Introductory Videos

These videos provide introductions into the brain structure and function, and molecular and cell biology. I will play some of them in class.

• The Brain: Structure and Function (13:55")
• Cell theory (watch the first 1:40")
• Types of brain cells (19")
• Glial Cells, White Matter and Gray Matter (1:50")
• Neuron (11:20")
• Split brain experiments (a Flash-based game)
• Inside the Brain: Unraveling the Mystery of Alzheimer's Disease (4:22")
• Mechanisms and secrets of Alzheimer's disease: exploring the brain (6:26")

These videos discuss general molecular and cell biology.

• The Cell (7:21"): a general overview of cell structure from Nucleus Medical Media
• DNA to protein - transcription and translation (Wellcome Trust, 3:18 min)
• The DNA learning center has created several interesting videos.
  • Cell signals (I played this video in class)
  • The Central Dogma (several videos)
• The textbook "Biology" by Raven, Johnson, Losos, and Singer has several relevant videos:
  • Molecular biology of the gene
  • Cell communication
  • Gene regulation

Schedule

Assignments

1. Assignment 1, released on February 13, 2018, due by 11:59pm on March 1, 2018
2. Assignment 2, released on March 1, 2018, due on March 15, 2018

Projects

There are over 100 algorithms developed for clustering graphs in order to find modules in them. The algorithms differ in several characteristics such as the definition of modules, the number of parameters used to define modules, whether modules can overlap or not, and the precise techniques used to compute them. This project will apply multiple such published algorithms to connectomes in order to discover how much the computed modules change based on the algorithm. The material in Chapter 9.3: Comparing and Aggregating Network Partitions of the textbook is relevant. I encourage the project group to read other papers on "consensus clustering" published in the literature, with my feedback and advice, of course. The steps in the project will include

• Decide a set of algorithms to compare after discussion with me.
• Download the code for each algorithm and making sure it runs well. If necessary, you may have to implement some algorithms, but I hope that work will be minimal.
• Develop and implement ideas and methods for comparing modules and partitions computed by different algorithms.
• Implement consensus clustering algorithms.
• Run these algorithms on connectomes and report your results.

The goal of this project is to find "network legos" of very large connectomes. You will use datasets where each connectome comes from one individual. The idea is to compute modules in each connectome (e.g., using the Louvain algorithm) and then to compare the modules systematically using the ideas in Network Legos: Building Blocks of Cellular Wiring Diagrams. I will explain this algorithm and simpler versions of it to the group that undertakes this project. The results of these comparison will yield "legos" from which we can reconstruct any interactome. This project is more challenging than the others simply because of the sizes of the connectomes: each of them contains millions of edges. Therefore, your code will need to be efficient in running time and in memory (RAM) usage. It might take some effort and trial-and-error to achieve satisfactory time and memory.

In mammals, the suprachiasmatic nucleus (SCN) is a network of neurons that communicate with each other to generate precise system-wide circadian rhythms (the daily sleep-wake cycle). Disruption of these neurons after long airplane flights is responsible for jet lag. The authors of Functional network inference of the suprachiasmatic nucleus created the first known network for these neurons in mammals. They used a neurotoxin to desynchronize this circadian network and then applied tools from information theory to determine which cells communicate in order to re-eestablish synchronization. Using techniques we have studied in this class and other related methods, this project will study the properties of this network, e.g., to replicate the paper's findings that "this functional network consists of two densely-connected cores, surrounded by sparsely connected shell regions." The networks themselves are available on GitHub.

A recent paper titled Organizing principles for the cerebral cortex network of commissural and association connections has published and analyzed a comprehensive connectome of the mouse brain, collected from findings in over 185 publications that appeared in the literature since 1974. This project will analyze the structural properties of this connectome. If you are ambitious, you can try to correlate this network with the mouse connectome created independently by the Allen Institute for Brain Science.

The Allen Center for Brain Science has created and made available some unique neuroscience datasets. This project will analyse a comprehensive dataset of gene expression in cells in medial temporal gyrus of brain slices from multiple human donors. For each donor, this dataset uses a technique called RNA-sequencing to measure the expression level of every gene. The project will integrate these data with interaction networks among human proteins to understand the different biological processes that are active in this region of the brain. The project will entail learning new algorithms and computational ideas that we did not have time to delve into in the class.

In the class, we have studied and analysed several type of connectomes. Each of these connectomes arose from experimental observations of the brains of different organisms. We studied several types of properties of these connectomes. Student presentations will explore other properties and methods of computational analysis. A fundamental question that arises now is what types of evolutionary processes in nature can generate the types of connectomes that exist in organisms. For example, the process is almost certainly not an Erdos-Renyi like model. This project will consider several mathematical models that have been proposed for connectomes and test these models for their ability to generate artificial networks with properties that match those of real connectomes such as the small world property and hierarchical modularity. The paper Resolving Structural Variability in Network Models and the Brain and Box 10.1: Growth Connectomics: Generative Models for Brain Networks in the textbook provide excellent starting points for this project. An important question to consider will be whether models should incorporate geometric constraints imposed by the structure of the brain.