Course: BIOEN 423/523 – Introduction to Synthetic Biology
Credits: 3, MWF 11:30-12:20, Office Hours are Mo, Fr, 10:00AM – 11:20AM, Sieg 128
Instructor: Eric Klavins
Texts and Supplemental Materials: The instructor assigns readings each week based on the topic. Examples include books, journal articles, and newspaper articles.
UW Catalog Description: Studies mathematical modeling of transcription, translation, regulation, and metabolism in cell; computer-aided design methods for synthetic biology; implementation of information processing, Boolean logic and feedback control laws with genetic regulatory networks; modularity, impedance matching and isolation in biochemical circuits; and parameter estimation methods.
Prerequisites by Course: Either MATH 136 or MATH 307, AMATH 351, or CSE 321 and MATH 308 or AMATH 352. Offered: jointly with EE 423/CSE 486.
Prerequisites by Topic: Elementary differential equations and applications, linear algebra and numerical analysis, basic computer programming.
Required or Elective: Elective
Students in this course will be using the program gro. gro is a language for programming, modeling, specifying and simulating the behavior of cells in growing microcolonies of microorganisms. The simulator models cell growth, cell division, intrinsic and extrinsic noise, difussing molecular signals, microchemostats, chemotaxis, and more. The gro framework is intended to be used in synthetic biology to prototype distributed, multicell behaviors and check that, logically, the local interaction rules you specify produce the desired global result. The language allows behaviors to be specified at whatever level of abstraction makes sense: from high level code, to low level biomolecular interactions. The gro framework has been also been used in the classroom, at UW and elsewhere, to teach synthetic biology to engineers.
Specific Outcomes: By the end of the course, students should be able to:
- Use mass action kinetics, Hill functions, Boolean networks, and Markov processes to model biochemical reaction networks and gene regulatory networks.
- Use the chemical master equation and extended moment generator to analyze stochastic chemical reaction networks.
- Use computational software such as Matlab, Mathematica, or R to simulate stochastic and deterministic chemical reaction networks.
- Design and model multi-celled behaviors involving cell-cell signaling such as pattern formation and differentiation utilizing the gro specification language and simulation environment.
- Discuss and think critically about scientific papers in synthetic biology.
Outcomes Addressed by this Course:
A. An ability to apply knowledge of mathematics, science, and engineering.
- In this course, students will apply knowledge of linear algebra, ordinary differential equations, and stochastic processes, to develop mathematical models of biological systems. As part of this process, students must think critically about their design choices in order to develop models that can both capture desired or observed behaviors as well as distinguish between hypotheses in an experimental system.
K. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice (i.e. Computers and analytical equipment)
- For all assignments, students will be required to use computational tools, such as Matlab, Mathematica, or R, to simulate and analyze dynamical models of biological circuits. Additionally students will develop expertise in designing and modeling multi-celled behaviors using the gro specification language and simulation environment.
M. The capability to apply advanced mathematics (including differential equations and statistics), science, and engineering to solve the problems at the interface of engineering and biology.
- In this course, students will be presented with genetic circuits from literature and required to model and analyze these circuits for desired behaviors as well as design novel circuits to meet a desired specification.
The following topics will be covered in this course, approximately in the order listed.
- Growth and cell division, mainly in bacteria. Dilution due to cell growth. Sources of noise. Sizes and numbers in cells.
- Gene expression and regulation. Bacterial promoters and ribosome binding sites. Inducers.
- Stohasticity. Intrinsic and extrinsic noise. Basics of probabilistic modeling. Negative feedback reduces noise. Noise in nature: bet-hedging, coin-flipping, symmetry breaking.
- State. The genetic toggle switch. Positive feedback. Distributed algorithms in bacterial micro-colonies. Leader election and symmetry breaking. Synthetic development and morphogenesis.
- Signaling. Two component systems, MAPK, quorum sensing, auxin signaling. Boolean logic, transfer functions, etc. implemented in cells.
- Dynamics. Synthetic oscillators: The repressilator. Synchronized oscillators. Other dynamic behaviors. Modeling and analysis of dynamic systems.
