• Transformation of Biology from a descriptive science into a predictive science; Integration of parts into wholes, across measurements, across disciplines, across levels of abstraction;
• Computation:Biology::Calculus:Physics; Historical development of computational thinking in biological sciences; What is a (mathematical or computational) model? What are models good for? How can we construct models? How can we evaluate models?
• Some challenges in computational systems biology.

• Klipp, E., Herwig, R., Kowald, A., Wierling,C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Chapter 1.
• Honavar, V. Lecture slides.
• Lazebnik, Y. (2002). Can a biologist fix a radio? - Or what I learned when studying apoptosis. Cancer Cell, 2:179.
• Ideker, T. (2004). Systems Biology 101 - What you need to know. Nature Biotechnology 22-4:473-475.
• Kitano, H. (2002). Computational Systems Biology. Nature 420:206-210.
• Ideker, T., Galitski, T., and Hood, L. (2001)A new approach to decoding Life: Systems Biology Annu. Rev. Genomics Hum. Genet. 2001. 2:343–72.
• Bruggeman, F.J. and Westerhoff, H.V. (2007). The nature of systems biology. Trends in Microbiology 15:45-50.
• You, L. (2004). Toward computational systems biology. Cell Biochemistry and Biophysics. 40:185-202.
• Finkelstein, A., Hetherington, J., Li, L., Margoninki, O., Saffrey, P., Seymour, R., and Warner, A. (2004) Computational challenges of systems biology. IEEE Computer, 37:26-33.

• Leroy Hood's lecture
• Doug Lauffenburger's lecture

• Cohen, J.E. (2004). Mathematics Is Biology’s Next Microscope, Only Better; Biology Is Mathematics’ Next Physics, Only Better PLOS Biology 2:2017-2023
• Wing, J. (2006)Computational thinking Communications of the ACM 49:33-35.
• Ashby, R.W. (1957).An introduction to cybernetics. London: Chapman & Hall.
• Cull, P. (2007) The mathematical biophysics of Nicolas Rashevsky Biosystems 88:178-184.
• Shannon, C. A mathematical theory of communication. Bell System Technical Journal, vol. 27, pp. 379-423 and 623-656.
• Wiener, N. (1948). Cybernetics, or Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press.
• Turing machines, Stanford encyclopedia of philosophy.
• Honavar, V. Some notes on McCulloch-Pitts neuron - example of an early attempt at computational thinking applied to biology The McCulloch-Pitts Neuron,Perceptron algorithm
• Biographies: Bertalanffy, Turing, von Neumann, Wiener, McCulloch, Pitts.
• Miller, J.G. (1975). Living Systems, New York: McGraw-Hill.

• What a metabolic network?
• Modeling chemical reactions as flows
• Enzyme Kinetics, Michaelis-Menten equation
• Transcription (Repression and Induction)
• Introduction to the Lambda Phage System: modeling a genetic switch.
• Mathematical notation and solving systems of ODEs computationally using numerical integration

• Klipp, E., Herwig, R., Kowald, A., Wierling,C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Chapter 3.2, 5.1, 5.2.
• Hasty, J., D. McMillen, et al. (2001). “Computational studies of gene regulatory networks: in numero molecular biology.” Nat Rev Genet 2(4): 268-79. ISU link
• Hasty, J., J. Pradines, et al. (2000). “Noise-based switches and amplifiers for gene expression.” Proc Natl Acad Sci U S A 97(5): 2075-80. PNAS link

• Stoichiometry and mass/energy balance
• Biomass basic formula: C H_a O_b N_c
• Key factors for measurement: respiratory quotient, yield coefficient
• Unstructured models and “one biophase” assumption
• Caveats and things to be careful of:
  • Effect of different time scales in a model
  • Mass units and measurements must all coincide (e.g., moles Carbon basis)
  • pH, temperature and other ways the system can behave differently
• Metabolic Control Analysis and Metabolic Flux introduction

• Phage biology background, model system analysis
• Definition of stability and multistability, effect of initial conditions.
• Hasty, J., J. Pradines, et al. (2000). “Noise-based switches and amplifiers for gene expression.” Proc Natl Acad Sci U S A 97(5): 2075-80. PNAS Link
• Matlab code: hasty.m, hastyfunc.m, plotstab.m for plotting the intersection of the creation and destruction terms at equilibrium.

