1
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Wan W, Zhang H, Niu L, Zhang M, Xu F, Li A, Pei D, Lin M, Cheng B. TGF-β1 promotes osteogenesis of mesenchymal stem cells via integrin mediated mechanical positive autoregulation. iScience 2024; 27:110262. [PMID: 39021801 PMCID: PMC11253692 DOI: 10.1016/j.isci.2024.110262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/18/2024] [Accepted: 06/10/2024] [Indexed: 07/20/2024] Open
Abstract
Positive autoregulation (PAR), one type of network motifs, provides a high phenotypic heterogeneity for cells to better adapt to their microenvironments. Typical mechanosensitive proteins can also form PAR, e.g., integrin mediated PAR, but the role of such mechanical PAR in physiological development and pathological process remains elusive. In this study, we found that transforming growth factor β1 (TGF-β1) and integrin levels decrease with tissue softening after the development of paradentium in vivo in rat model of periodontitis (an inflammatory disease with bone defect). Interestingly, TGF-β1 could induce the formation of mechanical PAR involving the integrin-FAK-YAP axis in mesenchymal stem cells (MSCs) by both in vitro experiments and in silico computational model. The computational model predicted a mechanical PAR involving the bimodal distribution of focus adhesions, which enables cells to accurately perceive extracellular mechanical cues. Thus, our analysis of TGF-β1 mediated mechanosensing mechanism on MSCs may help to better understand the molecular process underlying bone regeneration.
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Affiliation(s)
- Wanting Wan
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an 710004 P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi’an Jiaotong University, Xi’an 710049 P.R. China
| | - Hui Zhang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an 710004 P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi’an Jiaotong University, Xi’an 710049 P.R. China
| | - Lin Niu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an 710004 P.R. China
| | - Min Zhang
- State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi International Joint Research Center for Oral Diseases, Department of General Dentistry and Emergency, School of Stomatology, Fourth Military Medical University, Xi’an 710032 P.R. China
| | - Feng Xu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049 P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi’an Jiaotong University, Xi’an 710049 P.R. China
| | - Ang Li
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an 710004 P.R. China
| | - Dandan Pei
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an 710004 P.R. China
| | - Min Lin
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049 P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi’an Jiaotong University, Xi’an 710049 P.R. China
| | - Bo Cheng
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049 P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi’an Jiaotong University, Xi’an 710049 P.R. China
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2
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Ilia K, Shakiba N, Bingham T, Jones RD, Kaminski MM, Aravera E, Bruno S, Palacios S, Weiss R, Collins JJ, Del Vecchio D, Schlaeger TM. Synthetic genetic circuits to uncover the OCT4 trajectories of successful reprogramming of human fibroblasts. SCIENCE ADVANCES 2023; 9:eadg8495. [PMID: 38019912 PMCID: PMC10686568 DOI: 10.1126/sciadv.adg8495] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.
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Affiliation(s)
- Katherine Ilia
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Nika Shakiba
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3 Canada
| | - Trevor Bingham
- Stem Cell Program, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard University, Boston, MA 02115, USA
| | - Ross D. Jones
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3 Canada
| | - Michael M. Kaminski
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz-Association, Berlin 10115, Germany
- Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Medizinische Klinik m.S. Nephrologie und Intensivmedizin, Berlin 10117, Germany
- Berlin Institute of Health, Berlin 13125, Germany
| | - Eliezer Aravera
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Simone Bruno
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
| | - Sebastian Palacios
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - James J. Collins
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02215, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Domitilla Del Vecchio
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
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3
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Merat R. The human antigen R as an actionable super-hub within the network of cancer cell persistency and plasticity. Transl Oncol 2023; 35:101722. [PMID: 37352624 DOI: 10.1016/j.tranon.2023.101722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/30/2023] [Accepted: 06/12/2023] [Indexed: 06/25/2023] Open
Abstract
In this perspective article, a clinically inspired phenotype-driven experimental approach is put forward to address the challenge of the adaptive response of solid cancers to small-molecule targeted therapies. A list of conditions is derived, including an experimental quantitative assessment of cell plasticity and an information theory-based detection of in vivo dependencies, for the discovery of post-transcriptional druggable mechanisms capable of preventing at multiple levels the emergence of plastic dedifferentiated slow-proliferating cells. The approach is illustrated by the author's own work in the example case of the adaptive response of BRAFV600-melanoma to BRAF inhibition. A bench-to-bedside and back to bench effort leads to a therapeutic strategy in which the inhibition of the baseline activity of the interferon-γ-activated inhibitor of translation (GAIT) complex, incriminated in the expression insufficiency of the RNA-binding protein HuR in a minority of cells, results in the suppression of the plastic, intermittently slow-proliferating cells involved in the adaptive response. A similar approach is recommended for the validation of other classes of mechanisms that we seek to modulate to overcome this complex challenge of modern cancer therapy.
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Affiliation(s)
- Rastine Merat
- Dermato-Oncology Unit, Division of Dermatology, Geneva University Hospitals, Switzerland; Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Switzerland.
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4
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Gautam P, Sinha SK. Theoretical investigation of functional responses of bio-molecular assembly networks. SOFT MATTER 2023; 19:3803-3817. [PMID: 37191191 DOI: 10.1039/d2sm01530g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Cooperative protein-protein and protein-DNA interactions form programmable complex assemblies, often performing non-linear gene regulatory operations involved in signal transductions and cell fate determination. The apparent structure of those complex assemblies is very similar, but their functional response strongly depends on the topology of the protein-DNA interaction networks. Here, we demonstrate how the coordinated self-assembly creates gene regulatory network motifs that corroborate the existence of a precise functional response at the molecular level using thermodynamic and dynamic analyses. Our theoretical and Monte Carlo simulations show that a complex network of interactions can form a decision-making loop, such as feedback and feed-forward circuits, only by a few molecular mechanisms. We characterize each possible network of interactions by systematic variations of free energy parameters associated with the binding among biomolecules and DNA looping. We also find that the higher-order networks exhibit alternative steady states from the stochastic dynamics of each network. We capture this signature by calculating stochastic potentials and attributing their multi-stability features. We validate our findings against the Gal promoter system in yeast cells. Overall, we show that the network topology is vital in phenotype diversity in regulatory circuits.
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Affiliation(s)
- Pankaj Gautam
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
| | - Sudipta Kumar Sinha
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
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5
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Luo S, Zhang Z, Wang Z, Yang X, Chen X, Zhou T, Zhang J. Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221057. [PMID: 37035293 PMCID: PMC10073913 DOI: 10.1098/rsos.221057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.
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Affiliation(s)
- Songhao Luo
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Zhenquan Zhang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Zihao Wang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, People's Republic of China
| | - Xiaoxuan Chen
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
- School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province 510275, People's Republic of China
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6
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Aquino G, Rocco A. Noise-induced transitions in gene circuits: A perturbative approach for slow noise. Phys Rev E 2023; 107:044401. [PMID: 37198804 DOI: 10.1103/physreve.107.044401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/21/2023] [Indexed: 05/19/2023]
Abstract
We consider a generic class of gene circuits affected by nonlinear extrinsic noise. To address this nonlinearity we introduce a general perturbative methodology based on assuming timescale separation between noise and gene dynamics, with fluctuations exhibiting a large but finite correlation time. We apply this methodology to the case of the toggle switch, and by considering biologically relevant log-normal fluctuations, we find that the system exhibits noise-induced transitions. The system becomes bimodal in regions of the parameter space where it would be deterministically monostable. We show that by including higher order corrections our methodology allows one to obtain correct predictions for the occurrence of transitions even for not so large correlation time of the fluctuations, thereby overcoming limitations of previous theoretical approaches. Interestingly we find that at intermediate noise intensities the noise-induced transition in the toggle switch affects one of the genes involved, but not the other one.
