5th q-bio Summer School Themes
The school comprises four overlapping series of lectures and three major themes (stochastic gene regulation, cell signaling systems, and multiscale properties of biomolecules). A prospective student should have research interests in one of these areas.
- Stochastic Gene Regulation
- Cell Signaling Systems
- Spatio-Temporal Properties of Signaling Molecules
- Other Topics
Stochastic Gene Regulation
In this theme, 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, RNAs and proteins) 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 is organized by Brian Munsky. Please address all questions about this section of the summer school to its organizer.
Lecturers:
- Brian Munsky, Los Alamos National Laboratory
- Elizabeth Hong-Geller, Los Alamos National Laboratory
- Gregor Neuert, Massachusetts Institute of Technology
- Nikolai Sinitsyn, Los Alamos National Laboratory
- Jim Werner, Los Alamos National Laboratory
- Pieter Rein ten Wolde, FOM Institute AMOLF
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: 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.
- Signal Processing in Biochemical networks.
- Synthetic Biology
- siRNA Effects in Host Pathogen Interactions
Lectures and slides:
- Brian Munsky's introduction slides are available at: File:Munsky Intro.pdf
- The first half of Brian's slides is at: File:Munsky Part 1.pdf
- The second half is available at: File:Munsky Part 2.pdf
Homework and Group Projects:
- All homework problems are meant to be done in groups. Feel free to use Matlab, c++ fortran, python or whatever.
- Students should write and test Stochastic Simulation Algorithm representations of the toggle switch model in Homework 2. These should be analyzed with three different approaches: First Reaction method, Direct Reaction Method, and Tau Leaping. Run many trajectories and collect statistics. Comment upon the similarities and differences between the various methods.
- Homework 1: Media:QBIO HWK1.pdf
- Homework 2: Stochastic Analysis of a toggle Switch: Media:Toggle HWK.pdf
- Group projects will be announced in an afternoon session.
Software:
- FSP_Tool_Kit 1.0 Please email me, and I will send the software to you.
- Others.
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.
- [1] McAdams, M., and A. Arkin. 1999. Its a noisy business! Tren. Gen. 15:65–69.
- [2] Elowitz, M., A. Levine, E. Siggia, and P. Swain. 2002. Stochastic gene expression in a single cell. Science. 297:1183–1186.
- [3] Thattai, M., and A. van Oudenaarden. 2001. Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. 98:8614–8619.
- [4] 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.
- [5] Becskei, A., and L. Serrano. 2000. Engineering stability in gene networks by autoregulation. Nature. 405:590–593.
- [6] 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.
- [7] 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.
- [8] Raj, A., and A. van Oudenaarden. 2009. Single-molecule approaches to stochastic gene expression. Annual Review of Biophysics. 38:255–270
- [9] Gillespie, D. T. 1992. A rigorous derivation of the chemical master equation. Physica A. 188:404–425.
- [10] Gillespie, D. T. 1977. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81:2340–2360.
- [11] Elf, J., and M. Ehrenberg. 2003. Fast evaluations of fluctuations in biochemical networks with the linear noise approximation. Genome Research. 13:2475–2484.
- [12] Gmez-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.
- [13] 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.
- [14] Gillespie, D. T. 2000. The chemical Langevin equation. J. Chem. Phys. 113:297–306.
- [15] Salis, H., and Y. Kaznessis. 2005. Accurate hybrid stochastic simulation of a system of coupled chemical or biological reactions. J. Chem. Phys. 112.
- [16] 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.
- [17] Munsky, B., B. Trinh, and M. Khammash. 2009. Listening to the noise: random fluctuations reveal gene network parameters. Molecular Systems Biology. 5.
- [18] Bel, G., B. Munsky, and I. Nemenman. 2010. Simplicity of completion time distributions for common complex biochemical processes. Physical Biology. 7.
- [19] Paulsson, J., O. Berg, and M. Ehrenberg. 2000. Stochastic focusing: Fluctuation-enhanced sensitivity of intracellular regulation. PNAS. 97:7148–7153.
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 minor scientific publication. Projects will consider open problems in the analysis of stochastic gene regulation. General research projects will include: computational theory and software improvement, data fitting and parameter inference from single cell measurements, computer aided experiment selection, among others.
