Leanpub Podcast Interview #46: Alex Lancaster
published Feb 21, 2017
Alex Lancaster is co-author of the Leanpub book Python For The Life Sciences: A gentle introduction to Python for life scientists (you can also learn more about the book here). In this interview, Leanpub co-founder Len Epp talks with Alex about his career, his books, and his experience self-publishing on Leanpub.
This interview was recorded on November 7, 2016.
This interview has been edited for conciseness and clarity.
Len: Hi, this Len Epp from Leanpub, and in this Leanpub podcast, I’ll be interviewing Alex Lancaster.
Alex is an evolutionary biologist, engineer, writer and consultant based in Boston. He completed his doctorate in Computational and Genomic Biology at Berkeley, and has worked in R&D in the broadcasting and IT industries in the US and Australia. And he’s also helped research in faculty positions in academia, including a research position at the Whitehead Institute at MIT, and a faculty position at Harvard Medical School.
Along with his colleague Gordon Webster, Alex is co-author of the Leanpub book, Python For The Life Sciences: A gentle introduction to Python for life scientists. The book serves as an excellent introduction to computer programming for biologists, including those who have never written a line of code.
Along with the book, you also get copies of code samples that you can learn from and adapt to your own specific research.
In this interview, we’re going to talk about Alex’s professional interests, his book and his experience self-publishing through Leanpub.
So, thank you Alex for being on the Leanpub podcast.
Alex: Thanks Len, happy to be here.
Len: I usually like to start these interviews by asking people for their origin story. I know from your bio that you studied both physics and electrical engineering before you got into evolutionary biology, and I was wondering if you could talk a little bit about your path through all these disciplines, and how you ended up at Berkeley?
Alex: Yeah. How long have you got?
I started life thinking I would be an astrophysicist, basically. It was where I was originally when I was an undergrad. And actually I spent about a week in a radio telescope down in Canberra - a while ago now, shall we say? Another century. And I realized that that wasn’t really going to be it for me for the rest of my life. Astrophysics has changed a lot since, but there was a lot of sitting in very quiet, desolate places, pouring over data, and it sounds very glamorous on the outside - but the reality of the day-to-day just turned out that it didn’t really appeal to me.
So, trying to figure out what to do, I decided to finish my engineering degree, which I started with. But I was always interested in evolution from a very young age, I think [from] when I picked up Richard Dawkins’ The Blind Watchmaker, which was written sometime in the 80s. I was just fascinated with the idea of these biomorphs, which were these little creatures that he had built evolutionarily on a Mac. It was nothing to do with real biology, but it was very - basically you could construct these creatures from this very simple genetic code. And it sort of always stayed with me.
So I always sort of followed along, thinking that if I could get training in physics, then I could move that over to biology at some point. But I didn’t want to go back to academia straightaway. So after I’d finished up my undergrad, I went and worked in the software industry for about four years.
I started as a design engineer at the ABC - which is the Australian Broadcasting Corporation in Australia, it’s like the equivalent of BBC - in their R&D section for a while. And I cut my teeth on coding. I did a little bit of hardware stuff, but I rapidly realized that software was where it was at. And the web was growing, and - those were very early days.
And so I basically sent myself around the world doing software contracts. I went to the UK for about a year in the mid-90s. I worked a little bit in the banking sector, a little bit in the telecommunications sector, building my tool bag. But I always had this idea that at some point I’d come back and do grad school.
And then, sometime in the late 90s, I decided - I stumbled across - well I actually I hadn’t knew about it before, this place called the Santa Fe Institute in New Mexico. I had been following what they’d been doing out there, and they are sort of really on the cutting edge of complex systems and biology and all that stuff. And I thought, “Well that’s a great place - I should somehow get myself a job there.”
So I ended up downloading, I think, a very early version of the Swarm software in the late 90s, and basically playing around with it. I ended up moonlighting - while I was working in a bank, on their website - building these models, computational neuroscience models, with some folks that I knew in Australia, that I had found in Australia. And back then it wasn’t as easy to find collaborators, so I had to go to conferences and chase down physical papers and stacks and stuff like that.
I helped build a bunch of models for those folks. And in doing that, I learnt this software package, called Swarm. And then it turned out there were a couple of job openings. So I applied, and I got it. And so I found myself in Santa Fe, New Mexico in ‘97, not knowing a soul, wondering quite why I’d gone there. And I was part time in a PhD program at UNM, University of Mexico in Albuquerque.
And I was doing that for a while, trying to do it part time. But I was really having a lot more fun at the job, working at the Institute. So they hired me as one of their software developers, and I was sort of able to work with a lot of researchers. That solidified my interest in biology, basically, in moving back towards the evolution side of things.
