Showing posts with label career. Show all posts
Showing posts with label career. Show all posts

Wednesday 15 February 2017

How could you study epigenetics? Thought experiment from a dreamer

This blog is part autobiography part roadmap. 

I am often asked “Do you miss it-being a scientist”

The short answer is “I didn’t dream of being an IT consultant.”

Longer answer is, as with all things that are part of being an adult, more complicated.

  • I miss discovery- the joy designing a method to answer a question and then actually knowing- for a brief moment in time- something that no one else knows.
  • I miss immersing myself in a problem and building a solution.
  • I don’t miss, university politics; having to know who’s ass to kiss and watching my back for potential theft of ideas.
  • I don’t miss the bullshit publication process that sometimes is used for competitive reasons.
  • I don’t miss trying to write a grant that appears to be both novel and safe at the same time.
  • I don’t miss the bureaucracy of universities policies that protect senior faculty but burden junior faculty.

I have been “out of the game” for half a decade now but I still pay attention to science and design experiments in my head and sometimes write them down.

I was an Assistant Professor running a small lab for about 4 years, my lab was centered on a set of enzymes that control the expression of genes in response to cell signals and environmental stimuli. These enzymes are commonly known as epigenetic regulators. The work we did was pretty good given the lack of funding, the fact that the enzymes had been described literally a year before I started my lab and I was trying to combine a novel class of genes with a novel set of methods (I was part of one of the groups that published the early papers describing the histone demethylase enzymes).[Synopsis of my lab]

My real interest, however has always been in learning the answer to the fundamental question “How do you make a brain?”  

I think now, I would ask a slightly different question; which has “how do we fix the brain when it isn’t made right.” One thing that age and distance has given me is perspective or perhaps empathy I don’t honestly know the difference. I have two small children both of whom are pretty awesome- unfortunately both have inherited some of my flaws. So I am often struck by how does the brain manifest these “flaws” even if there are no major changes or developmental issues.

How would one go after such questions?

Five years ago the answer was Stem Cells with maybe some mouse genetics thrown in for good measure. Now? I would go another direction. I think the single biggest issue in epigenetic research as well as neuroscience is the lack correlation data between phenotype (what it looks like in the whole organism), genotype (what genes play a role) and biochemical output (how well does the “engine” function”). Much like astrophysics and quantum physics have mathematic models which provide probability maps for specific core particles and/or forces, Epigenetics needs probability maps for phenotype and genotype- a Heisenberg probability if you will.

As I have mentioned in other posts, epigenetics is essentially grammar for the genome. It is a big, unwieldy mess of a field that is likely at least three separate full fields that we do not have names for as of yet. Sticking with the analogy the “field” of epigenetics is at the point where Western civilization was in the late 1700s/early 1800s where we knew some words and potentially some word relationships in the Egyptian cuneiform but we were largely blind to what was actually being said in hieroglyphs until the Rosetta stone was found. To me the rosetta stone for epigenetics will be cross species mapping of real world consequences.

For example; we know that there is a link between obesity in dogs and their owners. That is a real world cross species phenotype- why don’t we look at what genes expression and epigenetic patterns are changed as both lose weight? There is still validity to the idea mammalian biology is conserved at the physiological level.

What I would do if I was starting now would be to focus on dogs as a main model; they live with us, they often eat like us, they have behaviours which at their core are similar enough to ours but distinct across breeds. Furthermore access to their health records would have less risk and potentially greater detail as most veterinarians have a depth of knowledge on their patients over a whole lifetime – and for some clients multiple dog lifetimes.

For me, I would focus on brain cancer- as a scientist it is a fascinating process to take a cell that is programmed to not multiply and make it multiply and it is a cancer type that has repercussions to ones body, dignity and family.
The lab would have five facets;

1.     Define a set of neurologic symptoms that could be tested for by a veterinarian in clinic.
a.     Use standard indications from observation.
b.     A set of typical blood markers that are used for “unhealthy’ as part of the analysis.
c.     X-rays to define rough location, size and prognosis

2.     Test brain tumour samples across gene expression, “Epigenetic profile”, potentially genome mutations

3.     Use samples to grow models tumours, testing their gene expression profile and epigenetics profile for changes in culture.
a.     Where possible have normal age specific controls across breeds (or at least a general “mutt” control)

4.     Longitudinal studies of dogs with tumors after various therapies.

5.     Map epigenetic changes in tumour versus normal as well as fresh tumour versus in culture.

I was “classically” trained as a mouse geneticist where we had the clean clear; I deleted a gene what happened to my cell type? I learned [the very hard way-  hello consulting ;{ ] that there are no one-to-one relationships in any cell type when we deal with epigenetics- it is the system that protects the cell from single points of failure.

