Parametric modeling of protein-DNA binding kinetics: a discrete event based simulation approach. (English) Zbl 1165.92013
Summary: To understand the stochastic behavior of biological systems, we adopt an “in silico” stochastic event based simulation methodology that can determine the temporal dynamics of different molecules. The main requirement for this technique are the event execution time models for the different biological functions of the system. This paper presents a parametric model to determine the execution time of one such biological function: protein-DNA binding. This biological function is modeled as a combination of microlevel biological events using a coarse-grained probability measure to estimate the stochastic parameters of the function. Our model considers the actual binding mechanism along with some approximated protein and DNA structural information. We use a collision theory based approach to transform the thermal and concentration gradients of this biological function into their corresponding probability measure. This information theoretic approach significantly removes the complexity of the classical protein sliding along the DNA model, improves the speed of computation and can bypass the speed-stability paradox.
This model can produce acceptable estimates of DNA-protein binding times necessary for our event based stochastic system simulator where the higher order (more than second order statistics) uncertainties can be ignored. The results show good correspondence with available experimental estimates. The model depends very little on experimentally generated rate constants and brings the important biological parameters and functions into consideration. We also present some “in silico” results showing the effects of protein-DNA binding on gene expression in prokaryotic cells.
This model can produce acceptable estimates of DNA-protein binding times necessary for our event based stochastic system simulator where the higher order (more than second order statistics) uncertainties can be ignored. The results show good correspondence with available experimental estimates. The model depends very little on experimentally generated rate constants and brings the important biological parameters and functions into consideration. We also present some “in silico” results showing the effects of protein-DNA binding on gene expression in prokaryotic cells.
MSC:
| 92C40 | Biochemistry, molecular biology |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 92C45 | Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) |
Software:
CelldesignerReferences:
| [1] | Adalsteinsson, D.; McMillen, D.; Elston, T. C., Biochemical network stochastic simulator (BioNets): Software for stochastic modeling of biochemical networks, BMC Bioinformatics, 5, 24 (2004) |
| [2] | Alberts, B.; Bray, D.; Lewis, J.; Raff, M.; Roberts, K.; Watson, J. D., Molecular Biology of the Cell (1994), Garland Publishing |
| [3] | Bell, C. E.; Lewis, M., A closer view of the conformation of the Lac repressor bound to operator, Nature Struct. Biol., 7, 209-214 (2000) |
| [4] | Bell, C. E.; Lewis, M., The Lac repressor: A second generation of structural and functional studies, Curr. Opin. Struct. Biol., 11, 19-25 (2001) |
| [5] | Berg, O. G.; von Hippel, P. H., Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters, J. Mol. Biol., 193, 723-750 (1987) |
| [6] | Berg, O. G.; Winter, R. B.; von Hippel, P. H., Diffusion-driven mechanisms of protein translocation on nucleic acids. 1. Models and theory, Biochemistry, 20, 6929-6948 (1981) |
| [7] | Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E., The protein data bank, Nucleic Acids Res., 28, 235-242 (2000) |
| [8] | Browning, D.; Busby, S., The regulation of bacterial transcription initiation, Nature Rev. Microbiol., 2, 57-65 (2004) |
| [9] | Cai, L., Stochastic protein expression in individual cells at the single molecule level, Nature Lett., 440, 358-362 (2006) |
| [10] | Funahashi, A.; Tanimura, N.; Morohashi, M.; Kitano, H., CellDesigner: A process diagram editor for gene-regulatory and biochemical networks, Biosilico, 1, 159-162 (2003) |
| [11] | S. Ghosh, P. Ghosh, K. Basu, S. Das, Modeling the stochastic dynamics of gene expression in single cells: A birth and death Markov chain analysis, in: IEEE International Conference on Bioinformatics and Biomedicine, 2007; S. Ghosh, P. Ghosh, K. Basu, S. Das, Modeling the stochastic dynamics of gene expression in single cells: A birth and death Markov chain analysis, in: IEEE International Conference on Bioinformatics and Biomedicine, 2007 |
| [12] | S. Ghosh, P. Ghosh, K. Basu, S. Das, S. Daefler, iSimBioSys: A discrete event simulation platform for in silico study of biological systems. in: Proceedings of 39th IEEE Annual Simulation Symposium, 2006, pp. 1-8; S. Ghosh, P. Ghosh, K. Basu, S. Das, S. Daefler, iSimBioSys: A discrete event simulation platform for in silico study of biological systems. in: Proceedings of 39th IEEE Annual Simulation Symposium, 2006, pp. 1-8 |
