 DOI: 10.1093/nar/gkw1235] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 11/09/2016] [Accepted: 11/24/2016] [Indexed: 12/15/2022] Open
Abstract
Timing is essential for many cellular processes, from cellular responses to external stimuli to the cell cycle and circadian clocks. Many of these processes are based on gene expression. For example, an activated gene may be required to reach in a precise time a threshold level of expression that triggers a specific downstream process. However, gene expression is subject to stochastic fluctuations, naturally inducing an uncertainty in this threshold-crossing time with potential consequences on biological functions and phenotypes. Here, we consider such ‘timing fluctuations’ and we ask how they can be controlled. Our analytical estimates and simulations show that, for an induced gene, timing variability is minimal if the threshold level of expression is approximately half of the steady-state level. Timing fluctuations can be reduced by increasing the transcription rate, while they are insensitive to the translation rate. In presence of self-regulatory strategies, we show that self-repression reduces timing noise for threshold levels that have to be reached quickly, while self-activation is optimal at long times. These results lay a framework for understanding stochasticity of endogenous systems such as the cell cycle, as well as for the design of synthetic trigger circuits.
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Affiliation(s)
- Alma Dal Co
- Department of Physics and INFN, Università degli Studi di Torino, via P. Giuria 1, Turin, Italy
| | - Marco Cosentino Lagomarsino
- Sorbonne Universités, Université Pierre et Marie Curie, Institut de Biologie Paris Seine, Place Jussieu 4, Paris, France.,UMR 7238 CNRS, Computational and Quantitative Biology, Paris, France.,IFOM, FIRC Institute of Molecular Oncology, Via Adamello 16, Milan, Italy
| | - Michele Caselle
- Department of Physics and INFN, Università degli Studi di Torino, via P. Giuria 1, Turin, Italy
| | - Matteo Osella
- Department of Physics and INFN, Università degli Studi di Torino, via P. Giuria 1, Turin, Italy
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12
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du Lac M, Scarpelli AH, Younger AKD, Bates DG, Leonard JN. Predicting the Dynamics and Heterogeneity of Genomic DNA Content within Bacterial Populations across Variable Growth Regimes. ACS Synth Biol 2017; 6:1131-1139. [PMID: 27689718 DOI: 10.1021/acssynbio.5b00217] [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: 11/30/2022]
Abstract
For many applications in microbial synthetic biology, optimizing a desired function requires careful tuning of the degree to which various genes are expressed. One challenge for predicting such effects or interpreting typical characterization experiments is that in bacteria such as E. coli, genome copy number varies widely across different phases and rates of growth, which also impacts how and when genes are expressed from different loci. While such phenomena are relatively well-understood at a mechanistic level, our quantitative understanding of such processes is essentially limited to ideal exponential growth. In contrast, common experimental phenomena such as growth on heterogeneous media, metabolic adaptation, and oxygen restriction all cause substantial deviations from ideal exponential growth, particularly as cultures approach the higher densities at which industrial biomanufacturing and even routine screening experiments are conducted. To meet the need for predicting and explaining how gene dosage impacts cellular functions outside of exponential growth, we here report a novel modeling strategy that leverages agent-based simulation and high performance computing to robustly predict the dynamics and heterogeneity of genomic DNA content within bacterial populations across variable growth regimes. We show that by feeding routine experimental data, such as optical density time series, into our heterogeneous multiphasic growth simulator, we can predict genomic DNA distributions over a range of nonexponential growth conditions. This modeling strategy provides an important advance in the ability of synthetic biologists to evaluate the role of genomic DNA content and heterogeneity in affecting the performance of existing or engineered microbial functions.
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Affiliation(s)
- Melchior du Lac
- Warwick
Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | | | | | - Declan G. Bates
- Warwick
Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
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13
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Min BE, Hwang HG, Lim HG, Jung GY. Optimization of industrial microorganisms: recent advances in synthetic dynamic regulators. ACTA ACUST UNITED AC 2017; 44:89-98. [DOI: 10.1007/s10295-016-1867-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 11/04/2016] [Indexed: 12/27/2022]
Abstract
Abstract
Production of biochemicals by industrial fermentation using microorganisms requires maintaining cellular production capacity, because maximal productivity is economically important. High-productivity microbial strains can be developed using static engineering, but these may not maintain maximal productivity throughout the culture period as culture conditions and cell states change dynamically. Additionally, economic reasons limit heterologous protein expression using inducible promoters to prevent metabolic burden for commodity chemical and biofuel production. Recently, synthetic and systems biology has been used to design genetic circuits, precisely controlling gene expression or influencing genetic behavior toward a desired phenotype. Development of dynamic regulators can maintain cellular phenotype in a maximum production state in response to factors including cell concentration, oxygen, temperature, pH, and metabolites. Herein, we introduce dynamic regulators of industrial microorganism optimization and discuss metabolic flux fine control by dynamic regulators in response to metabolites or extracellular stimuli, robust production systems, and auto-induction systems using quorum sensing.
