Reasons Not to Study Life Science or Anything Related
Human beings are becoming more ambitious, maybe more presumptuous to some extent, nowadays, as we want to understand everything about life and cure all diseases. This significantly motivated the industry and education of life science and its related disciplines. Lots of money goes there, lots of advertisement and progress reports shows up on media, lots of young promising students choose life science to study in college and graduate school.
While life science still belongs to science in general, unfortunately, the majority of the people in this field have turned it into a cult. Outsiders who did not know quite a lot about life science think highly of it, but lots of insiders are always being slaved and suffering.
In this blog post, I am going to describe what this field really is, and how it will ruin one’s dignity and career. I wish the people who are studying life science or related disciplines would quit, and the people who are going to study life science or related disciplines would think twice and not waste the talents there.
Most of the disciplines in science and engineering share lots in common. Every new finding is based on the mathematical axiom, the law of physics, rigorous mathematical derivation, or verifications in both experiments and practice. In principle, every student or researcher in science and engineering should have very solid skills to do mathematics, because that is the basic skill you do science and engineering. In college and graduate school, students from different science or engineering departments are taking the same fundamental courses which heavily emphasize on mathematics, or specialized courses that often share a lot of contents and basic ideas in common. This is how science and engineering works and how people should study science and engineering.
However, life science, although it has a “science” in its name, is totally different from other science and engineering disciplines. In college, at least in some colleges, students majoring in life science would still have to attend courses, such as calculus, linear algebra, and probability and statistics. This is usually because the college requires every student studying science and engineering to attend those courses, which is a good motivation. Those courses, however, would hardly be used in specialized life science studies. Because you would hardly use math in your study and research, you forgot. To my knowledge, almost all of my former colleagues who study life science totally forgot how to do mathematics, and how to read mathematical symbols and expressions, even though they have once studied it before. Many professors in life science, who claimed he or she discovered something via mathematical derivation, or would like to formulate some equations in the class but could not justify them using mathematical derivations, are often bluffing and know almost nothing about mathematics and physics. Life science is more close to chemistry than any other disciplines in science and engineering. So in principle, students or researchers who study life science should have very good knowledge of chemistry. However, based on my teaching and research experience, it is often not true.
So sounds like you don’t need to know anything and there is no prerequisite in order to do life science studies. This is true to some extent. Otherwise, you would not see there are so many middle school or high school students spending their summer doing life science research in some labs. The only one thing I think is useful for life science or you can learn from life science is the design of experiments. This is probably the only thing in life science that shares something in common with other disciplines. What makes you stand out in life science is not how well you are doing for the course work, but how well you know about using different kinds of experiment instruments, and your experiences to different kinds of life science experiments. These knowledge and skills are highly domain-specific, and they do not apply to other disciplines.
Wait, how about data analysis? Can we learn data analysis from life science? In life science, the experiments could be categorized based on the size of data you generated. For experiments generating a small amount of data, usually it is too simple to analyze, and you would learn nothing. Compute the mean and standard deviation of the samples, analyze whether there is any statistical difference between the control group and experiment group. Because usually the students and even the professors do not know too much about statistics, they often made mistakes in choosing the right statistical methods for analysis, thus resulting in error-prone conclusions. This is called “You don’t know what you were doing”. For experiments generating a large amount of data, such as genome sequencing experiments, it is usually handled and processed by professional software. Essentially you got results magically from a black-box software without knowing what the underlying analytical algorithms are. This is called “You don’t know what it was doing”.
Although lots of advanced experiment instruments have been invented to help the life science researchers to automate their workflow, most life science researchers still spend more than 90% of the time doing the labor-consuming work. Powerful researchers who manage a lot of resources, including funding and experiment instruments, do not have to do experiments in person. They have sufficient time to read literature, think potentially appropriate proposals, design experiments, and have funding to hire someone to do experiments for them. These powerful researchers are usually the professors in universities or research institutions, and the people being hired to do experiments are usually graduate students or postdoctoral fellows. Unfortunately, it will be many years for a junior life science student or researcher to finally become a powerful life science researcher, and the competition is extremely fierce.