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Affiliation(s)
- Gerardo Aquino
- Department of Computing, Goldsmiths, University of London, SE14 6AD London, United Kingdom
| | - Andrea Rocco
- Department of Physics, University of Surrey, GU2 7XH Guildford, United Kingdom
- Department of Microbial Sciences, University of Surrey, GU2 7XH, Guildford, United Kingdom
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7
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Ilia K, Shakiba N, Bingham T, Jones RD, Kaminski MM, Aravera E, Bruno S, Palacios S, Weiss R, Collins JJ, Del Vecchio D, Schlaeger TM. Synthetic genetic circuits to uncover and enforce the OCT4 trajectories of successful reprogramming of human fibroblasts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525529. [PMID: 36747813 PMCID: PMC9900859 DOI: 10.1101/2023.01.25.525529] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. Here, we identify the successful reprogramming trajectories of the core pluripotency TF, OCT4, and design a genetic controller that enforces such trajectories with high precision. By combining a genetic circuit that generates a wide range of OCT4 trajectories with live-cell imaging, we track OCT4 trajectories with clonal resolution and find that a distinct constant OCT4 trajectory is required for colony formation. We then develop a synthetic genetic circuit that yields a tight OCT4 distribution around the identified trajectory and outperforms in terms of reprogramming efficiency other circuits that less accurately regulate OCT4. Our synthetic biology approach is generalizable for identifying and enforcing TF dynamics for cell fate programming applications.
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Affiliation(s)
- Katherine Ilia
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Nika Shakiba
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, V6T 1Z3 Canada
| | - Trevor Bingham
- Boston Children’s Hospital Stem Cell Program, Boston Children’s Hospital, Boston, MA, 02115, USA
- Harvard University, Boston, MA, 02115, USA
| | - Ross D. Jones
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, V6T 1Z3 Canada
| | - Michael M. Kaminski
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany
| | - Eliezer Aravera
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Simone Bruno
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sebastian Palacios
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, 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
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, 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
| | - James J. Collins
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02139, USA
| | - Domitilla Del Vecchio
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Thorsten M. Schlaeger
- Boston Children’s Hospital Stem Cell Program, Boston Children’s Hospital, Boston, MA, 02115, USA
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8
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Inhibition of the DAPKs-L13a axis prevents a GAIT-like motif-mediated HuR insufficiency in melanoma cells. Biochem Biophys Res Commun 2022; 626:21-29. [DOI: 10.1016/j.bbrc.2022.07.086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/20/2022]
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9
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Chen L, Wang Y, Liu J, Wang H. Coloured noise induces phenotypic diversity with energy dissipation. Biosystems 2022; 214:104648. [PMID: 35218875 DOI: 10.1016/j.biosystems.2022.104648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/17/2022] [Accepted: 02/20/2022] [Indexed: 11/02/2022]
Abstract
Genes integrate many different sources of noise to adapt their survival strategy with energy costs, but how this noise impacts gene phenotype switching is not fully understood. Here, we refine a mechanistic model with multiplicative and additive coloured noise and analyse the influence of noise strength (NS) and autocorrelation time (AT) on gene phenotypic diversity. Different from white noise, we found that in the autocorrelation time-scale plane, increasing the multiplicative noise will broaden the bimodal region of the gene product, and additive noise will induce bimodal region drift from the lower level to the higher level, while the AT will promote this transition. Specifically, the effect of AT on gene expression is similar to a feedback loop; that is, the AT of multiplicative noise will elongate the mean first passage time (MFPT) from the low stable state to the high stable state, but it will reduce the MFPT from the high stable state to the low stable state, and the opposite is true for additive noise. Moreover, these transitions will violate the detailed equilibrium and then consume energy. By effective topology network reconstruction, we found that when the NS is small, the more obvious the bimodality is, the lower the energy dissipation; however, when the NS is large, it will consume more energy with a tendency for bimodality. The overall analysis implies that living organisms will utilize noise strength and its autocorrelation time for better survival in complex and fluctuating environments.
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Affiliation(s)
- Leiyan Chen
- School of Sciences, Hainan University, Haikou, 570228, Hainan, People's Republic of China
| | - Yan Wang
- Department of Neurology, The First Affiliated Hospital, University of South China, HengYang, 421001, Hunan, People's Republic of China
| | - Jinrong Liu
- School of Sciences, Hainan University, Haikou, 570228, Hainan, People's Republic of China
| | - Haohua Wang
- School of Sciences, Hainan University, Haikou, 570228, Hainan, People's Republic of China; Hainan University, Coll Forestry, Key Laboratory of Genetics & Germplasm Innovation Tropical Special Fo, Ministry of Education, Haikou, 570228, Hainan, People's Republic of China; Hainan University, Key Laboratory of Engineering Modeling and Statistical Computation of Hainan Province, Haikou, 570228, Hainan, People's Republic of China.
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10
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Yang X, Luo S, Zhang Z, Wang Z, Zhou T, Zhang J. Silent transcription intervals and translational bursting lead to diverse phenotypic switching. Phys Chem Chem Phys 2022; 24:26600-26608. [DOI: 10.1039/d2cp03703c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, P. R. China
| | - Songhao Luo
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zhenquan Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zihao Wang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
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11
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Justino JR, Reis CFD, Fonseca AL, Souza SJD, Stransky B. An integrated approach to identify bimodal genes associated with prognosis in câncer. Genet Mol Biol 2021; 44:e20210109. [PMID: 34617951 PMCID: PMC8495773 DOI: 10.1590/1678-4685-gmb-2021-0109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 07/08/2021] [Indexed: 02/08/2023] Open
Abstract
Bimodal gene expression (where a gene expression distribution has two maxima) is
associated with phenotypic diversity in different biological systems. A critical
issue, thus, is the integration of expression and phenotype data to identify
genuine associations. Here, we developed tools that allow both: i) the
identification of genes with bimodal gene expression and ii) their association
with prognosis in cancer patients from The Cancer Genome Atlas (TCGA).
Bimodality was observed for 554 genes in expression data from 25 tumor types.
Furthermore, 96 of these genes presented different prognosis when patients
belonging to the two expression peaks were compared. The software to execute the
method and the corresponding documentation are available at the Data access
section.
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Affiliation(s)
- Josivan Ribeiro Justino
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal de Rondônia, Departamento de Matemática e Estatística, Ji-Parana, RO, Brazil
| | - Clovis Ferreira Dos Reis
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil
| | - Andre Luis Fonseca
- Universidade de São Paulo, Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Sandro Jose de Souza
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Instituto do Cérebro, Natal, RN, Brazil.,Sichuan University, West China Hospital, Institutes for Systems Genetics, Chengdu, China
| | - Beatriz Stransky
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Centro de Tecnologia, Departamento de Engenharia Biomédica, Natal, RN, Brazil
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12
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Hortsch SK, Kremling A. Stochastic Models for Studying the Role of Cellular Noise and Heterogeneity. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11466-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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13
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Fluctuating-rate model with multiple gene states. J Math Biol 2020; 81:1099-1141. [PMID: 33000313 DOI: 10.1007/s00285-020-01538-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 08/28/2020] [Indexed: 10/23/2022]
Abstract
Multiple phenotypic states of single cells often co-exist in the presence of positive feedbacks. Stochastic gene-state switchings and low copy numbers of proteins in single cells cause considerable fluctuations. The chemical master equation (CME) is a powerful tool that describes the dynamics of single cells, but it may be overly complicated. Among many simplified models, a fluctuating-rate (FR) model has been proposed recently to approximate the full CME model in the realistic intermediate region of gene-state switchings. However, only the scenario with two gene states has been carefully analysed. In this paper, we generalise the FR model to the case with multiple gene states, in which the mathematical derivation becomes more complicated. The leading order of fluctuations around each phenotypic state, as well as the transition rates between phenotypic states, in the intermediate gene-state switching region is characterized by the rate function of the stationary distribution of the FR model in the Freidlin-Wentzell-type large deviation principle (LDP). Under certain reasonable assumptions, we show that the derivative of the rate function is equal to the unique nontrivial solution of a dominant generalised eigenvalue problem, leading to a new numerical algorithm for obtaining the LDP rate function directly. Furthermore, we prove the Lyapunov property of the rate function for the corresponding deterministic mean-field dynamics. Finally, through a tristable example, we show that the local fluctuations (the asymptotic variance of the stationary distribution at each phenotypic state) in the intermediate and rapid regions of gene-state switchings are different. Finally, a tri-stable example is constructed to illustrate the validity of our theory.
