Murder Your Darling Hypotheses But Do Not Bury Them

“Whenever you feel an impulse to perpetrate a piece of exceptionally fine writing, obey it—whole-heartedly—and delete it before sending your manuscript to press. Murder your darlings.”

Sir Arthur Quiller-Couch (1863–1944). On the Art of Writing. 1916

 

Murder your darlings. The British writer Sir Arthur Quiller Crouch shared this piece of writerly wisdom when he gave his inaugural lecture series at Cambridge, asking writers to consider deleting words, phrases or even paragraphs that are especially dear to them. The minute writers fall in love with what they write, they are bound to lose their objectivity and may not be able to judge how their choice of words will be perceived by the reader. But writers aren’t the only ones who can fall prey to the Pygmalion syndrome. Scientists often find themselves in a similar situation when they develop “pet” or “darling” hypotheses.

Hypothesis via Shutterstock
Hypothesis via Shutterstock

How do scientists decide when it is time to murder their darling hypotheses? The simple answer is that scientists ought to give up scientific hypotheses once the experimental data is unable to support them, no matter how “darling” they are. However, the problem with scientific hypotheses is that they aren’t just generated based on subjective whims. A scientific hypothesis is usually put forward after analyzing substantial amounts of experimental data. The better a hypothesis is at explaining the existing data, the more “darling” it becomes. Therefore, scientists are reluctant to discard a hypothesis because of just one piece of experimental data that contradicts it.

In addition to experimental data, a number of additional factors can also play a major role in determining whether scientists will either discard or uphold their darling scientific hypotheses. Some scientific careers are built on specific scientific hypotheses which set apart certain scientists from competing rival groups. Research grants, which are essential to the survival of a scientific laboratory by providing salary funds for the senior researchers as well as the junior trainees and research staff, are written in a hypothesis-focused manner, outlining experiments that will lead to the acceptance or rejection of selected scientific hypotheses. Well written research grants always consider the possibility that the core hypothesis may be rejected based on the future experimental data. But if the hypothesis has to be rejected then the scientist has to explain the discrepancies between the preferred hypothesis that is now falling in disrepute and all the preliminary data that had led her to formulate the initial hypothesis. Such discrepancies could endanger the renewal of the grant funding and the future of the laboratory. Last but not least, it is very difficult to publish a scholarly paper describing a rejected scientific hypothesis without providing an in-depth mechanistic explanation for why the hypothesis was wrong and proposing alternate hypotheses.

For example, it is quite reasonable for a cell biologist to formulate the hypothesis that protein A improves the survival of neurons by activating pathway X based on prior scientific studies which have shown that protein A is an activator of pathway X in neurons and other studies which prove that pathway X improves cell survival in skin cells. If the data supports the hypothesis, publishing this result is fairly straightforward because it conforms to the general expectations. However, if the data does not support this hypothesis then the scientist has to explain why. Is it because protein A did not activate pathway X in her experiments? Is it because in pathway X functions differently in neurons than in skin cells? Is it because neurons and skin cells have a different threshold for survival? Experimental results that do not conform to the predictions have the potential to uncover exciting new scientific mechanisms but chasing down these alternate explanations requires a lot of time and resources which are becoming increasingly scarce. Therefore, it shouldn’t come as a surprise that some scientists may consciously or subconsciously ignore selected pieces of experimental data which contradict their darling hypotheses.

Let us move from these hypothetical situations to the real world of laboratories. There is surprisingly little data on how and when scientists reject hypotheses, but John Fugelsang and Kevin Dunbar at Dartmouth conducted a rather unique study “Theory and data interactions of the scientific mind: Evidence from the molecular and the cognitive laboratory” in 2004 in which they researched researchers. They sat in at scientific laboratory meetings of three renowned molecular biology laboratories at carefully recorded how scientists presented their laboratory data and how they would handle results which contradicted their predictions based on their hypotheses and models.

In their final analysis, Fugelsang and Dunbar included 417 scientific results that were presented at the meetings of which roughly half (223 out of 417) were not consistent with the predictions. Only 12% of these inconsistencies lead to change of the scientific model (and thus a revision of hypotheses). In the vast majority of the cases, the laboratories decided to follow up the studies by repeating and modifying the experimental protocols, thinking that the fault did not lie with the hypotheses but instead with the manner how the experiment was conducted. In the follow up experiments, 84 of the inconsistent findings could be replicated and this in turn resulted in a gradual modification of the underlying models and hypotheses in the majority of the cases. However, even when the inconsistent results were replicated, only 61% of the models were revised which means that 39% of the cases did not lead to any significant changes.

The study did not provide much information on the long-term fate of the hypotheses and models and we obviously cannot generalize the results of three molecular biology laboratory meetings at one university to the whole scientific enterprise. Also, Fugelsang and Dunbar’s study did not have a large enough sample size to clearly identify the reasons why some scientists were willing to revise their models and others weren’t. Was it because of varying complexity of experiments and models? Was it because of the approach of the individuals who conducted the experiments or the laboratory heads? I wish there were more studies like this because it would help us understand the scientific process better and maybe improve the quality of scientific research if we learned how different scientists handle inconsistent results.

