On Becoming a Leader in Your Field
This is the first chapter of the book Assembled Chaos. You can get a free PDF copy of the full book by joining the BioSci Community or purchasing a copy here.
“And indeed, that IS the question: whether to float with the tide, or to swim for a goal. It is a choice we must all make consciously or unconsciously at one time in our lives. So few people understand this!”
- Hunter S. Thompson[i]
Increased Complexity
For decades, medical research has taken a reductionist or fragmented view of wellness and disease (e.g. looking for the one key protein that is produced by one gene that causes a disease). The lure of reductionist simplicity is powerful. Our minds struggle to grasp complexity, and therefore, it feels more comfortable to “Keep it simple, stupid.” During medical school, I learned a maxim known as Occam’s Razor, which goes like this: If you are trying to decide between two explanations for something, the simplest is most likely the truth. Unfortunately, this heuristic has been extended broadly to our approach in understanding human health and disease. You see this is in the oft-published overinflated good news about medical advances.
“Once we have the human genome sequenced, we will know which genes cause any disease and we will be able to prevent and treat all diseases.”
Though this is a gross paraphrase of what was said at the time, in the lead-up to completion of the Human Genome Project, there was a lot of hype. For the most part, that hype was driven by a craving for simplicity.
“Oh, how nice it would be if you could go to a doctor, let her scrape the inside of your cheek with a brush to get a sample of your DNA, and then, voila, you would know what diseases you were likely to get and exactly how to prevent them.”
Now, years after sequencing the human genome, this vision has not come to pass.
As it turns out, “one gene, one disease” thinking is a gross oversimplification of reality. In reality, numerous genes contribute to most common diseases. Asthma, for example, has been linked to over a hundred genes. Genetics makes up only a small part of the whole picture. Other factors such as your environment, your diet, infections you’ve had or not had, your mental state, your physical condition, and many others all combine together to determine if you have asthma (and what type of asthma). The complexity is even more staggering than we had previously thought.
Many believe this is an era of “groundbreaking scientific developments in high-resolution, high-throughput technologies”[ii]. Experts such as Sam Hawgood and Keith Yamamoto even believe that biomedical research, health, and healthcare are at an inflection point that will result in increasingly more precise medicine[iii]. The inflection point they are referring to is a shift from the linear accumulation of data and knowledge to exponential expansion of data and knowledge accumulation.
Previously, if a study included more than ten different measurements, it was considered a complex dataset. Now studies routinely include tens of thousands of measurements, including not only the human genome but also proteins, lipids, mRNA, metabolites, microbiome, and much more. Remarkably, you don’t have to be selective. Technological advances blur the lines between basic, translational, and real-world research – technology is the enabler. We are charging into an era where research is getting more complex because it can.
Medical research no longer proceeds in one direction from bench to bedside. Reverse translation, where clinical research provides the rationale for basic research, is now not only possible but becoming commonplace. It is also easier to link purely translational research, such as therapeutic target identification and screening, toxicology, and pharmacology, to both basic and clinical research. As a result, we are fast approaching a time when there is no longer a distinction between basic, clinical, and real-world research. There is just medical research that takes in all the different aspects of those previously distinct disciplines.
Even so, every researcher can’t possibly become an expert in all of those disciplines. Technology within those disciplines is simply advancing too fast. While technology enables us to gain a much more accurate understanding of wellness and disease, it also increases the complexity of medical research. There is, however, another consequence of the advance in technology that adds yet another layer of complexity.
Based upon Hawgood S., Sci Transl Med. 2015 Aug 12;7(300):300ps17
The increased ability to understand wellness and disease and the resulting advancements raise societal-level questions. Should we be able to edit genes to prevent or cure disease? The sharing of data will help attempts to gain better data-driven understanding of disease and wellness, but when even “anonymous” data can be used to re-identify individuals, what does this mean for data privacy? On top of that the societal-level elephant in the room is the increasing cost of technologically new ways to manage disease.
Ultimately, if you want to make a real difference, and shift a paradigm in medical research, you must integrate the collective efforts of multiple disciplines and understand the perspectives of diverse types of stakeholders. This all means that if you want to do meaningful medical research you cannot escape complexity.
Big or Simple?
When you think about strategy, you have two choices. You can either focus on the simple and expedient projects, or you can pursue big projects that have the potential to make a real difference. This choice occurs on multiple levels: on a career-planning level, on the level of choosing a funding strategy, and on the level of publishing. Once you have enough data to create a paper, do you publish it, or do you wait to assemble more data so you have enough to have a bigger and higher-impact journal article?
This choice also exists when you’re planning a clinical study. Do you put together the large definitive study, or do you conduct a study that will simply “add to the literature?” The size of your ambitions is often limited by the amount of funding and resources you have. The problem is that a bunch of small clinical studies do not equal one big definitive study. Often, researchers attempt to make a collection of small studies more impactful using meta-analyses, but meta-analyses are limited by the same mindset that generated the small studies in the first place.
This choice also exists in basic research. I like to think of basic research as having three components:
1) developing models that mimic human health and disease,
2) testing those models, and
3) gaining insights on mechanisms of disease.
