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Gautam P, Kumar Sinha S. Anticipating response function in gene regulatory networks. J R Soc Interface 2021; 18:20210206. [PMID: 34062105 DOI: 10.1098/rsif.2021.0206] [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] [Indexed: 12/23/2022] Open
Abstract
The origin of an ordered genetic response of a complex and noisy biological cell is intimately related to the detailed mechanism of protein-DNA interactions present in a wide variety of gene regulatory (GR) systems. However, the quantitative prediction of genetic response and the correlation between the mechanism and the response curve is poorly understood. Here, we report in silico binding studies of GR systems to show that the transcription factor (TF) binds to multiple DNA sites with high cooperativity spreads from specific binding sites into adjacent non-specific DNA and bends the DNA. Our analysis is not limited only to the isolated model system but also can be applied to a system containing multiple interacting genes. The controlling role of TF oligomerization, TF-ligand interactions, and DNA looping for gene expression has been also characterized. The predictions are validated against detailed grand canonical Monte Carlo simulations and published data for the lac operon system. Overall, our study reveals that the expression of target genes can be quantitatively controlled by modulating TF-ligand interactions and the bending energy of DNA.
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Affiliation(s)
- Pankaj Gautam
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology, Ropar 140001, India
| | - Sudipta Kumar Sinha
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology, Ropar 140001, India
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Landman J, Brewster RC, Weinert FM, Phillips R, Kegel WK. Self-consistent theory of transcriptional control in complex regulatory architectures. PLoS One 2017; 12:e0179235. [PMID: 28686609 PMCID: PMC5501422 DOI: 10.1371/journal.pone.0179235] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 05/25/2017] [Indexed: 11/24/2022] Open
Abstract
Individual regulatory proteins are typically charged with the simultaneous regulation of a battery of different genes. As a result, when one of these proteins is limiting, competitive effects have a significant impact on the transcriptional response of the regulated genes. Here we present a general framework for the analysis of any generic regulatory architecture that accounts for the competitive effects of the regulatory environment by isolating these effects into an effective concentration parameter. These predictions are formulated using the grand-canonical ensemble of statistical mechanics and the fold-change in gene expression is predicted as a function of the number of transcription factors, the strength of interactions between the transcription factors and their DNA binding sites, and the effective concentration of the transcription factor. The effective concentration is set by the transcription factor interactions with competing binding sites within the cell and is determined self-consistently. Using this approach, we analyze regulatory architectures in the grand-canonical ensemble ranging from simple repression and simple activation to scenarios that include repression mediated by DNA looping of distal regulatory sites. It is demonstrated that all the canonical expressions previously derived in the case of an isolated, non-competing gene, can be generalised by a simple substitution to their grand canonical counterpart, which allows for simple intuitive incorporation of the influence of multiple competing transcription factor binding sites. As an example of the strength of this approach, we build on these results to present an analytical description of transcriptional regulation of the lac operon.
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Affiliation(s)
- Jasper Landman
- Van ’t Hoff Laboratory for Physical & Colloid Chemistry, Utrecht University, Utrecht, the Netherlands
- European Synchrotron Radiation Facility, Grenoble, France
| | - Robert C. Brewster
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, United States of America
| | - Franz M. Weinert
- Department of Applied Physics, California Institute of Technology, Pasadena, California, United States of America
| | - Rob Phillips
- Department of Applied Physics, California Institute of Technology, Pasadena, California, United States of America
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Willem K. Kegel
- Van ’t Hoff Laboratory for Physical & Colloid Chemistry, Utrecht University, Utrecht, the Netherlands
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Vilar JMG, Saiz L. Systems biophysics of gene expression. Biophys J 2014; 104:2574-85. [PMID: 23790365 DOI: 10.1016/j.bpj.2013.04.032] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Revised: 04/08/2013] [Accepted: 04/12/2013] [Indexed: 01/16/2023] Open
Abstract
Gene expression is a process central to any form of life. It involves multiple temporal and functional scales that extend from specific protein-DNA interactions to the coordinated regulation of multiple genes in response to intracellular and extracellular changes. This diversity in scales poses fundamental challenges to the use of traditional approaches to fully understand even the simplest gene expression systems. Recent advances in computational systems biophysics have provided promising avenues to reliably integrate the molecular detail of biophysical process into the system behavior. Here, we review recent advances in the description of gene regulation as a system of biophysical processes that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. There is now basic mechanistic understanding on how promoters controlled by multiple, local and distal, DNA binding sites for transcription factors can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including precision and flexibility of the transcriptional responses.
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Affiliation(s)
- Jose M G Vilar
- Biophysics Unit CSIC-UPV/EHU and Department of Biochemistry and Molecular Biology, University of the Basque Country, Bilbao, Spain.
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Kubrycht J, Sigler K, Souček P. Virtual interactomics of proteins from biochemical standpoint. Mol Biol Int 2012; 2012:976385. [PMID: 22928109 PMCID: PMC3423939 DOI: 10.1155/2012/976385] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2012] [Revised: 05/18/2012] [Accepted: 05/18/2012] [Indexed: 12/24/2022] Open
Abstract
Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.
