Physics 434, 2016: Physical Biology

From Ilya Nemenman: Theoretical Biophysics @ Emory
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Back to Physics 434, 2016: Physical Biology.



  • Projects are up. Ilya 09:58, 14 November 2016 (EST)
  • No homework this week (will post new one on Wednesday). Please catch up if you are behind. Ilya 09:58, 14 November 2016 (EST)
  • We wil have a catchup class on Friday at 2pm. Ilya 09:58, 14 November 2016 (EST)
  • There's no homework due this week -- prepare for the midterm!
  • The midterm is scheduled for Nov 9.
  • Instead of the usual class on October 24 we will have a class on October 28, 3pm. Please come to MSC N117A.
  • Welcome to the class!

About the class

This class is aimed to teach you to think physically about biological systems. Thinking physically means, in the context of this class, creating simple predictive mathematical models of biological processes that provide insight. If you are still confused, we will talk a lot more during the lectures what it means to think physically. The class relies a lot on computer simulations as a tool to verify our understanding -- and you will learn Matlab or Python (your choice) during this class (no prior programming experience necessary, but PHYS 212/ BIOL 212 would be useful). The main ideas that we will explore are the ideas of dynamics, randomness, control, inference, and information -- all applied to biology in two different ways: first, how we model and learn biology, and, second, how biological organisms model and learn the world around them. These ideas will be explored in a variety of biological systems, from viruses and bacteria, to neural systems, and to entire populations.


See also student resources for the book.
Make sure to download the Matlab guide.
Or get Python Tutorial: J Kinder and P Nelson, Student Guide to Python for Physical Modeling,

Lecture Notes and Detailed Schedule

Detailed schedule and Lecture notes will be provided as needed (the latter only when we deviate substantially from the Nelson's textbook). As the class prerequisites keep changing, I expect stronger mathematically inclined students in the class, and hence we may be able to progress faster than in the previous years. Therefore, I am not providing a detailed schedule for the full semester, but will be updating it as the course progresses.

Aug 24, Week 1
Chapters 0, 1 from Nelson.
Lecture 1 additional notes (last edited 08/23/2016)
Aug 29, Week 2
Chapter 1 and 2, Appendix B -- briefly mentioned in class; you are responsible for this on your own
Aug 31, Week 2
Chapter 3
  • Discrete randomness: additional lecture notes, partially overlapping with the textbook (last edited on 10/21/2014).
  • Law of large numbers: additional lecture notes complementing the textbook (last edited on 10/16/2014).
Sep 5, Week 3
No class. Labor day
Sep 7, Week 3
Chapter 3, and see additional lecture notes above
Sep 12, Week 4
Chapter 3, and see additional lecture notes above
Sep 14, Week 4
Chapter 4
Sep 19, Week 5
Chapter 4
Sep 21, Week 5
Chapter 5
Sep 26, Week 6
Chapter 5
Sep 28, Week 6
No book chapter
  • Random walks and diffusion, Lecture notes (last edited 11/05/2014).
  • Search and first passage time , Lecture notes (last edited 11/06/2014).
Oct 3, Week 7
No book chapter
Oct 5, Week 7
No book chapter
Oct 10, Week 8 -- No classes
Fall break
Oct 12, Week 8
No book chapter
  • Search and first passage time , Lecture notes (last edited 11/06/2014).
Oct 17, Week 9
Chapter 7
  • Poisson processes.
Chapter 8
  • Randomness in cells
Oct 19, Week 9
Chapter 8
Oct 24, Week 10 -- rescheduled
Chapter 8
Oct 26, Week 10
Chapter 8
Oct 28, Week 10
Chapter 9
  • Cellular regulation
  • Negative Feedback
Oct 31, Week 11
Chapter 9
  • Negative Feedback
  • Biochemical kinetics, see also these notes
Nov 2, Week 11
Chapter 9
  • Negative feedback
Nov 7, Week 12 -- rescheduled, no class
pre-midterm preparation
Nov 9, Week 12 - Midterm
Nov 14, week 13
Chapter 10, Genetic Switches
  • Genetic Switches
Exam solutions
Nov 16, Week 13
Chapter 10
Nov XX, Week 13
Chapter 11
  • Cellular Oscillations,
Nov 21, Week 14
Intro to information theory
Nov 28, Week 15
Intro to information theory
Does biology care about bits?
Nov 30, Week 15
Examples of information transmission in various systems. What can we learn?
Information for reverse-engineering of cellular networks.
Dec 5, Week 16
In-class project presentations.