The problem is that those models often differ from lab to lab. Everyone has their own protocols and their own unique model that is the best. Whenever someone suggests aligning and harmonizing models across different labs, they encounter reluctance, and rightfully so. If you have developed a model and invested a lot of time and energy in it, when you change it, the new results will not be applicable to the previous ones. Consequently, we end up with at least as many models as there are labs. Do you stay focused on your individual model or do you engage in a bigger and more complex effort to align models across several research groups?
The emphasis on the simple and achievable is completely understandable. It is, after all, what you know works. Smaller teams are easier to manage, and when you know each other well, it becomes easier to build trust. Getting a diverse group of different disciplines and stakeholders together in a big project takes energy. Getting them to work together in a meaningful way takes even more energy. Being on a call or in a meeting when conflict arises, and the discussion melts down into a shouting match isn’t something anyone looks forward to. You face a very real risk that if you sink time and energy in a big consortium project, it will be a net loss. You must constantly balance the need to get research done, to get publications, and to get funding against your ambition to make a big difference in your field.
Of course, all that you do fits into a bigger picture. You have certainly thought about how each new project or set of experiments fits into the context of your focus, or you’ve at least found a way to make new projects fit into that context. However, that is quite different than working closely with a group on a big, ambitious consortium project.
In the face of complexity, it becomes easy to rationalize an incremental approach: “At least we will get a paper out of it.” Hence, much of the output of any researcher ends up as part of a body of evidence that may or may not become sufficient to make a difference.
Seth Godin has a fantastic analogy for the resistance one encounters in the creative process. He uses a syringe that is capped on the end. Then, on stage, he moves the plunger back and releases it, and it snaps back to the empty position. He does this repeatedly. Then he pulls back really hard, and the plunger flies out of the syringe with a characteristic “thwop.”[iv]
The point is that if you don’t apply enough effort to any creative process, you will get nowhere. The countless non-definitive research studies are very much like Seth repeatedly pulling back that plunger. However, the solution is not for individual researchers to pull harder. It is about combining forces to take on both the technical and the societal level complexity that defines medical research today.
If you want to shift paradigms, you have to be comfortable with complexity. You have to accept the attendant uncertainty that you experience when you embark upon an effort but don’t know or can’t imagine how you are going to achieve the end result.
Shaping your field
Ray Dalio[v], head of one of the only investment funds to do well in the wake of the 2008 stock market crash, attributes much of his firm’s success to the people within the firm and how they are managed. It begins with who they hire. Dalio uses the term “shaper” to describe the types of people he likes to hire. A shaper can combine strategy and action. They have a vision and make it happen regardless of what it takes. Shapers are leaders of their field. They don’t shy away from complexity.
How does one get to be a shaper? Using the phrase “get to be” is probably incorrect. That phrase suggests that you have to be lucky or are granted the honor. In reality, you have more choice in the matter than you might think.
The secret to being a shaper is relationships. To combine strategy and action in the current context of medical research, you need the help of others. These relationships have to go beyond the relationships in your research group, your institution, or even your country. If you don’t have relationships with individuals from a diversity of disciplines, it is unlikely that you will be able to shape your field.
I can't think of a better context for building relationships than trying to work together to accomplish something meaningful--something that will make a real difference. Sports teams strive to be the best, to win the championships. That is their common bond. Consider professional basketball teams. Even though anyone can take a shot or drive to the net, the best teams are made up of specialists who focus on specific aspects of the game. They talk to each other a lot on the court, and that communication binds them together in pursuit of a common goal: winning.
Similarly, a consortium is a team of specialists. Instead of striving to win games, a medical research consortium strives to change the future of medicine. Like any team, they are comprised of specialists, and they communicate with each other. Outside of a consortium, your relationship with other researchers is probably something like: “Here’s what I have done. What have you done? How can we share?” In a consortium project, the relationship is more like, “Here is a problem. It’s a big problem no one can solve on their own. How can we work together to solve it?”
Relationships where you are solving problems together are richer and more meaningful relationships.
Leadership 2.0
From: Hacking Leadership by Mike Myatt
In his book Hacking Leadership[vi], Mike Myatt delineates the differences between what he calls the old paradigm of leadership and a new paradigm:
As you scan down the two columns it becomes clear that the right-hand side is more about relationships, and doing something meaningful, something with real impact. Working in a consortium project is medical research’s version of leadership 2.0.
Ironically, when you enable a lot of interaction, conducting research in a complex and ambitious consortium project is a highly efficient way of working. So, even from an entirely selfish perspective a consortium project can be a personal force multiplier. But that is not a given. In fact, there is a very real risk that engaging in the wrong type of consortium project will be a waste of your time and energy. So, how can you be confident that engaging in a consortium project will be a sail that propels the vessel of your career forward with fulfilling work and not an anchor that moors you in the harbor of complex go nowhere ambitions?
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[i] Thompson HS. Letter to Hume Logan. 1958.
[ii] Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med. 2016 Dec 19;8(1):134. doi: 10.1186/s13073-016-0388-7.
[iii] Hawgood S, Hook-Barnard IG, O'Brien TC, Yamamoto KR. Precision medicine: Beyond the inflection point. Sci Transl Med. 2015 Aug 12;7(300):300ps17. doi: 10.1126/scitranslmed.aaa9970.
[iv] https://www.sethgodin.com
[v] Dalio R. Principles For Success 2019. ISBN-10: 1982147210
[vi] Myatt M. Hacking leadership: The 11 gaps every business needs to close and the secrets to closing them quickly. 2013. ISBN-10: 1118817419.