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Affiliation(s)
- Jaroslav Kubrycht
- Department of Physiology, Second Medical School, Charles University, 150 00 Prague, Czech Republic
| | - Karel Sigler
- Laboratory of Cell Biology, Institute of Microbiology, Academy of Sciences of the Czech Republic, 142 20 Prague, Czech Republic
| | - Pavel Souček
- Toxicogenomics Unit, National Institute of Public Health, 100 42 Prague, Czech Republic
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Saiz L. The physics of protein-DNA interaction networks in the control of gene expression. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2012; 24:193102. [PMID: 22516977 DOI: 10.1088/0953-8984/24/19/193102] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Protein-DNA interaction networks play a central role in many fundamental cellular processes. In gene regulation, physical interactions and reactions among the molecular components together with the physical properties of DNA control how genes are turned on and off. A key player in all these processes is the inherent flexibility of DNA, which provides an avenue for long-range interactions between distal DNA elements through DNA looping. Such versatility enables multiple interactions and results in additional complexity that is remarkably difficult to address with traditional approaches. This topical review considers recent advances in statistical physics methods to study the assembly of protein-DNA complexes with loops, their effects in the control of gene expression, and their explicit application to the prototypical lac operon genetic system of the E. coli bacterium. In the last decade, it has been shown that the underlying physical properties of DNA looping can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including the balance between robustness and sensitivity of the induction process. These physical properties are largely dependent on the free energy of DNA looping, which accounts for DNA bending and twisting effects. These new physical methods have also been used in reverse to uncover the actual in vivo free energy of looping double-stranded DNA in living cells, which was not possible with existing experimental techniques. The results obtained for DNA looping by the lac repressor inside the E. coli bacterium showed a more malleable DNA than expected as a result of the interplay of the simultaneous presence of two distinct conformations of looped DNA.
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Affiliation(s)
- Leonor Saiz
- Department of Biomedical Engineering, University of California, 451 East Health Sciences Drive, Davis, CA 95616, USA.
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Sequential-dissociation kinetics of non-covalent complexes of DNA with multiple proteins in separation-based approach: General theory and its application. Anal Chim Acta 2012; 724:111-8. [DOI: 10.1016/j.aca.2012.01.057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2011] [Revised: 01/27/2012] [Accepted: 01/29/2012] [Indexed: 11/20/2022]
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Tkačik G, Walczak AM, Bialek W. Optimizing information flow in small genetic networks. III. A self-interacting gene. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 85:041903. [PMID: 22680494 DOI: 10.1103/physreve.85.041903] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Indexed: 06/01/2023]
Abstract
Living cells must control the reading out or "expression" of information encoded in their genomes, and this regulation often is mediated by transcription factors--proteins that bind to DNA and either enhance or repress the expression of nearby genes. But the expression of transcription factor proteins is itself regulated, and many transcription factors regulate their own expression in addition to responding to other input signals. Here we analyze the simplest of such self-regulatory circuits, asking how parameters can be chosen to optimize information transmission from inputs to outputs in the steady state. Some nonzero level of self-regulation is almost always optimal, with self-activation dominant when transcription factor concentrations are low and self-repression dominant when concentrations are high. In steady state the optimal self-activation is never strong enough to induce bistability, although there is a limit in which the optimal parameters are very close to the critical point.
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Affiliation(s)
- Gašper Tkačik
- Institute of Science and Technology Austria, Am Campus 1, A-3400 Klosterneuburg, Austria.
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Petrov AP, Cherney LT, Dodgson B, Okhonin V, Krylov SN. Separation-Based Approach to Study Dissociation Kinetics of Noncovalent DNA–Multiple Protein Complexes. J Am Chem Soc 2011; 133:12486-92. [DOI: 10.1021/ja106782j] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Alexander P. Petrov
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Leonid T. Cherney
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Bryan Dodgson
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Victor Okhonin
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Sergey N. Krylov
- Department of Chemistry and Centre for Research on Biomolecular Interactions, York University, Toronto, Ontario, M3J 1P3, Canada
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Stamatakis M, Zygourakis K. Deterministic and stochastic population-level simulations of an artificial lac operon genetic network. BMC Bioinformatics 2011; 12:301. [PMID: 21791088 PMCID: PMC3181209 DOI: 10.1186/1471-2105-12-301] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2011] [Accepted: 07/26/2011] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND The lac operon genetic switch is considered as a paradigm of genetic regulation. This system has a positive feedback loop due to the LacY permease boosting its own production by the facilitated transport of inducer into the cell and the subsequent de-repression of the lac operon genes. Previously, we have investigated the effect of stochasticity in an artificial lac operon network at the single cell level by comparing corresponding deterministic and stochastic kinetic models. RESULTS This work focuses on the dynamics of cell populations by incorporating the above kinetic scheme into two Monte Carlo (MC) simulation frameworks. The first MC framework assumes stochastic reaction occurrence, accounts for stochastic DNA duplication, division and partitioning and tracks all daughter cells to obtain the statistics of the entire cell population. In order to better understand how stochastic effects shape cell population distributions, we develop a second framework that assumes deterministic reaction dynamics. By comparing the predictions of the two frameworks, we conclude that stochasticity can create or destroy bimodality, and may enhance phenotypic heterogeneity. CONCLUSIONS Our results show how various sources of stochasticity act in synergy with the positive feedback architecture, thereby shaping the behavior at the cell population level. Further, the insights obtained from the present study allow us to construct simpler and less computationally intensive models that can closely approximate the dynamics of heterogeneous cell populations.
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Affiliation(s)
- Michail Stamatakis
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA
| | - Kyriacos Zygourakis
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA
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