• Dr. Shanks Webpage
• Metabolic Engineering Theme Song
• Review paper on Metabolic Pathways, B. Markus Lange and Majid Ghassemian, “Comprehensive post-genomic data analysis approaches integrating biochemical pathway maps,” Phytochemistry, Volume 66, Issue 4, , February 2005, Pages 413-451.
• Science Direct Link

* Review paper on modeling plant metabolic networks, R. Rios-Estepa and B.M. Lange, “Experimental and mathematical approaches to modeling plant metabolic networks,” Phytochemistry, Vol 68, 2007, 2351-2374. ScienceDirect

• Dr. Shanks’ Notes and Reference List.

• Stoichiometric Matrix Analysis (Section 5.2 in Text)
• Linear Systems and Stability (Sections 3.1 and 3.2 in Text)
• Metabolomics (Dr. Nikolau)

• Sections 3.1, 3.2, 5.2 in Textbook
• For the best explanation that I have ever seen on the relationship of subspaces, least squares estimation, and the singular value decomposition, please read this paper by Gilbert Strang, “The Fundamental Theorem of Linear Algebra,” The American Mathematical Monthly, Vol 100, No 9, 848-855, 1993. Strang Paper
• Class Notes Feb 5.
• Palsson ea al. paper on Extreme Pathway Analysis for metabolic networks. C. H. SCHILLING, D. LETSCHER AND B. PALSSON, “Theory for the Systemic Definition of Metabolic Pathways and their use in Interpreting Metabolic Function from a Pathway-Oriented Perspective,” J. theor. Biol. (2000) 203, 229}248. http://www.eng.iastate.edu/~julied/classes/CE570/Notes/palsson_theorypaper.pdf
• Notes from Dr. Nikolau’s lecture Notes

• Extreme Pathway Analysis
• Flux Balance Analysis
• Metabolic Pathway Databases: KEGG, Biocyc Family of databases.

• CellNetAnalyzer / FluxAnalyzer has been developed by Steffen Klamt in the Systems Biology group of Prof. Ernst Dieter Gilles at the Max Planck Insitute for Dynamics of Complex Technical Systems in Magdeburg. Academic use: free academic license. Current version is 8.0. The requirements for using CellNetAnalyzer are:
  • MATLAB version 6.1 or higher
  • some functions require the optimization toolbox of MATLAB (or/and GLPKMEX with GLPK library)

• Systems biology - hierarchy of models, need for abstraction
• Review of transcription, translation, and gene regulation
• Transcriptome, transcriptomics, and some caveats
• Designing experiments that optimally exercise the expression space

• Honavar, V. Lecture Notes
• Klipp, E., Herwig, R., Kowald, A., Wierling,C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Section 2.4 (gene expression).
• Gibson, G. Genome-scale hypothesis scanning - A primer. PLOS Biology. Vol. 1. pp.029-.
• Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL (1996). Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996, 14:1675-1680
• P. Hegde, R. Qi, K. Abernathy, C. Gay, S. Dharap, R. Gaspard, J.E. Hughes, E. Snesrud, N. Lee and J.Quackenbush (2000) A concise guide to cDNA microarray analysis Biotechniques 29:548-562
• Ness, S. Microarray analysis: basic strategies for successful experiments, Mol Biotechnol (2007) 36:205–219
• Yamamoto,M., Wakatsuki,T., Hada, A. and Ryo, A. (2001). Use of serial analysis of gene expression (SAGE) technology, Journal of Immunological Methods Vol. 250:45-66
• Schaechter, M., and Ingraham, J. What Limits Genomics, Proteomics, Transcriptomics? - International Microbiology, 5: 51–52

• Gene expression data acquisition
• cDNA, microarray, SAGE, and related technologies
• Transgenic animals, knockouts, and RNAi
• Identifying differentially expressed genes - parametric and non-parametric approaches