In my own experience, I have also struggled with results which defied my scientific hypotheses. In 2002, we found that stem cells in human fat tissue could help grow new blood vessels. Yes, you could obtain fat from a liposuction performed by a plastic surgeon and inject these fat-derived stem cells into animal models of low blood flow in the legs. Within a week or two, the injected cells helped restore the blood flow to near normal levels! The simplest hypothesis was that the stem cells converted into endothelial cells, the cell type which forms the lining of blood vessels. However, after several months of experiments, I found no consistent evidence of fat-derived stem cells transforming into endothelial cells. We ended up publishing a paper which proposed an alternative explanation that the stem cells were releasing growth factors that helped grow blood vessels. But this explanation was not as satisfying as I had hoped. It did not account for the fact that the stem cells had aligned themselves alongside blood vessel structures and behaved like blood vessel cells.

Even though I “murdered” my darling hypothesis of fat –derived stem cells converting into blood vessel endothelial cells at the time, I did not “bury” the hypothesis. It kept ruminating in the back of my mind until roughly one decade later when we were again studying how stem cells were improving blood vessel growth. The difference was that this time, I had access to a live-imaging confocal laser microscope which allowed us to take images of cells labeled with red and green fluorescent dyes over long periods of time. Below, you can see a video of human bone marrow mesenchymal stem cells (labeled green) and human endothelial cells (labeled red) observed with the microscope overnight. The short movie compresses images obtained throughout the night and shows that the stem cells indeed do not convert into endothelial cells. Instead, they form a scaffold and guide the endothelial cells (red) by allowing them to move alongside the green scaffold and thus construct their network. This work was published in 2013 in the Journal of Molecular and Cellular Cardiology, roughly a decade after I had been forced to give up on the initial hypothesis. Back in 2002, I had assumed that the stem cells were turning into blood vessel endothelial cells because they aligned themselves in blood vessel like structures. I had never considered the possibility that they were scaffold for the endothelial cells.

This and other similar experiences have lead me to reformulate the “murder your darlings” commandment to “murder your darling hypotheses but do not bury them”. Instead of repeatedly trying to defend scientific hypotheses that cannot be supported by emerging experimental data, it is better to give up on them. But this does not mean that we should forget and bury those initial hypotheses. With newer technologies, resources or collaborations, we may find ways to explain inconsistent results years later that were not previously available to us. This is why I regularly peruse my cemetery of dead hypotheses on my hard drive to see if there are ways of perhaps resurrecting them, not in their original form but in a modification that I am now able to test.

 

Reference:

ResearchBlogging.org

Fugelsang, J., Stein, C., Green, A., & Dunbar, K. (2004). Theory and Data Interactions of the Scientific Mind: Evidence From the Molecular and the Cognitive Laboratory. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 58 (2), 86-95 DOI: 10.1037/h0085799

 

Note: An earlier version of this article first appeared on 3Quarksdaily.

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Literature and Philosophy in the Laboratory Meeting

Research institutions in the life sciences engage in two types of regular scientific meet-ups: scientific seminars and lab meetings. The structure of scientific seminars is fairly standard. Speakers give Powerpoint presentations (typically 45 to 55 minutes long) which provide the necessary scientific background, summarize their group’s recent published scientific work and then (hopefully) present newer, unpublished data. Lab meetings are a rather different affair. The purpose of a lab meeting is to share the scientific work-in-progress with one’s peers within a research group and also to update the laboratory heads. Lab meetings are usually less formal than seminars, and all members of a research group are encouraged to critique the presented scientific data and work-in-progress. There is no need to provide much background information because the audience of peers is already well-acquainted with the subject and it is not uncommon to show raw, unprocessed data and images in order to solicit constructive criticism and guidance from lab members and mentors on how to interpret the data. This enables peer review in real-time, so that, hopefully, major errors and flaws can be averted and newer ideas incorporated into the ongoing experiments.

Books

During the past two decades that I have actively participated in biological, psychological and medical research, I have observed very different styles of lab meetings. Some involve brief 5-10 minute updates from each group member; others develop a rotation system in which one lab member has to present the progress of their ongoing work in a seminar-like, polished format with publication-quality images. Some labs have two hour meetings twice a week, other labs meet only every two weeks for an hour. Some groups bring snacks or coffee to lab meetings, others spend a lot of time discussing logistics such as obtaining and sharing biological reagents or establishing timelines for submitting manuscripts and grants. During the first decade of my work as a researcher, I was a trainee and followed the format of whatever group I belonged to. During the past decade, I have been heading my own research group and it has become my responsibility to structure our lab meetings. I do not know which format works best, so I approach lab meetings like our experiments. Developing a good lab meeting structure is a work-in-progress which requires continuous exploration and testing of new approaches. During the current academic year, I decided to try out a new twist: incorporating literature and philosophy into the weekly lab meetings.