- Tuning. Sensitivity analysis. Fine tuning behaviors via promoters, ribosome binding sites, protein and RNA degradation rates. Recombineering and MAGE.
- Evolution. Basics of creating variation and selection. Mutation rates and DNA repair. Continuous culture devices. Examples of directed evolution.
Readings (Follow the links):
- “DNA Science, A First Course : Second Edition” (Book), David Micklos and Greg A. Frever
- “Biology is Technology” (Book), Robert H. Carlson
- “The changing economics of DNA synthesis”, Robert H. Carlson, Nature, 2009.
2 Synthetic Biology Overviews
- Special Issue of Science on Synthetic Biology: Read the introduction, news, reviews and perspectives sections.
- Five hard truths: http://www.nature.com/news/2010/100120/pdf/463288a.pdf
- Adventures in Synthetic Biology
- Arkin and Fletcher, Fast, Cheap and Somewhat in Control. Genome Biology 7(8), 114, 2006.
- Andrianantoandro, Basu, Karig and Weiss, Synthetic biology: new engineering rules for an emerging discipline. Molecular Systems Biology 2, 2006.
- Caleb J. Bashor, Andrew A. Horwitz, Sergio G. Peisajovich, and Wendell A. Lim, Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems. Annual Review of Biophysics 39, 515-537 (2010).
- Guillaume, Mutalik, and Arkin, Toward rational design of bacterial genomes. Current Opinion in Microbiology, 2011.
3 Biology Reviews
- A good text on biochemistry is essential. We like Lehninger Principles of Biochemistry, Fourth Edition, 2004.
- A Youtube video on transcription and translation.
- The Inner Life of the Cell
- Transcribe and translate a gene — interactive! (somewhat misleading in the translation step, as the code seems not complimented like it should be).
- Lots of other online tutorials are available. Try googling “Gene Expression Tutorial”. For example, this one.
- Raser and O’Shea, Noise in Gene Expression: Origins, Consequences, and Control, Science 2005.
- McAdams and Arkin. Stochastic mechanisms in gene expression, PNAS, 1997.
4 Chemical Kinetics
- Feinberg’s Notes. Read the first two chapters.
- S.S. Jang, K.T. Oishi, R.G. Egbert, and E. Klavins, Specification and simulation of multicelled behaviors, ACS Synthetic Biology, Vol. 1, No. 8, pp. 365–374. 2012.
- E.W. Dijkstra, Guarded Commands, Nondeterminacy and Formal Derivation of Programs, CACM, 1975.
- PRISM is a stochastic guarded command language like gro, except super formal and accompanied by a model-checker.
7 Gene Regulation
- Cox, Surrette, and Ellowitz describe engineered promoters.
- Isaacs, Swyer, and Collins discuss engineered riboregulators in RNA synthetic biology.
- Bayer and Smolke build riboregulators with RNA aptamers and ribozymes in Eukaryotes.
8 Gene Regulation
- A more recent paper on identifying gene networks based on expression patterns using boolean networks.
9 Synthetic Gene Networks
- A genetic toggle switch in E. coli
- rewritable digital storage in living cells
- transciptional oscillator in E. coli
- Another toggle switch/oscillator in E. coli
- A tunable oscillator in mammalian cells;
- Induced cellular differentiation.
- Yokobayashi, Weiss, and Arnold perform directed evolution on a genetic circuit
- Egbert and Klavins fine-tune genetic circuits using simple sequence repeats.
- Lou, Stanton, Chen, Munsky, and Voigt suggest a method for buffering synthetic circuits from genetic context.
- Bonnie Bassler’s TED Talk on quorum sensing .
- You, Cox, Weiss and Arnold, Programmed population control by cell–cell communication and regulated killing, Nature, 2004.
- Basu, Gerchman, Collins, Arnold and Weiss, A synthetic multicellular system for programmed pattern formation. Nature 434, 1130-1134, 2005.
11 Pattern Formation
- Howard Gutowitz, Cellular Automata, 1991.
- Stephen Wolfram, A New Kind of Science, 2002.
- Daniel Yamins, Radhika Nagpal, Automated Global-to-Local Programming in 1-D Spatial Multi-Agent Systems, AAMAS, 2008.
