Tips for Thriving in Your Research Career

This blog post is written by University Texas at Austin graduate student Rayna Harris, and was inspired by the “NIH and You: How to Survive and Thrive in Your Research Career” Symposium at the 2014 Society for Neuroscience Annual Meeting in Washington D.C. on Saturday, November 15, 2014.

NIH Panel Members included:

  • Stephen J. Korn, Director of the Office of Training, Career Development, and Workforce Diversity
  • Nancy L. Desmond, Office Director and Associate Director for Research Training and Career Development
  • Michelle Jones-London, Director of Diversity Training and Workforce Development
  • Alan L. Williard, Acting Deputy Director of NINDS

#1. When it comes to choosing mentors, be promiscuous!

Successful experimenting! L-R: Manisha Sinha, Hilary Katz, Dalia Salloum. Photo credit: Rayna Harris

Successful experimenting! L-R: Manisha Sinha, Hilary Katz, Dalia Salloum. Photo credit: Rayna Harris

Choosing the right mentor is one of the most critical decisions grad students and post-doctoral fellows must make (see # 2). However, don’t forget the importance of having multiple mentors during each stage of your research career.

Other mentors will not only nurture and advise you, but they can also fill the voids in your relationship with your primary mentor. For instance, if your principal investigator (PI) is not a statistician, seek the advice of one who is to verify that your results are statistically sound. Or, if your mentor is a single male and you are a soon to be mother, seek the guidance of a female PI with children to discuss work-family balance.

#2. But seriously, choose the right mentor for you

It is important to join a lab where you will be supported in your training and your career; receiving good mentorship support is pivotal for success in your career. When choosing a lab, do your homework first and find out where former trainees have gone. Did they continue down their chosen career path? Do they still have a good relationship with the PI? These are important questions you need to have the answers to.

A good mentor should have the experience and the connections to get you were you want to be!

Altmetric score for Barres 2013 Neuron article.

Altmetric score for Barres 2013 Neuron article.

Once you join a lab, develop a relationship with your mentor that is built on good communication. How, when, and how often you communicate will be different for each mentor-mentee relationship, so find a strategy that works for both of you. Don’t be afraid to talk to your mentor about your goals! Work together to create an individual development plan and revisit it periodically.

For more on this subject, the following articles are highly recommended:

  1. Barres, Ben A. (2013) How to pick a graduate advisor. Neuron 80: 275-9.
  2. Wood, Charles (2012) When lab leaders take too much control. Nature 491: 785-786
  3. Raman, Indira M (2014) How to Be a Graduate Advisee. Neuron 81: 9-11.

#3. Be a good advisee

It would not be fair to demand quality from your mentor without returning the favor. By being a good advisee, you can actually help your mentor be a good mentor. Be proactive, and ask for your mentor’s time or advice when you need it. This way, both of you can shine!

If you ever find yourself in the unfortunate situation of being in a toxic relationship, swallow your pride and ask for outside help. Talk to your graduate program director, your department chair, or one of your other mentors. These people can either help you work it out with your mentor or can help you find a new lab.

Be proactive and talk to your mentors. Downloaded from http://www.phdcomics.com/comics/archive.php?comicid=1025

Be proactive and talk to your mentors. Downloaded from http://www.phdcomics.com/comics/archive.php?comicid=1025

 

#4. Have plans and follow through with them.

I recall The Serial Mentor saying that the number one common mistake grad students make is proposing an overly ambitious thesis. Don’t be one of those folks! Propose a doable project. Then do it. Persist even when parts of it fail, and do not take rejection personally.

Stay focused and learn to balance the time and effort you spend on your projects with classes, grant writing (see #8), reading, publishing, exercising, relaxing, and the plethora of other responsibilities you may have.

If you are a post-doctoral fellow, your focus should be to develop a research program that you can take with you! Discuss this early on with your mentor, and don’t join if you suspect that you won’t be able to.

Of course, a healthy dose of ambition is fantastic. Ambition is probably one of the most common shared traits among people who are “the first” to do something. The trick is, though, to not be so overly ambitious that you have little to present in your next job talk or award acceptance speech.

