Analysis of Biomolecular and Cellular Systems

Modeling of Cellular Systems

This half of the course is taught by Prof. Forest White. This section contains lecture notes, reading material, and suggested study problems. There is also an associated protein networks project.

Overview

Cells must respond dynamically to changes in their environment in order to survive. Ultimately, all phenotypic changes can be traced to changes in the biomolecular interactions that we will cover in the second section of the course. To understand biological responses, the kinetics of these interactions must be considered to quantify how different input signals may give rise to a variety of responses. By the end of this unit you will be able to use differential equations to model cellular signaling pathways.

Lecture Notes

Teaching assistants Daniel Martin-Alarcon and Allison Claas took these lecture notes during class. Used with permission.

LEC #	TOPICS
1	Introduction, goals and rationale for the course, protein-ligand interactions (PDF)
2	Experimental techniques: Titration analysis, fractional saturation, the “pseudo-first order approximation” (PFOA), isothermal titration calorimetry (ITC), mass spectrometry (MS) (PDF)
3	Co-immunoprecipitation (Co-IP) and mass spectrometry (MS), Fӧrster resonance energy transfer (FRET), primary ligation assay (PLA), surface plasmon resonance (SPR) (PDF - 1.6MB)
4	Enzyme kinetics, Michaelis-Menten kinetics (PDF)
5	Kinase engineering, competitive inhibition, non-competitive inhibition, epidermal growth factor receptor (EGFR) in cancer, regulation of kinase activity (PDF)
6	Constitutive kinases, modeling kinases, how to shut down kinases, multiple substrates, modeling abstraction, mitogen-activated protein kinase (MAPK) cascade (PDF)
7	Ultrasensitivity / amplification in the MAPK cascade (PDF)
8	Signal shut-down (PDF - 1.6MB)
9	Ligand depletion (Notes for this lecture are not available.)
10	Transcription factor (TF) phosphorylation (PDF - 1.9MB)
11	Transcriptional regulation: Simple regulation, positive and negative autoregulation (PDF - 2.9MB)

Reading Material (Optional)

Söderberg, Ola, et al. “Direct Observation of Individual Endogenous Protein Complexes in Situ by Proximity Ligation.” Nature Methods 3, no. 12 (2006): 995–1000.

Gusev, Yuriy, et al. “Rolling Circle Amplification: A New Approach to Increase Sensitivity for Immunohistochemistry and Flow Cytometry.” The American Journal of Pathology 159, no. 1 (2001): 63–9.

Yarden, Y. “The EGFR Family and its Ligands in Human Cancer: Signalling Mechanisms and Therapeutic Opportunities.” European Journal of Cancer 37 (2001): 3–8.

Huang, Chi-Ying, and James E. Ferrell, Jr. “Ultrasensitivity in the Mitogen-activated Protein Kinase Cascade.” Proceedings of the National Academy of Sciences 93, no. 19 (1996): 10078–83.

Santos, Silvia D. M., et al. “Growth Factor-induced MAPK Network Topology Shapes Erk Response Determining PC-12 Cell Fate.” Nature Cell Biology 9, no. 3 (2007): 324–30.

Turke, Alexa B., et al. “MEK Inhibition Leads to PI3K/AKT Activation by Relieving a Negative Feedback on ERBB Receptors.” Cancer Research 72, no. 13 (2012): 3228–37.

Kim, Sun Young, and James E. Ferrell, Jr. “Substrate Competition as a Source of Ultrasensitivity in the Inactivation of Wee1.” Cell 128, no. 6 (2007): 1133–45.

Carlson, Scott M., et al. “Large-scale Discovery of ERK2 Substrates Identifies ERK-mediated Transcriptional Regulation by ETV3.” Science Signaling 4, no. 196 (2011): rs11.

Albeck, John G., et al. “Quantitative Analysis of Pathways Controlling Extrinsic Apoptosis in Single Cells.” Molecular Cell 30, no. 1 (2008): 11–25.

Friedman, Lilach M., et al. “Synergistic Down-regulation of Receptor Tyrosine Kinases by Combinations of mAbs: Implications for Cancer Immunotherapy.” Proceedings of the National Academy of Sciences 102, no. 6 (2005): 1915–20.

Spangler, Jamie B., et al. “Combination Antibody Treatment Down-regulates Epidermal Growth Factor Receptor by Inhibiting Endosomal Recycling.” Proceedings of the National Academy of Sciences 107, no. 30 (2010): 13252–7.

Haugh, Jason M. “Mathematical Model of Human Growth Hormone (hGH)‐Stimulated Cell Proliferation Explains the Efficacy of hGH Variants as Receptor Agonists or Antagonists.” Biotechnology Progress 20, no. 5 (2004): 1337–44.