Long term would be to identify a set of parameters that can be linked to cancer and then go back and start testing the enzymes that are directly linked to the epigenetic modifications that are related to the phenotype. 

Tuesday 21 January 2014

Twenty skills that I -or any Ph.D- has that are in demand

A while ago Christopher Buddle posted a blog on SciLogs about what you needed to know before becoming a professor. Many of those skills are the ones in demand outside of academia. 

It got me thinking generally what skills I have amassed over a Ph.D, Post-doc and faculty position. For any other "recovering scientists" reading this please feel free to steal this list, add to it or perfect it. Any comments or critique would be welcome. 
  1. Project managementover my academic career I managed to publish several papers in top journals. Some required precise planning of tasks and experiments on a short deadlines against competition. This requires ensuring that each set of experiments is finishes with a high quality deliverable.
  2. Human resource- as a professor I had to hire, fire and develop staff. This included students and early career professionals where you are balancing what they are capableof today, with their career goals. I picked projects for them that they matched their skills.
  3. Project planning- a PhD is a set of projects, that need to be planned out, with a full timeline, deliverables and costs set out. In addition a key part of a successful PhD or post-doc is knowing when to kill a project.
  4. Stakeholder relationship- each stage of a PhD requires you to set out goals with your faculty advisory committee. These people will provide guidance and advice for where you should spend your time. Part of success is ensuring that you cogent show progress toward each of the members ideas ofyour success. The stakes get higher as you move to a post-doc where you are expected to manage the project and manage the expectations of your boss.
  5. Budget building- as a professor I needed to build RFPs, prioritize purchases based on project needs-as well as the long term strategy of the lab, source infrastructure, mange vendors and raise funds.
  6. Publications- part of a scientists job is to communicate results to the community. This includes typical writing skills but also graphic design, matching the presentation visualizations to the message and audience.
  7. Data management- all aspects of data management including ensuring high quality data recording metadata, designing database considerations. Build database querying, integrating public and owned data into a complete set.
  8. Analytics- a key part of my PhD was defying how to quantitate behavior and images. This requires a clear analytic method that allows reproducibility through clear, logical rubric for scoring purposes.
  9. Web based research-not just the query but also the decision on good sources and bad ones.
  10. Public speaking- I have given hundreds of lectures to all sizes of groups both lay groups and expert groups. This gives me a large set of tools to fall back on for presentation design
  11. Individual drive- to do a PhD you need to an internal drive to do what must be done.
  12. Intellectual flexibility- as part of my PhD I learned at least 12 different technical skills at a high enough level to use them in peer reviewed publications and teach them to others. I learned these through reading and just dpingi didn't need to be walk through them multiple times.
  13. Records management- my laboratory work in a high demand, high competition environment. We needed to have all experiments documented in a way that would stand up to legal review and could be used as part of a patent process.
  14. Understanding of several healthcare related regulations- part of my work was related to drug discovery and some of it was in collaboration with clinicians. Meaning that we ensured that all documents and protocols met the required standards.
  15. Graphic design- genetics is a hard area to explain without pictures. I designed many successful visualizations using Photoshop, powepoint and old matte photography techniqies.
  16. Process design- my laboratory was at the bleeding edge of genetics. This meant that we were constantly building new processes and testing resources that would be best for that process.
  17. Process optimization- due to the unique methods we constantly needed to set production standards and build analytics that allowed us to evaluate and optimize process and make changes that reduced cost and increased reproducibility and accuracy.
  18. Contract negotiations-as part of my job, I have negotiated service contracts, terms of employment 
  19. Fund raising- academic labs are also look for new sources of funding and interacting with potential investors/funders
  20. Strategic product planning -a key part of success is understanding where government priorities are now and the next five years to develop a funding strategy. Successful scientists also have a understanding of the competitive landscape and position their employees and infrastructure to keep up.