#5. Learn to cope with failure and develop grit

In addition to technical training, accumulate transferable skills throughout your career. These skills will help you succeed no matter what you choose to pursue and include (but are not limited to) critical thinking, communication, leadership, reasoning, grit, and perseverance.

Empowerment, resiliency, and grit are essential characteristics in a good researcher. Learn to cope with failure and you will have much more success in life. Take control of your academic environment rather than stumbling along after failure. Your mentors are there to help you up when you fall, but you must empower yourself.

#6. “You’ve got to know when to hold ‘em, know when to fold ‘em”

Don’t let failure stress you out! Image from: http://goo.gl/XOrfHq

Don’t let failure stress you out! Image from: http://goo.gl/XOrfHq

This quote is actually from a song about gambling by Kenny Rogers, but I think the advice really applies publishing goals and whether or not you really want to stay on the tenure track.

Set your aims high. If you aim to publish in top tier journals, then will you have a good chance of publishing in journals ranging from good to the very best. However, don’t spend 6 years trying to get one project into the best journal and then never publish. Ask yourself if publishing small bits early in a solid journal is a better career move or if you really want to hold out for that chance to revolutionize the field with one great piece.

Remember, industry is not easier; it’s just different.

Many of my peers struggle with deciding whether or not to stay in academia. The most common advice I’ve heard is to stick with research as long as you passionately love it and to not quit until you have to. Every minute you spend in academia is useful, so don’t think that you’re wasting your time. If you are considering leaving academia, peruse opportunities as they present themselves and seize the right one when it comes along.

#7. Network whenever possible and don’t burn bridges.

Networking at conferences is a must #SfN14

Networking at conferences is a must #SfN14

When you go to meetings, don’t just socialize with people from home. Schedule lunch or coffee with your letter writers to keep them updated or with potential employers to get to know them better. Meet new people at posters or socials or during interactive sessions.

Along those lines, try to keep positive relationships with all your colleagues and don’t burn bridges. Our communities are small, so try to be nice to even to your bad colleagues. You never know you will need something from them or someone they know.

#8. Talk to your program officer before and after applying for grants

I’ve saved the final tip for the topic of funding. This could probably be a 1000 word blog all by itself, but I’ll keep it short. Visit the National Institute for Allergy and Infectious Disease (NIAID) for more online resources.

Remember, your program officer (PO) is there to help you get funding! I’m sure you have heard that you should call or email them before submitting a grant, but what’s the best approach? The POs say that the best way is to send an email with your Specific Aims page and your Biosketch attached.

Also, contact your PO to discuss interpreting the summary statement of a grant that is not funded. This is especially useful if you have a hard time understanding the essence of the comments or if the reviews are conflicting.

Applying for grants as a grad student or post doc is a great idea because it gives you experience with the whole process and will help you thrive in your research career. However, you don’t need a grant at this stage to get a faculty position. If you have heard this, know that it is a myth! According toDr. Stephen J. Korn only 15% of new assistant professors had a K99 award.

Final thoughts

There is a pretty good chance you have heard most of this advice before. My mentors (yes I have multiple) and other great scientists have said this over and over again. But, sometimes it’s good to hear things more than once

I hope you found pieces of advice contained herein useful and worth sharing with others. Best wishes in your journey as a research scientist!

E.O. Wilson’s advice for thriving in sciencing

E.O. Wilson’s advice for thriving in sciencing

 

Disclaimer

I have a great mentor and a good relationship with him. But, I strive for perfection and am always looking for advice on how to do things better.

One of my live tweets from #SfN14

One of my live tweets from #SfN14

 

Acknowledgments

Many thanks to @karinaalbab and @maruca221 for comments and suggestions for this blog, the organizers of #SfN14 for providing a great forum for discussion, and to @PLOSNeuro and @emilyjanedennis for inspiring me to blog and tweet at #SfN14.

This story was originally published here on Medium and here on PLOS as part of the PLOS Neuroscience Community.

For more information, you can contact Rayna at rayna.harris at utexas dot edu.