I ended up postponing my program, and then I reapplied, ended up going to Berkeley, and studying population genetics and theoretical models of biology for grad school. But all the way along, I was interested keeping up my software skill. So I always had a foot on the computational side, and a foot in the biology.
Then I ended up doing the standard academic track, of doing a postdoc. I did a couple of postdocs, and then I was faculty at medical school briefly for a couple of years. And I just decided that the way - well, a lot of things have happened since I’ve moved here, but one of the things that have happened in academia, as you may know - it’s become a very tough environment to do more “out there” research.
And in the way that I see academia going for probably a while now - it’s really accelerated in the years since the crash, I think. So I decided that it would be more interesting to try my hand at some kind of hybrid career. And at some point, Gordon and I met, and we kicked around this idea of creating this company. And we really got going about a year ago. And that’s what bought us to Amber Biology.
That’s probably an overly long-winded answer to a shorter question. But that takes us right up to the present. I’m happy to go back into any of the eddies that you found interesting there. That’s how I got here.
Len: That’s a really great answer, thanks very much for that. I’ve had a bit of a - in conventional terms, I suppose - meandering career myself. So it’s really interesting to hear from someone who follows their curiosity, where it takes them. Which is, it sounds like, what’s motivated you.
Alex: Pretty much, pretty much. I’ve never been one for really mapped out career plans. And in a funny kind of way, I think the way things are moving now, that notion of the career plan is becoming somehow less relevant. But that’s something we can definitely talk about if you want.
Len: I have a doctorate myself, in English, not in biology, but I could talk about academia and things that have been happening there forever -
Alex: I’d love to hear your experience in that too.
Len: But actually I was wondering - when it comes to academia and the sciences, this is a topic that one sees in the news recently, about the difficulty that young scientists have getting tenure, and the importance that’s placed on getting published, regardless of necessarily the relevance of the publication. Is this something that you’ve had direct experience with?
Alex: Yeah. I would say that it’s more than just the people getting tenures. People getting the tenure track positions in the first place. The bottleneck is I think even greater there. And you have a lot of very highly trained, highly motivated people who are competing for a very, very limited number of slots.
The slots are certainly not increasing, and if anything, they’re probably decreasing, because universities are oftentimes cutting their budget, and they’re often looking to supplement the people that they do bring on.
It’s one of these things, it could be overstated, but I certainly think that at the level of the higher administrations, there is definitely a push towards finding sort of faculty and research areas that are sort of highly fundable, because a lot of the costs of running a university have been sort of shifted toward federal grant money, especially in United States, and so that puts a lot of pressure on those administrators. And that gets translated down into in the department. And I think now in the department levels it’s probably - the picture’s a bit more mixed, because I think that most people there really want to hire people that are doing interesting things. And I think that, in general, most people want to do the right thing, and are interested in intellectual balance, and the usual things that academia’s known for. But they find themselves under a lot of pressure.
So I think that that combination… it’s sort of a system, the pressure to publish in prestigious journals, and how they’re ranked in terms of grants, has a tendency to factor into the grant making decisions. And so that feeds back to the faculty. So there are a set of interlocking factors, you might say, that drive the system towards a setup where you want to minimize risk and maximize return.
That militates against people doing more unconventional and risky approaches. And it also militates against doing smaller scale, and actually cheaper research. Which is sort of a strange thing, because oftentimes these little side rivulets can be the things that can actually drive science forward. And you really don’t know where the next big discovery’s going to come from. So yeah, definitely the publishing part is part of a larger network of problems, but it’s definitely a big driver.
Len: It’s really fascinating to me, to watch, in North America, what I call “admin creep,” like mission creep, but happening at universities, where tuition costs are rising and rising. The cost of running a university is increasing. And yet there’s this budgetary pressure on professors and scientists, and people doing research. And so costs are going up, and yet there’s this squeeze. And it’s -
Alex: No, absolutely. And in fact, we were pretty much talking about this for almost a full day on Saturday at the Ronin Institute’s first Unconference. I don’t know if you might have seen that on my blog?
Len: Oh no, I didn’t see it, but I know what an Unconference is. Actually if you could describe that, that would be good, I think.
Alex: So the Unconference - I’d actually never done one myself before, but it was pretty cool. The idea is that the topics and the areas that get discussed, and the talks are effectively self-organized by the participants. And the way that they did it on Saturday, I was loosely involved in organizing it.
There were three seed speakers at the beginning. They spoke for about 10 minutes each. And then that generated a list of things in people’s heads, and people would write down on a piece of paper a topic they’d like to discuss. And then you put those pieces of paper around the room. And then people walk around and identify the things they’d like to discuss.
And then we proposed topics that were similar, you kind of merge. And then out of that, we got about three distinct groups. And we have a discussion for about an hour and a half, I think, or an hour or so. Really interesting group of people. And then we break for lunch, and then it’s a repeat in the afternoon, then summarize it at the end.