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14
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Qiu B, Zhou T, Zhang J. Molecular-memory-driven phenotypic switching in a genetic toggle switch without cooperative binding. Phys Rev E 2020; 101:022409. [PMID: 32168703 DOI: 10.1103/physreve.101.022409] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 01/17/2020] [Indexed: 06/10/2023]
Abstract
A genetic toggle switch would involve multistep reaction processes (e.g., complex promoter activation), creating memories between individual reaction events. Revealing the effect of this molecular memory is important for understanding intracellular processes such as cellular decision making. We propose a generalized genetic toggle switch model and use a generalized chemical master equation theory to account for the memory effect. Interestingly, we find that molecular memory can induce bimodality in this memory system although the corresponding memoryless counterpart is not bimodal. This finding implies a plausible alternative mechanism for phenotypic switching that is driven by molecular memory rather than by ultrasensitivity or cooperative binding as shown in previous studies. We also find that unbalanced memories arising from the processes by which mutually inhibiting transcription factors are produced can give rise to asymmetric bimodality without changing the positions of two peaks in the bimodal protein distribution. Given the prevalence of molecular memory in gene regulation, our findings would provide insights into cell fate decisions in growth and development.
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Affiliation(s)
- Baohua Qiu
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, 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, Guangdong Province, Sun Yat-Sen University, Guangzhou 510275, 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, Guangdong Province, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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15
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Qiu H, Yuan Z, Zhou T, Chen L. Different effects of fast and slow input fluctuations on output in gene regulation. CHAOS (WOODBURY, N.Y.) 2020; 30:023104. [PMID: 32113227 DOI: 10.1063/1.5133148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 01/14/2020] [Indexed: 06/10/2023]
Abstract
An important task in the post-gene era is to understand the role of stochasticity in gene regulation. Here, we analyze a cascade model of stochastic gene expression, where the upstream gene stochastically generates proteins that regulate, as transcription factors, stochastic synthesis of the downstream output. We find that in contrast to fast input fluctuations that do not change the behavior of the downstream system qualitatively, slow input fluctuations can induce different modes of the distribution of downstream output and even stochastic focusing or defocusing of the downstream output level, although the regulatory protein follows the same distribution in both cases. This finding is counterintuitive but can have broad biological implications, e.g., slow input rather than fast fluctuations may both increase the survival probability of cells and enhance the sensitivity of intracellular regulation. In addition, we find that input fluctuations can minimize the output noise.
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Affiliation(s)
- Huahai Qiu
- School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, China
| | - Zhanjiang Yuan
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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16
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Aris H, Borhani S, Cahn D, O'Donnell C, Tan E, Xu P. Modeling transcriptional factor cross-talk to understand parabolic kinetics, bimodal gene expression and retroactivity in biosensor design. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Circuit-Host Coupling Induces Multifaceted Behavioral Modulations of a Gene Switch. Biophys J 2019; 114:737-746. [PMID: 29414718 DOI: 10.1016/j.bpj.2017.12.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 11/12/2017] [Accepted: 12/01/2017] [Indexed: 12/30/2022] Open
Abstract
Quantitative modeling of gene circuits is fundamentally important to synthetic biology, as it offers the potential to transform circuit engineering from trial-and-error construction to rational design and, hence, facilitates the advance of the field. Currently, typical models regard gene circuits as isolated entities and focus only on the biochemical processes within the circuits. However, such a standard paradigm is getting challenged by increasing experimental evidence suggesting that circuits and their host are intimately connected, and their interactions can potentially impact circuit behaviors. Here we systematically examined the roles of circuit-host coupling in shaping circuit dynamics by using a self-activating gene switch as a model circuit. Through a combination of deterministic modeling, stochastic simulation, and Fokker-Planck equation formalism, we found that circuit-host coupling alters switch behaviors across multiple scales. At the single-cell level, it slows the switch dynamics in the high protein production regime and enlarges the difference between stable steady-state values. At the population level, it favors cells with low protein production through differential growth amplification. Together, the two-level coupling effects induce both quantitative and qualitative modulations of the switch, with the primary component of the effects determined by the circuit's architectural parameters. This study illustrates the complexity and importance of circuit-host coupling in modulating circuit behaviors, demonstrating the need for a new paradigm-integrated modeling of the circuit-host system-for quantitative understanding of engineered gene networks.
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18
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Ali Al-Radhawi M, Del Vecchio D, Sontag ED. Multi-modality in gene regulatory networks with slow promoter kinetics. PLoS Comput Biol 2019; 15:e1006784. [PMID: 30779734 PMCID: PMC6396950 DOI: 10.1371/journal.pcbi.1006784] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 03/01/2019] [Accepted: 01/14/2019] [Indexed: 12/27/2022] Open
Abstract
Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic Slow Promoter Kinetics (SPK), a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator, and a trans-differentiation network.
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Affiliation(s)
- M. Ali Al-Radhawi
- Departments of Electrical and Computer Engineering and of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Eduardo D. Sontag
- Departments of Electrical and Computer Engineering and of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America
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19
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Production of chemicals using dynamic control of metabolic fluxes. Curr Opin Biotechnol 2018; 53:12-19. [DOI: 10.1016/j.copbio.2017.10.009] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 10/25/2017] [Accepted: 10/27/2017] [Indexed: 01/21/2023]
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20
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Linear mapping approximation of gene regulatory networks with stochastic dynamics. Nat Commun 2018; 9:3305. [PMID: 30120244 PMCID: PMC6098115 DOI: 10.1038/s41467-018-05822-0] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 07/20/2018] [Indexed: 01/09/2023] Open
Abstract
The presence of protein-DNA binding reactions often leads to analytically intractable models of stochastic gene expression. Here we present the linear-mapping approximation that maps systems with protein-promoter interactions onto approximately equivalent systems with no binding reactions. This is achieved by the marriage of conditional mean-field approximation and the Magnus expansion, leading to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method's accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dynamics. The method is also used to study the first-passage time distribution of promoter switching, the sensitivity of the size of protein number fluctuations to parameter perturbation and the stochastic bifurcation diagram characterizing the onset of multimodality in protein number distributions.
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21
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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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22
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Dasgupta S, Bader GD, Goyal S. Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics. Biophys J 2018; 115:429-435. [PMID: 30033145 DOI: 10.1016/j.bpj.2018.07.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 06/29/2018] [Accepted: 07/03/2018] [Indexed: 01/04/2023] Open
Abstract
Single-cell genomics has recently emerged as a powerful tool for observing multicellular systems at a much higher level of resolution and depth than previously possible. High-throughput single-cell RNA sequencing techniques are able to simultaneously quantify expression levels of several thousands of genes within individual cells for tens of thousands of cells within a complex tissue. This has led to development of novel computational methods to analyze this high-dimensional data, investigating longstanding and fundamental questions regarding the granularity of cell types, the definition of cell states, and transitions from one cell type to another along developmental trajectories. In this perspective, we outline this emerging field starting from the "input data" (e.g., quantifying transcription levels in single cells), which are analyzed to define "identities" (e.g., cell types, states, and key genes) and to build "interactions" using models that can infer relations and transitions between cells.
