ARL/ICB Crash Course in Systems Biology, August 2010
This course is geared toward biologists who want to become familiar with current computational biology software and capabilities, emphasizing quantitative applications for understanding and modeling complex biological systems. The course is taught by researchers from the Army Institute for Collaborative Technology and the Army Research Laboratory.
To register and obtain lodging and transportation information for the workshop, please go to:
Schedule
The course will consist of four sessions, each lasting approximately 3.5 hours (including a break in the middle of the session).
Monday, 9 Aug
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Tuesday, 10 Aug
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Lecture Outline
Session 1: Modeling and Analysis using Differential Equations
This session will provide an introduction to modeling of core processes in biology using differential equations. The first lecture will focus on the cell as a multi-layered feedback system. Scientists need to build ad hoc models to analyze the cellular complexity in a quantitative manner. Ordinary differential equations (ODEs) are a good choice when considering high copy number molecules in a well mixed environment. Several transcriptional regulation pathways in bacteria, for instance, have been successfully modeled with ODEs. We will overview the general methods to build macroscopic deterministic models of biological processes, referring to the trp operon and the iron starvation pathways as application examples. Classical control and dynamical systems analysis tools (equilibria, bifurcations and frequency analysis) will also be reviewed. Finally, we will provide some fundamental notions from the theory of chemical reaction networks. The second lecture will close the modeling process cycle by covering the model identification theory and practice. Once a model structure (system of equations) is proposed, the validity of this structure should be tested by means of an identifiability analysis, e.g. making use of sensitivity analysis tools that can help to identify critical and negligible parameters and to establish a parameter ranking. If experimental data are available, parameter estimation is then carried out, leading to a first model. Otherwise a set of experiments must be devised by means of optimal experimental design and performed before the parameter estimation. The quality of these estimators should be assessed by checking the correlation between them and computing their confidence intervals. This initial model must be validated with new experiments, which in most cases will reveal a number of deficiencies. Thus, a new model structure and/or a new experimental design must be planned, and the process is repeated iteratively until the validation step is considered satisfactory.
Lecture 1: Core Processes in Cells (Elisa Franco, Caltech) This lecture will provide an introduction to modeling of core processes in biology using differential equations. Specific topics to be covered include:
- The Cell as a Dynamical System with different layers of feedback
- Modeling techniques and ordinary differential equations
- Examples: transcriptional and post-transcriptional regulation
- Control and dynamical systems analysis tools (equilibria, bifurcations, frequency analysis)
- Basic notions of chemical reaction networks theory
Reading list:
- H. de Jong (2002). "Modeling and simulation of genetic regulatory systems: a literature review", J Comput Biol, 9(1):67-103
- M. Santillán and M. C. Mackey (2001). "Dynamic regulation of the tryptophan operon: A modeling study and comparison with experimental data", Proc. Natl. Acad. Sci. USA. 98(4):1364-1369
- E. Levine, Z. Zhang, T. Kuhlman and T. Hwa (2007). "Quantitative Characteristics of Gene Regulation by Small RNA", PLoS Biol, 5(9):e229
- M. Feinberg (1987). "Chemical reaction network structure and the stability of complex isothermal reactors-I. The deficiency zero and deficiency one theorems", Chemical Engineering Science, 42(10):2229-2268
Lecture 2: Model analysis and identification (Maria Rodriguez Fernandez, UCSB)
This lecture will provide an introduction to model identification theory and practice. Specific topics to be covered include:
- Global and local sensitivity analysis
- Identifiability analysis
- Optimal experimental design
- Robust parameter identification
- Confidence intervals of the estimated parameters
- Model identification tools
Reading list:
- M. Joshi, A. Seidel-Morgenstern, and A. Kremling (2006). "Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems", Metabolic Engineering, 8:447–455
- A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola (2008). "Global Sensitivity Analysis: The Primer", John Wiley & Sons Ltd.