It makes for a really interactive kind of format, as opposed to a traditional conference, where everybody’s like half paying attention, and on their laptops and that kind of stuff. It was perfect for the kind of thing that we were trying to do, which was to really generate a robust discussion around the future of scholarship in general. And not just in sciences, but in humanities as well - to try and think about ways that we could do things that don’t necessarily involve the traditional kind of institutions that we’re using.
So that was big, that was great, because we got - there were people, as I said, from like theology and English literature. And there were a lot of biologists represented there, because they tend to be over-represented in the Boston area. But yeah, that was really interesting.
Len: That is really interesting. Do you feel that there’s a pressure building to push people to a model of education that’s not university-based?
Alex: That’s an interesting question. That may happen. I mean, I think there’s certainly room for a lot of different paths to getting knowledge, that don’t involve going through the ivory tower. I think there’s sort of a realization - I kind of get the sense, that sort of realization that, when you get a scarcity of positions, or a scarcity of - what’s the right word I’m trying to think of?
Basically a scarcity of educational good, shall we say? Everyone’s so focused on getting into sort of colleges, so they can get top jobs, and so on. And I think what happens is that people attach a sort of monetary value to that - to that luxury good. And then oftentimes, that becomes the goal rather than the education.
I always assumed for many years that that was a byproduct - that should just be a byproduct of getting education. But I do think there’s a lot of pressure to getting, sort of, credentialism, I call it. I don’t want to be the pot calling the kettle black, beause I played that game too. But I realized the limitations of that sort of thinking. And so I do think there’s a realization that maybe having everyone go to college in the standard way won’t necessarily work for everybody and may not be desirable in all cases.
Especially with the student loan thing. I feel like that’s the next bubble, that’s the next scary bubble that people haven’t really confronted - is the student loan crisis in the United States. Because if everybody’s told they have to get this kind of education, but then they go through life saddled with all this debt, that they always feel they need to get the kind of job that can support that debt. Then that cuts down on career options that people can pursue.
Len: Yeah, and very crucially, one of the interesting aspects of student loan debt in the States, which is over a trillion dollars, and greater than credit card debt, is that you can’t -
Alex: Can’t go bankrupt.
Len: You can’t go bankrupt. It’s incredible. I was listening to a podcast interview by Ezra Klein, with Joseph Stiglitz recently - the Nobel Prize winning Economist. And he was saying two of the most consequential decisions that the United States made in the last 20 or so years, is - one was that if a company went bankrupt, rather than the people working for it - having the primary claim upon the assets, it was people who held derivatives in the company.
And the second one was that if you go bankrupt as a student, you can’t clear your debts. And this can happen. It can happen where, if there’s a student debt that’s associated with a parent, the child can actually die before completing the degree. And if the parents go bankrupt, they can’t clear the debt. It’s very perverse. And when you add into that the importance that’s placed on the rank of the university that you’ve attended, and when you think about the pressures that are on someone who’s 17 or 18 - You know if you don’t get into one of these universities at this age, you can still get ahead in life, but you feel like you’re behind. And you will be, in a sense - in the conversation, behind, your whole life - you really will be, if you don’t get on that track, if you don’t go to Berkeley, or Harvard or something like that. And the pressures are extremely intense.
One of the topics that comes up on this podcast, just because of the type of people that often publish Leanpub books, is, if you want to become a computer programmer or software engineer, developer - should you go to university in 2016?
I was wondering what your opinion is about that very specific question - if your goal is not to get an education, not to become an educated person, but to be a developer and work, do you think that people in the States, should get university degrees in computer science?
Alex: It’s a good question. My sense is - not, if that’s just the one thing you want to do. Like if you know that’s the one thing you want to do, and that you can educate yourself in the areas, in other ways, then I’d say that probably - at least it shouldn’t be a necessity. I mean I do think that it’s kind of silly to force people into…. the more general answer to that is we’ve still got a kind of one-size-fits-all system, that doesn’t really take into account the nature of people’s individual, quirky career paths. There’s an expectation that there’s a set of norms that you follow. And if you’re off those norms, then you’re probably a little bit weird, and you’re probably some kind of person who’s failed.
Which is kind of weird. Because at the same time we laud all the college dropouts - like Bill Gates and Mark Zuckerberg, for starting all those things. But at the same time, they’re kind of the exception that proves the rule a little bit. I feel like, in general, I would say also - growing up in Australia, I never felt that same pressure in the same way. Partly because the culture is different. Now things have changed a lot in the 20 or 25 years since I was an undergrad. But there was never a feeling that -
There were always options, if you really wanted to do those super-professional things. But there was a sense that you could get ahead if you didn’t finish school or you didn’t go to grad school. Maybe you went to college, but you didn’t really necessarily want to build a career in whatever thing that you studied. But it was a period of your life, and it didn’t define you in the same way that I feel that it defines people here. Or at least it feels like they’re defined by that experience.