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Affiliation(s)
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Sidhartha Goyal
- Department of Physics, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
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23
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Bokes P, Lin YT, Singh A. High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression. Bull Math Biol 2018; 80:1871-1899. [DOI: 10.1007/s11538-018-0438-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 04/19/2018] [Indexed: 01/18/2023]
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24
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Ochab-Marcinek A, Jędrak J, Tabaka M. Hill kinetics as a noise filter: the role of transcription factor autoregulation in gene cascades. Phys Chem Chem Phys 2018; 19:22580-22591. [PMID: 28809965 DOI: 10.1039/c7cp00743d] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
An intuition based on deterministic models of chemical kinetics is that population heterogeneity of transcription factor levels in cells is transmitted unchanged downstream to the target genes. We use a stochastic model of a two-gene cascade with a self-regulating upstream gene to show that, counter to the intuition, there is no simple mapping (bimodal to bimodal, unimodal to unimodal) between the shapes of the distributions of transcription factor numbers and target protein numbers in cells. Due to the presence of the two regulations, the system contains two nonlinear transfer functions, defined by the Hill kinetics of transcription factor binding. The transfer function of the regulator can "interfere" with the transfer function of the target, converting the bimodal input into a unimodal output or vice versa. We show that this effect can be predicted by a geometric construction. As an example application of the method, we present a case study of a system of several downstream genes of different sensitivities, controlled by a common transcription factor which also regulates its own transcription. We show that a single regulator can induce qualitatively different patterns (binary or graded) of responses to a signal in different downstream genes, depending on whether the sensitivity regions of the transfer functions of the upstream and downstream genes overlap or not. Alternatively, the same model can be interpreted as describing a single downstream gene that has different sensitivities in different cell lines due to mutations. Our model shows, therefore, a possible kinetic mechanism by which different genes can interpret the same biological signal in a different manner.
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Affiliation(s)
- Anna Ochab-Marcinek
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland.
| | - Jakub Jędrak
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland.
| | - Marcin Tabaka
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland.
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25
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Razooky BS, Cao Y, Hansen MMK, Perelson AS, Simpson ML, Weinberger LS. Nonlatching positive feedback enables robust bimodality by decoupling expression noise from the mean. PLoS Biol 2017; 15:e2000841. [PMID: 29045398 PMCID: PMC5646755 DOI: 10.1371/journal.pbio.2000841] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 09/19/2017] [Indexed: 12/28/2022] Open
Abstract
Fundamental to biological decision-making is the ability to generate bimodal expression patterns where 2 alternate expression states simultaneously exist. Here, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV's fate decision between active replication and viral latency. We find that the HIV transactivator of transcription (Tat) protein manipulates the intrinsic toggling of HIV's promoter, the long terminal repeat (LTR), to generate bimodal ON-OFF expression and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit's noncooperative "nonlatching" feedback architecture is optimized to slow the promoter's toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that nonlatching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV's decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.
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Affiliation(s)
- Brandon S. Razooky
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, New York, United States of America
- The Gladstone Institutes (Virology and Immunology), San Francisco, California, United States of America
- Biophysics Graduate Group, University of California, San Francisco, San Francisco, California, United Sates of America
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Youfang Cao
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies (CNLS), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Maike M. K. Hansen
- The Gladstone Institutes (Virology and Immunology), San Francisco, California, United States of America
| | - Alan S. Perelson
- Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Michael L. Simpson
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Tennessee, United States of America
- * E-mail: (MLS); (LSW)
| | - Leor S. Weinberger
- The Gladstone Institutes (Virology and Immunology), San Francisco, California, United States of America
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, United States of America
- QB3: California Institute of Quantitative Biosciences, University of California, San Francisco, San Francisco, California, United States of America
- Department of Pharmaceutical Chemistry University of California, San Francisco, San Francisco, California, United States of America
- * E-mail: (MLS); (LSW)
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26
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Noise slows the rate of Michaelis-Menten reactions. J Theor Biol 2017; 430:21-31. [PMID: 28676416 DOI: 10.1016/j.jtbi.2017.06.039] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 06/02/2017] [Accepted: 06/29/2017] [Indexed: 01/28/2023]
Abstract
Microscopic randomness and the small volumes of living cells combine to generate random fluctuations in molecule concentrations called "noise". Here I investigate the effect of noise on biochemical reactions obeying Michaelis-Menten kinetics, concluding that substrate noise causes these reactions to slow. I derive a general expression for the time evolution of the joint probability density of chemical species in arbitrarily connected networks of non-linear chemical reactions in small volumes. This equation is a generalization of the chemical master equation (CME), a common tool for investigating stochastic chemical kinetics, extended to reaction networks occurring in small volumes, such as living cells. I apply this equation to a generalized Michaelis-Menten reaction in an open system, deriving the following general result: 〈p〉≤p¯ and 〈s〉≥s¯, where s¯ and p¯ denote the deterministic steady-state concentration of reactant and product species, respectively, and 〈s〉 and 〈p〉 denote the steady-state ensemble average over independent realizations of a stochastic reaction. Under biologically realistic conditions, namely when substrate is degraded or diluted by cell division, 〈p〉≤p¯. Consequently, noise slows the rate of in vivo Michaelis-Menten reactions. These predictions are validated by extensive stochastic simulations using Gillespie's exact stochastic simulation algorithm. I specify the conditions under which these effects occur and when they vanish, therefore reconciling discrepancies among previous theoretical investigations of stochastic biochemical reactions. Stochastic slowdown of reaction flux caused by molecular noise in living cells may have functional consequences, which the present theory may be used to quantify.
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27
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Das D, Dey S, Brewster RC, Choubey S. Effect of transcription factor resource sharing on gene expression noise. PLoS Comput Biol 2017; 13:e1005491. [PMID: 28414750 PMCID: PMC5411101 DOI: 10.1371/journal.pcbi.1005491] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 05/01/2017] [Accepted: 03/31/2017] [Indexed: 12/31/2022] Open
Abstract
Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it’s TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression. Genetically identical cells, even when they are exposed to the same environmental conditions, display incredible diversity. Gene expression noise is attributed to be a key source of this phenotypic diversity. Transcriptional dynamics is a dominant source of expression noise. Although scores of theoretical and experimental studies have explored how noise is regulated at the level of transcription, most of them focus on the gene specific, cis regulatory elements, such as the number of transcription factor (TF) binding sites, their binding strength, etc. However, how the global properties of transcription, such as the limited availability of TFs impact noise in gene expression remains rather elusive. Here we build a theoretical model that incorporates the effect of limiting TF pool on gene expression noise. We find that competition between genes for TFs leads to enhanced variability in mRNA copy number across an isogenic population. Moreover, for gene copies sharing TFs with other competitor sites, mRNA variance as a function of the mean shows distinct imprints for one gene copy and multiple gene copies respectively. This stands in sharp contrast to the universal behavior found in mean expression irrespective of the different scenarios of competition. An interesting feature of competition is that introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution, which could lead to phenotypic variability.