My research group studies stem cells and tissue engineeringcellular metabolism in cancer cells and stem cells and the inflammation of blood vessels. Most of our work focuses on identifying molecular and cellular pathways in cells, and we then test our findings in animal models. Over the years, I have noticed that the increasing complexity of the molecular and cellular signaling pathways and the technologies we employ makes it easy to forget the “big picture” of why we are even conducting the experiments. Determining whether protein A is required for phenomenon X and whether protein B is a necessary co-activator which acts in concert with protein A becomes such a central focus of our work that we may not always remember what it is that compels us to study phenomenon X in the first place. Some of our research has direct medical relevance, but at other times we primarily want to unravel the awe-inspiring complexity of cellular processes. But the question of whether our work is establishing a definitive cause-effect relationship or whether we are uncovering yet another mechanism within an intricate web of causes and effects sometimes falls by the wayside. When asked to explain the purpose or goals of our research, we have become so used to directing a laser pointer onto a slide of a cellular model that it becomes challenging to explain the nature of our work without visual aids.

This fall, I introduced a new component into our weekly lab meetings. After our usual round-up of new experimental data and progress, I suggested that each week one lab member should give a brief 15 minute overview about a book they had recently finished or were still reading. The overview was meant to be a “teaser” without spoilers, explaining why they had started reading the book, what they liked about it, and whether they would recommend it to others. One major condition was to speak about the book without any Powerpoint slides! But there weren’t any major restrictions when it came to the book; it could be fiction or non-fiction and published in any language of the world (but ideally also available in an English translation). If lab members were interested and wanted to talk more about the book, then we would continue to discuss it, otherwise we would disband and return to our usual work. If nobody in my lab wanted to talk about a book then I would give an impromptu mini-talk (without Powerpoint) about a topic relating to the philosophy or culture of science. I use the term “culture of science” broadly to encompass topics such as the peer review process and post-publication peer review, the question of reproducibility of scientific findings, retractions of scientific papers, science communication and science policy – topics which have not been traditionally considered philosophy of science issues but still relate to the process of scientific discovery and the dissemination of scientific findings.

One member of our group introduced us to “For Whom the Bell Tolls” by Ernest Hemingway. He had also recently lived in Spain as a postdoctoral research fellow and shared some of his own personal experiences about how his Spanish friends and colleagues talked about the Spanish Civil War. At another lab meeting, we heard about “Sycamore Row” by John Grisham and the ensuring discussion revolved around race relations in Mississippi. I spoke about “A Tale for a Time Being” by Ruth Ozeki and the difficulties that the book’s protagonist faced as an outsider when her family returned to Japan after living in Silicon Valley. I think that the book which got nearly everyone in the group talking was “Far From the Tree: Parents, Children and the Search for Identity” by Andrew Solomon. The book describes how families grapple with profound physical or cognitive differences between parents and children. The PhD student who discussed the book focused on the “Deafness” chapter of this nearly 1000-page tome but she also placed it in the broader context of parenting, love and the stigma of disability. We stayed in the conference room long after the planned 15 minutes, talking about being “disabled” or being “differently abled” and the challenges that parents and children face.

On the weeks where nobody had a book they wanted to present, we used the time to touch on the cultural and philosophical aspects of science such as Thomas Kuhn’s concept of paradigm shifts in “The Structure of Scientific Revolutions“, Karl Popper’s principles of falsifiability of scientific statements, the challenge of reproducibility of scientific results in stem cell biology and cancer research, or the emergence of Pubpeer as a post-publication peer review website. Some of the lab members had heard of Thomas Kuhn’s or Karl Popper’s ideas before, but by coupling it to a lab meeting, we were able to illustrate these ideas using our own work. A lot of 20th century philosophy of science arose from ideas rooted in physics. When undergraduate or graduate students take courses on philosophy of science, it isn’t always easy for them to apply these abstract principles to their own lab work, especially if they pursue a research career in the life sciences. Thomas Kuhn saw Newtonian and Einsteinian theories as distinct paradigms, but what constitutes a paradigm shift in stem cell biology? Is the ability to generate induced pluripotent stem cells from mature adult cells a paradigm shift or “just” a technological advance?

It is difficult for me to know whether the members of my research group enjoy or benefit from these humanities blurbs at the end of our lab meetings. Perhaps they are just tolerating them as eccentricities of the management and maybe they will tire of them. I personally find these sessions valuable because I believe they help ground us in reality. They remind us that it is important to think and read outside of the box. As scientists, we all read numerous scientific articles every week just to stay up-to-date in our area(s) of expertise, but that does not exempt us from also thinking and reading about important issues facing society and the world we live in. I do not know whether discussing literature and philosophy makes us better scientists but I hope that it makes us better people.

 

Note: An earlier version of this article was first published on the 3Quarksdaily blog.

ResearchBlogging.org

Thomas Kuhn (2012). The Structure of Scientific Revolutions University of Chicago Press DOI: 10.7208/chicago/9780226458106.001.0001