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BEACON Researchers at Work: Can’t we all get along? Overcoming evolutionary conflict

This week’s BEACON Researchers at Work blog post is by University of Washington postdoc Sylvie Estrela.

SylvieEstrela_photoConflict is widespread in nature and that is no exception in the microbial world. Examples of competitive interactions between microbes include competition for shared limiting nutrients, competition for space, and the production of compounds such as toxins and antibiotics that inhibit or kill competitors. In the face of such conflict, how can we explain the occurrence of mutually beneficial associations between unrelated organisms, known as mutualisms?

Microbes are intrinsically leaky, that is, they produce a broad range of metabolites into their environment as a result of their metabolism. When these waste products of metabolism are used as nutrients for growth, this is called cross-feeding. Thus, a cross-feeder reaps some benefit from the association with a producer. If the waste product is toxic to the producer, then waste removal by the cross-feeder is beneficial to the producer. This can be seen as trading a service (detoxification) for a resource (food). At a first glance, it seems that both partners would benefit from the association, setting out the ground for mutualism to occur. To gain a better insight into the dynamics of this interaction, I started by developing a simple mathematical model. The model revealed that this simple cross-feeding interaction can generate a variety of possible ecological outcomes, spanning mutualism, exploitation, and competition. Furthermore, it highlighted the importance of the metabolic constraints of individual species and the features of their shared environment, such as toxicity level and decay rate of the waste product, in determining the conditions for mutualism [1].

This was the beginning of my academic journey into exploring how mutualism may arise at the first place and be maintained, and which ended up being the main focus of my PhD research supervised by Dr. Sam Brown at the University of Edinburgh. At this point, the model described two species growing in a well-mixed (planktonic-like) environment. But in natural environments, most microbes live in surface-attached, spatially-structured communities such as biofilms. An interesting feature of growth in a structured environment is the stronger potential for demographic feedbacks between interacting partners. This is mostly due to the fact that an individual cell has a stronger effect (either positive or negative) on its neighbouring cells than on the cells that are further apart, which in turn feeds back on its own growth. So how do metabolic interactions and demographic feedbacks combine to shape the spatial organisation and functioning of polymicrobial communities?

Figure 1. Simulation of a two species community where species are engaged in a food for detoxification metabolic interaction. While strong metabolic interdependence drives species mixing, weak metabolic interdependence drives species segregation.

Figure 1. Simulation of a two species community where species are engaged in a food for detoxification metabolic interaction. While strong metabolic interdependence drives species mixing, weak metabolic interdependence drives species segregation.

To address this question, I used a spatially-explicit model that simulates the growth of the two-species community on a surface. I found that strong metabolic interdependence generates mutualism and species mixing, and community behaviour is less sensitive to variation in initial conditions (initial species frequency and spatial distribution). In contrast, weak metabolic interdependence generates competition and species segregation, and community behaviour is highly contingent on initial conditions (fig. 1, [2]). Hence, these findings suggest that demographic feedbacks between species are central to the community development, shaping whether and how potential metabolic interactions come to be strengthened or attenuated between expanding species [3].

Now as a postdoc in Prof. Ben Kerr’s lab (UW), I’m interested in exploring further some of these questions by specifically focusing on the evolution of mutualisms and interdependencies when traits are costly to perform rather than just a waste product of metabolism. Because of the lack of relatedness between partners, evolutionary conflicts of interest will be strong. But despite conflict, interspecific mutualism can prevail when the conditions are such that partners’ interests are aligned and potential conflicts are kept in check. A critical question is how this can be achieved. In collaboration with Prof. Ben Kerr and Prof. Eric Klavins (UW), I’m using the ‘gro’ simulation platform to address this question (fig. 2).

Figure 2. Snapshot of a ‘gro’ simulation showing the emergent spatial pattern of two species exchanging costly essential functions.

Figure 2. Snapshot of a ‘gro’ simulation showing the emergent spatial pattern of two species exchanging costly essential functions.