Doing experiments is extremely tedious and usually trivial. Once you become familiar with some experiments, you do those experiments routinely and hardly learn anything new. Doing life science experiments requires extremely high concentration. If you made an error during the experiment, say preparing a bottle of solution with incorrect concentrations of some components, it could hardly be traced back. Your experiment results will thus be wrong and irreproducible. Some experiments cannot be fully automated by advanced instruments, and they require good hands to do fine operations. If you don’t have good hands, usually your experiment results would be inconsistent and untrustworthy. Experiments would usually take an extremely long time to conduct. Unlike computer programs, they usually could not be “saved” in halfway. Many experiment materials and samples are fragile, sensitive to environments, such as temperature and light, and they have their “life cycles” as well. This means that the fresh experiment materials and samples should be used as soon as possible to ensure their quality. The same experiment material or sample is likely different from what it was two hours ago. If somehow you realize that anything went wrong in the experiments, usually you would have to start from scratch again, whereas for computer programs you could always start from somewhere in the middle as long as you saved it. This further means that your life will be managed by experiments and you will no longer have control over your own life. For example, once you started a scheduled 24-hour experiment, you would have to follow exactly the experiment plan. If you scheduled to do something for the experiment, even if it was at 2:00 AM and there was a storm outside, you would still need to show up in the lab and start to do experiments on time. Otherwise, the entire experiment might screw up, and you would need to restart the entire experiment from scratch on another day.
You could see that there are so many variables we could hardly control in experiments, and these poorly controlled variables contribute to the noise in our experiment results. In fact, even with these variables appropriately controlled, most of the experiments are intrinsically chaotic, and the data will always be noisy. Someone may argue that the datasets from other disciplines might be noisy as well, and extracting valuable information from noisy data is the goal of science and engineering. This is true without any doubt. But there are two factors behind helping us extracting valuable information from noisy data, signal-to-noise and the number of samples. Due to the cost of life science experiments, the number of experiment replicates is usually not large. With poor signal-to-noise, extracting valuable information from limited samples would not be possible. In addition, the experiment results might be determined by some hidden variables in the environment that you are not aware of. Just like computer programs, if the software environment or hardware environment does not match what the computer program requires, the computer program might generate incorrect results, or it might just not run at all. Many life science experiments could only be reproduced in the original researcher’s lab, and could not be reproduced in other labs. While the experiment results might still be valid in the original lab, it might be just some artifact, and cannot be generalized to the practice and nature.
In my opinion, the correct way to do science is one of the followings:
- Deriving theoretical proofs for hypothesis, and doing experiments to verify.
- Doing thought experiments, such as Einstein’s pursuing of a beam of light experiment.
The correct way to do engineering is:
- Proposing a model, finding parameters for the model using the data from experiments, and improving the model using new data.
Because life science currently does not have solid mathematics and physics foundations, the two correct ways for doing science could hardly be applied to life science. The way to do life science research is actually always the way of doing engineering, “proposing a model, finding parameters for the model using the data from experiments, and improving the model using new data”. However, the underlying assumption for the correct way to do engineering is that “the model is always wrong”. This means that no matter how much data and evidence you collected, the model is likely to be wrong and will fail in some unprecedented cases. This means that life science studies only find models but never learn what nature is. This is in a sense that the life science which currently the people are doing is actually engineering instead of science.
Having talked so much, we learned that doing life science is hard, given it is chaotic, and has no perfect way to study. Regardless of whether the life science researchers know much about general mathematics, physics, and chemistry, in principle, we should still admire the life science researchers, because they are working so hard to find the truth from all different kind of difficulties. However, the time is different from several decades ago when people’s motivation to understanding life is pure. The majority of the people in the field of life science have turned this discipline corrupted now.