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Affiliation(s)
- Byung Eun Min
- grid.49100.3c 0000000107424007 Department of Chemical Engineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu 37673 Pohang Gyeongbuk Korea
| | - Hyun Gyu Hwang
- grid.49100.3c 0000000107424007 School of Interdisciplinary Bioscience and Bioengineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu 37673 Pohang Gyeongbuk Korea
| | - Hyun Gyu Lim
- grid.49100.3c 0000000107424007 Department of Chemical Engineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu 37673 Pohang Gyeongbuk Korea
| | - Gyoo Yeol Jung
- grid.49100.3c 0000000107424007 Department of Chemical Engineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu 37673 Pohang Gyeongbuk Korea
- grid.49100.3c 0000000107424007 School of Interdisciplinary Bioscience and Bioengineering Pohang University of Science and Technology 77 Cheongam-ro, Nam-gu 37673 Pohang Gyeongbuk Korea
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14
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Advances in synthetic dynamic circuits design: using novel synthetic parts to engineer new generations of gene oscillations. Curr Opin Biotechnol 2015; 36:161-7. [PMID: 26342588 DOI: 10.1016/j.copbio.2015.08.020] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 08/11/2015] [Accepted: 08/18/2015] [Indexed: 01/27/2023]
Abstract
As bioengineering applications expand, the need to design and implement circuits that exhibit dynamic properties increases. In particular, schemes that control precise patterns of gene expression as a function of time are essential for balancing multiple metabolic objectives in natural and synthetic systems. Given that modularity has been an important component of dynamic circuits, recent efforts to improve dynamic circuits have focused on replacing old parts for new components that increase the robustness, stability, and tunability. In this review, we show that incorporation of novel components such as regulatory noncoding RNAs (ncRNAs), promoter-transcription factor pairs, and metabolite sensors have allowed traditional dynamic circuits to obtain more robust functionality and improved dynamic properties.
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Heng BC, Aubel D, Fussenegger M. Prosthetic gene networks as an alternative to standard pharmacotherapies for metabolic disorders. Curr Opin Biotechnol 2015; 35:37-45. [PMID: 25679308 DOI: 10.1016/j.copbio.2015.01.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Revised: 01/28/2015] [Accepted: 01/28/2015] [Indexed: 12/19/2022]
Abstract
Synthetic biology makes inroads into clinical therapy with the debut of closed-loop prosthetic gene networks specifically designed to treat human diseases. Prosthetic networks are synthetic sensor/effector devices that could functionally integrate and interface with host metabolism to monitor disease states and coordinate appropriate therapeutic responses in a self-sufficient, timely and automatic manner. Prosthetic networks hold particular promise for the current global epidemic of closely interrelated metabolic disorders encompassing obesity, type 2 diabetes, hypertension and hyperlipidaemia, which arise from the unhealthy lifestyle and dietary factors in the modern urbanised world. This review will critically examine the various attempts at constructing prosthetic gene networks for the treatment of these metabolic disorders, as well as provide insight into future developments in the field.
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Affiliation(s)
- Boon Chin Heng
- Department of Biological Sciences, Sunway University, Bandar Sunway 47500, Selangor Darul Ehsan, Malaysia; Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, CH-4058 Basel, Switzerland
| | - Dominique Aubel
- IUTA Département Génie Biologique, Université Claude Bernard Lyon 1, Boulevard du 11 Novembre 1918, F-69622 Villeurbanne Cedex, France
| | - Martin Fussenegger
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich, Mattenstrasse 26, CH-4058 Basel, Switzerland; Faculty of Science, University of Basel, Mattenstrasse 26, CH-4058 Basel, Switzerland.
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Moriya T, Yamamura M, Kiga D. Effects of downstream genes on synthetic genetic circuits. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 4:S4. [PMID: 25521010 PMCID: PMC4290693 DOI: 10.1186/1752-0509-8-s4-s4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background In order to understand and regulate complex genetic networks in living cells, it is important to build simple and well-defined genetic circuits. We designed such circuits using a synthetic biology approach that included mathematical modeling and simulation, with a focus on the effects by which downstream reporter genes are involved in the regulation of synthetic genetic circuits. Results Our results indicated that downstream genes exert two main effects on genes involved in the regulation of synthetic genetic circuits: (1) competition for regulatory proteins and (2) protein degradation in the cell. Conclusions Our findings regarding the effects of downstream genes on regulatory genes and the role of impedance in driving large-scale and complex genetic circuits may facilitate the design of more accurate genetic circuits. This design will have wide applications in future studies of systems and synthetic biology.
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Jaruszewicz J, Kimmel M, Lipniacki T. Stability of bacterial toggle switches is enhanced by cell-cycle lengthening by several orders of magnitude. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 89:022710. [PMID: 25353512 DOI: 10.1103/physreve.89.022710] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2013] [Indexed: 06/04/2023]
Abstract
Bistable regulatory elements are important for nongenetic inheritance, increase of cell-to-cell heterogeneity allowing adaptation, and robust responses at the population level. Here, we study computationally the bistable genetic toggle switch-a small regulatory network consisting of a pair of mutual repressors-in growing and dividing bacteria. We show that as cells with an inhibited growth exhibit high stability of toggle states, cell growth and divisions lead to a dramatic increase of toggling rates. The toggling rates were found to increase with rate of cell growth, and can be up to six orders of magnitude larger for fast growing cells than for cells with the inhibited growth. The effect is caused mainly by the increase of protein and mRNA burst sizes associated with the faster growth. The observation that fast growth dramatically destabilizes toggle states implies that rapidly growing cells may vigorously explore the epigenetic landscape enabling nongenetic evolution, while cells with inhibited growth adhere to the local optima. This can be a clever population strategy that allows the slow growing (but stress resistant) cells to survive long periods of unfavorable conditions. Simultaneously, at favorable conditions, this stress resistant (but slowly growing-or not growing) subpopulation may be replenished due to a high switching rate from the fast growing population.
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Affiliation(s)
- Joanna Jaruszewicz
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Marek Kimmel
- Departments of Statistics and Bioengineering, Rice University, Houston, Texas 77005, USA and Systems Engineering Group, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Tomasz Lipniacki
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland and Department of Statistics, Rice University, Houston, Texas 77005, USA
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