Cell Signaling Systems
This series of lectures will be focused on modeling cell signaling. We will begin with an overview of the inherent features of cell signaling systems, including multisite phosphorylation, ubiquitination and regulated recruitment (co-localization of enzymes and substrates). We will then discuss how these features complicate efforts to develop predictive mechanistic models of cell signaling systems and possible solutions, in particular the rule-based modeling approach. We will cover methods for simulating a model, visualizing and annotating a model, and fitting procedures. To cover the latter topic, we will also discuss the types of quantitative data available to support modeling efforts. We will make extensive use of software tools that are compatible with the BioNetGen language (BNGL) or the closely related Kappa language. An example of such a tool is BioNetGen (http://bionetgen.org).
Students will participate in a group project designed to introduce students to the essential steps in formulation and analysis of a rule-based model for a cell signaling system.
This lecture series in 2011 q-bio Summer School is organized by William S. Hlavacek. Please address all questions about this part of the summer school to its organizer.
Lecturers
- James R. Faeder, PhD, University of Pittsburgh School of Medicine
- Byron Goldstein, PhD, Los Alamos National Laboratory
- Ryan N. Gutenkunst, PhD, University of Arizona
- William S. Hlavacek, PhD, Los Alamos National Laboratory and University of New Mexico
- Edward C. Stites, MD, PhD, Translational Genomics Research Institute
Tutors
The experts listed below will be available to assist students during at least one of the lab exercises.
- Dipak Barua, Los Alamos National Laboratory
- Bin Hu, Los Alamos National Laboratory
- Jin Yang, Chinese Academy of Sciences and Max Planck Institute for Computational Biology, Shanghai
Lectures and Slides
All lectures will pertain to modeling of cell signaling systems, with an emphasis on the rule-based modeling approach. A schedule of lectures is provided below. Links to slides will appear below as lecture slides become available.
- Overview of cell signaling and introduction to rule-based modeling, (Hlavacek), followed by afternoon lab on BNGL, July 25 (slides)
- Methods for simulating rule-based models (Faeder), followed by afternoon lab on BioNetGen, August 1 (for slides and other materials, see below)
- Software for rule-based modeling and applications (Faeder), followed by afternoon lab on RuleBender and NFsim, August 2 (for slides and other materials, see below)
- Parameter estimation, sensitivity analysis and sloppiness (Gutenkunst), August 3 (slides)
- Mechanistic rule-based models for understanding cancer biology (Stites), followed by afternoon lab, August 4
- Parameters (Goldstein), followed by afternoon lab on parameter values and units, August 5 [Dr. Goldstein's plan is to talk about the three types of parameters that must be entered in a rule-based model of a signaling cascade: 1. Geometric factors such as the cytosolic volume, the surface area of the plasma membrane and the external volume associated with each cell; 2. The concentrations of the components of the model; and 3. The rate constants that describe the dynamics of the biochemistry that are encoded in the rules.]
Materials from Dr. Faeder:
Topics
- Inherent features of cell signaling systems
- Combinatorial complexity
- Conventional modeling approaches
- The rule-based modeling approach
- Pitfalls of model specification
- Visualizing and annotating a rule-based model
- Generate-first simulation
- On-the-fly simulation
- Network-free simulation
- Software tools for simulation
- Fitting
- Sensitivity analysis
- Sloppiness
Labs
The following subjects will be covered:
- BioNetGen language (BNGL)
- Anatomy of a BioNetGen input file
- Using RuleBender, BioNetGen and NFsim
- Recommended units for rate constants
- Online resources useful for model building (e.g., UniProt, HPRD, and Phospho.ELM)
- Guidelines for model visualization and annotation - drawing an extended contact map and assembling a model guide
A link to a course that covers rule-based modeling at Pittsburgh is here.
Project
Two teams of students will work on a group project. The teams will be led by Sarah Olson and Karen Tkach.
Software
Reading
Students are encouraged to read the following papers before the summer school.
- Scott JD, Pawson T (2009) Cell signaling in space and time: where proteins come together and when they're apart. Science 326, 1220-1224.
- Mayer BJ, Blinov ML, Loew LM (2009) Molecular machines or pleiomorphic ensembles: signaling complexes revisited. J. Biol. 8:81.
Course Credit
You may receive course credit from the University of New Mexico (3 credit hours) for participating in the Modeling Cell Signaling section of the 2011 q-bio Summer School. To learn more about the courses listed below, please contact Dr. Hlavacek (bhlavacek@tgen.org).