That’s a part of a cultural difference. And also the fact that we do have student loans, but they work totally differently. That’s, again, a long winded answer. But yeah, I would like to see a world in which we didn’t push people into career paths which they either don’t want, or aren’t really a necessity. Just a general openness to like people finding a different way to whatever their passion is.
Because I feel like - ultimately - that’s the thing that matters. And that’s the thing that’s going to make people productive members of society. It’s not to say you have to do it this way. But, figure out ways to support what they do - rather than sort of pre-defining it for them, and map it out. Because things are changing so fast anyway, that I feel like almost any career advice these days is going to be like five years out of date.
Len: And where did you grow up in Australia?
Alex: I grew up in Sydney, in the suburbs of Sydney. I was there until like the mid/late 90’s. And I went to the UK and I came back. I bounced around before I come to the United States.
Len: You did the walkabout.
Alex: I did the walkabout, yes. And we’re sort of known for that - we tend to like go overseas and then come back.
Len: When I was living in London, I always had an Australian roommate. Which meant I always had at least three Australian roommates, because people would always be visiting. Including parents on the couch for two weeks - that kind of thing.
Len: But I saw - who was it? Paul Rudd. Not Paul Rudd. [Note: This might be Len’s funniest gaffe ever - eds.]
Alex: Kevin Rudd.
Len: Kevin Rudd.
Len: I worked for Macquarie Bank for a couple of years in London.
Alex: Oh really? I worked for Macquarie Bank for about six months.
Len: No kidding.
Len: At 1 Ropemaker Street?
Alex: One right at the stock exchange there in Sydney.
Len: Oh, in Sydney.
Alex: Yeah Sydney, yeah not–
Len: Oh pardon me, I worked for them in London. But I had my training in Sydney. Yeah, that’s funny.
Alex: I worked on their first website ever.
Len: Oh really?
Alex: Yes, I was on the team that helped build the first Macquarie Bank website. It was about ‘96 I think?
Len: That’s fantastic.
Alex: Running Perl objects and stuff like that. Yeah, it was an interesting learning experience too. I realized that banks weren’t really my future at that point.
Len: Yeah, I realized that after two and a half years. It took me a bit longer. But it was an exciting experience. Especially working for an Australian bank, trying to make its way in London. It was quite curious.
Alex: Which side of the banking pot were you on?
Len: Investment banking. I was doing mergers and acquisitions. It was pretty interesting. In fact, actually quite a few of my colleagues - one of the curious and great things about working for Macquarie, was that people weren’t - there were fewer business school graduates than you might get at other investment banks.
Alex: Oh that’s interesting.
Len: People were - for example - one of my colleagues, who was brilliant, had done maths and had gone to London on a holiday. And the job he got was being an investment banker for Macquarie Bank, or Macquarie Group, as it came to be known.
Len: I remember the word “holiday” played a role in his passwords and stuff like that. But there were a lot of people - a chemist from Perth and people from all over, from all kinds of different backgrounds. This was in the mid-2000’s, and it was a really interesting, interesting time. I’m sure things have developed a lot since then.
I wanted to ask you about Swarm - it was one of the first open source agent-based modeling tools. I was wondering if you could talk a little bit, for people who might not know what those are, what that is, and why Swarm was important?
Alex: Swarm came originally from folks, before my time, at the Santa Fe Institute in New Mexico. And there, there were… I sometimes call it a think tank. They don’t like to use that term. It’s a small, private, non-profit research institute that’s dedicated to what they call a science of complex systems.
And that, in practice, means people building computational and mathematical models of all kinds of systems. From natural systems, like biological systems through to model the economy. And even to things like archaeology. And so you might think of it as a sort of set of language primitives that a lot of these models are built in. Sort of in the language of agents.
Where, rather than like a lot of typical mathematical models, where you sort of have an equation that describes the aggregate behavior of a bunch of individuals, using say differential equations and things like that - you build models where you represent the actual individuals, as code. And the natural representation is - in computer science terms, is that of an object.
And so, there was basically a push. People basically found they were building code structures that were almost identical to each other. And then there was a realization that, “Well maybe we should have sort of a common platform that we could reuse, and then build our domain-specific stuff on top?” So you’d have a library that you would then write your code for your model in, that would call functions from that library.
And that’s basically how Swarm came about in the mid 90’s. It went through a number of iterations. But I wasn’t involved in the prototype. I came into the project a little later. And we were at the point then where we were now interacting. So my job was actually interacting with the scientists at the Institute, and visiting people to talk about the science, and think about how we could translate that science into the model, and then work with the other people. Building the kernel to create the right infrastructure.
