How to Survive a STEM PhD

I recently wrote a short eBook, which is now available on Amazon! It was a fun project and primarily a way to collect lots of advice I’ve received over the years. I’m including a short excerpt here—for the full book, please follow the link to Amazon! The book includes sections on a wide range of topics, including how to pick the right lab, how to manage your time, and how to be a good scientist.

ALS Cover

Chapter 3: How to manage your time

Many people—including me—struggle with time management in graduate school. This can be for many reasons. For some, graduate school is their first experience with a serious, full-time job. For others, the incredible flexibility of academia takes time to get used to. Depending on your lab, it’s possible that you won’t have a direct manager or won’t report to anyone directly. If you miss a day of work (or show up at noon), it may be that no one will even notice. This freedom can be both liberating and demotivating.

Many programs, including my own, do not have specific milestones between years two and five. After passing qualifying exams in year two, the ensuing years can feel unchanging, even when you are making good progress toward graduation. To me, the lack of month-to-month milestones made time management seem like a daunting task.

However, the good news is that there are techniques to improve your time management skills! I’ve presented a few of my favorite suggestions here.

Track your progress

Graduate school lacks monthly or weekly milestones, so many successful graduate students find ways to make their own milestones. One great way to do this is through biweekly or monthly reports. Ideally, these reports are sent to your PI or mentor to help keep you accountable for writing them even when you are busy, but reports can be written for yourself as well. In each report, I reflect on the big picture objective of each project, where the project stood the last time I wrote a report, and what progress I’ve made in the last two to four weeks. Failed experiments also go in the report: learning that something didn’t work is an important part of progress in graduate school and is worth revisiting. Each report shouldn’t take more than a few hours to write, especially if all of your i’s and t’s are already dotted—for example, if your data are already worked up into presentable figures. I like this strategy because it allows me to track my progress month-by-month, and it gets me into the habit of working up data as soon as it’s generated.

Writing biweekly reports also helps me figure out what experiments I am avoiding: I notice when experiments have been in my “future directions” section for the past few months. Maybe those experiments are actually not as relevant as I though, or maybe (more likely) they use new techniques that will require extra time to learn. Either way, it’s worth knowing what I am unintentionally avoiding. On tough days, I look back at old reports to remind myself how much I’ve learned, even if it feels like I’m not progressing as fast as I should.

There’s a lot to be learned from pursuing whatever science is most interesting to you or following a line of inquiry out of sheer curiosity. But science is also measured in publications, and you eventually want to be guided by experiments that are leading towards a tangible product, particularly if you are interested in having a job in academia. An easy way to keep future manuscripts in mind is to sketch out figures for how you would present each project as a story. And when I say sketch, I mean sketch! I usually sit down with a blank 8×11 sheet of printer paper and draw boxes with sketched out data, showing what I think each figure will look like. If I’m not close enough to a manuscript to know what the graphs will look like, I will use words to describe the content (i.e. Figure 4—Phylogeny?). This can be done even if you’re in the very early stages of a project. This exercise essentially forces you to ask yourself how you would frame your project as a story to the scientific community. From there, your research can be focused towards answering the questions that bring the story to life.

I should note: not every scientist agrees with this approach. Some feel that creativity is stagnated if a scientist always keeps a manuscript format in mind. However, in my experience, sketching out papers is a way to keep focused on experiments that are related and building toward a PhD. That said, spending time on experiments that are tangents or side projects can be a great benefit—so maintain a healthy balance!

Set goals

I found myself in a rut in the middle of my fourth year. I would arrive at work, then make a list of everything I needed to accomplish that day. This strategy of day-by-day scheduling ended up feeling overwhelming to me—there’s only so much you can do in one day, and I felt frustrated by my slow progress. I discussed my frustration with a friend, and he suggested that I make my to-do list week-by-week instead of day-by-day. This allows for longer term planning, accommodates busy days, and identifies days with large chunks of time for longer experiments. More importantly, weekly to-do lists helped me feel less overwhelmed by any single day’s tasks.

