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After more than 10 years in administration, including 9 as Dean of | |
Arts and Sciences and 1 as interim Provost at UConn, I have returned | |
to my faculty position. I am spending a year as a visiting scientist | |
at the Jackson Laboratory for Genomic Medicine (JAX-GM) in Farmington, | |
Connecticut, trying to get a grip on some of the mathematical problems | |
of interest to researchers in cancer genomics. In this talk, I will offer some personal | |
observations about being a mathematician and a high-level administrator, talk a bit about | |
the research environment at an independent research institute like JAX-GM, outline | |
a few problems that I've begun to learn about, and conclude with a | |
discussion of how these experiences have shaped my view of graduate training in mathematics. | |
Observations as an administrator | |
Challenges/pleasures of administration: | |
intellectual diversity | |
other types of diversity | |
truly intractable problems | |
teamwork | |
opportunity to be a force for good | |
issues: | |
- doubts about the curriculum | |
- isolation of the department | |
- stubbornly slow process of diversification | |
broader observations: | |
-- length of academic career is a challenge for many people; change can be good. | |
-- relative impermeability of academic mathematics | |
Jackson Labs | |
Founded 1929 in Bar Harbor Maine | |
Originally supported by Edsel Ford and Roscoe Jackson (Hudson Motor cars) | |
Home of mouse genetics | |
26 Nobel Prizes associated with JAX | |
George Snell, JAX Faculty, won in 1980 for genetics of the immunte system. | |
Huge business in mice -- 3M mice per year | |
88M per year grant support | |
256M per year in the mouse business and other services | |
Mouse genome database | |
New facility in Connecticut dedicated to 'precision medicine' | |
-- 350 employees | |
~ 30 faculty | |
huge technical support infrastructure | |
-- information technology and "computational services" | |
-- imaging | |
-- sequencing | |
Math departments think of NSA or Finance as places where there are jobs for PhD's. | |
Place like JAX offers a research environment (no teaching) but can be a steady job -- compare | |
for example with a permanenent teaching faculty job. | |
Here's a sample job ad: | |
Responsibilities | |
The successful candidate will develop and apply computational methods | |
for genomic data analyses, have novel opportunities at the | |
intersection of algorithms, data interpretation, and experimental | |
design in close collaboration with the experimental team in the | |
lab. The applicant is expected to have Ph.D. in computational biology, | |
cancer genomics, or a similar discipline, and demonstrate strong | |
computing expertise, publication, and communication skills. We also | |
consider exceptional applicants who are new to computational biology, | |
including statistics, applied math, physics, and computer science. | |
Qualifications | |
PhD. in a quantitative area: computational biology, computer science, statistics, physics and applied math | |
Programming in R, Python, Perl or C++ | |
Experience in genomic dataset analysis and integration | |
Three (3) years of postdoctoral training or equivalent research experience, and demonstrated meritorious scientific performance. | |
Strong publication record (two or more peer-reviewed publications). | |
Ability to integrate and apply feedback in a professional manner, prioritize and drive to results with a high emphasis on quality, and work as part of a team. | |
Proven ability to analyze and synthesize information, and demonstrated resourcefulness in finding appropriate solutions to problems and initiative in presenting alternatives and implementing solutions to ensure effective change and/or eliminate or mitigate potential negative effects. | |
Exceptional organizational and project management skills, including the ability to plan, schedule, and successfully carry out multiple projects at the same time to successful conclusion with minimal supervision. | |
Quality orientated including delivering accuracy and attention to detail. | |
Some examples of people at JAX -- many non-biologists -- changing field draws in people from lots of areas. | |
Jeff Chuang (Assoc Prof, faculty member) PhD from MIT in statistical physics | |
Bill Flynn, "Application computational scientist", PhD in Physics (large molecules) from Rutgers | |
