Frozen Evolution. Or, that’s not the way it is, Mr. Darwin. A Farewell to Selfish Gene.

When I still worked in the Department of Parasitology, I often had to defend my research methods and approach to molecular biologists and biochemists, who gradually grew to predominate there. Finally, I gave up my efforts and moved with my entire team to the Department of Philosophy and History of Natural Sciences, which has a branch of theoretical and evolutionary biology, where my approach didn’t stick out. My colleagues in the Department of Parasitology often reproached my results for being inexact and ambiguous. They used to say that there was nothing or almost nothing to conclude from them – the performance of Toxo positives and Toxo negatives differs by only a couple percent, and only after running the data through a complicated statistical test, can we discover a relationship. When we conduct the exact same study on a different subject group, the relationship we originally discovered appears completely different – sometimes even opposite. When we use another, more sophisticated statistical test, we suddenly find a relationship we didn’t see before. In short, they say that our data allows for too much casual interpretation, and sometimes complete subjectivity. In contrast, the sort of results that biochemists and molecular biologists get are clearly unambiguous and objective, which must mean that they are perfectly exact.

From personal experience researching in the field of molecular biology and immunology, I well know that such results only seem unambiguous. It’s because biochemists and molecular biologists use very simplified systems, which are standardized to the extent that they will always give the same results. But in reality, just by using reagents from a different company, they will also get different results for the same experiment. And this is not even the biggest problem. Besides, in today’s globalized world, the number of independent producers of research reagents is rapidly dwindling, so researchers are often forced to buy chemicals from the same sources anyway, so their results are more comparable. A much larger problem is the biological material. If we use a different strain of lab mice, then the results of the same experiment (especially in my favorite field: immunology) will often be completely opposite to those obtained with the other strain. To get rid of the nuisance of ambiguous results, immunologists always try to use a system standardized to the smallest detail, that corresponds with the system used by colleagues in other work places.

But it’s a question whether this effort for maximum standardization doesn’t come at too a high price, and sacrifice some of the quality of the research It’s great that molecular biologists, unlike scientists such as experimental psychologists, can repeat the same experiment a hundred times over and get the same results, if they picked any one of the 200 strains of laboratory mice. But it’s less impressive that if they conducted their experiment only twice, but each time on a different strain of mice, then they might find that their “highly reproducible” results looked quite different. The reproducibility of results in current molecular biology and biochemistry is often achieved at the cost of their validity (Box 75 What is test validity, and what does the increase of interleukin 12 in Toxo positive mice indicate?).

Results obtained under highly standardized, and often also highly artificial conditions are clear and reproducible. However, they may tell us very little about the real biological significance of the observed phenomenon. Let me explain. Probably the most interesting molecules in organisms are enzymes – biological catalysts (Box 76 Why are enzymes such lousy catalysts?). When biochemists try to describe a newly isolated enzyme, they must first purify it from the rest of the cellular homogenate, and then find its physical and biochemical characteristics, including various enzymatic constants and the reaction kinetics. Few people realize that the standard conditions under which this purified enzyme is studied, are drastically different from the conditions which exist in a live cell.

So while biochemists are able to measure the characteristics of an enzyme, these characteristics may be completely different in the cell. Under cellular conditions, an enzyme which, according to biochemical tests, catalyzes a reaction in one direction, may actually catalyze it in the opposite direction, or also be the catalyst for a different reaction.

In other words, the exactness of current molecular biology and biochemistry is only skin-deep. Even these fields are prey to the arbitrary interpretation of the researcher. The difference is that the evolutionary psychologist can’t buy a genetically identical group of human subjects from a specialized firm, a group of subjects who were exposed to the same conditions their entire life. If we’re studying humans, we must accept that we’re working with a genetically diverse species, whose individuals undergo unique and disparate experiences. That is why the same experimental or observational study conducted on the students of the Department of Natural Sciences in 1995, 2005 and 2010 can give us different results. The population changed over the years, and the different cohorts of students can react differently to the same factor – in this case, infection by Toxoplasma gondii.