While this strategy worked for me, there are many ways to set goals for yourself during graduate school. While my experiments lent itself better to weekly goals, some people may benefit from shorter- or longer-term planning, such as daily or monthly goals. Pick a strategy and try it out! Even after you decide on a plan, make sure you are reevaluating how it works for you and are open to trying new methods. Finding a way to motivate yourself via goal setting can help make the mountainous goals found in a PhD program—such as publishing or graduating—feel more attainable.

Track your time

At times, graduate students boast about how many hours per week they work. In a strange way, students can feel almost prideful about working long and hard hours. I am guilty of this as well.

One of the most interesting ideas on how to better manage my time was suggested at a conference in my second year. A female physics professor, much to my surprise, said that most people who say they work long hours are either lying or exaggerating. These people work inefficiently, she told me, and that inefficiency has actually been quantified in a research studies. She recommended that I read I Know How She Does It, by Laura Vanderkam.

Vanderkam studies the lives and habits of successful women across many fields using hour-by-hour weekly schedules. These meticulously documented scheduled revealed a gap between the number of hours some claims to work, and the number of hours that person actually works. For example, people who claim to work 90 hours per week are actually working closer to 50 or 60 hours when they annotate their weekly schedule. More likely, these people are either inefficient with their time—taking long lunch breaks, spending hours on the internet or taking coffee breaks—or they are simply overestimating their work week.

Since this conversation, I’ve become far more aware of my own scheduling inefficiencies. When I work late, it’s usually because I struggled to begin an experiment when I should, or I had many non-research meetings during the day. Of course, this is not universally true, and in some unusual circumstances, graduate students are expected to work many hours per week. But this tends to be the exception rather than the rule.

The most productive people I know in graduate school figured out early how to manage their time well, then spent the next several years working 40 hours or less while making fast progress. When I have documented my time, I’ve found that I spend a lot of time stressing about upcoming meetings, answering non-urgent emails, or reading the news. These inefficiencies lead me to 9 pm days far more often than does actual scientific need. In the past few years, the advice to document my time at least once per year has arisen several times, and it’s one of the more surprising and enlightening exercises I’ve done to improve my time management skills.

Finally, there are an incredible number of distractions in graduate school that are not necessary for you to graduate. These distractions can include mentorship, teaching, classes, extracurricular activities, coffee breaks, lunch with coworkers, and the internet. Some of these activities are important for developing your CV, avoiding burnout, networking with colleagues, and learning non-lab skills; however, I’ve found that these activities can also drastically lengthen my day. Tracking my time helped me document the frequency with which extracurricular commitments remove me from lab. With that knowledge, I was able to decide whether the time away from lab was time well spent.


Download the whole eBook for less than $2 at Amazon.com!

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Plantation Peril

Replacing rainforests with farms has many obvious consequences, but a recent paper from Nature Communications suggests that the ecological effect of deforestation is even greater than previously imagined. In this study, researchers, including several from UC Berkeley, showed that species that thrive in farmlands can negatively affect ecosystems of rainforests nearby—even if those rainforests are protected areas.

The team used decades of data from a protected rainforest in Malaysia bordered by palm tree plantations to conduct this study. In particular, the researchers studied wild boar, which thrive in palm tree plantation ecosystems by raiding farmland to eat fallen palm tree fruit. Wild boars travel long distances and leave distinctive markings in forests, meaning the boars could be tracked more easily than other farmland animals. Researchers also took advantage of the fact that palm trees generate fruit continuously for 20-25 years, then are cleared and replanted, leaving the same area clear for four to six years. This allowed researchers to correlate boar nest abundance directly with palm tree plantations. In the time frame without productive palm trees, the number of wild boars in the forest plummeted.