So it’s kind of a bit of a translator between a lot of different disciplines. To try and sort of figure out, okay, how do we represent these things, that would work for the largest number of people, and the largest number of kinds of scientists. And then also figure out at what abstractions you would want to use, that’d be generally useful, and what abstractions that are very specific - say economics or sociology or things like that.
The fundamental notion is that you have a bunch individuals that all interact. And they have a set of rules, and they have state. It’s like SimCity or something. You set the things up, then you sort of let the thing go, and you see what happens.
So Swarm was the first tool kit to do that. It’s inspired several others. And there’s still the SwarmFest meeting, that we started back in the late 90’s, it’s still going strong. It’s been for about 20 years, and I hadn’t been for 10 years, and I went again - the first time in many years, 10 or 11 years - two years ago. It’s sort of great to see that the community is still sort of out there, trying to push the boundaries. Because it’s still, in some sense, it’s still kind of a little bit on the edge. I am surprised that it’s not more mainstream actually.
Len: And why do you think it’s not more mainstream than it is?
Alex: Probably because models are complicated. I think, there was probably some early overselling. This happens a lot in areas of science where everyone’s very excited. They start making promises that they probably can’t deliver. And so there’s been a little bit of a backlash, to complex system approaches in general and agent-based modeling is one of them. So I think that had some role.
They are more complicated to analyze than just traditional models. You don’t have the same set of tool kits you can use to do sensitivity analysis. And I think it sort of dovetails a little bit, with the problem with academia in general. There’s just not as much - I feel like there was more appetite in the late 90’s, to just try new stuff in general, in science.
It could just be that I’m getting older. Or it may be in my head. But my sense is that it’s harder to do stuff that’s a little on the edge now. People really want to see something like return on an investment, whatever that means in science.
I think it’s unfortunate. Because I still think that it’s important, for people doing stuff, to do stuff that doesn’t always work, and might fail. Just because it hasn’t worked yet, doesn’t mean it will never work.
Len: It’s interesting that the theme of stress in academia’s coming up. Because it’s something I think about a lot. I mean - Einstein goes for a walk, and sees a workman on a scaffold, and imagines him falling down, and has a great insight that changes the world. You can’t possibly quantify Einstein going for a walk.
There was something that happened a few years ago in the UK, called the Research Assessment Exercise. Where basically, an incredible amount of professors’ time was wasted in assessment of work, under the futile illusion that you can quantify research. And you end up with the people who should be doing the forward thinking, subordinated to - to put it crudely - politicians who are trying to make a point to people who are skeptical about higher education fundamentally.
It’s very cart before the horse kind of stuff.
Actually, that’s probably a bad metaphor. It’s kind of like - people who really don’t have an appreciation for what happens at higher levels of research - being skeptical about it, because they don’t see progress. And you need to give people the time to have the revelation in the shower. And to pursue paths that may actually ultimately be fruitless. Because that’s what cutting edge thinking is.
I was actually wondering, what was the work you did for your thesis, for your doctoral thesis back in the day?
Alex: I kind of switched gears a little bit away from the purely agent-based stuff when I was working in immunogenetics, or immune system genes - it was actually fairly empirical. I was looking at all these data sets coming from different populations around the world, where they would go up and genotype sets of genes called HLA genes - human leukocyte antigen. They’re basically involved in the immune system, and they’re the things that help detect - like when you have a bad pathogen that’s invaded your body. And one of the questions that have puzzled evolutionary biologists, and people who study population genetics - why this region of the human genome is incredibly polymorphic. Why are there so many alleles in different variants?
And so, I was working on trying to quantify that, that nature of that variation, and then building tools to analyze it and see if we could basically measure the strength of selection at the level, not just in the whole gene itself, but also at the level of the individual residues, amino acids in the actual 3D structure of this molecule.
So, I’m going off on a tangent here. But it’s effectively trying to figure out if we could use the population data to get at functional questions about how evolution has shaped the sort of nature of these molecules. And so there were some statistical analysis, and a fair amount of coding. I built a pipeline for that, that’s still used today, called PyPop - Python for Population Genetics.
I was also developing this methodology for figuring out like how to use things like Monte Carlo, Markov chain stuff to better analyze this data - it’s basically on that sort of interface. That is really quite classic computational biology meets evolutionary biology. It’s where I was at. And I’ve still got colleagues at projects that will probably see the light of day eventually.
But it really got me interested in the - it sort of cut my teeth on learning one specific biological system really in a lot of detail. Because the complex system stuff is great, but you can often find yourself going off into abstract speculation. So I feel like even though it’s not really what I still do on a day to day basis, I still think it was a valuable, for someone like me - I will always like to be trying new things, it’s good to have the training in one area. I think of it like the fox and the hedgehog, if you’ve heard that analogy? Which is why I call one of my blogs, “the curious foxhog”, because it’s like, I feel it’s good to have deep training in one area, but at the same time it’s good not to get tunnel vision. So yeah, that was where I came in on that stuff.