Homework assignments



Below are some of the papers we will be mentioning in class. Projects will be based on some of them. Enjoy the reading.

Additional Textbooks

  1. R Phillips, J Kondev, J Theriot. Physical Biology of the Cell (Garland Science, 2008)
    • Sizing up E. coli. PDF
  2. CM Grinstead and JL Snell, Introduction to Probability.
  3. W Bialek, Biophysics: Searching for Principles (2011).
    • Information theory overview is in Chapter 4.
  4. For information about Wiener processes and diffusion, a good source is: Wiener Process article in Wikipedia.
  5. The most standard textbook on information theory is: T Cover and J Thomas, Elements of Information Theory, 2nd ed (Wiley Interscience, 2006).

Sensory Ecology and Corresponding Evolutionary Adaptations

  1. T Cronin, N Shashar, R Caldwell. Polarization vision and its role in biological signaling. Integrative and Comparative Biology 43(4):549-558, 2003. PDF.
  2. D Stavenga, Visual acuity of fly photoreceptors in natural conditions--dependence on UV sensitizing pigment and light-controlling pupil. J Exp Biol 207 (Pt 10) pp. 1703-13, 2004. PDF.

Transcriptional regulation

  1. O Berg and P von Hippel. Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. J Mol Biol. 193(4):723-50, 1987. PDF.
  2. O Berg et al. Diffusion-driven mechanisms of protein translocation on nucleic acids. 1. Models and theory. Biochemistry 20(24):6929-48, 1981. PDF.
  3. C Guet, M Elowitz, W Hsing, S Leibler. Combinatorial synthesis of genetic networks. Science 296:1466, 2002. PDF.
  4. M Slutsky and L Mirny. Kinetics of protein-DNA interaction: facilitated target location in sequence-dependent potential. Biophysical J 87(6):4021-35, 2004. PDF.
  5. E Ozbudak, M Thattai, H Lim, B Shraiman, A van Oudenaarden. Multistability in the lactose utilization network of Escherichia coli. Nature 427: 737, 2004. PDF.
  6. D Dreisigmeyer, J Stajic, I Nemenman, W Hlavacek, and M Wall. Determinants of bistability in induction of the Escherichia coli lac operon. IET Syst Biol 2:293-303, 2008. PDF.

Signal Processing in Vision

  1. P Detwiler et al. Engineering aspects of enzymatic signal transduction: Photoreceptors in the retina. Biophys. J., 79:2801-2817, 2000. PDF.
  2. A Pumir et al. Systems analysis of the single photon response in invertebrate photoreceptors. Proc Natl Acad Sci USA 105 (30) pp. 10354-9, 2008. PDF.
  3. F Rieke and D Baylor. Single photon detection by rod cells of the retina. Rev Mod Phys 70, 1027-1036, 1998. PDF.
  4. T Doan, A Mendez, P Detwiler, J Chen, F Rieke. Multiple phosphorylation sites confer reproducibility of the rod's single-photon responses. Science 313, 530-533, 2006. PDF.

Bacterial chemotaxis

  1. J Adler. Chemotaxis in bacteria. Annu Rev Biochem 44 pp. 341-56, 1975. PDF
  2. H Berg and D Brown. Chemotaxis in Escherichia coli analysed by three-dimensional tracking. Nature 239 (5374) pp. 500-4, 1972. PDF.
  3. E Budrene and H Berg. Dynamics of formation of symmetrical patterns by chemotactic bacteria. Nature 376 (6535) pp. 49-53, 1995. PDF
  4. E Budrene and H Berg. Complex patterns formed by motile cells of Escherichia coli. Nature 349 (6310) pp. 630-3, 1991. PDF
  5. E Purcell. Life at low Reynolds number. Am J Phys 45 (1) pp. 3-11, 1977. PDF
  6. H Berg. Motile behavior of bacteria. Phys Today 53 (1) pp. 24-29, 2000. PDF
  7. C Rao and A Arkin. Design and diversity in bacterial chemotaxis: a comparative study in Escherichia coli and Bacillus subtilis. PLoS Biol 2 (2) pp. E49, 2004. PDF
  8. C Rao et al. The three adaptation systems of Bacillus subtilis chemotaxis. Trends Microbio l16 (10) pp. 480-7, 2008. PDF.
  9. A Celani and M Vergassola. Bacterial strategies for chemotaxis response. Proc Natl Acad Sci USA107, 1391-6, 2010. PDF.