- Elowitz, Levine, Siggia, and Swain. Stochastic Gene Expression in a Single Cell, Science, 2006.
- Swain, Elowitz, Siggia. Intrinsic and Extrinsic Contributions to Stochasticity in Gene Expression, PNAS, 2002.
- Raj and van Oudenaarden, Single-molecule approaches to stochastic gene expression, Annual Review of Biophysics, 2009.
- Thattai and van Oudenaarden, Intrinsic noise in gene regulatory networks. PNAS, 2001.
- Lestas, Vinnecombe, and Paullson, Fundamental limits on the suppression of molecular fluctuations, Nature, 2010.
14 Modeling Noise
- Go look up all the definitions in today’s lecture on Wikipedia or Planet Math.
- Feller, An Introduction to Probability Theory and its Applications. This is simply the best book out there on probability.
- Wilkinsen, Stochastic Modelling for Systems Biology. This is a good book for how probability is used in biology. Also covers the basic definitions.
15 Noise in Gene Expression
- Klavins, 2009 Course Notes on Stochasticity, 2009.
- Ozbudak, Thattai, Kurtser, Grossman and van Oudenaarden, Regulation of noise in the expression of a single gene, Nature Genetics 2002.
16 Ethics, Biosafety, Biowarfare
- American experience material on bio warfare: http://www.pbs.org/wgbh/americanexperience/features/timeline/weapon-timeline/
- Video on polio reconstruction: http://www.youtube.com/watch_popup?v=hfElVabRh2Y&vq=medium#t=31.
- The CDC’s biosafety guidelines: http://www.cdc.gov/biosafety/publications/bmbl5/index.htm
- Presidential commission on synthetic biology: http://bioethics.gov/sites/default/files/PCSBI-Synthetic-Biology-Report-12.16.10.pdf
Course schedule by week/topic:
BIOEN 423 Weekly Schedule
Readings and Other Materials
|1||9/24||History||A variety of optional readings are listed below.||A1, due 10/1|
|9/26||Synthetic Biology||Special Issue of Science on Synthetic Biology
and optional overviews of synthetic biology.
|9/28||Gene Expression||Find a tutorial that speaks to you|
ODEs in Matlab
|For more on chemical reactions, check out Lecture 2 of Feinberg’s notes. The notation is a bit different, but the ideas are the same.||A2, due 10/8|
|10/3||Introduction to gro||Read the paper on gro. A few other papers are listed below.|
|10/5||iGEM!||Check out UW’s iGEM wiki page and also read about other teams.|
|3||10/8||Gene Regulation||Rewiring cells: synthetic biology as a tool to interrogate the organizational principles of living systems. Optional readings are listed below.||A3, due 10/15|
|10/10||Modeling Gene Regulation||Boolean networks|
|10/12||Synthetic Gene Networks||A Robust and tunable gene oscillator|
|4||10/15||Signaling||Check out Bonnie Bassler’s TED Talk on quorum sensing .0.gro,1.gro,2.gro,3.gro. Optional readings are listed below.||A4, due 10/22|
|10/17||Pattern Formation||This paper demonstrates some of the impossibility ideas we discussed. More papers are listed below.|
|10/19||Extra Pattern Formation Slides||le_example.gro,midpoint.gro|
|5||10/22||Noise||Stochastic Gene Expression. Other noise papers listed below.||A5, due 10/29|
|10/24||Modeling Noise||Nothing required, but you might want to check out the list below of books on probability.|
|10/26||Noise in Gene Expression||You can read my 2009 course notes on stochasticity. Links to other resources are below.|
|11/1||Bioweapons, biosecurity, and ethics||Look at the PBS Documentary on the Living Weapon.|
|7||11/5||Midterm Results and Solutions|
|11/7||Moment Equations||A6, due 11/14|
|11/14||Molecular Programming||A7, due 11/21|
|11/16||Chemotaxis and Reaction/Diffusion|
|11/21||Guest Lecture: Rob Carlson|
|10||11/26||Guest Lecture: Georg Seelig|
|11/28||Guest Lecture: James Carothers|
|11/30||Grad Presentations||A8, Due 12/12|
|12/12||FINAL MEETING : Simulation presentations!|