The career of life science is driven by publications. It does not matter how much you know about the specific domains in life science or how proficient you are doing some kind of experiments, publications are always the key to getting you a job. However, getting a publication in life science is relatively harder, compared to other disciplines, such as computer science. It is because, even if the idea is simple, doing experiments takes a lot of time and effort, and getting a consistent self-contained “story” for publication takes even more time and effort. Being smart, even as smart as Albert Einstein, would not help you in doing life science career, since being smart could hardly improve the chance of proposing the “correct” model from the enormous model candidates. Because life science students and researchers usually do not know much about general science, such as mathematics, physics, and chemistry, getting more publications in life science becomes their proof and identity card as a “researcher”.
Because all the life science experiments come at a cost, and usually they are expensive, people who have more resources get more publications. Large labs get more publications, and more publications get the labs more fundings. Smaller labs get fewer publications, and fewer publications hardly get the labs more fundings. It becomes a cycle. Because the principal investigators (PI) for labs, especially for large labs, need to apply for fundings to feed the whole lab, some of them would have no time to instruct students and junior researchers. But whenever there is a publication from the lab, regardless of how much scientific contribution the PI made, the PI’s name will always show up in the publication, probably because the PI fed you and it is a convention to get permission from PI to get a paper published.
Competitions for publications and resources are a disaster in life science.
Because the number of life science research topics are somewhat limited compared to other disciplines, many research groups all over the world are studying exactly the same research topic. If they published before you publish, and it happens that their conclusion is almost the same as yours, your many year studies would just be in vain. If this happens in other disciplines, usually you could still get it published in a decent journal or conference. But for life science, this is never the case.
Resource competition is also everywhere. Because the total budget from NIH or the research institution is limited. Some applicants get funded, some applicants do not. While this is true for other disciplines as well, in life science, there are a significant amount of the competition for the shared resources, such as million-dollar lab instruments. Even within the same lab, there are competitions for research topics and experiment resources between students and researchers.
Because of these and the fact that definitely there are some selfish people around, people started to dislike, or maybe hate, each other.
Students need publications for graduation and jobs, junior researchers need publications for promotion, senior researchers need publications for being elected as a member in the national academy of science and getting more research resources. This usually causes hierarchy in the lab.
For students and researchers in other disciplines, if you are good enough and could learn everything on your own, you could be almost entirely independent. However, people in the field of life science could hardly be independent. You could usually tell this from the number of authors in the publication. People studying life science usually only have the skill set to do certain kinds of experiments. They also manage a small portions of the lab resources, including instruments and experiment materials, that they don’t want to share unless there is a guarantee for authorship. Because doing experiments is expensive and it relies on the instruments, self-independent study does not work in life science, and experiment noobs would have to learn from other colleagues. This kinds of relationships could be unhealthy because if there are often the cases the “master” did not want you to learn all the stuff he or she knows. So it is likely that you become affiliated to your “master”. All these weird things cause hierarchy in the lab, and the lower level people have to obey the high level people. Because of such unequal relationships, people become slaved. They become cheap labor and have to work enormously long in the lab. Students with critical thinking could hardly survive in the lab. No matter how wrong you think your mentor’s ideas are, you will have to obey their instructions, or they will force you to obey by any kind of means. In other disciplines, the relationships in the lab are relatively equal, because people are more independent, and you could use mathematics, which is the universal tool for correctness, to prove things are correct or wrong. Being demanded to work more than 60 hours per week is also not something unusual. Even if later it turns out that the mentor’s idea is totally wrong, it is your precious time getting wasted, but not theirs. When doing experiments, you would also have to be careful not to break anything valuable. If it happens, I am sure your life would not be easy in the lab.
To attract more cheap labors, especially young inexperienced college students, and get the cheap labors motivated, the PI in the lab would often promise that the students’ credits will be reflected in the authorship of the publications. However, such kind of promise is cheap and in many scenarios it will not become true even if you have spent a good amount of time in it. The order of authors listed in the publication also matters. I have seen a lot of cases that the credits of people not being reflected in the authorship or their rank in the authorship is low even if they have contributed a lot.