Eukaryotic chemotaxis

  1. J Franca-Koh et al. Navigating signaling networks: chemotaxis in Dictyostelium discoideum. Curr Opin Genet Dev 16 (4) pp. 333-8, 2006. PDF.
  2. W-J Rappel et al. Establishing direction during chemotaxis in eukaryotic cells. Biophysical Journal 83 (3) pp. 1361-7, 2002. PDF.

Random walks

  1. G Bel, B Munsky, and I Nemenman. The simplicity of completion time distributions for common complex biochemical processes. Physical Biology 7 016003, 2010. PDF.

Information theory

  1. J Ziv and A Lempel. A Universal Algorithm for Sequential Data Compression. IEEE Trans. Inf. Thy 3 (23) 337, 1977. PDF.
  2. N Tishby, F Pereira, and W Bialek. The information bottleneck method. arXiv:physics/0004057v1, 2000. PDF.
  3. E Ziv, I Nemenman, and C Wiggins. Optimal signal processing in small stochastic biochemical networks. PLoS ONE 2: e1077, 2007. PDF.
  4. S Strong, R Koberle, R de Ruyter van Steveninck, and W Bialek. Entropy and information in neural spike trains. Phys Rev Lett 80:197–200, 1998. PDF.
  5. R Cheong, A Rhee, CJ Wang, I Nemenman, and A Levchenko. Information Transduction Capacity of Noisy Biochemical Signaling Networks. Science doi:10.1126/science.1204553, 2011. PDF.
  6. A Margolin, I Nemenman, K Basso, U Klein, C Wiggins, G Stolovitzky, Riccardo D Favera, and A Califano. ARACNE: An algorithm for reconstruction of genetic networks in a mammalian cellular context. BMC Bioinformatics, 7 (Suppl. 1):S7, 2006. PDF
  7. I Nemenman, Information theory and adaptation. In Quantitative biology: From molecules to Cellular Systems, ME Wall, ed. (Taylor and Francis, 2012). PDF.
  8. A Levchenko and I Nemenman. Cellular noise and information transmission. Current Opinion Biotech 28, 156, 2014. PDF.

Noise in biochemistry, population biology, and neuroscience

  1. S Luria and M Delbruck. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491-511, 1943. PDF.
  2. E Schneidman, B Freedman, and I Segev. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Comp. 10, p.1679-1704, 1998. PDF.
  3. T Kepler and T Elston. Stochasticity in transcriptional regulation: Origins, consequences, and mathematical representations. Biophys J. 81, 3116-3136, 2001. PDF.
  4. M Elowitz, A Levine, E Siggia & P Swain. Stochastic gene expression in a single cell. Science 207, 1183, 2002. PDF.
  5. W Blake, M Kaern, C Cantor, and J Collins. Noise in eukaryotic gene expression. Nature 422, 633-637, 2003. PDF.
  6. J Raser and E O’Shea. Control of stochasticity in eukaryotic gene expression. Science 304, 1811-1814, 2004. PDF.
  7. G Lahav. et al. Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat Genet 36, 147–150, 2004. PDF.
  8. J Paulsson. Summing up the noise in gene networks. Nature 427, 415, 2004. PDF, Supplement.
  9. J Pedraza and A van Oudenaarden. Noise propagation in gene networks, Science 307, 1965-1969, 2005. PDF.
  10. N Rosenfeld, J Young, U Alon, P Swain, M Elowitz. Gene Regulation at the Single-Cell Level. Science 307, 1962, 2005. PDF.
  11. B Averbeck et al. Neural correlations, population coding and computation. Nat Rev Neurosci 7, 358-66, 2006. PDF.
  12. D Gillespie. Stochastic Simulation of Chemical Kinetics. Ann Rev Phys Chem 58, 35-55, 2007. PDF.
  13. T Cağatay et al. Architecture-dependent noise discriminates functionally analogous differentiation circuits. Cell 139:512-22, 2009. PDF, supplement.
  14. A Walczak, G Tkacik, and W Bialek. Optimizing information flow in small genetic networks. II. Feed-forward interactions. Phys Rev E 81, 041905, 2010. PDF.

