BioE 434/534, ChemE 498/599, EE 424, CSE3 487: Advanced Systems and Synthetic Biology

Instructors

Georg Seelig gseelig@uw.edu
Herbert Sauro uw.hsauro@gmail.com

Overview

Synthetic biology concerns the design and construction of new genetic parts, bio-molecular devices, and synthetic organisms. The topic is highly interdisciplinary, involving molecular biology, genetic engineering, protein design, metabolic engineering, computer engineering and mathematical modeling. Synthetic biology emphasizes the use of rigorous, quantitative engineering methods. Approaches and results in synthetic biology are rapidly being translated to industry and its applications are wide ranging.

Course structure

Classes will involve a mixture of paper discussions, lecture presentations by the instructors and in-class working sessions.

Pre-requisites

EE/BIOEN 423/523, CSE 486; or EE/BIOEN 425, CSE 488; or permission of the instructors. Students who are taking Advanced Synthetic Biology are expected to have a working knowledge of gene regulation and of the tools used to model genetic or metabolic circuits (e.g. differential equations, chemical reaction networks, stochastic chemical equations and similar). Familiarity with Matlab, Python or similar is required.

Approach

Synthetic biology covers an enormous wealth of different sub-fields and approaches. There is no established syllabus for synthetic biology and there is no single technical skill that is absolutely necessary to be successful. Still, there is one essential meta-skill that you need to acquire and that is the ability to identify an interesting problem that you have a chance to tackle with the tools you have. Moreover, once you think you have identified a potentially interesting problem, you need to be able to tell others (your peers, funding agencies, ...) about it and convince them that your problem is interesting, solvable and original. The goal of this class is to develop such skills through a set of examples.

The examples will come from four resarch directions that I personally find interesting:

Designing smart diagnostics with DNA nanotechnology: We will use DNA strand displacement to engineer molecular diagnostics that are multiplexed, i.e. they can detect and analyze multiple target, highly specific, that is they can discriminate between nucleic acids that differ in only a single position and sensitive, they can amplify a signal resulting from a low concentration of analyte.
Synthetic development and engineered multi-cellularity: We will start from the Turing model, which explains how spatial patterns can emerge from an initially homogenous state. The Turing mechanism is believed to have roles in development and is responsible for animal coat patterns. Guided by the model we will identify genetic parts that would allow us to engineer such a synthetic developmental program in a population of single-celled yeast. This module will use concepts from dynamical systems theory.
Metabolic engineering: We will identify a molecule of commercial interest and design a pathway to produce such a molecule.
From massively parallel measurements to targeted therapeutics: We will design gene expression constructs that produce a functional protein in one cell type but not in another. We will take advantage of gene splicing to create targeted construct. This module will use NextGen sequencing data and machine learning tools to analyze that data.

Within each topic, I will present some of the background and (mathematical) methods that could be used to tackle the problem. Then, we will flesh out a project together and see whether we can come up with a coherent story that could be the foundation for a research grant.

Grading

You will prepare a written report for three of the four topic areas. Two of these reports take the form of a 2-3 page white paper or pre-proposal. For your favorite topic you will write a longer (up to 6 page) research grant. Reports will typically include a succinct problem statement including specific aims, preliminary data in the form of a model and a background section with citations. Each of the two shorter white papers accounts for 25% of your grade, the full proposal accounts for 50%.

Lecture notes

Part 1: Designing smart diagnostics with DNA nanotechnology:
Notes on DNA diagnostics (week 1 and 2)
Slides on DNA strand displacment etc. (week 1 and 2)

Part 2: Synthetic development and engineered multi-cellularity:
Notes on gradient patterning, feed-forward loops etc (week 3)
Notes on Activator inhibitor patters (week 3)
Slides on Engineering Turing patterns (week 4)

Part 3: Metabolic control analysis:
Notes on metabolic control analysis (week 5 and 6)

Part 4: From massively parallel measurements to targeted therapeutics:
Notes on DNA storage(week 7)
Slides on DNA synthesis, storage and sequencing (week 7)
Slides on building a model for splicing from synthetic data (week 8)

Reading

Part 1: Designing smart diagnostics with DNA nanotechnology:
Sherry Xi Chen and Georg Seelig, An Engineered Kinetic Amplification Mechanism for Single Nucleotide Variant Discrimination, J. Am. Chem. Soc., DOI: 10.1021/jacs.6b00277 (2016).
David Yu Zhang and Erik Winfree, Control of DNA strand displacement kinetics using toehold exchange, J. Am. Chem. Soc. 131, 17303-17314 (2009).

Part 2: Synthetic development and engineered multi-cellularity:
Stansilav Y. Shvartsman and Ruth E. Baker, Mathematical models of morphogen gradients and their effects on gene expression, WIREs, Dev. Biol. doi:10.1002/wdev.55 (2012).
R. Basu et al., A synthetic multicellular system for programmed pattern formation, Nature, 434, 1130 (2005).
Y. Schaerli et al., A unified design space of synthetic stripe-forming networks, Nat. Comm. DOI: 10.1038/ncomms5905 (2014).

Part 4: From massively parallel measurements to targeted therapeutics:
Sriram Kosuri and George Church, Large-scale de novo DNA synthesis: technologies and applications, Nature Methods 11, 499 (2014).
A. Rosenberg et al., Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences, Cell, 163, 698 (2015).
McKenna et al., Whole organism lineage tracing by combinatorial and cumulative genome editing, (2016).

Assignments

Part 2: Synthetic development and engineered multi-cellularity: Pattern formation assignment, due 5/4 midnight

Part 3: Metabolic control analysis: Metabolic control assignment 1: Due 5/5