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Affiliation(s)
- Dipjyoti Das
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
| | - Supravat Dey
- Laboratoire Charles Coulomb, Université de Montpellier and CNRS, Montpellier, France
| | - Robert C. Brewster
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- * E-mail: (RCB); (SC)
| | - Sandeep Choubey
- FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, United States of America
- * E-mail: (RCB); (SC)
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28
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Liu P, Wang H, Huang L, Zhou T. The dynamic mechanism of noisy signal decoding in gene regulation. Sci Rep 2017; 7:42128. [PMID: 28176840 PMCID: PMC5296728 DOI: 10.1038/srep42128] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 01/06/2017] [Indexed: 11/08/2022] Open
Abstract
Experimental evidence supports that signaling pathways can induce different dynamics of transcription factor (TF) activation, but how an input signal is encoded by such a dynamic, noisy TF and further decoded by downstream genes remains largely unclear. Here, using a system of stochastic transcription with signal regulation, we show that (1) keeping the intensity of the signal noise invariant but prolonging the signal duration can both enhance the mutual information (MI) and reduce the energetic cost (EC); (2) if the signal duration is fixed, the larger MI needs the larger EC, but if the signal period is fixed, there is an optimal time that the signal spends at one lower branch, such that MI reaches the maximum; (3) if both the period and the duration are simultaneously fixed, increasing the input noise can always enhance MI in the case of transcription regulation rather than in the case of degradation regulation. In addition, we find that the input noise can induce stochastic focusing in a regulation-dependent manner. These results reveal not only the dynamic mechanism of noisy signal decoding in gene regulation but also the essential role of external noise in controlling gene expression levels.
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Affiliation(s)
- Peijiang Liu
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
| | - Haohua Wang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
- Department of Mathematics College of Information Science and Technology Hainan University, Haikou 570228, People’s Republic of China
| | - Lifang Huang
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, People’s Republic of China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
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29
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Chakravarty S, Barik D. Steady state statistical correlations predict bistability in reaction motifs. MOLECULAR BIOSYSTEMS 2017; 13:775-784. [DOI: 10.1039/c7mb00052a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
We show that steady state cross-moments and cross-cumulants predict bistability in feedback-regulated reaction networks.
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Affiliation(s)
- Suchana Chakravarty
- School of Chemistry
- University of Hyderabad
- Central University P.O
- Hyderabad
- India
| | - Debashis Barik
- School of Chemistry
- University of Hyderabad
- Central University P.O
- Hyderabad
- India
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30
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Li XT, Jun Y, Erickstad MJ, Brown SD, Parks A, Court DL, Jun S. tCRISPRi: tunable and reversible, one-step control of gene expression. Sci Rep 2016; 6:39076. [PMID: 27996021 PMCID: PMC5171832 DOI: 10.1038/srep39076] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 11/17/2016] [Indexed: 02/06/2023] Open
Abstract
The ability to control the level of gene expression is a major quest in biology. A widely used approach employs deletion of a nonessential gene of interest (knockout), or multi-step recombineering to move a gene of interest under a repressible promoter (knockdown). However, these genetic methods are laborious, and limited for quantitative study. Here, we report a tunable CRISPR-cas system, "tCRISPRi", for precise and continuous titration of gene expression by more than 30-fold. Our tCRISPRi system employs various previous advancements into a single strain: (1) We constructed a new strain containing a tunable arabinose operon promoter PBAD to quantitatively control the expression of CRISPR-(d)Cas protein over two orders of magnitude in a plasmid-free system. (2) tCRISPRi is reversible, and gene expression is repressed under knockdown conditions. (3) tCRISPRi shows significantly less than 10% leaky expression. (4) Most important from a practical perspective, construction of tCRISPRi to target a new gene requires only one-step of oligo recombineering. Our results show that tCRISPRi, in combination with recombineering, provides a simple and easy-to-implement tool for gene expression control, and is ideally suited for construction of both individual strains and high-throughput tunable knockdown libraries.
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Affiliation(s)
- Xin-tian Li
- Section of Molecular Biology, Division of Biological Sciences and Department of Physics, UC San Diego, La Jolla, CA 92093, USA
| | - Yonggun Jun
- Section of Molecular Biology, Division of Biological Sciences and Department of Physics, UC San Diego, La Jolla, CA 92093, USA
| | - Michael J. Erickstad
- Section of Molecular Biology, Division of Biological Sciences and Department of Physics, UC San Diego, La Jolla, CA 92093, USA
| | - Steven D. Brown
- Section of Molecular Biology, Division of Biological Sciences and Department of Physics, UC San Diego, La Jolla, CA 92093, USA
| | - Adam Parks
- RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland 21702, USA
| | - Donald L. Court
- RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute at Frederick, Frederick, Maryland 21702, USA
| | - Suckjoon Jun
- Section of Molecular Biology, Division of Biological Sciences and Department of Physics, UC San Diego, La Jolla, CA 92093, USA
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31
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Gene expression noise is affected differentially by feedback in burst frequency and burst size. J Math Biol 2016; 74:1483-1509. [PMID: 27665109 DOI: 10.1007/s00285-016-1059-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 08/08/2016] [Indexed: 12/31/2022]
Abstract
Inside individual cells, expression of genes is stochastic across organisms ranging from bacterial to human cells. A ubiquitous feature of stochastic expression is burst-like synthesis of gene products, which drives considerable intercellular variability in protein levels across an isogenic cell population. One common mechanism by which cells control such stochasticity is negative feedback regulation, where a protein inhibits its own synthesis. For a single gene that is expressed in bursts, negative feedback can affect the burst frequency or the burst size. In order to compare these feedback types, we study a piecewise deterministic model for gene expression of a self-regulating gene. Mathematically tractable steady-state protein distributions are derived and used to compare the noise suppression abilities of the two feedbacks. Results show that in the low noise regime, both feedbacks are similar in term of their noise buffering abilities. Intriguingly, feedback in burst size outperforms the feedback in burst frequency in the high noise regime. Finally, we discuss various regulatory strategies by which cells implement feedback to control burst sizes of expressed proteins at the level of single cells.
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32
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Hahl SK, Kremling A. A Comparison of Deterministic and Stochastic Modeling Approaches for Biochemical Reaction Systems: On Fixed Points, Means, and Modes. Front Genet 2016; 7:157. [PMID: 27630669 PMCID: PMC5005346 DOI: 10.3389/fgene.2016.00157] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 08/17/2016] [Indexed: 12/29/2022] Open
Abstract
In the mathematical modeling of biochemical reactions, a convenient standard approach is to use ordinary differential equations (ODEs) that follow the law of mass action. However, this deterministic ansatz is based on simplifications; in particular, it neglects noise, which is inherent to biological processes. In contrast, the stochasticity of reactions is captured in detail by the discrete chemical master equation (CME). Therefore, the CME is frequently applied to mesoscopic systems, where copy numbers of involved components are small and random fluctuations are thus significant. Here, we compare those two common modeling approaches, aiming at identifying parallels and discrepancies between deterministic variables and possible stochastic counterparts like the mean or modes of the state space probability distribution. To that end, a mathematically flexible reaction scheme of autoregulatory gene expression is translated into the corresponding ODE and CME formulations. We show that in the thermodynamic limit, deterministic stable fixed points usually correspond well to the modes in the stationary probability distribution. However, this connection might be disrupted in small systems. The discrepancies are characterized and systematically traced back to the magnitude of the stoichiometric coefficients and to the presence of nonlinear reactions. These factors are found to synergistically promote large and highly asymmetric fluctuations. As a consequence, bistable but unimodal, and monostable but bimodal systems can emerge. This clearly challenges the role of ODE modeling in the description of cellular signaling and regulation, where some of the involved components usually occur in low copy numbers. Nevertheless, systems whose bimodality originates from deterministic bistability are found to sustain a more robust separation of the two states compared to bimodal, but monostable systems. In regulatory circuits that require precise coordination, ODE modeling is thus still expected to provide relevant indications on the underlying dynamics.