Len: And is that related to evolutionary systems biology? Which I’ve read you’re involved in - or is that something different?
Alex: It’s part of it. After I left my grad program, I started working in models of evolvability, but specifically related to prions. And it turns out that prions are this interesting mechanism for storing variation that can be released when an organism’s in a moment of stress. It’s kind of a fascinating, and I think of that as a classic example of evolution systems biology. Because systems biology, I think of it as like the mechanisms and the networks that are ultimately sculpted by evolution. And that’s why - I think ultimately, people that are trying to integrate sort of….
Evolution for a long time was very - evolutionary theory’s very abstract, and it didn’t refer to any sort of real systems. Just they would have these models about, consider these two alleles and play around with it. But now we have a lot more data, and we can say things like, “Well, now we know this trait is generated by these networks. What are the different evolutionary paths that you might take? Will the system take?” And so that’s kind of what I think of as evolutionary systems biology.
But my PhD wasn’t really in - it didn’t really exist even as a discipline. But, again, it was the training I needed to get into that area later.
Len: This is kind of a selfish question, because it’s a preoccupation of mine, but what do you think of evolutionary psychology?
Alex: I don’t really think about it a lot. I used to read a lot about it back in the day. I’m a little bit skeptical of it in general. I’m always a little bit skeptical of explanations that involve pre-defining our notion of what’s fit in the environment. And I think the problem that I see with evolutionary psychology, or some of it anyway, is that it tends to overestimate the role of competition and the survival of the fittest side of evolution.
Whereas evolution as a whole, includes all kinds of - not just competitive processes, but cooperative processes, and symbiosis and mutualisms. And all these rich dynamics that I feel with some of the evolution psychology stuff, it’s a little on the simplistic side.
It also interfaces with these arguments that come more from the political and economic side, that can easily be used to justify some of the existing power structure…. There’s always a danger when you go into nature and say, “Oh look, it’s done this in nature, so it must be - that must be right” - you know what I mean?
Alex: That’s my main problem with some of the evolutionary psychology. Having said that, I don’t have a problem in principle with studying - using evolutionary principles in all kinds of areas. In economics and psychology. I really like the stuff that David Sloan Wilson works on, which is integrating economics and evolutionary thinking, complexity thinking. I’ve been reading his Evonomics blog quite a bit lately, and that involves psychology.
But yeah - so as far as the classic evolutionary psychology stuff, again I haven’t looked at it in a while. But I felt like at least the version of it in the 90s, early 2000’s, was always a little bit iffy to me.
Len: The fundamental question I have about it, is how do you do experiments? If you can’t do experiments, it’s not science, and it seems, to put it crudely, and so you see things - like for some reason, I think probably now, the former editor of the Science and Technology section of The Economist, loved evolutionary science just-so stories. And you see this in the science press generally.
I mean to think they actually did this in The Economist once. But they said that they’ve proven that women have a preference for pink. And that this is cross cultural. And that it’s probably because when, in the olden times, in the long, long ago, women had to search for berries. And so they were selected for experiencing pleasure when seeing color in nature. And this was in like the 2000’s. This wasn’t in like 1810. This was in like 2010. [See the last paragraph of this article from 2007 - eds.] It just seems like - I mean, the stuff that surfaces in the press, is obviously going to tend towards nonsense. But how can one possibly run experiments on human psychological evolution?
Alex: That would be my problem with it too. The just-so nature of it rears its head in those circumstances. And the danger is it can easily reinforce people’s preconceptions and scientize something that really shouldn’t be scientized. To coin a term.
Len: That’s a great word. On the subject of science and programming, which is what your book is about, I wanted to ask you how important is it for scientists these days to learn how to program?
Alex: I think it’s pretty important. Especially as… the more quantitative disciplines like the life sciences are becoming rapidly…. But I also don’t think that one should assume that that’s the only thing you need, and that everything should be - that you should drop your pipettes and just do coding. I have done pipetting once but, I know that I’m never going to be a great bench biologist.
I think getting your head around, going a little bit beyond spreadsheets - I mean, we say this in the book blurb - is going to be really important. And it’s interesting actually. Especially with the SwarmFest, I’m meeting a lot of people who are not even scientists, and the digital humanities people are really picking up on the programming side of it too.
And so yeah, I think it’s important. I think also, you have to simultaneously keep in mind that programming is ultimately still just a hammer, and you don’t want to make everything a nail. So on one hand, I’m like yeah, people should get used to computational and quantitative thinking and all that good stuff. But at the same time we shouldn’t get rid of people who work in museums and that love collecting specimens.
