This guest lecture by Prof. George Church is on the topic of causality, in particular, how to use genomic data and the tools of natural computing to differentiate between correlation and causation.

This lecture is guided by the question "Where is missing heritability found?" Prof. David Gifford discusses computational models that can predict phenotype from genotype. He then discusses how to discover and model quantitative trait loci.

This lecture begins with the epigenetic state, which regulates gene function without changing DNA. Then, how to estimate the protein occupancy of the genome via computational methods. Lastly, how to map enhancers to their regulatory targets.

This lecture by Prof. Ernest Fraenkel is about gene regulatory networks. He begins by finishing Lecture 14's discussion of protein-protein interactions.

Prof. Doug Lauffenburger delivers a guest lecture on the topic of logic modeling of cell signaling networks. He begins by giving a conceptual background of the subject, and then discusses an example involving hepatocyes (liver cells).

This lecture is on predicting protein interactions. He discusses structural predictions of protein-protein interactions. He then talks about how measurements of protein-protein interactions are made and Bayes Net prediction of protein-protein interactions.

This guest lecture by Prof. Ron Weiss is on synthetic biology. Prof. Weiss describes how he came to be a synthetic biologist, followed by an overview of the field. He covers basic , technologies for scalability, and programmable therapeutics.

This lecture by Prof. David Gifford is on human genetics. He covers how scientists discover variation in the human genome. He discusses how to prioritize variants based on their importance. And then covers how to prove causation, not just correlation.

In this lecture, Professor Burge reviews classical and next-generation sequencing. He then introduces local alignment (BLAST) and some of the associated statistics.

This lecture by Prof. Christopher Burge covers modeling and discovery of sequence motifs. He gives the example of the Gibbs sampling algorithm. He covers information content of a motif, and he ends with parameter estimation for motif models.

This lecture by Prof. Ernest Fraenkel is on protein interaction networks. He covers network models, including their structure and an analysis. He asks, "can we use networks to predict function?" He ends with a data integration example.

In this lecture, Prof. Burge discusses global sequence alignment and gapped local sequence alignment. He later talks about substitution matrices for protein comparison.

Prof. Gifford talks about library complexity as it relates to genome sequencing. He explains how to create a full-text minute-size (FM) index, which involves a Burrows-Wheeler transform (BWT). He ends with how to deal with the problem of mismatching.

Prof. Burge discusses comparative genomics. He begins with a review of global alignment of protein sequences, then talks about Markov models, the Jukes-Cantor model, and Kimura models. He discusses types of selection: natural, negative, and positive.

This lecture on predicting protein structure covers refining a partially correct structure. Methods include energy minimization, molecular dynamics, and simulated annealing. He moves on to methods for predicting structure from a sequence of amino acid.

Professor Ernest Fraenkel begins his unit of the course, which moves across scales, from atoms to proteins to networks. This lecture is about the structure of proteins, and how biological phenomena make sense in light of protein structure.

Lecture 11 is about RNA secondary structure.  Prof. Christopher Burge begins with an introduction and biological examples of RNA structure. He then talks about two approaches for predicting structure: covariation and energy minimization.

Prof. Christopher Burge begins by reviewing Lecture 9, then begins his lecture on hidden Markov models (HMM) of genomic and protein features. He addresses the terminology and applications of HMMs, the Viterbi algorithm, and then gives a few examples.

This lecture by Prof. David Gifford is about RNA-seq (RNA sequencing), a method of characterizing RNA molecules through next-generation sequencing. He begins with the principles of RNA-seq, and then moves on to how to analyze the data generated by RNA-seq.

Prof. Gifford talks about two different ways to assemble a genome de novo. The first approach is overlap layout consensus assemblers, as exemplified by string graph assemblers. The second approach is de Bruijn graph-based assemblers.

In this lecture, Prof. David Gifford discusses transcriptional regulation. He talks about techniques that can elucidate how genes are regulated, and how gene regulators interact with the genome.

In this lecture, Professors Burge, Gifford, and Fraenkel give an historical overview of the field of computational and systems biology, as well as outline the material they plan to cover throughout the semester.