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Affiliation(s)
- Sayuri K. Hahl
- Specialty Division for Systems Biotechnology, Faculty of Mechanical Engineering, Technische Universität MünchenGarching, Germany
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33
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Influence of gene copy number on self-regulated gene expression. J Theor Biol 2016; 408:222-236. [PMID: 27528448 DOI: 10.1016/j.jtbi.2016.08.018] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 07/15/2016] [Accepted: 08/11/2016] [Indexed: 02/06/2023]
Abstract
Using an analytically solvable stochastic model, we study the properties of a simple genetic circuit consisting of multiple copies of a self-regulating gene. We analyse how the variation in gene copy number and the mutations changing the auto-regulation strength affect the steady-state distribution of protein concentration. We predict that one-reporter assay, an experimental method where the extrinsic noise level is inferred from the comparison of expression variance of a single and duplicated reporter gene, may give an incorrect estimation of the extrinsic noise contribution when applied to self-regulating genes. We also show that an imperfect duplication of an auto-activated gene, changing the regulation strength of one of the copies, may lead to a hybrid, binary+graded response of these genes to external signal. The analysis of relative changes in mean gene expression before and after duplication suggests that evolutionary accumulation of gene duplications may, at a given mean burst size, non-trivially depend on the inherent noisiness of a given gene, quantified by the inverse of the maximal mean frequency of bursts. Moreover, we find that the dependence of gene expression noise on gene copy number and auto-regulation strength may qualitatively differ, e.g. in monotonicity, depending on whether the noise is measured by Fano factor or coefficient of variation. Thus, experimentally-based hypotheses linking gene expression noise and evolutionary optimisation in the context of gene copy number variation may be ambiguous as they are dependent on the particular function chosen to quantify noise.
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34
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Hsu C, Jaquet V, Maleki F, Becskei A. Contribution of Bistability and Noise to Cell Fate Transitions Determined by Feedback Opening. J Mol Biol 2016; 428:4115-4128. [PMID: 27498164 DOI: 10.1016/j.jmb.2016.07.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 07/26/2016] [Accepted: 07/29/2016] [Indexed: 01/06/2023]
Abstract
Alternative cell fates represent a form of non-genetic diversity, which can promote adaptation and functional specialization. It is difficult to predict the rate of the transition between two cell fates due to the strong effect of noise on feedback loops and missing parameters. We opened synthetic positive feedback loops experimentally to obtain open-loop functions. These functions allowed us to identify a deterministic model of bistability by bypassing noise and the requirement to resolve individual processes in the loop. Combining the open-loop function with kinetic measurements and reintroducing the measured noise, we were able to predict the transition rates for the feedback systems without parameter tuning. Noise in gene expression was the key determinant of the transition rates inside the bistable range. Transitions between two cell fates were also observed outside of the bistable range, evidenced by bimodality and hysteresis. In this case, a slow transient process was the rate-limiting step in the transitions. Thus, feedback opening is an effective approach to identify the determinants of cell fate transitions and to predict their rates.
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Affiliation(s)
- Chieh Hsu
- Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland; School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Vincent Jaquet
- Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland
| | - Farzaneh Maleki
- Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland
| | - Attila Becskei
- Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056 Basel, Switzerland.
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35
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Wang H, Yuan Z, Liu P, Zhou T. Mechanisms of information decoding in a cascade system of gene expression. Phys Rev E 2016; 93:052411. [PMID: 27300928 DOI: 10.1103/physreve.93.052411] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Indexed: 06/06/2023]
Abstract
Biotechnology advances have allowed investigation of heterogeneity of cellular responses to stimuli on the single-cell level. Functionally, this heterogeneity can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. However, the mechanism of how this response heterogeneity is generated remains elusive. Here, by systematically analyzing a representative cellular signaling system, we show that (1) the upstream activator always amplifies the downstream burst frequency (BF) but the noiseless activator performs better than the noisy one, remarkably for small or moderate input signal strengths, and the repressor always reduces the downstream BF but the difference in the reducing effect between noiseless and noise repressors is very small; (2) both the downstream burst size and mRNA mean are a monotonically increasing function of the activator strength but a monotonically decreasing function of the repressor strength; (3) for repressor-type input, there is a noisy signal strength such that the downstream mRNA noise arrives at an optimal level, but for activator-type input, the output noise intensity is fundamentally a monotonically decreasing function of the input strength. Our results reveal the essential mechanisms of both signal information decoding and cellular response heterogeneity, whereas our analysis provides a paradigm for analyzing dynamics of noisy biochemical signaling systems.
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Affiliation(s)
- Haohua Wang
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Department of Mathematics, College of Information Science and Technology, Hainan University, Haikou 570228, People's Republic of China
| | - Zhanjiang Yuan
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Peijiang Liu
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Tianshou Zhou
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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36
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Transcription factor levels enable metabolic diversification of single cells of environmental bacteria. ISME JOURNAL 2015; 10:1122-33. [PMID: 26636554 DOI: 10.1038/ismej.2015.193] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 08/27/2015] [Accepted: 09/22/2015] [Indexed: 12/19/2022]
Abstract
Transcriptional noise is a necessary consequence of the molecular events that drive gene expression in prokaryotes. However, some environmental microorganisms that inhabit polluted sites, for example, the m-xylene degrading soil bacterium Pseudomonas putida mt-2 seem to have co-opted evolutionarily such a noise for deploying a metabolic diversification strategy that allows a cautious exploration of new chemical landscapes. We have examined this phenomenon under the light of deterministic and stochastic models for activation of the main promoter of the master m-xylene responsive promoter of the system (Pu) by its cognate transcriptional factor (XylR). These analyses consider the role of co-factors for Pu activation and determinants of xylR mRNA translation. The model traces the onset and eventual disappearance of the bimodal distribution of Pu activity along time to the growth-phase dependent abundance of XylR itself, that is, very low in exponentially growing cells and high in stationary. This tenet was validated by examining the behaviour of a Pu-GFP fusion in a P. putida strain in which xylR expression was engineered under the control of an IPTG-inducible system. This work shows how a relatively simple regulatory scenario (for example, growth-phase dependent expression of a limiting transcription factor) originates a regime of phenotypic diversity likely to be advantageous in competitive environmental settings.
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37
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Yu X, Zeng T, Li G. Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples. BMC Genomics 2015; 16:918. [PMID: 26556243 PMCID: PMC4641376 DOI: 10.1186/s12864-015-2188-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Accepted: 11/02/2015] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Pathway enrichment analysis is a useful tool to study biology and biomedicine, due to its functional screening on well-defined biological procedures rather than separate molecules. The measurement of malfunctions of pathways with a phenotype change, e.g., from normal to diseased, is the key issue when applying enrichment analysis on a pathway. The differentially expressed genes (DEGs) are widely focused in conventional analysis, which is based on the great purity of samples. However, the disease samples are usually heterogeneous, so that, the genes with great differential expression variance (DEVGs) are becoming attractive and important to indicate the specific state of a biological system. In the context of differential expression variance, it is still a challenge to measure the enrichment or status of a pathway. To address this issue, we proposed Integrative Enrichment Analysis (IEA) based on a novel enrichment measurement. RESULTS The main competitive ability of IEA is to identify dysregulated pathways containing DEGs and DEVGs simultaneously, which are usually under-scored by other methods. Next, IEA provides two additional assistant approaches to investigate such dysregulated pathways. One is to infer the association among identified dysregulated pathways and expected target pathways by estimating pathway crosstalks. The other one is to recognize subtype-factors as dysregulated pathways associated to particular clinical indices according to the DEVGs' relative expressions rather than conventional raw expressions. Based on a previously established evaluation scheme, we found that, in particular cohorts (i.e., a group of real gene expression datasets from human patients), a few target disease pathways can be significantly high-ranked by IEA, which is more effective than other state-of-the-art methods. Furthermore, we present a proof-of-concept study on Diabetes to indicate: IEA rather than conventional ORA or GSEA can capture the under-estimated dysregulated pathways full of DEVGs and DEGs; these newly identified pathways could be significantly linked to prior-known disease pathways by estimated crosstalks; and many candidate subtype-factors recognized by IEA also have significant relation with the risk of subtypes of genotype-phenotype associations. CONCLUSIONS Totally, IEA supplies a new tool to carry on enrichment analysis in the complicate context of clinical application (i.e., heterogeneity of disease), as a necessary complementary and cooperative approach to conventional ones.
