Welcome to the q-bio Summer School and Conference!

Stochastic Gene Regulation, 2015

From Q-bio

This series of lectures and projects will be held at the new Fort Collins Colorado campus. We will explore stochasticity and cell-to-cell variability in the measurement and modeling of biochemical systems. In particular, we will concentrate on the effects that small numbers of important molecules (i.e. genes, RNA, and protein) have on the dynamics of living cells. We will review experimental manifestations of stochastic effects in molecular biology, as can be measured using single-cell and single-molecule techniques. We will discuss the most recent analytical and numerical methods that are used to model these systems and show how these methods can improve interpretation of experimental data. We will study how different cellular mechanisms control and/or exploit randomness in order to survive in uncertain environments. Similarly, we will explore how single-cell measurements of cell-to-cell variability can reveal more information about underlying cellular mechanisms.

This section of the summer school will include a number of instructor-suggested group projects, in which students will apply various numerical techniques to formulate, identify and solve stochastic models for gene regulatory systems. Students will then apply these tools to model experimental flow cytometry or other single-cell data. Access and knowledge of Matlab will be helpful, but is not strictly necessary.

This section of the summer school will be co-organized by Brian Munsky and Doug Shepherd. Please address all questions about this section of the summer school to an organizer.

Speakers

Lecturers (Tentative)

Project Mentors

Course Materials

Topics

  • Introduction to Stochasticity. The importance of stochasticity in gene regulatory networks. Key examples from the literature.
  • Discussion of the importance of stochasticity in small populations. Stochastic Phenomena: switching, focusing, resonance, filtering.
  • The effects of positive and negative feedback.
  • The physics behind stochastic chemical kinetics.
  • Connection between deterministic and stochastic reaction rates.
  • Derivation of the Master Equation for discrete stochastic processes.
  • Solving the Chemical Master Equation: exact solutions for linear propensity functions,
  • Kinetic Monte Carlo algorithms: Gillespie algorithm, Tau Leaping. Chemical Langevin equation. Time separation schemes. Hybrid methods.
  • Density Computation Approaches: Finite State projections techniques, Moment Generating Function Techniques, Moment Closure Techniques, Fokker Planck equation.
  • Simplification of complex biochemical processes.
  • Switch rate analyses, waiting/completion times.
  • Single cell measurement techniques: flow cytometry, fluorescence microscopy, time lapse microscopy.
  • Using fluctuations to infer system mechanisms and parameters.
  • Markov Chain Monte Carlo methods.

Lectures and slides

  • To be announced

Homework

  • TBA

Software

  • To be announced

Journal Club Readings

All students with an emphasis in this section should be prepared to read and discuss the following articles on the effects, importance, and analysis of single-cell variability. This list is by no means comprehensive. Additional materials will be sent to you directly, and other articles will be posted here as the course progresses. The citations in bold face are probably to the most useful and accessible for the course.

