Chapter 10: A Controversial Department Evolves: On Recruitment, Flexibility, and the Value of Failure

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Chapter 10: A Controversial Department Evolves: On Recruitment, Flexibility, and the Value of Failure

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Dr. Mills first lists the faculty he recruited to develop a breadth of perspectives in the Department of Systems Biology. Next, he sketches the research "precept" at work in the Department: when a researcher builds a model, its failure to work can be as revealing as a model that does work. He gives an example of a model built for the pi3 kinase pathway. Dr. Mills then follows up with an anecdote about the most challenging lecture he ever had to give: a lecture on the theme of failure at Rice University in which he stressed, If we do not fail, we are not doing work that is high-risk. He talks about the conservatism of current funding agencies. He then talks about how founding a Department of Systems Biology was risky and controversial, but notes that over the past ten years acceptance has grown and that the Department's approaches are well accepted now, with many collaborative relationships outside the department. He talks about his own role as a representative of the Department.

Identifier

Mills,GB_02_20160707_C10

Publication Date

7-1-2016

Publisher

The Historical Resources Center, The Research Medical Library, The University of Texas MD Anderson Cancer Center

City

Houston, Texas

Topics Covered

The University of Texas MD Anderson Cancer Center - Building the Institution; Overview; Discovery and Success; On Research and Researchers; Building/Transforming the Institution; Growth and/or Change; Understanding Cancer, the History of Science, Cancer Research; Professional Practice; The Professional at Work; Collaborations; Leadership; On Leadership; On the Nature of Institutions; Research

Transcript

Tacey Ann Rosolowski, PhD:

How did the department evolve from this idea that this had to happen, to something that's now recognized as being so important?

Gordon B. Mills, MD, PhD :

Some of the things that happened is evolution of people, so I took a lot of what we were doing and started to move it in that direction. Now, I have an outstanding team that can drive processes of that level, but we also were allowed to recruit, by the institution. I had had a number of positions that were promised in my recruitment package, but for various reasons were put on hold for a period of time, and those were released, and we specifically went out to recruit people who could cross those boundaries. Some of the people we were recruiting were more reductionist oriented, with an understanding that the systems approach were important. Others were very much mathematically oriented but understood that the biology was important. So my goal was to bring in people that had the different skillsets, put them in physical proximity, and give them the support to begin to build such a program.

Tacey Ann Rosolowski, PhD:

Who were some of these folks?

Gordon B. Mills, MD, PhD :

Well, my department members. Prahlad Ram is one of the few individuals who does a really good job of both the math and the laboratory wet bench work. We have brought in Ju-Seog Lee, who integrates information across human, mouse, and genomics. Phoebus Lin, who has done a similar job of trying to understand not how molecules are involved in DNA damage repair, but rather understand DNA damage repair somewhat agnostic to which molecules are involved. The idea here is to understand concepts and focus on concepts, rather than I studied P-53.

Tacey Ann Rosolowski, PhD:

Interesting.

Gordon B. Mills, MD, PhD :

Now, we need people who study P-53 or we can't build those concepts. This is not an either/or, it's not a good or bad, it's a level that needs to happen on top of everything else if we're going to integrate what some outstanding scientists like Gigi Lozano are doing with P-53, into what's happening in terms of alterations in signaling, and putting those together. That's what the systems biology is. It's an integration of all of that information or at least an attempt to do so in a manner that allows it to become comprehensible. There's also a precept that if we build models, you will learn two or three things. One is, is if your model works, great, you know a lot of what you need to know, but if you build a model, you're going to find many of the times it doesn't work, which means you are missing something. And so the idea that a model will tell you that the pieces you thought worked don't fit. The other is that sometimes in building a model, there are new concepts that arise, that are not immediately obvious from other approaches, so these types of new knowledge that come out of a mathematical process, that can't be picked up by other approaches are also important.

Tacey Ann Rosolowski, PhD:

Can you give me an example?