Surprisingly, the researchers found that rainforests over a kilometer away from palm oil plantations saw a dramatic increase in wild boar birthing nests, resulting in a 62 percent decline in the density of tree saplings over a 24-year period. This is particularly concerning given that protected areas are often central to efforts to conserve and preserve biodiversity—and current protected areas include sea, mountain, and rainforest preserves around the world. An improved understanding of cross-boundary effects could help guide improved conservation efforts and better use of protected areas.

Article published via the Berkeley Science Review. Original story can be found here.

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Book Review—Why We Sleep: Unlocking the Power of Sleep and Dreams

Lying awake at three o’clock in the morning, I began silently cursing the pillow, the blankets, the streetlight filtering in through my window. I had never had trouble sleeping before, but a perfect storm of deadlines and responsibilities had made for several miserable nights in the past week.

It was around this time that I first stumbled upon Why We Sleep by UC Berkeley Professor Matthew Walker. I admit that I was primarily hoping for a quick fix for improving the quality of my sleep during stressful times. While I did learn a number of useful tips, I also received a host of terrifying statistics, anecdotes, and well-researched studies on the effects of sleep deprivation.

Why We Sleep undertakes an enormous task. Most people know that more exercise and a better diet are two of the easiest ways to improve one’s health. However, Walker argues that sleep is the third pillar of health—or even that the other two pillars actually rest on a foundation of a good night’s sleep. Walker sets out to convince the reader that a lack of sleep in this country causes, correlates with, or exacerbates nearly every disease and leads to enormous social and economic costs. At times, Walker’s tone borders on alarmist, yet he keeps the reader engaged with a self-aware tone and frequent breaks into lighthearted banter. Why We Sleep is also extensively referenced with numerous studies to support Walker’s points. By the end of the book, I found myself convinced by his argument.

Walker explains that lack of sleep can impact an impressive number of diseases, including Alzheimer’s, cancer, and cardiovascular disease. Sleep deprivation can also affect memory, learning, fertility, obesity, the immune system, and overall lifespan. In one passage, Walker compares the impairment of not sleeping for a night with the impairment of being drunk to the legal limit—a comparison supported by many convincing studies. In particular, Walker writes about a phenomenon called microsleep, where a person becomes unresponsive for only a second or two, which is long enough to cause serious damage while driving. After missing a night of sleep, the incidence of microsleeps increased over 400 percent compared to a group that slept eight hours. More concerningly, the same study found that participants who slept six hours per night for ten days also experienced a 400 percent increase in microsleeps. These results have remarkable health and safety implications for the many people who do not sleep the recommended seven to nine hours per night.

In addition to discussions about the effects of unhealthy sleeping habits, Walker provides fascinating insight into the evolutionary benefits and origins of sleep. For example, it is well-documented that teenagers’ body clocks naturally shift to be later than adults’. Walker proposes that this mechanism could have provided teenagers with time away from a watchful parental eye, facilitating the leap to independence. Similarly, a group of early humans that contained a mixture of early- and late-risers likely were far better protected from night predators and other nighttime dangers, since there were only a few hours when every person was asleep. Walker also notes that every animal ever documented experiences some kind of sleep, underscoring its evolutionary importance.

The main drawback to Why We Sleep is how hard it is to avoid the idea of taking a nap while reading this book. That’s right, I think to myself. I am sleep deprived! I should rest, just for a minute. Thankfully, the author strongly encourages this practice in the introduction, making my nap in the middle of chapter four entirely excusable. “I will take no offense,” he says to readers who fall asleep while reading his book. “On the contrary, I would be delighted.”

Article published via the Berkeley Science Review. Original story can be found here.

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Berkeley Science Review: From the Editor (Fall 2017, Issue 33)

The past year has been a whirlwind as Editor in Chief of the Berkeley Science Review. This is a reprint of my final From the Editor, which describes some of the work I’ve helped edit and produce over the past semester. The entire issue can be found here!

Dear Readers,

Autumn has arrived, with its crisp air and bustling streets. Sidewalks and coffee shops teem with students and researchers, chatting, exchanging ideas, and discussing science. In many ways, science is forged through these connections—between people, concepts, techniques—as ideas become woven together into true progress.