There’s room for all of those skills and people. So I get a little bit nervous when people say, “Well everyone needs to learn to code, because everyone’s going to be coding in the future - I don’t think that’s true.
Having said that, I think another reason to code, for everybody - and scientists in general - is that it’s good to know the thinking. Because a lot of the systems that you’ll be interacting with are going to be engineered - which means knowing the fact that there’s code behind that, what does that actually mean, and what the limitations are, just to be a generally scientifically literate citizen.
There’s a great book by Douglas Rushkoff, called *Program or Be Programmed. I don’t know if you saw that? It’s really small. I always give it to people who wonder about programming, because he has a good spiel about, “Yes you should probably code, but you don’t have to. But you should definitely know what’s involved as this becomes more part of your world kind of thing.”
My position is maybe a little bit more nuanced than you might expect, just because I also realize the limitations of the data-driven, metric-obsessed kind of thing that we often can get ourselves into. But at the same time, I feel like it’s good to know those things - know how to analyze and use code, even if you don’t do it as your full time job, it’s for no other reason than you know what’s going on behind the scenes. And you know code is being deployed. So I think from that perspective, I think, yes, people should learn to code, even if it’s not their only thing.
Len: And my last question is - why did you decide to publish your book on Leanpub? I should say - by the way, Python For The Life Sciences is a really great book. A ton of work went into it. It’s really well done.
Len: I mean that. I see a lot of books. I was wondering why you chose to publish it with us?
Alex: We kicked around a lot of different things when we started our consulting firm. And one of the things that Gordon and I both agree on is, we don’t love gatekeepers. We love the idea of people doing things from the bottom up. And so we thought about approaching a publisher and proposing it. But we just felt, that’s going to add a lot of stuff in front of us, and let’s just write the damn thing and see where it lands.
And then when we started looking around - actually a friend of mine on a Slack channel that I recommend, he’s always got cool things, he writes a lot of cool things about programming and science and complex systems, his name’s Bill Tozier vaguery.com is his website - he just mentioned it in passing. I was like, “Oh, I should check this thing out.” And I went over there, and I said, “This is really interesting.”
So we were initially thinking, “Oh well, we’ll just sort of put it up and see what happens.” We really thought that… fit our general ethos. And obviously you have a really great revenue model, which I think is really good. And to be honest, looking at the way Amazon works, and the big e-publishers, they are starting to act more like rentier-type models.
Len: Like what sorry?
Alex: They’re sort of monopolizing the market now. And so they’re able to set the - like able to set monopoly prices. And I would rather support, in general, new, emerging businesses and organizations that are trying to make a way to make a living without necessarily having to create a massive infrastructure. That’s why Leanpub and self-publishing seems the way to go.
And that said - if a major publisher was to pick us up tomorrow, I guess we could continue doing both. But… for example, with our consultancy, we’re trying to build a sustainable business. We don’t necessarily want to be taking over the world. We’re not after world domination, we’re after - earn enough money to keep paying the bills, so that we can do the cool science or research or art or whatever it is that we’re doing. You know what I mean?
So that was part of it. I like it when I see other people doing cool new things. I’m always like, “I want to support that thing.” Even if it means like, okay, I don’t get the massive return in the immediate. I feel like in the long run, we’ll all be better if we do that.
Len: Thanks, that’s a really great answer. In many ways your book is the classic Leanpub book. And I mean that in the sense of books that we love to see. Because I think both your minimum and your suggested price is $34.99. It’s 304 pages. And if you’re in the life sciences or a biologist, and you’re looking to learn to code - I mean, this is a book you get great value from. Definitely worth 35 bucks.
Alex: Right, and we think so too.
Len: One of the interesting things - we’re still learning about this new model of self-publishing. But Amazon decreases the royalty rate that it pays when you go over $9.99 for a book.
Alex: Yeah, we noticed that too.
Len: I mean from 70% to 30%, right? [Note: On Amazon it’s actually 35% royalties for books over $10, not 30%–which is still terrible compared to Leanpub’s 90% minus 50 cents royalty rate - eds.] So they’re basically saying, “All ebooks are interchangeable, and the price should be less than $10.”
Alex: Right, exactly. They drive you towards that.
Len: I think that that’s probably an appropriate price point for novels. And I still think if you are writing a novel, you should have it on Leanpub as well. Because you will make more money, because it’s a 90% royalty. But if your book is worth more than $9.99, and I don’t just mean, because the price is more than $9.99. If it’s actually worth more than $9.99, you should not be publishing in a place that’s meant for novels.
Books like yours, books like so many other Leanpub books - well I mean, not specifically your book, but like for other types of books - they can change the amount you can charge people for the work that you do. Because you’ve learnt something new, and you’ve got skills that you didn’t have before. They can help increase the skill level that you have. And something like that, people are willing to pay more for. And authors should earn more from, I think.