• Chris Tuggle's lecture notes
• Dan Nettleton's lecture notes
• Klipp, E., Herwig, R., Kowald, A., Wierling,C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Sections 9.1 - 9.2.5 (gene expression analysis).
• Draghici, S. (2002). Statistical intelligence: effective analysis of high-density microarray data Drug Discovery Today Vol. 7. No. 11. pp. S55-S63.
• Dudoit, S., et al. (2003) Multiple hypothesis testing in microarray experiments. Statistical Science 18(1): 71-103.
• Nettleton, D., Recknor, J., Reecy, J.M. (2007). Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis. Bioinformatics. 24 192-201.
• Storey, J. D., and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences 100, 9440-9445
• Reiner, A., et al. (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19(3):368-375.
• Affymetrix GeneChip Expression Analysis - Data Analysis Fundamentals (Skim)

• Gene expression data normalization
• Identifying co-expressed genes
• Commonly used distance or similarity measures and caveats
• Clustering Algorithms - k-means clustering and its variants
• Limitations of k-means clustering
• Gaussian mixture models and Expectation Maximization
• Hierarchical Clustering

• Gene expression data analysis Notes, Vasant Honavar
• Klipp, E., Herwig, R., Kowald, A., Wierling,C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Sections 9.3 (clustering algorithms).
• Sherlock, G. (2000) Analysis of Large-Scale Gene Expression Data Current opinion in immunology Volume 12, pp. 201-205.
• Yeung, K.Y. et al. (2001). Model-based clustering and data transformations for gene expression data. Bioinformatics, Vol. 17 no. 10, pp. 977-987.
• Berkhin, P. (2002)A Survey of Clustering Algorithms
• Tibshirani, R., Walther, G., Hastie, T. (2001) Estimating the number of clusters in a data set via the gap statistic Journal of the Royal Statistical Society: Series B (Statistical Methodology) Vol. 63 (2), pp 411–423 doi:10.1111/1467-9868.00293.
• Eisen, M.B., Spellman, P.T., Browndagger, P.O., and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns, PNAS Vol. 95, Issue 25, 14863-14868.
• Hecker, L., Alcon, T., Honavar, V., and Greenlee, H. Analysis and Interpretation of Large-Scale Gene Expression Data Sets Using a Seed Network. Journal of Bioinformatics and Biology Insights. Vol. 2. pp. 91-102, 2008.

• Spectral clustering of gene expression data
• Analyzing clusters - GO annotations, shared regulatory motifs
• Finding connected components and modules
• Topological characteristics of gene networks
• Small world networks
• Hierarchical networks
• Modular networks

• Gene expression data analysis Notes, Vasant Honavar
• Klipp, E., Herwig, R., Kowald, A., Wierling, C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Sections 8.1-8.2 (promoter identification).
• Tavazoie, S. Hughes, J.D., Campbell, M.J., Cho, R.J. and Church, G.M. (1999) Systematic determination of genetic network architectureNature Genetics 22, 281 - 285 (1999) doi:10.1038/10343
• Pilpel, Y., Sudarsanam, P. and Church, G.M. identifying regulatory networks by combinatorial analysis of promoter elementsNature Genetics DOI: 10.1038/ng724 (2001).
• Bussemaker HJ, Li H, Siggia ED Regulatory element detection using correlation with expression.Regulatory element detection using correlation with expression. Nature Genetics 2001, 27:167-171.
• von Luxburg, U. A tutorial on spectral clustering Statistics and Computing vol. 17, pp. 95-416 (2007).
• Isbell, C. Tutorial on Information Theory.
• Barabási, A-L., and Oltvai, Z.M.Network Biology: Understanding the Cells's Functional Organization Nature Reviews Genetics 5, 101-113 (2004)
• Z. N. Oltvai and A.-L. Barabási Life's complexity pyramid Science 298, 763-764 (2002).
• H. Jeong, Z. N. Oltvai, A.-L. Barabási Prediction of protein essentiality based on genomic data ComplexUs 1, 19-28 (2003).
• I. Farkas, H. Jeong, T. Vicsek, A.-L. Barabási, Z.N. Oltvai The topology of transcription regulatory network in the yeast, Saccharomyces cerevisiae Physica A 318, 601-612 (2003).
• S. Y. Yook, Z. N. Oltvai, A.-L. Barabási Functional and topological characterization of protein interaction networks Proteomics 4, 928-942 (2004).
• E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A.-L. Barabási Hierarchical organization of modularity in metabolic networks Science 297, 1551-1555 (2002).
• Khanin, R. and Wit How Scale-Free are Biological Networks Journal of Computational Biology,13(3): 810-818. doi:10.1089/cmb.2006.13.810. (2006).
• Emilsson et al.Genetics of Gene Expression and its Effect on Disease Nature, doi:10.1038/nature06758, 2008.