 

Key references

[1] Estrela, S. et al. (2012) From Metabolism to Ecology: Cross-Feeding Interactions Shape the Balance between Polymicrobial Conflict and Mutualism. Am. Nat. 180, 566–576

[2] Estrela, S. and Brown, S.P. (2013) Metabolic and demographic feedbacks shape the emergent spatial structure and function of microbial communities. PLoS Comput. Biol. 9, e1003398

[3] Estrela S, Whiteley M, and Brown SP (in press) The demographic determinants of human microbiome health. Trends in Microbiology

For more information about Sylvie’s work, you can contact her at sestrela at uw dot edu.

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BEACON Researchers at Work: Teaching a Robot to Learn

This week’s BEACON Researchers at Work blog post is by University of Idaho graduate student Travis DeVault.

TravisI imagine it would be difficult to find someone working in the field of computer science that did not start with a love of working with a computer. Likewise, I doubt many people choose to work with robots unless they love robots and the future that robots hold for us. We live in a world where personal, mobile computers are more limited by fashion trends than by hardware requirements, but it was only a few decades ago that personal computers were just starting to enter the average home. And so, it is the same for robots today as it was for computers decades ago.

The promise that robots offer us for tomorrow is that of cheap, reliable machines that can perform any number of complex or simple tasks that are currently performed by people. We have robots working on other planets, robots that explore our oceans, robots that perform surgery, and robots that build cars; in the near future though, robots will be common in every home and business. Robot surgeons and explorers will need less human supervision, and the cars will be robots. I’m personally most looking forward to a robot maid that can do a good job cleaning dishes.

Training trackBut for now, I think we’ve got to admit robots are pretty stupid. All the cool robots are either teleoperated by people, or at least heavily monitored and given instructions. Sure, I’ve got a robot vacuum that can do a better job than I can, but according to my wife, I’ve always found a way to make the house more of a mess when I try to clean. The robot vacuum never learns a better way to clean, it misses spots, it never knows where the dirty areas are, it scares my dog, and it still can’t figure out how to empty its own dirt bin. It’s really just an RC car with a vacuum and some infrared sensors to make sure it doesn’t bump into walls (I still bump into walls when I vacuum).

The research I do at the University of Idaho Laboratory for Artificial Intelligence and Robotics (LAIR) uses the principles of evolution in many different ways to enhance robotic learning. Our goal is to make robots that can learn over time, either through observing people or by receiving instruction from a human trainer or from other robots. One aspect that is very unique about the LAIR is that we use real robots for all of our work. Most groups doing robotics research will do most of the work in simulation, and then maybe transfer a finished control structure to a physical robot in order to create a youtube video. At the LAIR, the entire experiment is conducted on the robot.

Because the work is done with a physical robot, one of the challenges of the work is creating a robot that is able to sense its environment. Although many sensors have been created for robots such as infrared and ultrasonic eyes, we’ve chosen to rely more on the built-in cameras of a smartphone. Image processing is a slow job even on a beefy PC, on a smartphone it because a very slow process. One of the ways that we use evolution is in an evolved vision algorithm; the evolution uses a genetic algorithm to decide what parts of an image it should process in order to make decisions.

Our goal is to create robots capable of learning in a large variety of environments, which includes taking the robots outside as part of our experiments. We create robotic brains which can evolve different behaviors based on the situations presented to the robots by a human trainer. Our robots have used an evolved brain to travel on indoor and outdoor paths. The learning is done at run time when the robot is driven on the road by the trainer. Using this type of evolved learning, the robots have achieved a 95% success rate at navigating roads which the robot had never been trained on.

Continuing on this work, we have decided to focus on distributing the evolutionary learning over a network of several robots. Some of the questions we’ve asked leading into the work are: Does distribution increase the learning rate? Does a robot perform better with distribution? Do multiple trainers matter? Can we make the robots train other robots to perform better on a more difficult problem? Currently, the roads following results are so good without distribution that we are creating a more difficult experiment for the robot, so that we can effectively test all of these questions.

trvis robotFuture plans for the LAIR include working with the agriculture department at the University of Idaho to make evolve robots capable of weeding potato and wheat fields. We intend to try to use an evolved vision algorithm to identify invasive species and plant illnesses using smartphone cameras and sensors. The smartphones could then create a GPS map of areas that farmer would need to investigate. We will eventually have robots with sophisticated enough behaviors that we can rely on them to kill the unwanted plants.