The necessity to work with very diverse subject groups may complicate our research, but in my eyes, it also gives us a great advantage. Our results aren’t as easily reproducible, but they’re obtained in a natural system; so they can be more safely extrapolated to the general population than results obtained in more “exact” fields. If we used a company-produced, genetically identical (but obviously non-existent) group of humans, we could never be sure that the results obtained from these experiments were applicable to the general human population. And scientists are definitely more interested in how toxoplasmosis, or any other factor, influences the real, genetically and phenotypically diverse, human population, than in how it influences an individual with a specific (and possibly rare) genotype (combination of genes) and a unique life experience. With a group of genetically identical humans, we could easily prove (or disprove) the effect of toxoplasmosis much more easily than with a representative group of people, and our results would be simple to reproduce. But a large part of these results would apply only to that strain of humans, and we couldn’t extrapolate them to the general population (Box 77 Pseudoreplications, and why statisticians fear them like the plague).

Another peculiarity of working with people, which causes many methodological complications, is that our test subjects participate in our studies of their own will. If people don’t wish

to participate, there’s no way to force them. This means we are never certain about how representative our subject group is – and to what extent their results are applicable to the population they came from.

The third peculiarity (and difficulty) of working with people, is that we often can’t study a problem using experiments. Instead, we must turn to observational studies – even when the problem better fits an experimental study. Let’s say we want to determine whether infection by Toxoplasma would cause lower tendency novelty-seeking in mice. To this end, we take two identical groups of mice, infect one of them with Toxoplasma, then wait and observe if the groups begin to exhibit differences in novelty seeking. If so, it’ll be obvious that the infection caused the change; as opposed to the possibility that differences in novelty seeking influenced which mouse was infected. But as I’ve mentioned several times (with thinly veiled regret) humans cannot be experimentally infected with Toxoplasma. We must use appropriate (and not always completely reliable) diagnostic techniques to separate individuals into those who were already naturally infected, and those not yet infected. Then we test their tendency towards novelty seeking and hope that individuals with an extremely high or extremely low tendency aren’t trying to hide their unusual characteristic, consciously or subconsciously – or at least, that they won’t succeed. And even if we find a difference between the Toxo positives and negatives, we can’t necessarily conclude that infection by Toxoplasma causes lower novelty-seeking. Lower novelty-seeking might increase the risk of catching Toxoplasma, or a third factor, such as the size of a person’s hometown, might influence both these factors.

Working with such a complicated population of test subjects, it’s understandable that we can’t rely on simple methods to evaluate our data. In molecular biology, researchers usually make do without statistics. They look at the results of an electrophoresis and determine, for example, that after the addition of soluble iron salt, the band which marks the location of a certain protein has been enhanced, grown fainter or disappeared entirely. A visible change in the levels of a certain protein on the electrophoretic gel is an unambiguous sign that needs no statistical evaluation. In molecular biology, we often need just natural intelligence to draw conclusions from our experiments. Fields like evolutionary psychology usually call for an additional, complicated step of statistical evaluation. We may be working with humans or animals caught in the wild, individuals who differ in age, gender and a number of other traits. Because this creates a very heterogeneous subject group, we must control for the effect of factors that may influence the trait we are studying (for example, reaction time) more than the factor that we study (e.g. toxoplasmosis), but are not the topic of our research. These confounding factors (e.g. age, health) must be filtered out, so that we can see the relationship between the studied factor (toxoplasmosis) and the dependent factor (subject reaction time). To minimize the effect of confounding variables, we must often employ complicated statistical techniques (see Box 78 How to deal with confounding variables). Such techniques are readily available today. Often, we even have a relatively easy-to-use computer programs that take on the complicated and time-consuming work. Of course, without knowledge of the statistical techniques, the operations we carry out with our original data may seem like New Age shamanism.

In reality, our complex world leaves us with little choice. Sophisticated methods are often the only way to understand complex phenomena and systems. This means that data analysis takes up a significant chunk of time. In many cases, collecting the data is much less difficult and time-consuming than it is to evaluate them.