  • Arkin, A., J. Ross, and M. H. 1998. Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected escherichia coli cells. Genetics. 149:1633–1648.
  • Becskei, A., and L. Serrano. 2000. Engineering stability in gene networks by autoregulation. Nature. 405:590–593.
  • Bel, G., B. Munsky, and I. Nemenman. 2010. Simplicity of completion time distributions for common complex biochemical processes. Physical Biology. 7.
  • Cagatay, T., M. Turcotte, M. Elowitz, J. Garcia-Ojalvo, and G. Suel. 2009. Architecture- dependent noise discriminates functionally analogous differentiation circuits. Cell. 139:512–522.
  • Dunlop, M., R. Cox III, J. Levine, R. Murray, and M. Elowitz. 2008. Regulatory activity revealed by dynamic correlations in gene expression noise. Nature Genetics. 40:1493–1498.
  • Elowitz, M., A. Levine, E. Siggia, and P. Swain. 2002. Stochastic gene expression in a single cell. Science. 297:1183–1186.
  • Elf, J., and M. Ehrenberg. 2003. Fast evaluations of fluctuations in biochemical networks with the linear noise approximation. Genome Research. 13:2475–2484.
  • Gandhi, S. J., Zenklusen, D., Lionnet, T., & Singer, R. H. (2010). Transcription of functionally related constitutive genes is not coordinated. Nature Structural & Molecular Biology, 18(1), 27–34.
  • Gillespie, D. T. 1992. A rigorous derivation of the chemical master equation. Physica A. 188:404–425.
  • Gillespie, D. T. 1977. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81:2340–2360.
  • Gillespie, D. T. 2000. The chemical Langevin equation. J. Chem. Phys. 113:297–306.
  • Gomez-Uribe, C., and G. Verghese. 2007. Mass fluctuation kinetics: Capturing stochastic effects in systems of chemical reactions through coupled mean-variance computations. J. Chem. Phys. 126.
  • Kobayashi, H., M. Kaern, M. Araki, K. Chung, T. Gardner, C. Cantor, and J. Collins. 2004. Programmable cells: Interfacing natural and engineered gene networks. PNAS. 101:8414–8419.
  • Liepe, J., Kirk, P., Filippi, S., Toni, T., Barnes, C. P., & Stumpf, M. P. H. (2014). A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. Nature Protocols, 9(2), 439–456. doi:10.1038/nprot.2014.025
  • McAdams, M., and A. Arkin. 1999. Its a noisy business! Tren. Gen. 15:65–69.
  • Munsky, B., and M. Khammash. 2008. The finite state projection approach for the analysis of stochastic noise in gene networks. IEEE Trans. Automat. Contr./IEEE Trans. Circuits and Systems: Part 1. 52:201–214.
  • Munsky, B., B. Trinh, and M. Khammash. 2009. Listening to the noise: random fluctuations reveal gene network parameters. Molecular Systems Biology. 5.
  • Munsky, B., Neuert, G., van Oudenaarden, A. Using Gene Expression Noise to Understand Gene Regulation, Science, 337:183-187.
  • Neuert, G., Munsky, B., Tan, R. Z., Teytelman, L., Khammash, M., & van Oudenaarden, A. (2013). Systematic identification of signal-activated stochastic gene regulation. Science (New York, N.Y.), 339(6119), 584–587.
  • Paulsson, J., O. Berg, and M. Ehrenberg. 2000. Stochastic focusing: Fluctuation-enhanced sensitivity of intracellular regulation. PNAS. 97:7148–7153.
  • Raser, J. M. (2004). Control of Stochasticity in Eukaryotic Gene Expression. Science (New York, N.Y.), 304(5678), 1811–1814.
  • Raj, A., and A. van Oudenaarden. 2009. Single-molecule approaches to stochastic gene expression. Annual Review of Biophysics. 38:255–270
  • Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., & Tyagi, S. (2006). Stochastic mRNA Synthesis in Mammalian Cells. PLoS Biol, 4(10), e309.
  • Ruess, J., Milias-Argeitis, A., & Lygeros, J. (2013). Designing experiments to understand the variability in biochemical reaction networks. Journal of the Royal Society Interface, 10(88).
  • Salis, H., and Y. Kaznessis. 2005. Accurate hybrid stochastic simulation of a system of coupled chemical or biological reactions. J. Chem. Phys. 112.
  • So, L.-H., Ghosh, A., Zong, C., Sepúlveda, L. A., Segev, R., & Golding, I. (2011). General properties of transcriptional time series in Escherichia coli. Nature Genetics, 43(6), 554–560.
  • Taniguchi, Y., Choi, P. J., Li, G. W., Chen, H., Babu, M., Hearn, J., et al. (2010). Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells. Science (New York, N.Y.), 329(5991), 533–538.
  • Thattai, M., and A. van Oudenaarden. 2001. Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. 98:8614–8619.
  • Villaverde, A. F., & Banga, J. R. (2013). Reverse engineering and identification in systems biology: strategies, perspectives and challenges. Journal of the Royal Society Interface, 11(91), 20130505–20130505.
  • Zechner, C., Unger, M., Pelet, S., Peter, M., & Koeppl, H. (2014). Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings. Nature Methods, 11(2), 197–202.
  • Zenklusen, D., Larson, D. R., & Singer, R. H. (2008). Single-RNA counting reveals alternative modes of gene expression in yeast. Nature Structural & Molecular Biology, 15(12), 1263–1271.

Group Projects

  • A variety of group projects will be proposed and discussed in the first few days of class. Projects are expected to last beyond the dates of the school and may result in a scientific publications. Projects will consider open problems in the integration of experimental and computational investigations of stochastic gene regulation. General research projects will include: single cell image processing, computational analysis of single cell and single molecule measurements, computer aided experiment selection, among others.
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