In this issue of the Berkeley Science Review, several authors uncover the connections that are central to scientific progress. Within these pages, find out how an on-campus program brings researchers together in “Breaking barriers”, and how international mentorship has sparked strong basic research programs in “World-wide science”. Explore how extreme life on our own planet connects to the study of life beyond Earth in “Life on the edge”, and how a paradigm shift in research results in better patient outcomes in “Ditching discovery”. This issue of the Berkeley Science Review also contains twin “From the Field” articles describing two very different kinds of research at UC Berkeley.

Each issue of the Berkeley Science Review is unique, shaped by the many writers, editors, designers, and photographers who invest  countless hours in its creation. I especially want to thank the design team, led by Art Director Ashley Truxal, for this bright and beautifully designed issue. I would also like to thank our managing editor, Katie Deets, who ensures that we continue to publish each new issue and grow as a student organization. I want to give special  acknowledgements to Nicole Haloupek, our blog Editor in Chief, and Dat Mai, our web director, for directing and expanding our online presence.

This is my last issue as Editor in Chief of the Berkeley Science Review. Being Editor in Chief has been one of the most challenging, rewarding, and surprising experiences I’ve had in graduate school. I have been fortunate to work with a talented and growing team of editors, writers, designers, and photographers. I am thrilled to introduce Dat Mai as the next Editor in Chief of the Berkeley Science Review. He is a talented third year graduate student in integrative biology, and he has acted as both an editor and web director for the Berkeley Science Review for the past several issues.

Enjoy Issue 33 of the Berkeley Science Review!

Sincerely,

Emily Hartman

Editor in Chief

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Beyond the Controversy: How CRISPR/Cas9 Permanently Modified Molecular Biology

CRISPR/Cas9, a new, easy, and rapid genome editing technique, is at the center of heated debates over gene therapy, human embryo manipulation, and genetically-modified crops. While the most controversial applications of CRISPR/Cas9 likely will not affect the general public for years, this technique immediately and irreversibly changed the landscape of both basic and applied molecular biology research. Fast and easy genome editing is changing the way we think about disease models, drug development, and even organ transplantation.

The Beginning

Many people have written about the history of CRISPR/Cas9 in the last few years (here and here, to name a few). The story behind the discovery is fascinating, involving a yogurt company, virus-fighting bacteria, the plight of basic research, and cross-country patent wars.  Moreover, technology itself is controversial, mostly because it is much, much easier to use than other genome editing techniques.

So what makes CRISPR/Cas9 so much better—and more ethically ambiguous—than other genetic editing machines?

Before CRISPR/Cas9, if you wanted to alter a mouse’s genome—for example, if you wanted to make a mouse that will develop Huntington’s Disease—you would likely need to create protein machine that is designed to target a single position in a mouse’s genome. This was possible, but it took time, effort, and money.

Later on, say you decided that you instead wanted to study a different genetic disorder, targeting a new position in the mouse genome. Before CRISPR/Cas9, you would need to create an entirely new protein machine. This is because older genome editing technologies can’t easily switch to target new locations on a genome, and significant work was needed to accomplish any new genetic edit.

With CRISPR/Cas9, scientists can now use the same protein machine over and over again, customizing it to target any position on a genome. Making a new genetic edit became more like playing around with snap blocks and less like building a Lego set without instructions. Because of this, CRISPR/Cas9 has simplified and democratized genome editing, and the technology was rapidly adopted by labs across the world.

“The past two years has seen a nearly unprecedented acceleration and adaptation of a new biological technology,” said Kevin Doxzen, a UC Berkeley Biophysics graduate student, to The Berkeley Graduate. Doxzen works for Professor Jennifer Doudna, who co-discovered CRISPR/Cas9 with several others. “The combined simplicity and efficacy of CRISPR-Cas9 creates an endless spectrum of applications.”

These applications include several controversial ones that consistently dominate news headlines. However, CRISPR/Cas9 has also altered molecular biology in more subtle ways.