For more information about Travis’ work, you can contact him at zerill at gmail dot com.

 
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BEACON Researchers at Work: What Every Scientist Needs to Know

This week’s BEACON Researchers at Work blog post is by University of Texas graduate student Amir Shahmoradi.

asmSummary: In a world in which science and technological breakthroughs dominate all aspects of almost every individual human life, scientists and researchers are under an ever increasing pressure to cross and expand the borders of human knowledge. As new discoveries require higher levels of precision and reproducibility, excess workload and hyper-competitive work environments have made researchers more prone to human cognitive biases. A solution to this emerging problem is to introduce all graduate students in STEM fields with the limitations of human mind and scientific instruments and their potential role in false positive discoveries and misconduct of scientific research. I suggest that a full-semester course that covers relevant topics including those mandated by NSF as Responsible Conduct of Research should be developed and tailored for each individual STEM field of research and be offered as an integral core course of every graduate program across the world.

Growing up in a traditional and highly religious society, I was drawn from an early age to the romantic mystique of ancient religious and philosophical writings. I joined study sessions and participated in lively discussions with religious scholars. But living in an academic household, I gradually developed a sense of scientific skepticism that led me to question the basic tenets of this knowledge. By contrast, science and mathematics seemed so captivating to me as a teenager for a very simple reason: Science is based on observation, evidence, and mathematics. It is universal, independent of people, society, religion and ideologies.

My passion for science, in particular Astronomy, Physics and Biology kept growing, until I stumbled on a post dubbed “The Same Color Illusion” in Astronomy Picture of the Day (APOD), which profoundly changed the way I view and perceive the world around me ever since. This APOD post showcased a simple example of human cognitive bias and how it can affect our perception of similar and different colors, with a simple clear message: “What human senses perceive of the world, does not necessarily reflect the reality.”

SameColorIllusion

The psychological literature is full of studies that demonstrate how human’s limited senses can result in cognitive flaws and biases in our understanding of the universe. In fact, psychologists have pinpointed many types of biases that affect not only the way we see but how we think about and react to the world around us. Confirmation bias, for example, is the tendency to notice, accept, and remember data that confirms what we already believe, and to ignore, forget, or explain away data that is contradictory to our beliefs. To make things worse, add the (unknown) limitations of instruments by which human probes the universe. The combined effects of human and instrument biases can result in erroneous conclusions and predictions.

Fortunately, many of such biases are now well understood by scientists, in particular, by experimental physicists, biologists and observational astronomers. A worked-out example is the well-known Malmquist bias in observational astronomy. Nevertheless, as our circle of knowledge expands, so does the circumference of darkness surrounding it, bringing new types of instrumental and human cognitive biases with it, that might affect human’s understanding of natural phenomena.

Today, we live in a world that relies heavily on science and technology. As a result, the number of scientists has also grown exponentially rapidly over the past century. With limited funds and resources now available to the community of scientists, the competition and work stress has also increased steadily among researchers.

In such a hyper-competitive atmosphere, scientists are more prone to perception and cognitive biases due to excess workload and stress. There already exist websites, such as Retraction Watch, that regularly report new examples of wrong scientific papers, and papers that contain fake or irreproducible results, forgeries and plagiarism.

The two major funding resources of science in the United States, the National Science Foundation and the National Institute of Health have already stepped in to mitigate the increasing trend that is seen in irreproducibility of scientific discoveries and retractions of scientific articles, before scientists lose the public’s trust in their work. Examples of actions taken include new rules for validating scientific discoveries and mandatory Responsible Conduct of Research (RCR) for all students and postdocs supported by NIH and NSF funds.

Personally, I cannot believe that any scientist in the world would intentionally want to fake results or commit plagiarism or be involved in any other unethical action. Over the past decade, I have witnessed how human cognitive biases can affect the minds and scientific results of numerous scientists. I have seen scientists who insist on the accuracy of their wrong discoveries, and in many cases, I have become convinced that there is no personal intention involved in their stance. I have been very fortunate to work on some specific research projects that opened my mind to many of the limitations that we humans and our scientific instruments face in probing and understanding the universe.