- M. Rodriguez-Fernandez and J. R. Banga (2010). "SensSB: a software toolbox for the development and sensitivity analysis of systems biology models", Bioinformatics, 26(13):1675-1676
- E. Walter and L. Pronzato (1997). "Identification of Parametric Models from Experimental Data", Springer
- D. E. Zak, G. E. Gonye, J. S. Schwaber, and F. J. Doyle III (2003). "Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: Insights from an identifiability analysis of an in silico network", Genome Research, 13:2396–2405
Session 2: Stochastic Modeling and Simulation
In microscopic systems formed by living cells, the small numbers of some reactant molecules can result in dynamical behavior that is discrete and stochastic rather than continuous and deterministic. An analysis tool that respects these dynamical characteristics is the stochastic simulation algorithm (SSA). Despite recent improvements, as a procedure that simulates every reaction event, the SSA is necessarily inefficient for most realistic problems. There are two main reasons for this, both arising from the multi-scale nature of the underlying problem: (1) the presence of multiple timescales (both fast and slow reactions); and (2) the need to include in the simulation both chemical species that are present in relatively small quantities and should be modeled by a discrete stochastic process, and species that are present in larger quantities and are more efficiently modeled by a deterministic differential equation. In the first half of the session, we will first describe the SSA, and then outline the methods such as tau-leaping, hybrid, slow-scale SSA and finite state projection that have been developed to accelerate the process of discrete stochastic simulation for well-mixed chemically reacting systems. Then we will examine the state of the art in algorithms and software for discrete stochastic simulation of spatially-dependent biochemical systems. The second half of the session will focus on StochKit, a software package for simulation of stochastic models. StochKit provides command-line executable for running stochastic simulations using variants of Gillespie’s Stochastic Simulation Algorithm (SSA) and Tau-leaping. Among the numerous implementations of the SSA, StochKit provides solvers for the most well used and efficient methods: SSA Direct Method, Optimized Direct Method [Cao et al. 2004], Logarithmic Direct Method, and a Constant-Time Algorithm [Slepoy et al. 2008]. As for the Tau-leaping algorithm, we provide a solver for an Adaptive Explicit Tau-leaping method. To further increase the computational efficiency, StochKit provides automatic parallelization and a converter for SBML files. We will give a comprehensive review of the available algorithms and illustrate how to use Matlab functions in StochKit to process output files. For advanced developers, we will briefly illustrate how to build a custom solver for specific needs.
Lecture 3: Multiscale Discrete Stochastic Simulation of Biochemical Systems (Linda Petzold, UCSB)
- Algorithms for well-mixed systems
- SSA
- Tau-Leaping
- Hybrid
- Slow-Scale SSA
- Finite-State Projection
- Algorithms and software for spatially-dependent systems
- Inhomogeneous SSA
- Diffusive Finite State Projection
- Complex geometries and URDME software
Reading list:
Lecture 4: Stochkit (Min Roh, UCSB)
- Presentation on StochKit
- Available stochastic solvers
- Creating a model
- SBML conversion
- Output processing
- Examples
- Custom models and drivers
Reading list:
Available software:
Session 3: Data Acquisition and Analysis
Since its inception 15 years ago, the DNA microarray has become a staple experimental tool for exploring the effects of biological intervention on gene expression. By measuring the abundance of tens of thousands of mRNA transcripts at once, microarrays provide a genome-wide characterization of biological function. While the high dimensionality of microarray data provides a distinct advantage over smaller scale experimental platforms, it also requires intelligent use of data processing and analysis techniques to control for sources of noise, systematic bias, and statistical artifacts. This session will provide an overview of the workflow required to transform raw microarray data into biological insight. In the first lecture, we will begin by introducing the R/Bioconductor analysis platform. We will then describe data preprocessing techniques including background subtraction, dye bias normalization, and scale normalization. These techniques reduce the effects of noise and systematic biases often associated with high dimensional data. Next, we will discuss methods to identify differentially expressed genes--i.e., genes whose transcripts are expressed at different levels between experimental conditions. Through the use of sophisticated statistical methods, we will obtain subsets of genes showing reproducible expression changes across experimental replicates. These individual genes provide the first clues into the underlying biological processes acting in the experiment of interest. In the second lecture, we will focus on functional classification and ontological analyses of genes of interest. We will apply pathway enrichment and network analysis to identify functions and networks highly enriched in a set of genes. We will discuss ways for functional analyses of time series data sets and ways to present gene expression data.