Memory in noisy environments

  1. T Gardner et al. Construction of a genetic toggle switch in Escherichia coli. Nature 403: 339-42, 2000. PDF
  2. W Bialek. Stability and noise in biochemical switches. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Advances in Neural Information Processing Systems 13, pages 103-109. MIT Press, 2001. PDF
  3. E Aurell and K Sneppen. Epigenetics as a first exit problem. Phys Rev Lett 88, 048101, 2002. PDF.
  4. E Korobkova, T Emonet, JMG Vilar, TS Shimizu, and P Cluzel. From molecular noise to behavioural variability in a single bacterium. Nature, 438:574-578, 2004. PDF.
  5. N Balaban, J Merrin, R Chait, L Kowalik, S Leibler. Bacterial persistence as a phenotypic switch. Science 305:1622, 2004. PDF.
  6. Y Tu and G Grinstein. How white noise generates power-law switching in bacterial flagellar motors. Phys Rev Lett, 2005. PDF.
  7. E Kussell and S Leibler. Phenotypic diversity, population growth, and information in fluctuating environments. Science 309:2075–2078. 2005. PDF.
  8. D Sprinzak et al. Cis-interactions between Notch and Delta generate mutually exclusive signalling states. Nature 465, 86–90, 2010. PDF.


  1. N Barkai and S Leibler. Robustness in simple biochemical networks. Nature 387, 913–917, 1997. PDF.
  2. U Alon, M Surette, N Barkai, and S Leibler. Robustness in bacterial chemotaxis. Nature 397, 168–171, 1999. PDF.
  3. N Brenner et al. Adaptive rescaling maximizes information transmission. Neuron 26, 695-702. PDF.
  4. P Cluzel, M Surette, and S Leibler. An ultrasensitive bacterial motor revealed by monitoring signaling proteins in single cells. Science, 287:1652-1655, 2000. PDF.
  5. B Andrews et al. Optimal noise filtering in the chemotactic response of Escherichia coli. PLoS Comput Biol 2, e154, 2006. PDF.
  6. T Sharpee et al. Adaptive filtering enhances information transmission in visual cortex. Nature 439, 936-42, 2006. PDF.
  7. A Fairhall et al. Efficiency and ambiguity in an adaptive neural code. Nature 412, 787-92, 2001. PDF.
  8. I Nemenman et al. Neural coding of natural stimuli: information at sub-millisecond resolution. PLoS Comput Biol 4, e1000025, 2008. PDF.
  9. T Friedlander and N Brenner. Adaptive response by state-dependent inactivation. Proc Natl Acad Sci USA 106, 22558-63, 2009. PDF.
  10. W Ma et al. Defining network topologies that can achieve biochemical adaptation. Cell 138, 760-73, 2009. PDF.


  1. A Eldar, D Rosin, B-Z Shilo, and N Barkai. Self-Enhanced Ligand Degradation Underlies Robustness of Morphogen Gradients. Developmental Cell, Vol. 5, 635–646, 2003. PDF.
  2. T Gregor, W Bialek, R de Ruyter van Steveninc, D Tank, and E Wieschaus. Diffusion and scaling during early embryonic pattern formation. PNAS 102:18403, 2005. PDF.
  3. T Doan, A Mendez, P Detwiler, J Chen, F Rieke. Multiple phosphorylation sites confer reproducibility of the Rod's single-photon responses. Science 313, 530-3, 2006. PDF.
  4. A Lander et al. The measure of success: constraints, objectives, and tradeoffs in morphogen-mediated patterning. Cold Spring Harb Perspect Biol 1, a002022, 2009. PDF


  1. CR Gallistel et al. The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect. J Exp Psychol Anim Behav Process 27, 354-72, 2001. PDF.
  2. CR Gallistel et al. The learning curve: implications of a quantitative analysis. Proc Natl Acad Sci USA 101, 13124-31, 2004. PDF.
  3. B Andrews and P Iglesias. An information-theoretic characterization of the optimal gradient sensing response of cells. PLoS Comput Biol 3, e153, 2007. PDF.
  4. M Vergassola et al. 'Infotaxis' as a strategy for searching without gradients. Nature 445, 406-9, 2007. PDF.

Eukaryotic signaling

  1. C-Y Huang and J Ferrell. Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc Natl Acad Sci USA 93:10078, 1996. PDF.
  2. N Markevich et al. Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. J Cell Biol 164:353-9, 2004. PDF.
  3. C Gomez-Uribe, G Verghese, and L Mirny. Operating regimes of signaling cycles: statics, dynamics, and noise filtering. PLoS Comput Biol 3:e246, 2007. PDF.
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