- Summer semester 2011, Undergraduate Problems (BIOL 499, Section 035, CRN:17299), Department of Biology, University of New Mexico
- Summer semester 2011, Research Problems (BIOL 551, Section 049, CRN:19876), Department of Biology, University of New Mexico
Spatio-Temporal Properties of Signaling Molecules
Description of Theme As system biology thrives to excel in providing cellular level behavior of complex biological systems, it has become imperative to integrate the molecular level events for better understanding at the system level. The objective of this theme is to provide training on computational methodologies to extract molecular level events at different resolutions. In addition we intend to provide a brief review on recent theoretical and computational methods and state-of the-art computing architectures. Finally, we will use examples from our own research to show that the real strength of these computational methodologies can only be materialized when combined with experimental studies. This section of the summer school is organized by "Gnana" S Gnanakaran. Please address all questions about this section of the summer school to its organizer.
Lecturers:
- "Gnana" S Gnanakaran, Los Alamos National Laboratory
- [Partha Ramakrishnan], Los Alamos National Laboratory
- Anurag Sethi, Los Alamos National Laboratory
- Giovanni Bellesia, Los Alamos National Laboratory
- [Jianhui Tian], Los Alamos National Laboratory
Topics:
- Introduction to Computational Structural Biology
- Molecular Modeling Approaches for biomolecular recognition
- Quantum mechanics
- Methods for enzyme catalysis
- Protein Dynamics using Molecular simulation methods
- Molecular communication over long distances using network theory approaches
- Coarse-graining approaches to access biologically relevant time and length scales
- Limitations: Dealing In vivo conditions, Sampling and force fields
Lectures and slides:
- To be announced.
Homework:
- To be announced.
Software:
The students in the spatio-temporal modeling are expected to have the following software installed and working on their laptops for the tutorial sessions in the afternoon.
Journal Club Readings:
The papers recommended for the spatio-temporal modeling journal club are listed below. The students will choose an article to present and discuss during the Journal club session.
- Equation of state calculations by fast computing machines. Metropolis et al. Journal of Chemical Physics 21(6), 1087-1092 (1953).
- Understanding modern molecular dynamics: techniques and applications M.E. Tuckerman and G.J. Martyna, Journal of Physical Chemistry B 104(2), 159-178 (2000)
- M. Levitt, A Simplified Representation of Protein Conformations for Rapid Simulation of Protein Folding. J. Mol. Biol. 104, 59-107 (1976)
- D.E. Shaw et al. Atomic-Level Characterization of the Structural Dynamics of Proteins Science 330(6002), 341-346 (2010)
- M.A. Fisher et al. De Novo Designed Proteins from a Library of Artificial Sequences Function in Escherichia Coli and Enable Cell Growth PLoS One 2011, 6(1): e15364
- Y.C. Kim and G. Hummer. Coarse-grained models for simulations of multi-protein complexes Application to ubiquitin binding. J Mol. Biol. 2008, 375:1416-1433
- H Zhou. Quantitative Relation between intermolecule and intramolecular binding to pro-rich peptides to SH3 domains. Biophys. J. 2006, 91: 3170-3181
- P.C. Whitford, et al., Conformational Transitions of Adenylate Kinase: Switching by Cracking. J. Mol. Biol., 2007, 366: 1661-1671.
- S Yang, N.K. Banavali, and B. Roux, Mapping the conformational transition in Src activation by cumulating the information from multiple molecular dynamics trajectories. PNAS, 2009, 106:3776-3781.
- M Karelson and V.S. Lobanov, and A. R. Katritzky Quantum-Chemical Descriptors in QSAR/QSPR Studies Chem. Rev., 1996, 96 (3), pp 1027–1044
- A. Cavalli, P. Carloni, and M. Recanatini, Target-Related Applications of First Principles Quantum Chemical Methods in Drug Design. Chem. Rev., 2006, 106 (9), pp 3497–3519
- M. Lundberg, T. Kawatsu, T. Vreven, M. J. Frisch, and K. Morokuma, Transition States in the Protein Environment -- ONIOM QM:MM Modeling of Isopenicillin N Synthesis,” J. Chem. Theory and Comput., 5 (2009) 222-34.
- K. Morokuma, D. G. Musaev, T. Vreven, H. Basch, M. Torrent and D. V. Khoroshun, "Model studies of the structure, reactivities, and reaction mechanisms of Metalloenzymes," IBM Journal of Research & Development 45(May/July 2001) 367.
- Stabilization and Structure Calculations for Noncovalent Interactions in Extended Molecular Systems Based on Wave Function and Density Functional Theories Kevin E. Riley, Michal Pitok, Petr Jureka, and Pavel Hobza Chem. Rev., 2010, 110 (9), pp 5023–5063
Group Projects
- To be announced.