| [13] | P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, An analytical model to estimate the time taken for cytoplasmic reactions for stochastic simulation of complex biological systems, in: 2nd IEEE Granular Computing Conference, 2006, pp. 79-84; P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, An analytical model to estimate the time taken for cytoplasmic reactions for stochastic simulation of complex biological systems, in: 2nd IEEE Granular Computing Conference, 2006, pp. 79-84 · Zbl 1162.92306 |
| [14] | P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, Modeling the diffusion process in the PhoPQ signal transduction system: A stochastic event based simulation framework, in: Intl. Symp. on Computational Biology & Bioinformatics, ISBB, 2006, pp. 1-6; P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, Modeling the diffusion process in the PhoPQ signal transduction system: A stochastic event based simulation framework, in: Intl. Symp. on Computational Biology & Bioinformatics, ISBB, 2006, pp. 1-6 |
| [15] | P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, A stochastic model to estimate the time taken for Protein-Ligand Docking, in: 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB, 2006, pp. 1-8; P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, A stochastic model to estimate the time taken for Protein-Ligand Docking, in: 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB, 2006, pp. 1-8 |
| [16] | P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, Stochastic modeling of cytoplasmic reactions in complex biological systems, in: 6th IEE International Conference on Computational Science and its Applications, ICCSA, 2006, pp. 566-576; P. Ghosh, S. Ghosh, K. Basu, S. Das, S. Daefler, Stochastic modeling of cytoplasmic reactions in complex biological systems, in: 6th IEE International Conference on Computational Science and its Applications, ICCSA, 2006, pp. 566-576 · Zbl 1162.92306 |
| [17] | Gillespie, D. T., Exact stochastic simulation of coupled chemical reactions, J. Phys. Chem., 81, 25, 2340-2361 (1977) |
| [18] | Greive, S. J.; von Hippel, P. H., Thinking quantitatively about transcriptional regulation, Nature Rev. Mol. Cell Biol., 6, 221-232 (2005) |
| [19] | Grillo, A. O.; Brown, M. P.; Royer, C. A., Probing the physical basis for Trp repressor-operator recognition, J. Mol. Biol., 287, 539-554 (1999) |
| [20] | Harbison, C. T., Transcriptional regulatory code of a eukaryotic genome, Nature, 431, 99-104 (2004) |
| [21] | Hasty, J.; Collins, J. J., Translating the noise, Nature Genet., 31, 13-14 (2002) |
| [22] | Keseler, I. M.; Collado-Vides, J.; Gama-Castro, S.; Ingraham, J.; Paley, S.; Paulsen, I. T.; Peralta-Gil, M.; Karp, P. D., EcoCyc: A comprehensive database resource for Escherichia coli, Nucleic Acids Res., 33 (2005), D334-337 |
| [23] | Kierzek, A. M.; Zaim, J.; Zielenkiewicz, P., The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression, J. Biol. Chem., 276, 11, 8165-8172 (2001) |
| [24] | Lomakin, A.; Frank-Kamenetskii, M., A theoretical analysis of specificity of nucleic acid interactions with oligonucleotides and peptide nucleic acids (PNAs), J. Mol. Biol., 276, 57-70 (1998) |
| [25] | Luscombe, N. M.; Austin, S. E.; Berman, H. M.; Thornton, J. M., An overview of the structures of protein-DNA complexes, Genome Biol., 1, 1-37 (2000) |
| [26] | McAdams, H.; Arkins, A., Stochastic mechanisms in gene expression, Proc. Natl. Acad. Sci., 94, 814-819 (1997) |
| [27] | Morton-Firth, C. J.; Bray, D., Predicting temporal fluctuations in an intracellular signalling pathway, J. Theoret. Biol., 192, 117-128 (1998) |
| [28] | Paulsson, J., Models of stochastic gene expression, Phys. Life Rev., 2, 157-175 (2005) |
| [29] | Samoilov, M. S.; Arkin, A. P., Deviant effects in molecular reaction pathways, Nature Biotechnol., 24, 10, 1235-1240 (2006) |
| [30] | Shimamoto, N., One-dimensional diffusion of proteins along DNA. Its biological and chemical significance revealed by single-molecule measurements, J. Biol. Chem., 274, 15293-15296 (1999) |
| [31] | Slutsky, M.; Mirny, L. A., Kinetics of protein-DNA interaction: Facilitated target location in sequence-dependent potential, Biophys. J., 87, 4021-4035 (2004) |
| [32] | Spolar, R. S.; Record, M. T., Coupling of local folding to site specific binding of proteins to DNA, Science, 263, 777-784 (1994) |
| [33] | Stormo, G. D.; Fields, D., Specificity, free energy and information content in protein-DNA interactions, Trends Biochem. Sci., 23, 109-113 (1998) |
| [34] | Takeda, Y.; Sarai, A.; Rivera, V. M., Analysis of the sequence specific interactions between Cro repressor and operator DNA by systematic base substitution experiments, Proc. Natl. Acad. Sci. USA., 86, 439-443 (1989) |
| [35] | von Hippel, P. H.; Berg, O. G., Facilitated target location in biological systems, J. Biol. Chem., 264, 675-678 (1989) |
| [36] | Winter, R. B.; Berg, O. G.; von Hippel, P. H., Diffusion-driven mechanisms of protein translocation on nucleic acids. 3. The Escherichia coli Lac repressor-operator interaction: kinetic measurements and conclusions, Biochemistry, 20, 6961-6977 (1989) |
| [37] | Yu, J., Probing gene expression in live cells, one protein molecule at a time, Science, 311, 5767, 1600-1603 (2006) |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