I personally believe the RCR trainings mandated by NIH and NSF can become even more efficient, if they were instead offered as a mandatory comprehensive full-semester course, for all graduate students in all STEM fields, a course that would also cover the myriad of human cognitive biases and instrumental limitations that would meddle with reasoning of every scientist and their understanding of natural phenomena. Regardless of where these students end up, whether academia or industry, whether they are funded by NSF/NIH or not, every student in science programs must learn about the limitations of human mind and its potential adverse effects in scientific reasoning and discoveries.

For more information about Amir’s work, you can contact him at a dot shahmoradi at gmail dot com.

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BEACON Researchers at Work: Engineering life

This week’s blog post is by University of Washington graduate student Leandra Brettner.

LeandraAll living organisms share a universal programming language—DNA. Long strings of unit molecules A’s, T’s, C’s and G’s dictate the unique traits of each individual, but the code is read ubiquitously across each species. This means that a gene that encodes a protein in one organism would encode the same protein if transplanted to another creature. Synthetic biologists use this property to engineer life by doing just that, rearranging genes from different species to program new behaviors into organisms. I am a synthetic biology graduate student in the lab of Professor Eric Klavins, and I work with genetically programmed bacteria, specifically Escherichia coli.

Microbes such as viruses, bacteria and yeast, are cheap and easy to grow, making them excellent platforms for synthesizing traditionally expensive organic chemicals such as fuels, pharmacologicals, and commodities like plastics. By performing the chemistry to create these products in microorganisms, we can potentially both decrease cost and increase sustainability and performance. Researchers like Jay Keasling at UCSF and Angela Belcher at MIT are demonstrating the amazing utility of living chemistry by manufacturing drugs such as artemisinic acid in yeast and building record breaking batteries out of viruses.

However, when we introduce foreign behaviors into cells, we are competing with millions if not billions of years of evolutionary history. Microbes, like all organisms, work hard to maintain the energy balance that supports life. Synthetic programs mess with that equilibrium, limiting the engineering complexity we have currently been able to achieve.

I work on developing ways to increase the complexity of engineered behaviors in microbes by isolating them into working groups—kind of like how factories use assembly lines, everyone has a specific task that contributes to the whole. These division of labor schemes are seen through every hierarchy of biology, from symbiotic bacteria to eusocial insects.

Our system’s goal is to digest complex carbohydrates like those in plant waste and turn it into usable biomass that can go towards producing carbon-based products like the biofuels and therapeutics mentioned, further reducing the cost and making production carbon neutral.

schematic of systemThe population of engineered bacteria start out in a consumer state where their only job is to grow and reproduce. Then, every so often, a cell will switch to an altruistic state where it produces an enzyme that breaks down cellulose and lyses to deliver the goods to the extracellular environment. The digested sugars can then be used as food for the consumer cells.

This cooperative architecture has allowed us to build in the complex behavior of novel nutrient use that can be coupled with chemical production in the future.  

However, this system suffers from an interesting form of community evolutionary instability called “the tragedy of the commons.” In well mixed culture, any variants that arise that cease to perform the cooperative behavior (cheaters) can still reap the public good provided by the altruists. Because they fail to lyse, the cheaters have an increased fitness advantage and can sweep the population—but to their ultimate demise. Without the altruists, cellulose digestion comes to a halt and the population crashes. Previous work has shown that if, however, there is some spatial organization to the environment, the communal benefit applies only to nearby, closely related cells who are likely fellow altruists. The cheaters are left stranded with limited or no access to the resource. This phenomena, dubbed kin selection, propagates the cooperative behavior through many generations. Members of Professor Ben Kerr’s lab are currently working with my system to investigate if they can evolve strains that exhibit increased cooperation by propagating cell lines in structured environments.

I look forward to continuing to collaborate with the Kerr lab, and potentially extending their research to the design and tuning of new synthetic organisms.

For more information about Leandra’s work, you can contact her at leandra dot brettner at gmail dot com.

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