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Affiliation(s)
- Xiangtian Yu
- School of Mathematics, Shandong University, Jinan, 250100, China.
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Cell Building Level 3, YueYang Road 320, Shanghai, 200031, China.
| | - Guojun Li
- School of Mathematics, Shandong University, Jinan, 250100, China.
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38
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Abstract
Microbes transiently differentiate into distinct, specialized cell types to generate functional diversity and cope with changing environmental conditions. Though alternate programs often entail radically different physiological and morphological states, recent single-cell studies have revealed that these crucial decisions are often left to chance. In these cases, the underlying genetic circuits leverage the intrinsic stochasticity of intracellular chemistry to drive transition between states. Understanding how these circuits transform transient gene expression fluctuations into lasting phenotypic programs will require a combination of quantitative modeling and extensive, time-resolved observation of switching events in single cells. In this article, we survey microbial cell fate decisions demonstrated to involve a random element, describe theoretical frameworks for understanding stochastic switching between states, and highlight recent advances in microfluidics that will enable characterization of key dynamic features of these circuits.
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Affiliation(s)
- Thomas M Norman
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115; , ,
| | - Nathan D Lord
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115; , ,
| | - Johan Paulsson
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115; , ,
| | - Richard Losick
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138;
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39
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Liu P, Yuan Z, Huang L, Zhou T. Roles of factorial noise in inducing bimodal gene expression. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 91:062706. [PMID: 26172735 DOI: 10.1103/physreve.91.062706] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Indexed: 06/04/2023]
Abstract
Some gene regulatory systems can exhibit bimodal distributions of mRNA or protein although the deterministic counterparts are monostable. This noise-induced bimodality is an interesting phenomenon and has important biological implications, but it is unclear how different sources of expression noise (each source creates so-called factorial noise that is defined as a component of the total noise) contribute separately to this stochastic bimodality. Here we consider a minimal model of gene regulation, which is monostable in the deterministic case. Although simple, this system contains factorial noise of two main kinds: promoter noise due to switching between gene states and transcriptional (or translational) noise due to synthesis and degradation of mRNA (or protein). To better trace the roles of factorial noise in inducing bimodality, we also analyze two limit models, continuous and adiabatic approximations, apart from the exact model. We show that in the case of slow gene switching, the continuous model where only promoter noise is considered can exhibit bimodality; in the case of fast switching, the adiabatic model where only transcriptional or translational noise is considered can also exhibit bimodality but the exact model cannot; and in other cases, both promoter noise and transcriptional or translational noise can cooperatively induce bimodality. Since slow gene switching and large protein copy numbers are characteristics of eukaryotic cells, whereas fast gene switching and small protein copy numbers are characteristics of prokaryotic cells, we infer that eukaryotic stochastic bimodality is induced mainly by promoter noise, whereas prokaryotic stochastic bimodality is induced primarily by transcriptional or translational noise.
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Affiliation(s)
- Peijiang Liu
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Zhanjiang Yuan
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Lifang Huang
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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40
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Sala A, Shoaib M, Anufrieva O, Mutharasu G, Jahan Hoque R, Yli-Harja O, Kandhavelu M. Transcription closed and open complex dynamics studies reveal balance between genetic determinants and co-factors. Phys Biol 2015; 12:036003. [PMID: 25988584 DOI: 10.1088/1478-3975/12/3/036003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In E. coli, promoter closed and open complexes are key steps in transcription initiation, where magnesium-dependent RNA polymerase catalyzes RNA synthesis. However, the exact mechanism of initiation remains to be fully elucidated. Here, using single mRNA detection and dual reporter studies, we show that increased intracellular magnesium concentration affects Plac initiation complex formation resulting in a highly dynamic process over the cell growth phases. Mg2+ regulates transcription transition, which modulates bimodality of mRNA distribution in the exponential phase. We reveal that Mg2+ regulates the size and frequency of the mRNA burst by changing the open complex duration. Moreover, increasing magnesium concentration leads to higher intrinsic and extrinsic noise in the exponential phase. RNAP-Mg2+ interaction simulation reveals critical movements creating a shorter contact distance between aspartic acid residues and Nucleotide Triphosphate residues and increasing electrostatic charges in the active site. Our findings provide unique biophysical insights into the balanced mechanism of genetic determinants and magnesium ion in transcription initiation regulation during cell growth.
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Affiliation(s)
- Adrien Sala
- Molecular Signaling Lab, Computational Systems Biology Research Group, Signal Processing Department, Tampere University of Technology, PO Box 553, 33101, Tampere, Finland
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41
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Dobrzyński M, Nguyen LK, Birtwistle MR, von Kriegsheim A, Blanco Fernández A, Cheong A, Kolch W, Kholodenko BN. Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses. J R Soc Interface 2015; 11:20140383. [PMID: 24966234 DOI: 10.1098/rsif.2014.0383] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
We show theoretically and experimentally a mechanism behind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally.
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Affiliation(s)
- Maciej Dobrzyński
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
| | - Lan K Nguyen
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
| | - Marc R Birtwistle
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | | | - Alfonso Blanco Fernández
- Flow Cytometry Core Technologies, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
| | - Alex Cheong
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland School of Life and Health Sciences, Aston University, Birmingham B4 7ET, UK
| | - Walter Kolch
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland
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42
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Ge H, Qian H, Xie XS. Stochastic phenotype transition of a single cell in an intermediate region of gene state switching. PHYSICAL REVIEW LETTERS 2015; 114:078101. [PMID: 25763973 DOI: 10.1103/physrevlett.114.078101] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Indexed: 06/04/2023]
Abstract
Multiple phenotypic states often arise in a single cell with different gene-expression states that undergo transcription regulation with positive feedback. Recent experiments show that, at least in E.coli, the gene state switching can be neither extremely slow nor exceedingly rapid as many previous theoretical treatments assumed. Rather, it is in the intermediate region which is difficult to handle mathematically. Under this condition, from a full chemical-master-equation description we derive a model in which the protein copy number, for a given gene state, follows a deterministic mean-field description while the protein-synthesis rates fluctuate due to stochastic gene state switching. The simplified kinetics yields a nonequilibrium landscape function, which, similar to the energy function for equilibrium fluctuation, provides the leading orders of fluctuations around each phenotypic state, as well as the transition rates between the two phenotypic states. This rate formula is analogous to Kramers' theory for chemical reactions. The resulting behaviors are significantly different from the two limiting cases studied previously.
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Affiliation(s)
- Hao Ge
- Biodynamic Optical Imaging Center (BIOPIC), Peking University, Beijing 100871, People's Republic of China
- Beijing International Center for Mathematical Research (BICMR), Peking University, Beijing 100871, People's Republic of China
| | - Hong Qian
- Department of Applied Mathematics, University of Washington, Seattle, Washington 98195, USA
| | - X Sunney Xie
- Biodynamic Optical Imaging Center (BIOPIC), Peking University, Beijing 100871, People's Republic of China
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA
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43
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Wang H, Yuan Z, Liu P, Zhou T. Division time-based amplifiers for stochastic gene expression. MOLECULAR BIOSYSTEMS 2015; 11:2417-28. [DOI: 10.1039/c5mb00391a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
While cell-to-cell variability is a phenotypic consequence of gene expression noise, sources of this noise may be complex – apart from intrinsic sources such as the random birth/death of mRNA and stochastic switching between promoter states, there are also extrinsic sources of noise such as cell division where division times are either constant or random.