Modeling disease

In the past hundred years, scientists have used mice to study an enormous number of diseases. While early studies involved breeding mice for traits of interest, more recent technology allowed researchers to study mice with genetic mutations. The 2007 Nobel Prize in Physiology was awarded to the scientists who developed ways to genetically manipulate small mammals, including mice. However, this technique has limitations. For example, it doesn’t work well with larger mammals, and it’s challenging to create multiple genetic edits.

Ultimately, while mice are an invaluable model system, humans are far more complex organisms. Some researchers argue our reliance on mice is part of why so many promising drug treatments fail when tested in humans. Recently, scientists have begun using CRIPSR/Cas9 to create more complex animal models of disease—including models in larger organisms such as pigs and sheep.

Last January, a team of researchers in China published a study where pigs were modified with CRISPR/Cas9 to contained mutations associated with Parkinson’s Disease. Other studies have generated large animal models of Alzheimer’s, Huntington’s, and ALS (Lou Gehrig’s Disease). Scientists hope that these models will provide more accurate models of these complex diseases, enabling better drugs and a better understanding of how these diseases progress.

Which piece matters?

In recent months, Zika virus has developed into a significant public health concern, prompting a flurry of effort to develop preventative vaccines or curative therapies. The Zika virus has only been minimally studied, and until recently researchers had few leads on how to develop new drugs.

Scientists from the University of Massachusetts worked to identify which pieces of a human cell necessary for the Zika virus to successfully infect and kill it. Like most viruses, Zika co-opts cellular machinery from its host cells—so identifying which host proteins are required for infection is key to developing new and effective drugs.

The researchers used CRISPR/Cas9 to systematically delete genes from a human cell line. The cells were then infected with Zika virus. At the end of 8 days, 95% of the modified cells had died. However, a lonely 5% survived and were sequenced. These survivors likely contained CRISPR/Cas9-mediated deletions that helped them survive the virus—meaning, Zika might require those genes to survive and proliferate. This study gave the scientific community a list of leads on how to develop new Zika therapies.

“These genetic screens give us our first look at what these viruses need to survive,” said Dr. Brass, lead author on the study, in a press release. He emphasized that the CRISPR/Cas9 screen could be used to study other emerging viral threats. “We plugged Zika virus into our system and immediately began studying it.”

Humanize organs

GMO pigs

Genetically modified pigs may provide scientists with better models of human disease. Flickr, credit thornypup.

Over a hundred thousand people are currently on the waiting list to receive a new organ. Pig organs, which are similar in size and shape to human organs, have long been studied as a possible alternative to human organ transplantation. However, pig organs contain a health hazard encoded in their genomes: porcine viral DNA, often dormant, can cause viral infections in humans. Because some of these pig viruses can infect humans, pig-to-human transplants are dangerous to the recipient.

Last year, scientists from Harvard snipped away 62 instances of pig viral DNA from a pig kidney cell line with a single CRISPR/Cas9 protein machine. These pieces of viral DNA were very similar—so similar, in fact, that one pair of molecular scissors effectively removed all unwanted DNA in one pass. This “cleansed” cell line effectively prevented viral infection and transmission to human cell lines. While this technology has yet to be tested in the (far) more complex pig animal, this incredible study brings a new angle to xenotransplantation.

The future

It will likely be many years before CRISPR/Cas9 is used to treat cancer, or to edit viable human embryos. Despite the advantages of CRISPR/CAS9 in molecular biology research, there’s a significant gap between using a tool in a lab, and using it on a human. These molecular scissors would have to cut precisely, 100% of the time, with no mistakes, before they can be used on humans.

Take, for example, the idea that we can use CRISPR/Cas9 to edit human embryos. In reality, to edit a human embryo requires utter precision—you need to be able to change the genome in exactly one place, no more no less. This level of precision is challenging in biology. “Scientists are still trying to make Cas9 as precise as possible in order to prevent any “off-target” effects,” said Doxzen, “but some error is unavoidable in noisy biological systems.”