And that brings me to another objection voiced against our research by colleagues who study so-called white biology (see pp. xxx) – the molecular biologists and biochemists. Often they complain that our laboratory does too little manual work. According to them, a student of biology should start pipetting solutions before (or rather instead of) breakfast, then switch to running from one complicated instrument to the other, harvesting cells and collecting them with a centrifuge, homogenizing tissue, sequencing or loading samples on chromatography columns – so in the evening, completely worn out, his other pair (their third this month) of scuffed-out lab sandals on his feet, he falls asleep on the living room couch, for his sore feet won’t carry him all the way to bed. In our lab, we spend most of the time sitting at the computer, entering in data from paper forms, checking that we entered it correctly; at best we might be conducting an analysis, but of course we’re never sure whether we understand it in detail, and whether it’s an analysis which really fits our data (fortunately, a random passerby can’t tell). Furthermore, the methods we use to collect data are not similar to the usual methods carried out in biological laboratories. We test many hypotheses using a questionnaire. A lot more of our studies are observational than experimental. I usually heard remonstrances against my work in connection to the complaint that my lab students did too little hands-on work and learned few methods. Apparently, it wouldn’t have been that bad if only I worked in this manner – but students in the college of natural sciences should learn modern methods, which they will use in future research. So the main objection against my research style was that I am in fact corrupting the youth. In the end, I preferred to go to another department. My less fortunate colleague Socrates, may he rest in peace, had it much worse.

Even in this case, I think that the complaint isn’t justified (which probably doesn’t surprise you).Today, science develops at such a rate that the instruments students used in their undergraduate or graduate work, will undoubtedly be different from the ones they’ll be working with when they graduate and begin their own research. And this applies not only to instruments, but to all experimental techniques. I remember, back in the day, when we’d walk reverently by the door of the laboratory, where they knew how to sequence DNA. Today we can fly our samples to be sequenced in Korea and get it cheaper than if we did it ourselves with the lab’s expensive machine. And the kind and clever Koreans (I hope it’s clear that I’m talking about South Korea here) will thank the person who sends them the most samples to be sequenced in a year, by mailing them a digital camera for Christmas. Today we pass reverently by the laboratories which can sequence an entire genome or proteome. I’m sure that in another couple of years none will bother with something so laughably routine, and will send genomes or protein mixtures they want sequenced straight to a specialized company in Korea or maybe China. And the amiable and skilled Chinese will give whoever sent them the most genomes, a digital ping-pong table with a chocolate fountain for Christmas.

In my opinion, our undergraduate and graduate students should leave our laboratories with the ability to set up a scientific study (be it observational or experimental) in order to answer a question; as the ability to analyze data, and particularly to interpret their results. This last thing is perhaps the most important – a student must learn to look at his results realistically, whether they agree with his hypothesis or not. In short, during his undergraduate or at least during his graduate work, he should learn to think scientifically and deal honestly with the data he obtains. I believe that the work carried out in workplaces like mine is much more apposite for teaching this, than the kind of work carried out in many labs focused on experimental science. In laboratories that are using the most sophisticated techniques of the day, students usually master one or two complicated techniques, learn to manipulate a certain machine or several ones. Often the measured data needs little evaluation; the student only has to believe his mentor and the producer of the machine, that measurements given really mean what the manual says they mean. In our laboratory, most students soon realize that data are very treacherous, and that their analysis must be handled with kid gloves. They discover (sometimes through bitter experience) all the problems that must be tackled when collecting data – all the things that can skew or completely invalidate results. From the perspective of training future scientists, the experience that students gain, for example, in our laboratory of evolutionary psychology is more valuable than that gained in a typical modernly equipped molecular biology lab. But frankly, the most valuable thing students can learn from a good laboratory (and such labs fortunately began popping up in the past 10 years in the college of natural sciences) – whether he spends his undergraduate years working with a single dilapidated machine, handling ten cutting-edge instruments, or studying in our lab, where the most complicated machine he’ll meet is a computer or a scanner to transfer questionnaires from paper onto the computer – the basic principle he should learn, is that anything he does in science, should be done honestly and thoroughly.