Gordon B. Mills, MD, PhD :

Well, if I was to give you an example, I guess here, a pretty good one I guess. So we draw one of the pathways that we work on, a nice vertical model. And so we've looked at that and we've built the math around that, and we've done that in great detail in a number of cell lines and a number of cell systems, and as soon as we move from one to another it doesn't work. Now that says that even though we think of this as an irreducible, canonical pathway that works this way in every cell, that's wrong, because if it did, if I built a model in one cell it would work in others. The answer is, is these pathways are very different in almost cell lineage, and normal cells and potentially in almost every cancer, and that the different pieces interact differently, sometimes subtly, sometimes quite massively. The other is, is when we built this, there were things we just couldn't explain, no matter how hard we put every piece together, and what it turns out is that there are other pathways out there that impinge very strongly on the PI-3 kinase pathway, and we simply hadn't been paying attention to them. And finally, what we thought in terms of the output of the PI-3 kinase pathway, down at the bottom, from many animal models and other studies, as being the key part and the PI-3 kinase being the key regulator, it turns out that that's not true, that although the PI-3 kinase pathway regulates that output, there are other pathways that are just, and actually more important in that process, in the epithelial cells we study for cancers, epithelial cancer, as compared to the fibroblasts, where those models were developed. So, the fact that you build a mathematical model and it doesn't work, or it does work, you learn things that you can't learn otherwise. Those are called emergent properties, things that emerge that you could not have predicted by approaches you were using otherwise.

Tacey Ann Rosolowski, PhD:

I'm smiling because it's so often there are conversations about how failures are not made public, but failures are kind of the way people learn.

Gordon B. Mills, MD, PhD :

Now you're going totally off topic. One of the most challenging lectures that I ever gave was a colloquia at Rice University. Rice University assigns a topic across the whole institution, this is not just biology or cancer, it's to be very broad, and one year the topic was failure. Now they asked me to give a talk and I'm going, Is this supposed to be a message, have I failed, or is it that they want me to talk about cancer as a failure of biology, which I think is probably what they wanted. But what I decided instead to present was the fact that we are not allowed to fail, and that if we are not failing most of the time, we're not doing high enough risk studies. The whole way in which this field, and the institution operates, is that failure is considered a problem, a major problem, and in many cases a fatal problem, where in contrast, if you don't fail most of the time you're not trying hard enough to do something different. And so I think that as I said, this took a lot of work, to try and put this together in a way that made sense and would be an acceptable and broadly interesting lecture across all of the different fields; across engineering, across the biology, across music. And so the challenge of this being a very broad audience, and I think a critically important target that we haven't dealt with. If you go to NIH for a grant, if you basically haven't done it all already and know what the answer is, you're not going to get funded. What is funded right now, where funding is tight, is in general so low risk, as to be not worth doing. Now that doesn't mean that good things aren't done with the money that is given out to the investigators, but if there's even the slightest potential problem, it will be destroyed by a study section. I have an example that I love, which is a colleague of mine had put a grant in and it was being reviewed, and it had nine parts to it, and one of the reviewers says, I'm really worried about this part, and went on for thirty minutes or so, describing one part of the grant that was a problem. I'm a very young investigator and finally I stopped them I said, you know, let me ask you a question. Tell me about the problems or strengths of the other eight-ninths of this grant, and they said oh, this is the most spectacular things I've ever read and I said well, do you know you are in the process of killing this grant because it has one part that you don't like? Would it matter if they never did that one-ninth? No, of course not, the rest of it is incredible. Then why are you telling me about that? Why aren't you telling me about what's great about the rest? But that's where we are now. We are so conservative, that if there's a little problem somewhere, we'll focus in on that and use it as a reason to say well, you know, we shouldn't fund this because it has one little flaw. Contrast. That flaw says this guy or this person is trying to do something a little more challenging and risky, and you need to have that, and so you shouldn't expect everything to succeed. If you do it's boring, it's predictable. And so I think we've gotten into a place where things are far too safe. I must admit that perhaps setting up the Department of Systems Biology, which was not safe, was not necessarily supported broadly.

Tacey Ann Rosolowski, PhD:

I was going to ask about that.

Gordon B. Mills, MD, PhD :

Because people didn't understand what it was or why. It was a very interesting and challenging step.