The precision, reproducibility, and control of CRISPR/Cas9 must reach an entirely new plane before we start envisioning a Gattaca-like future. Still, even today the technology has altered molecular biology forever, enabling studies that seemed impossible only a few years ago.

Title Image: Flickr, credit NIH Image Gallery. The CRISPR/Cas9 protein machine, pictured here, acts as molecular scissors, snipping double stranded DNA in a genome.

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The business of microbiology

Last night, Bill Gates spoke to a thousands of scientists at the opening night of the American Society for Microbiology conference. Over an hour, he discussed past and current work on various diseases including polio, malaria, tuberculosis, and others.

In each case, Gates’ language and perspective were far more financial than a typical keynote speech. Gates didn’t wonder how we can save a single life; he wondered how we can save the most lives possible with a limited number of dollars. Which disease is most cost-effective to target. And ultimately, how we can save a life for under $1000. “A little bit of investment done properly is catalytic,” Gates said.

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Flickr, Credit United States Mission Geneva. Photo taken at Meeting organized by Bill and Melinda Gates Foundation at the WHO in 2011.

The language of cost effectiveness changed, when discussing eradication, though the emphasis on tracking outcomes per dollar was still clear. “Zero is a magic number,” Gates said, because future intervention costs are eliminated. While the last 1%, or 0.1%, of a disease is the most difficult to eliminate, it may also be the most important. Using polio as an example, he showed how 400,000 cases in 1985 has dropped to 16 so far in 2016—though the magic number still remains elusive, due largely to politics, instability, and infrastructure. Looking forward, Gates identified malaria and poor nutrition as global health problems that could be solved for under $1000 per life.

Hearing Bill Gates discuss the nuances of microbiology from a business perspective was eye-opening. I appreciated the focus on effective change per dollar, and on tracking “success metrics”. Patience is still necessary in research, yet hearing speakers like Gates inspires me to think beyond the day-to-day of academia.

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Editing sensationalism (or: Don’t throw away your cellphone)

Earlier this week, a top story on my Google News feed caught my eye. “Report claims cellphones cause cancer!” one site blared. “Mobile phones can cause cancer,” read another headline. One was particularly bad: from the headline, to the buried critiques, to the opening sentence (“It’s the moment we’ve all been dreading.”)

I immediately sigh: it seemed highly unlikely to me that the study, whatever it said, would say that cellphones definitely cause cancer.

Out of morbid curiosity, I pulled up the original article. Even the title—bland, like most scientific articles—immediately contained a clear and obvious caveat: “Report of Partial Findings from the…Carcinogenesis Studies of Cell Phone Radiofrequency Radiation…” (emphasis mine). Partial findings! The study wasn’t even fully released yet. I scrolled down, noting that the pdf actually contained reviewer comments, a practice that is not overly common in my field. I liked it. It allowed me to immediately hear what other experts thought of the article. Generally, the reviewers were cautious. Every review contained a criticism, some harsher than others.

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Credit: Steve Paine on Flickr

Others have already written about why this study does not prove that cell phones cause cancer, or why we shouldn’t immediately stop using our phones. The short version is that there were some concerns about the control group, and the statistical significance was weak—meaning the positive result (ie cellphones cause cancer in rats) may be false, even though it seems true given the data. Male rats and female rats did not produce the same results. Studies done in rats don’t always translate to humans. And of course, you should never trust a single study anyways. Maybe the most obvious argument against the headlines is that we’ve been using cell phones for years without a huge increases in brain cancer, and that many other studies do not agree with these finding.

Sensationalization has real consequences for science and scientists. Articles like these lead John Oliver to tear apart my field on HBO, correctly identifying sensationalization as a reason why the public no longer trusts science news.

To be clear, this doesn’t mean the study wasn’t well done, or important. It just means that cellphones may or may not cause cancer. Science moves slowly, and this is one study among many on the effect of cellphones.

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