• Differential Equation models
• Information-theoretic approaches to inferring genetic networks
• Boolean Networks and Temporal Boolean Networks

• Gene Network Inference Notes, Vasant Honavar
• Klipp, E., Herwig, R., Kowald, A., Wierling, C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Section 8.3 (models of gene networks).
• Hidde de Jong. Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. Journal of Computational Biology. January 1, 2002, 9(1): 67-103.
• Patrick D’Haeseleer, Shoudan Liang, and Roland Somogyi Tutorial on Genetic Network InferencePacific Symposium on Biocomputing, 1999.
• Nils Nilsson, Decision Trees
• Claude Shannon A Mathematical Theory of Communication
• Shoudan Liang, Srephanie Fuhrman, and Roland Somogyi REVEAL, A general reverse engineering algorithm for inference of genetic network architectures, Pacific Symposium on Biocomputing, Vol. 3, pp. 18-29. 1998.
• Tatsuya Akutsu Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Proceedings of the Pacific Symposium on Biocomputing, Vol. 4. pp. 17-28. 1999.
• Adrian Silvescu and Vasant Honavar Temporal Boolean Network Models of Genetic Networks and Their Inference from Gene Expression Time Series. Complex Systems. Vol. 13. No. 1. pp. 54-75, 2001.

• Bayesian Networks
• Temporal Bayesian Networks
• Representative Applications

• Gene Network Inference Notes, Vasant Honavar
• Klipp, E., Herwig, R., Kowald, A., Wierling, C., and Lehrach, H. (2005). Systems Biology in practice, New York: Wiley-VCH. Section 8.3 (models of gene networks).
• Eugene Charniak Bayesian Networks Without Tears, AI Magazine, pp. 50-63, Winter 1991.
• Dana Pe’er Bayesian Network Analysis of Signaling Networks: A Primer Sci. STKE 2005 (281), [DOI: 10.1126/stke.2812005pl4], 2005.
• Rose F. Liu and Rusmin Soetjipto Analysis of Three Bayesian Network Inference Algorithms: Variable Elimination, Likelihood Weighting, and Gibbs Sampling, 2004.
• David Heckerman, A Tutorial on Learning with Bayesian Networks Tech. Rep. MSR-TR-95-06. Microsoft Research, 1995.
• C.K. Chou and C.N. Liu Approximating Discrete Probability Distributions with Dependence Trees. IEEE Transactions on Information Theory. 14(3), 1968. pp. 462-467.
• W. Lam and F. Bacchus Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence, 10(4), 1994.
• Nir Friedman Inferring Cellular Networks Using Probabilistic Graphical Models, Science, Vol. 303, pp. 799-805, 2004.
• N. Friedman, M. Linial, I. Nachman, and D. Pe’er Using Bayesian Networks to Analyze Expression Data, Journal of Computational Biology, 7:601–620, 2000.
• Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., and Friedman, N. Module Networks: Identifying Regulatory Modules and their Condition-Specific Regulators from Gene Expression Data, Nature Genetics Vol. 34, pp. 166-176. 2003.
• Karen Sachs, Omar Perez, Dana Pe’er, Douglas A. Lauffenburger, Garry P. Nolan Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data, Science 308(5721):523-9. 2005.
• Murphy, K. and Mian, S. Modelling gene expression using dynamic bayesian networks, 1999.
• Dojer N, Gambin A, Mizera A, Wilczyński B, Tiuryn J.Applying dynamic Bayesian networks to perturbed gene expression data BMC Bioinformatics. 2006, 7:249.

