Week 1

Aim of Research Project

My research project aims to extend upon the original PATHLINKER paper. In particular, I hope to investigate different weighting schemes both from the perspective of utilizing different data sets and data-summary methods as well as potentially maybe different statistical tools for manipulating the data in meaningful ways. This is motivated by the fact that there appears to be little overlap between interactomes consisting of the same proteins but weighted differently. Therefore, this project will allow us to analyze different schemes and develop an heuristic for weighting interactomes. As a result, we will be able to apply algorithms like PATHFINDER on graphs such that they provide results that have valid biological meaning rather than being affected by superficial factors such as trends in scientific research or biases of the scientific community. Right now, I am focusing on Bayesian Weighting schemes which attempt to use experimental evidence to provide a weight that represent the probability of a given interaction occurring in a given signaling pathway by leveraging Bayes’ theorem. I will talk more about this in future posts.

Week 1 Progress

Week 1 was spent mostly acquainting myself with the necessary prerequisite knowledge required for research during coming weeks such as Dijkstra’s algorithm (and its implementation) and Bayesian Weighting Schemes and in that regard reading some relevant papers. For the sake of keeping track the particular papers were titled :-

  1. Pathways on demand: automated reconstruction of human signalling networks
  2. Top-Down Network Analysis to Drive Bottom-Up Modeling of Physiological Processes
  3. Bridging high-throughput genetic and transactional data reveals cellular responses to alpha-synuclein toxicity.

The first was the original PATH LINKER paper which our research hopes to extend upon. The next two provided some information on Bayesian Weighting but did not provide too much else due to some ambiguous mathematical notation. I hope to attempt to write a document that annotates the relevant portions of the papers for the sake of clarity and future reference.


Kicking Off the Collaborative REU

As the summer winds down and classes begin at Reed College, we are excited to begin a new project that sits at the intersection of computer science and biology.  With mentoring expertise on both sides of the aisle (Anna is a computer scientist, and Derek is a cell biologist), our interdisciplinary team will apply computer science techniques to predict potential players in disease.

The Biological Question: How is cell migration regulated in patients with schizophrenia?

Schizophrenia is a psychiatric disorder that affects how a person thinks, feels, and behaves, with potentially severe symptoms.  While we know that susceptibility of this disease runs in families, there are many mysteries about which genes, or “instructions” encoded in DNA, drive schizophrenia.  A paper recently demonstrated that cell migration patterns are altered in patients with schizophrenia – the cells become more motile and less “attached” compared to the same type of cells from healthy patients.  Since genes associated with cell migration have also been implicated in other diseases, we want to identify genes that may be potentially involved in altered cell migration and schizophrenia.

The Computational Approach: Machine learning to predict disease genes

While experiments can test whether a particular gene is associated with cell migration, we can’t simply test all 20,000 possible genes – it would take way too long, be way too expensive, and a vast majority of the experiments will be uninformative.  Instead, we will develop computational approaches to predict a small subset of candidate genes for further experimental testing.  These in silico experiments (which is just a fancy word for computer-simulated experiments) may not be incredibly accurate, but they will sure be fast!

How do we go about developing a computational method to predict candidate cell migration and schizophrenia-associated genes? As we’ll detail in future blog posts, we will search for these genes within large, publicly-available datasets.  We will build a list of the genes that are known to be associated with cell migration or schizophrenia, and then look for other genes that have similar properties to the known genes.  This general technique is called machine learning, where we design instructions for a computer to make predictions.  In our case, we wish to predict whether an unknown gene could be associated with cell migration, schizophrenia, or both.

Experimental Validation: Testing the computational predictions

An important aspect of computational biology research is to experimentally test the predictions to see if we discovered new players involved in schizophrenia and cell migration. In Derek’s lab, the team will test the top candidates in two ways.  First, will see whether each candidate gene affects cell migration in fly cells by “knocking down” the gene product in the cells and observing the change in cell movement.  Next, we will take the top candidates from the first step and observe migration patterns in fly neuroblasts (cells that are destined to become neurons). From these experiments, candidate genes that alter migration patterns in fly neuroblasts may affect neuron cell migration in humans.

There is lots to learn and lots to do!  It will be a fun year – stay tuned.