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CharonY

Are biologists poorly trained?

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I wanted to start a new topic based on the question whether biology training of biologists is worse than that of other natural sciences. Generally I would argue that it is largely dependent on the university/institute, however the question arises, whether there are intrinsic features common to all biology student courses.

Phil (thanks for the correction, Paralith) gave the following points:

 

#1; Some courses are too easy. If you take a look at the textbooks used in; "intro. to microbiology", “ecology”, courses related to a particular group or organism… they are so simple; they could easily be used in high school. A few concepts, some things to remember, et voilà ! Of course, it depends of the teacher, still…

 

I would argue that it largely depends on the uni. I have been in three different (German) universities and institutes so far. In two of them we (the teachers) created our own courses from scratch and wrote our own scripts. Mine were notoriously feared as I usually set the difficulty rather high when I had small groups (OK, but then I was rather generous with marks).

In the institutes generally textbook variant courses were offered, but I was allowed to set my own too. I have to add that all courses that I offered were practical courses. In the end I'd say that it is largely dependent on the uni and possibly on the system used in different countries. Until recently in Germany almost all courses were more of a practical nature (with the exception of mathematics, of course.

 

#2; Biologists are notoriously inept with numbers. I don't like statistics, it's boring, still, it's very important for biologists. In fact, statistics was in great part built for biology. Yet, most universities have only one (very easy) mandatory course in statistics. It's not enough. And what about mathematics! How many ecologists can understand the theoretical works of Peter Abrams or Stephen Hubbell ? Getting some info on an ecosystem, or about the structure of the genome of some drosophilids, it's not enough. We have to use these information to improve our theories. It can rarely be done without some maths.

 

I agree with that one. I think it has two main problems. First, many biology students chose biology because they think they can get away without using maths. Personally I would argue that in most disciplines (especially ecology!) one has to have a firm grasp on at least statistics. I would argue that a biologist needs it more than the average chemists.

The second point is related to the first one, statistics (or mathematics for biologists or any equivalent courses) are often poorly visited if they are not mandatory, and even then the students tend to slack off. I regularly force my students to use statistics whenever possible, though at least Germany apparently the math requirements are getting reduced. Big error in my opinion.

 

#3; I think one of the weaknesses of biology comes from our need to get a huge amount of information. Given the complexity of our subject, it's normal, however it has a serious drawback. Many students have been able to get a Ph.D. mostly by collecting data, which can often be made even with a mediocre understanding of biology.

 

I would argue that it may be the same for other disciplines. Many if not all areas of science have accumulated so much information that it is necessary to specialize heavily. Physics students specializing in biophysics for instance often lack knowledge in molecular physics, although both areas have overlaps.

It really depends on when the specialization begins. In most universities (in Germany) you specialize after two years of studying general biology (bachelor's equivalent) then you basically choose an area (e.g. ethology, genetics, microbiology, etc.) where more practical courses are performed (and finally you do the diploma/master thesis).

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Here we go again. If it isn't math, it isn't rigorous.

 

Math is a way of thinking...one way. There are others. Try a few.

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You give me too much credit, CharonY. It was Phil, not I, who listed the above points as you see them, though I do for the most part agree with them.

 

Point number 2 I agree with completely - I myself am pretty guilty of it. I'm not very good at math, and I most certainly don't enjoy it. Despite that, though, I knew that I would really need it, and voluntarily took a research statistics course (that was not required for my major) in undergrad. And I was really glad I did - it helped that the teacher was excellent, and illustrated every point with a practical example taken from real, natural science research. In fact, I would have taken a second semester with her had my schedule permitted it.

 

I think I would combine points 1 and 3 into the fact that simply getting into the field of biology requires a that a very broad knowledge base be established first - even so you can decide where in biology you want to specialize, if nothing else. I think that problem is just that the field of biology is so huge - a bio 101 class will only briefly touch on the surface of all the subjects it tries to cover. Even the specific field of ecology is also very broad, and again, an ecology 101 class will only be able to go into so much detail if they want to achieve good coverage of the field as a whole. Thus why it seems that these classes are relatively easy - it's because they are. And I'm honestly not sure of a good way around it, especially since it's pretty universal to require the broader, more general classes before you can get to the upper level, more detailed ones, where the science really does get challenging. And in comparison to engineers, biology students spend less time in that zone of challenge while they're in school, because they spent so much time covering the basics.