Vida Ravanmehr, PhD in Electrical/Computer Engineering (working on error correcting codes) BA/MA in Applied Math | |
Peter Robinson (Professor), BA and MS in Computer Science, MD from Penn. | |
But not many math PhD's (pure or applied) overall. | |
What are they working on? | |
Many different things, but here are two examples of problems. | |
1. (Robinson group) The Human Phenotype Ontology (HPO) | |
Phenotype = symptom. HPO is a large directed acyclic graph, where each node represents a symptom/phenotype and they are classified | |
from general to specific. Items ("terms") in the HPO are curated by experts. | |
For example: | |
[Term] | |
id: HP:0010082 | |
name: Symphalangism affecting the distal phalanx of the hallux | |
synonym: "Fused outermost bone of big toe" EXACT layperson [ORCID:0000-0001-5208-3432] | |
xref: UMLS:C4024063 | |
is_a: HP:0001859 ! Distal foot symphalangism | |
is_a: HP:0010053 ! Abnormality of the distal phalanx of the hallux | |
is_a: HP:0010091 ! Symphalangism affecting the proximal phalanx of the hallux | |
is_a: HP:0010191 ! Symphalangism affecting the distal phalanges of the toes | |
created_by: doelkens | |
creation_date: 2009-05-29T12:16:28Z | |
DAG is annotated by (mendelian genetic) diseases (So a disease is a subtree of the ontology) | |
Possible applications: | |
useful for clinical studies because it nails down symptoms in a systematic nomenclature. | |
Can be used for more interesting problems -- given a set of symptoms, find potential diseases consistent with those symptoms. | |
This type of reasoning problem from ontologies has a long history. | |
(give examples) | |
Also used to identify cross-species similarities and to find appropriate animal models | |
And to try to pair specific genetic variants with diseases. | |
2. (Chuang group) broader problem: tumor evolution. Typical tumor is made up of subpopulations of cells with common | |
genetic profile. Some of these subpopulations might be susceptible to treatment with a particular chemotherapy agent | |
to which others are resistant. | |
Patient-derived xenografts (PDX): take tumors from people, grow them in immuno suppressed mice so there is space to experiment on them. | |
Some attempts to reconstruct history of cell populations using ideas from evolutionary biology, but still an active area of research | |
Management of PDX studies is a large complicated business. | |
Key tool is sequencing -- different types -- DNA, RNA, single-cell. | |
My current project is to look at issues in single cell RNA sequencing, so here is a brief overview. | |
Recall how genetics works: DNA -> mRNA -> protein | |
Different cells in your body have same* DNA but different expression profiles -- different levels of mRNA and so different collection of proteins. | |
Prior technology (RNA-seq) looked at a sample from a mixture of cells and computed the relative abundance of different mRNA molecules. | |
So a typical RNA-seq experiment might take 3 replicates of a control and 3 of a treatment (say drugs or whatever) and compute the expression levels | |
of the genes in the two cases, then try to draw conclusions. So you have a matrix with 30000 genes and six samples. Various strategies to extract | |
meaning from this situation. | |
single-cell works differently. Cells get sequenced one at a time and you get an expression profile for all of the genes for each cell. | |
Result might be an integer matrix of 3000 cells by 30000 genes. Each integer counts the number of mRNA associated with that gene. | |
BUT: the data is confounded because the small amounts of material in each cell mean that many things DON't get counted that should. So the | |
matrix has lots and lots of zeros. | |
A typical goal is: | |
-- to cluster the cells by their expression profiles; | |
-- identify the genes whose expression levels characterize the clusters; | |
-- find a biological interpretation for these genes. | |
In the context of tumor heterogeneity, perhaps the clusters might be associated with certain biochemical pathways that can be targeted with particular drugs. | |
Example of one associated statistical model (there are many proposals for this): | |
Typical approach would be to use principal componenet analysis or some fancier method to reduce the dimension and identify clusters. | |
Problem: How to deal with the issues of: | |
-- normalization of the data | |
-- what effect does the dropout have on the dimensionality reduction? | |
-- should one try to impute the zero values? if so, how? | |