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Affiliation(s)
- Haohua Wang
- School of Mathematics and Computational Science
- Sun Yat-Sen University
- Guangzhou 510275
- People's Republic of China
- Department of Mathematics College of Information Science and Technology Hainan University
| | - Zhanjiang Yuan
- School of Mathematics and Computational Science
- Sun Yat-Sen University
- Guangzhou 510275
- People's Republic of China
| | - Peijiang Liu
- School of Mathematics and Computational Science
- Sun Yat-Sen University
- Guangzhou 510275
- People's Republic of China
| | - Tianshou Zhou
- School of Mathematics and Computational Science
- Sun Yat-Sen University
- Guangzhou 510275
- People's Republic of China
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44
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Mackey MC, Santillán M, Tyran-Kamińska M, Zeron ES. The utility of simple mathematical models in understanding gene regulatory dynamics. In Silico Biol 2015; 12:23-53. [PMID: 25402755 PMCID: PMC4923710 DOI: 10.3233/isb-140463] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Revised: 10/22/2014] [Accepted: 10/23/2014] [Indexed: 11/17/2022]
Abstract
In this review, we survey work that has been carried out in the attempts of biomathematicians to understand the dynamic behaviour of simple bacterial operons starting with the initial work of the 1960's. We concentrate on the simplest of situations, discussing both repressible and inducible systems and then turning to concrete examples related to the biology of the lactose and tryptophan operons. We conclude with a brief discussion of the role of both extrinsic noise and so-called intrinsic noise in the form of translational and/or transcriptional bursting.
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Affiliation(s)
- Michael C. Mackey
- Departments of Physiology, Physics & Mathematics, McGill University, Montreal, Quebec, Canada
| | - Moisés Santillán
- Centro de Investigación y de Estudios Avanzados del IPN, Unidad Monterrey, Parque de Investigación e Innovación Tecnológica, Apodaca NL, México
| | | | - Eduardo S. Zeron
- Departamento de Matemáticas, Centro de Investigación y de Estudios Avanzados del IPN, Apartado Postal, México DF, México
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Ochab-Marcinek A, Tabaka M. Transcriptional leakage versus noise: a simple mechanism of conversion between binary and graded response in autoregulated genes. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 91:012704. [PMID: 25679640 DOI: 10.1103/physreve.91.012704] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Indexed: 06/04/2023]
Abstract
We study the response of an autoregulated gene to a range of concentrations of signal molecules. We show that transcriptional leakage and noise due to translational bursting have the opposite effects. In a positively autoregulated gene, increasing the noise converts the response from graded to binary, while increasing the leakage converts the response from binary to graded. Our findings support the hypothesis that, being a common phenomenon, leaky expression may be a relatively easy way for evolutionary tuning of the type of gene response without changing the type of regulation from positive to negative.
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Affiliation(s)
- Anna Ochab-Marcinek
- Institute of Physical Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Marcin Tabaka
- Institute of Physical Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, 01-224 Warsaw, Poland
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Kim KH, Qian H, Sauro HM. Nonlinear biochemical signal processing via noise propagation. J Chem Phys 2014; 139:144108. [PMID: 24116604 DOI: 10.1063/1.4822103] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cellular phenotypes based on identifying a system's nonlinearities and noise propagations. We observe that such noise can simultaneously enhance sensitivities in one behavioral region while reducing sensitivities in another. Employing this novel phenomenon we designed three biochemical signal processing modules: (a) A gene regulatory network that acts as a concentration detector with both enhanced amplitude and sensitivity. (b) A non-cooperative positive feedback system, with a graded dose-response in the deterministic case, that serves as a bistable switch due to noise-induced ultra-sensitivity. (c) A noise-induced linear amplifier for gene regulation that requires no feedback. The methods developed in the present work allow one to understand and engineer nonlinear biochemical signal processors based on fluctuation-induced phenotypes.
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Affiliation(s)
- Kyung Hyuk Kim
- Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, Washington 98195, USA
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Tabaka M, Kalwarczyk T, Hołyst R. Quantitative influence of macromolecular crowding on gene regulation kinetics. Nucleic Acids Res 2013; 42:727-38. [PMID: 24121687 PMCID: PMC3902910 DOI: 10.1093/nar/gkt907] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
We introduce macromolecular crowding quantitatively into the model for kinetics of gene regulation in Escherichia coli. We analyse and compute the specific-site searching time for 180 known transcription factors (TFs) regulating 1300 operons. The time is between 160 s (e.g. for SoxS Mw = 12.91 kDa) and 1550 s (e.g. for PepA6 of Mw = 329.28 kDa). Diffusion coefficients for one-dimensional sliding are between for large proteins up to for small monomers or dimers. Three-dimensional diffusion coefficients in the cytoplasm are 2 orders of magnitude larger than 1D sliding coefficients, nevertheless the sliding enhances the binding rates of TF to specific sites by 1–2 orders of magnitude. The latter effect is due to ubiquitous non-specific binding. We compare the model to experimental data for LacI repressor and find that non-specific binding of the protein to DNA is activation- and not diffusion-limited. We show that the target location rate by LacI repressor is optimized with respect to microscopic rate constant for association to non-specific sites on DNA. We analyse the effect of oligomerization of TFs and DNA looping effects on searching kinetics. We show that optimal searching strategy depends on TF abundance.
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Affiliation(s)
- Marcin Tabaka
- Institute of Physical Chemistry, Polish Academy of Sciences, 44/52 Kasprzaka, 01-224 Warsaw, Poland
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48
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Kurata H, Maeda K, Onaka T, Takata T. BioFNet: biological functional network database for analysis and synthesis of biological systems. Brief Bioinform 2013; 15:699-709. [PMID: 23894104 DOI: 10.1093/bib/bbt048] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.
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Stochastic models of transcription: from single molecules to single cells. Methods 2013; 62:13-25. [PMID: 23557991 DOI: 10.1016/j.ymeth.2013.03.026] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2012] [Revised: 11/29/2012] [Accepted: 03/22/2013] [Indexed: 11/23/2022] Open
Abstract
Genes in prokaryotic and eukaryotic cells are typically regulated by complex promoters containing multiple binding sites for a variety of transcription factors leading to a specific functional dependence between regulatory inputs and transcriptional outputs. With increasing regularity, the transcriptional outputs from different promoters are being measured in quantitative detail in single-cell experiments thus providing the impetus for the development of quantitative models of transcription. We describe recent progress in developing models of transcriptional regulation that incorporate, to different degrees, the complexity of multi-state promoter dynamics, and its effect on the transcriptional outputs of single cells. The goal of these models is to predict the statistical properties of transcriptional outputs and characterize their variability in time and across a population of cells, as a function of the input concentrations of transcription factors. The interplay between mathematical models of different regulatory mechanisms and quantitative biophysical experiments holds the promise of elucidating the molecular-scale mechanisms of transcriptional regulation in cells, from bacteria to higher eukaryotes.
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Abstract
The biochemical processes leading to the synthesis of new proteins are random, as they typically involve a small number of diffusing molecules. They lead to fluctuations in the number of proteins in a single cell as a function of time and to cell-to-cell variability of protein abundances. These in turn can lead to phenotypic heterogeneity in a population of genetically identical cells. Phenotypic heterogeneity may have important consequences for the development of multicellular organisms and the fitness of bacterial colonies, raising the question of how it is regulated. Here we review the experimental evidence that transcriptional regulation affects noise in gene expression, and discuss how the noise strength is encoded in the architecture of the promoter region. We discuss how models based on specific molecular mechanisms of gene regulation can make experimentally testable predictions for how changes to the promoter architecture are reflected in gene expression noise.
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Affiliation(s)
- Alvaro Sanchez
- Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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