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ELL796: Signals and Systems in Biology

If you are doing the course, please join the Piazza forum (the access code has been announced in class and e-mailed to you).

This course will be an attempt to look at how ideas and tools from electrical engineering and computer science can be applied to understand the workings of living organisms. We will be looking at both the signals that characterise biological information and dynamics, and the systems which integrate these and carry out the functionality of life. Our perspective will be to view biological systems as information-processing or computational systems: unlike engineered systems, these have not been intelligently designed but have evolved naturally over billions of years, meaning that no one truly understands their inner workings. However, we can observe their input-output characteristics in a range of experimental settings; and the ultimate goal of this sort of approach is to be able to fit such experimental data to plausible, predictive models of the system's structure and dynamics. In other words, we seek to reverse engineer the circuitry of life.

We will be looking at some of the specific areas or problems in biology where such techniques have been usefully applied, and also seek to explore connections between them. A rough list of topics is below.

Instructor: Sumeet Agarwal
3 credits (3-0-0)
II Semester 2017–18
Tu F 14:00–15:20, LH 603

Evaluation components

References

  1. Neil C. Jones and Pavel A. Pevzner. An Introduction to Bioinformatics Algorithms. MIT Press, 2004.
  2. Uri Alon. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman and Hall/CRC, 2006.
  3. Brian P. Ingalls. Mathematical Modeling in Systems Biology: An Introduction. MIT Press, 2013.
[Relevant research papers etc.]
[Previous lecture notes (intranet)]

Planned schedule

Serial no. Lecture nos. Topics Slides
1 1–3 Evolutionary and Systems thinking in Biology
2 3–5 Introduction to Evolution; Modelling Evolution Evolution; Genetic Algorithms
3 6 Introduction to Cellular and Molecular Biology Molecular Biology of the Cell
4 6–10 Computational Genomics: Gene Identification, Sequence Alignment
5 11 Discussion session on evolution and genomics
6 12–13 The Data Revolution: Genomics/Transcriptomics/Proteomics DNA sequencing; Gene/Protein expression & interactions
7 13–15 Primer on networks; Protein interaction networks Date and Party hubs
8 16–19 Gene regulatory networks, motifs, and dynamics
9 20–21 Large-scale network modelling and inference Dynamics and Inference on Biological Networks; Learning Predictive Models of Gene Dynamics
10 22–23 Signal transduction and developmental networks
11 24 Discussion session on network biology
12 25 Student debate: Journal club —
13 26–27 Evolvability and Learning Evolvability as Learnability
14 28 Student presentations: Term project/paper —

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