Tacey Ann Rosolowski, PhD:

I was going to ask if it was a controversial move.

Gordon B. Mills, MD, PhD :

Vastly so.

Tacey Ann Rosolowski, PhD:

Yeah. And what were the comments that people made?

Gordon B. Mills, MD, PhD :

Well, some people said that this is just an aspect of everything else, there's nothing independent about it to warrant there being a department. Others said we've never seen anything like this, it's different so therefore it can't be good.

Tacey Ann Rosolowski, PhD:

Can't be good. Change is hard.

Gordon B. Mills, MD, PhD :

[00:36:[00] That's right. Finally, the other was, is well it's risky, why would you do something risky, let's stick to what we know. We don't see how this will necessarily help us understand what is happening in cancer. We also had an external review board come in and one of the people on the review board says well, I have somebody in my lab that does that, I can't see why you would want to do this broadly, it shouldn't be independent. And I had investigators, very senior investigators in the institution, come and tell me I should resign because I had made a bad mistake, it wasn't a great idea. So you do get that type of response whenever you publicly say, I want change. Now you can frequently make change happen just by doing it quietly, but this was very public. Because it was a new name, it had to go to the regents, it was a new direction, it had to go through multiple different committees. But in the end, there was a clear understanding that if this worked, if we made this process better than it was, it would be very worthwhile. Now, this has gotten to the point where the National Cancer Institute, the Library of Congress, actually have grants specifically related to this area. There is a variant of systems biology called computational biology. The difference to a degree is a computational biologist generally doesn't do wet bench work. They really just do the math, whereas a wet bench biologist doesn't do the math, and you sometimes get them to work together. Our goal is to have people who can work with, or at least across both sides, and make this happen in a better way, but even the computational biology is now one of the areas of emphasis of CPRIT, saying we need the ability to take this incredible trove of information we're generating, the amount of information we're generating, and find ways to begin to make sense out of it so that you can then test this experimentally. What we do is try and do both of those. Now, one of the other things that we have done is bring in collaboratively, a fairly large number of computational biologists or bioinformaticians in this case, in to close and strong collaborations with the department, where we will provide data, resources, ability to test hypotheses, to the computational biology group and vice versa. If they come up with something neat, a concept that we will test those hypotheses, if possible, experimentally, for them, and that back and forth has been, I think very important for the success, not just of department members, but of many across the institution.

Tacey Ann Rosolowski, PhD:

The department was founded in 2006.

Gordon B. Mills, MD, PhD :

Yeah.

Tacey Ann Rosolowski, PhD:

And I mean it's ten years later. So what was the arc of acceptance, you know the profile of the department within the institution?

Gordon B. Mills, MD, PhD :

I'm not sure I'm the right person to answer that question.

Tacey Ann Rosolowski, PhD:

Well, you know, it's your perspective.

Gordon B. Mills, MD, PhD :

Yeah, I guess it's my perspective. I think conceptually, it is extremely well accepted within the institution, and I think there are two reasons for that. One is, it is a group of very good scientists and we've maintained that level and that bar by bringing in new people, turning over people, helping people find new career positions. Some of them have gone on to be from assistant professors in my group, to be co-directors of research institutes. So, it's been very productive for those that have stayed and for those that have left. But I think within the other part that I've emphasized, the group is collaborative and that means we help a lot of other people do more than they could do otherwise. Further, we've done a massive job of setting up technologies and supporting those technologies and making them available to others in the institution, nationally and internationally. One of the platforms we've developed, which is our protein array platform, which is necessary for this type of a process, to develop information on a large number of proteins in perturbed systems, has now had over a hundred and twenty thousand samples sent for analysis, with about half of those from outside of MD Anderson and from all around the world. And so that type of data generation support of others, because we need it for ourselves, but them immediately make it available to others, I think has resulted in the department being highly accepted. And then I put a lot of effort as a representative of the department, into trying to make MD Anderson a more successful institution. So I think it's come across very well.

Chapter 10: A Controversial Department Evolves: On Recruitment, Flexibility, and the Value of Failure

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