Because it is usually hard to find cheap and tamed labors for the lab, once you get it, you will not let it go. The PI has many ways to keep people in the lab. They could write negative recommendation letters for graduating students, give grade “B” or “C” to students for courses without justifications, lie to graduating students the project will end and publications with the students’ authorship will come out, etc., you name it.
The results of many experiment results in the publication could hardly be reproduced, not even mention the validness of the conclusion. This is mainly due to two reasons. One reason is that the experiment is intrinsic disordered or the experiment results were controlled by some hidden variables that people were not aware of. The other reason is that people are fabricating data or intentionally not processing data with scientific justification. On one hand, because there are missing data or the original data does not support the conclusion being proposed, people fabricate data for the publication. These data, of course, could not be reproduced by any means. On the other hand, the data might be real, but people violate scientific common sense when they are processing the data. For example, people did many experiments but only select to use the results that match the conclusion being proposed. It is possible that the data selected does not reflect the true distribution and therefore the experiment results could not be reproduced.
Many research discoveries and findings are over-advertised and exaggerated. A lot of findings, even though they might be real, are completely useless. But in order to get more further fundings, it has to be described to have potential in some aspects, such as curing diseases. Investors, who even have life science education background, were often misled by the advertisement of life science discoveries, and wasted a lot of time and money on something useless.
Because there are lots of competitions in life science researches, people hide key technical details in the publication so that other people could hardly reproduce. Sending Emails asking for technical details and getting no response is also common. As I said, the competition also exists inside the lab, stealing colleagues’ research ideas, experiment accomplishments, research credits, experiment materials, and samples are not news.
Of course, some biological experiments are unethical at all. But in order to get attention, reputation, or profits from the society, some people don’t care about ethics at all. For example, there were labs which tried to clone human beings, or edited genomes for human offsprings. Do remember, in science fiction, if there are villains, they are always somewhat related to life science researches.
Nowadays, the prosper of life science research is mainly due to the advancements in physics, mechanical engineering, and computer science. Life science researchers did not invent those but simply use them without understanding too much how those work. To some extent, I think this is not the correct way to do science. But we should not blame the life science researchers for this. Because of the restrictions and limitations I described above, they could not or they are not able to study those advancements in too many details.
Another weird feeling of studying life science is that you would feel useless outside a lab. Essentially because all the skills and knowledge you learned are highly domain-specific and do not work outside the lab. The lab becomes a prison, and you are the prisoner. Once having been in jail for too long, you don’t know how to live outside a prison, just like Brooks Hatlen in the film “The Shawshank Redemption”.
As a devoted life scientist, such as what I used to be, you would devote almost all of your time to research both passively or actively. Therefore, you would have almost no time to study anything else. If your career in life science does not work out, your life becomes ruined. Because the knowledge you have is highly domain-specific and it does not generalize to other disciplines, it is almost impossible to find a job in other fields. However, for students and researchers in other disciplines, because their math skills are usually very good, and all the disciplines in science and technology except life science share knowledge in common, changing career is usually not hard.
How about life science related interdisciplinary studies, such as biophysics, biostatistics, bioinformatics, etc.? I would still suggest staying away from them. Because the knowledge you will learn and use for these interdisciplinary studies are orchestrated to work for life science only. Biophysics, biostatistics, bioinformatics are derivatives of physics, statistics, and computer science, but you would not actually learn the real physics, statistics, and computer science. They are highly domain-specific, and do not generalize to other fields. There is also a lack of innovations there. All the advancements in biophysics, biostatistics, and bioinformatics are essentially the effort from physics, statistics, and computer science.
My suggestions to the public are not to risk your career by studying life science or anything related too early, because it is one of most complicated subjects to study in the world and the whole field has been corrupted. If you have already obtained a PhD in mathematics, physics, and computer science, you may give it a shot to life science researches. If you don’t like it, you could still go back to what you were doing.
I also have suggestions to governments and education institutions. Please remove life science or any interdisciplinary majors related to life science from college. The purpose of college education is to lay good foundations for science and engineering for students. Highly domain-specific education waste students’ precious time, and mislead students when they are going to make their important career decisions.
Reasons Not to Study Life Science or Anything Related