Alex: Right. It was basically - a little under a year, I mean, not full-time, it wasn’t like a 40 hours a week, 7 days, 24/7 type-thing. But we put a fair amount of… and the other reason I liked it, the way we did it was that, because we had the freedom to do it at the pace we wanted, we could write the book that we wanted to write. Not the book that someone else had decided like fit the right market niche. So we could take our time and make it a little bit lighter, and put in jokes and things like that, that might not have passed muster, or may not have been into some other publisher’s conception of what they wanted.
So we liked the idea of having a book that has a bit of personality. Beause textbooks can be kind of on the dry side. But that was another nice thing about it. And also the iterative nature of it, because it just goes with the whole software development philosophy that we kind of use in our own consulting. The sort of Agile type thing of - release it, get feedback, improve. I feel like that’s a really good model to….
And we didn’t fully embrace it. We didn’t do chapter by chapter. But we’re certainly going to upload new versions. So I feel like that that approach is really, it’s still pretty new for the book publishing world still, I think. But you see how effective it is in software. At getting a better, more robust, if it’s done properly of course - kind of result. So that was another thing that was cool about the Leanpub thing. I liked that. I liked that idea that - doing anything creative or anything new. Get something out there, it’s not perfect. Improve it. Improve it. Do it in public. Improve it, get better. I’m learning this, just myself by taking improv classes. The same kind of thing. Like you’re not that great at the beginning, but you - when you get in front of an audience, you get better, so it’s a bit like that. I mean, just like not taking the risk, exposing yourself. People might not like every little bit of it, but that’s okay - you get better at it. So I feel like it fits with that sort of philosophy.
Len: Just before we go, I feel like I would be remiss if I didn’t ask someone living in the United States the day before the big election what’s the mood like in - well you’re in Arlington, Massachusetts - you were saying before we started this interview - just north of Boston, I think. What’s the mood like down there?
Alex: In the greater Boston area?
Alex: I think - what’s the thing, your acceptance, grievance, with the stages of grief. I feel like we’re at the acceptance phase now, with, just in a sense of like, whatever will be will be. Everyone’s just so over it. So much, so many - I don’t know. I can’t read anymore editorials. I can’t read fivethirtyeight.com. I feel like almost everything has been said that is going to be said, and it’s just going to be up to the voters now.
But yeah, people are concerned obviously, I think, definitely in our community. Because we’re around a lot of academics and scientists and people who - what the results will be for all of that. But that’s something I think that definitely comes up when we’re in conversation with friends and colleagues.
Len: Well thanks very much for that, that’s very well said. It’ll be an interesting artifact, because this will come out after people know the results.
Alex: Yeah what the result was. May I ask you what your - what’s the view from Canada?
Len: I could talk about that for a long time, I suppose. I think that probably most Canadians are quite concerned that Donald Trump could win, to put it straightforwardly.
Canada’s a more complicated place than Canadians pretend it is. But the spectacle over the last like two years of the campaign we have one border, right? And it’s with the United States. And to see someone who appears to be driven by no sense of responsibility, and not constrained by reason - within a hair’s breadth of being the President of the United States, is something that I think people are concerned by.
Knock on wood - when this comes out, we’ll all know.
Alex: Hopefully we will be able to breathe that sigh of relief.
Len: I think people here are in a sense, complacent. I mean, we just kind of take it for granted that Americans will make the reasonable choice. I don’t want to speak on behalf of all sorts of people who disagree with me, and generalize dramatically, but like, it’s - we’re watching. We’re always watching what’s happening in the States - we’re definitely watching now. I’m just getting a memory of when I stayed up late in 2000, to make sure that Al Gore won Florida, before going to bed. And woke up to a different world.
Alex: Yes, yes, I remember that election well. I had only been in the States for a few years at that point. And that’s when I started paying attention to politics…. I think it’s true, I gave a massive generalization, to say that academics and researchers tend to try and go about their business, to pretend that like nothing’s going to affect them. Or it’s like, it’s above them, or….
I think this election has certainly made people sit up and pay attention. Because even in the other elections of other Presidents, candidates, there’s a general agreement that probably the NIH would continue, and NSF always - they’re not about to abolish them, but sort of all bets are off with Trump. I think that’s what makes people really scared. Because it’s like, you really don’t know. It’s like a lot a lot of the things that you take for granted, just could not be true anymore.
Len: I hadn’t actually thought about that, but that’s the National Institutes of Health and the National Science Foundation - nd I hadn’t thought about that.
Len: Thanks very much Alex. I guess–
Alex: Yeah on that cheery note!
Len: On that cheery note, thanks very much for taking the time to do this really fun interview. And thanks for publishing your book on Leanpub.
Alex: Oh, you are most welcome, it’s been really fun.