Lecture 5: Microarray data preprocessing and differential expression analysis (Bernie Daigle, UCSB)
- Introduction to R/Bioconductor
- Preprocessing
- Data import
- Within-array normalization
- Between-array normalization
- Data visualization
- Differential expression analysis
- Computation of test statistics
- Statistical significance and multiple testing
Reading list:
- J. Quackenbush (2002). "Microarray data normalization and transformation", Nat Genet, 32 Suppl:496-501
- X. Cui and G.A. Churchill (2003). "Statistical tests for differential expression in cDNA microarray experiments", Genome Biol, 4(4):210
- DNA microarray data analysis using Bioconductor
Required software:
- R software environment
- Bioconductor packages for genomic data analysis: Biobase, affy, limma, marray, hgug4110b
- Tutorial source code
Required data:
Lecture 6: Data mining, ontological classification and pathway analysis of microarray gene expression (Rasha Hammamieh, WRAIR)
- Data presentation
- Annotation
- Functional Analysis
- Pathway Analysis
Available software:
- Cytoscape with plugins: BisoGenet, Bingo, ClusterMaker, Agilent Literature search
- Cluster
- TreeView
- Short Time-series Expression Miner (STEM)
Required data:
Session 4: Applications
Lecture 7: Polarization in Yeast Mating (Mike Lawson, UCSB)
One of the most well-studied examples of cell polarization is the growth of the mating projection in Saccharomyces cerevisiae. A single molecular entity located at the front of the cell, termed the polarisome, helps to organize structural, transport, and signaling proteins. We have developed a spatial stochastic model (utilizing the reaction-diffusion master equation) of polarisome formation in mating yeast, focusing on the tight localization of proteins on the membrane. Prior work has produced deterministic (PDE) mathematical models that describe the spatial dynamics of yeast cell polarization in response to spatial gradients of mating pheromone; however, these required special mechanisms (e.g. high cooperativity) to match the characteristic punctate of the polarisome. This new model is built on simple mechanistic components, but is able to achieve a highly polarized phenotype even in relatively shallow input gradients. Preliminary results highlight the need for spatial stochastic modeling because deterministic simulation fails to achieve a sharp break in symmetry.
Lecture 8: Biological variability and model uncertainty (Camilla Luni, UCSB)
Structured Singular Value (SSV) analysis is a tool developed in control theory useful to analyze uncertain biological models. A range of applications will be presented that focus on its use for analysis of fragility points in the network, drug screening, model discrimination, and model extension.
Reading list:
- Jacobsen, E. W. and C. Trane (2010). "Using dynamic perturbations to identify fragilities in biochemical networks." International Journal of Robust and Nonlinear Control 20(9): 1027-1046
- Jacobsen, E. W. and G. Cedersund (2008). "Structural robustness of biochemical network models-with application to the oscillatory metabolism of activated neutrophils." Iet Systems Biology 2(1): 39-47
- Kim, J. S., N. V. Valeyev, et al. (2010). "Analysis and extension of a biochemical network model using robust control theory." International Journal of Robust and Nonlinear Control 20(9): 1017-1026
- Shoemaker, J. E. and F. J. Doyle (2008). "Identifying fragilities in biochemical networks: Robust performance analysis of Fas signaling-induced apoptosis." Biophysical Journal 95(6): 2610-2623
Lecture 9: Biofuels (Adam Arkin, LBNL)