• Data Sources for Protein-Protein Interaction Networks
• Motifs
• Effect of Data Sets
• Comparing networks
• Introduction to Signal Transduction Networks

• Protein-Protein Interaction Notes, Julie Dickerson
• Signal Transduction Notes, Julie Dickerson
• R. Sharan & Trey Ideker, Modeling cellular machinery through biological network comparison, Nature Biotechnology 24, 427 - 433 (2006).
• R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon, Network Motifs: Simple Building Blocks of Complex Networks, Science 25 October 2002: Vol. 298. no. 5594, pp. 824 - 827.
• Gandhi, et al., Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets, Nat Genet. 2006 Mar;38(3):285-93.
• Kiemer, L. and G. Cesareni (2007). “Comparative interactomics: comparing apples and pears?” Trends Biotechnol 25(10): 448-54.
• Tyson, J. J., K. C. Chen, et al. (2003). “Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.” Curr Opin Cell Biol 15(2): 221-31.
• Nature Comic on Synthetic Biology Requires Flash Plugin.
• International Genetically Engineered Machine Competition iGEM Website. Should we have a team from the ISU BCB program next year??

• Biology of Signal Transduction Networks
• Methods for measuring signal transduction
• Modeling of the p53 and Mdm2 feedback loop

• Dr. Dobbs’ notes for Tuesday April 8, powerpoint and PDF.
• Modeling of Signal transduction system in p53: Zatorsky, N., et al., “Oscillations and variability in the p53 system,” Molecular Systems Biology, doi:10.1038/msb4100068, 2006.

• Database for Systems Biology Models, Biomodels.net. Models are available in SBML level 2.1. These models, except for the delay function, can be directly read into many modeling packages including the Matlab Systems Biology workbench for analysis. Also, the models are curated and are linked to publications.

• SBML: Systems Biology Markup Language
• Validating and constructing models
• Feedback in Biological Systems
• Dr. Guru Rao: Proteomics measurements

• p53 modeling notes(Dickerson)
• p53 SBML Files .zip
• Modeling of Signal transduction system in p53: Zatorsky, N., et al., “Oscillations and variability in the p53 system,” Molecular Systems Biology, doi:10.1038/msb4100068, 2006.
• Dr. Rao’s Proteomic Lecture: Proteomics Notes

• SBML.org
• Biomodels Database

• Biological Oscillators
• Cell cycle

• Class Notes pdf (Dickerson)
• Class Text: Sections 3.2.4, 7.1-7.2
• Biological Oscillator Analysis Paper: Dynamics of two-component biochemical systems in interacting cells; Synchronization and desynchronization of oscillations and multiple steady states“, Jana Wolf, Reinhart Heinrich, BioSystems 43 (1997) 1–24.
• Yeast Cell Cycle Paper:“Kinetic Analysis of a Molecular Model of the Budding Yeast Cell Cycle,” Katherine C. Chen, Attila Csikasz-Nagy, Bela Gyorffy, John Val, Bela Novak, and John J. Tyson, Molecular Biology of the Cell, Vol. 11, 369–391, January 2000.

• X-windows phase plane analysis program: Very powerful for analysis of ODEs and bifurcation analysis. Interface is definitely old style, but very powerful, no fancy GUI stuff here.

• Yeast Cell Cycle Paper:“Kinetic Analysis of a Molecular Model of the Budding Yeast Cell Cycle,” Katherine C. Chen, Attila Csikasz-Nagy, Bela Gyorffy, John Val, Bela Novak, and John J. Tyson, Molecular Biology of the Cell, Vol. 11, 369–391, January 2000.
• Aldridge, B.B., G. Haller, P.K. Sorger, and D.A. Lauffenburger, “Direct Lyapunov Exponent Analysis Enables Parametric Study of Transient Signaling Governing Cell Behavior”, IEE Proc. Systems Biol. 153: 425-432 (2006).PDF