 

Here we go again. If it isn't math, it isn't rigorous.

 

Math is a way of thinking...one way. There are others. Try a few.

 

We're not saying that biology isn't rigorous. We're saying that the training of its practitioners isn't as rigorous, which is detrimental to the functioning of the field.

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With engineering, one is trained to solve problems using basic principles of science. Often exams are open book, since bulk memorizing is not as important as applying basic rational principles of science, in situations, one has not encountered before.

 

With biology memorizing is more important. One is not encouraged to use ingenuity to speculate possible solutions to problems. One is obliged to play by the experimental rules of the game, which is often based on the blackbox of statistics. For example, whether we have a bow-arrow, hand-ball, gun or cannon, the engineering sees projectile motion. The biologist sees each as a separate phenomena requiring empirical experiments. The engineering can see the similarity in the compound bow and crossbow, but the biologists sees these as further sub-categories for investigation. The engineer can solve them all on the back of an envelop. The biologist is required, by training, to put in hard labor with the proper resources.

 

The reason biology is memory intensive, and procedure orientated, is the state of the art has not be able to get beyond observational empiricsm. It would be like the engineer not having a rational understanding of heat transfer and then having to memorize empirical data. It becomes more of an artform where memorizing allows one quicker access to data. Luckily for the engineer, large memory storage is reduced to a handful of working relationships, that allows them to address new things, quickly. The biologist does not yet have a good set of practical compact relationships.

 

A good analogy are two woodsmen hiking in the woods. The engineer is given a light compact backback, in school, with all his needed supplies. The biologist is given this huge backback with his needed supplies. The net result is the biologist can only cover a small amount of ground each day. He also has to be trained how to walk so he doesn't tip over. The engineer, can move quickly, at many angles, due to his agile speed.

 

When I was young I was always attracted to biology. But I always avoided it because there was just too much memory work for my tastes. I always felt it would be nice, if biology was like engineering where basic principles could be taught, that would allow one to rationally solve problems without needing experiments up front. After the rational prediction, then you run one or two quickie experiments, since you know what to expect.

 

I invented the H-potential analysis for the cell, so I could have a compact backpack for moving around the living state. That way I could avoid having to put on the huge memory backpack, and still enjoy biology. But the problem is, current biology training is so biased by their huge needed backpack, they seem to assume, it is impossible to cover ground fast. I have been trying to show that biology now has compact relationships that allows more agility and speed. Somebody just needs to be willing to take off their huge backpack and try it out. It makes it much easier to move around with agility and/or to cover more distance, quickly.

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Are biologists poorly trained?

IMO absolutely!

 

I have been in three different (German) universities and institutes so far. In two of them we (the teachers) created our own courses from scratch and wrote our own scripts.

 

I Israel its the same system, I give immunology course for undergraduate students and what I decide to teach I will teach. There are some lecturers that for last 20 years teach the same course without changing/editing any contents... and no one tells them nothing..

 

#2; Biologists are notoriously inept with numbers. I don't like statistics, it's boring, still, it's very important for biologists. In fact, statistics was in great part built for biology. Yet, most universities have only one (very easy) mandatory course in statistics.

 

agree with every word!

I have to admit.. that till now (finishing my PhD this year) when I have to do complicated (for me) and important statistical calculations, like for in-vivo results, I'm going to statistician, and ask him to check me ..(can you imagine we, ''dumb'' biologists, have a special man in our faculty who do statistical calculations for us..what a shame..).

But how can I blame the students when we had only one basic statistical course in our first year study, when more than a half of the students didn't knew for what they even need this..?!

 

In most universities (in Germany) you specialize after two years of studying general biology (bachelor's equivalent) then you basically choose an area (e.g. ethology, genetics, microbiology, etc.) where more practical courses are performed (and finally you do the diploma/master thesis).

 

we have pretty much the same system, so I don't think that in professional side we have problems. and even if we feel we have gap in some subject we always can use a PubMed;)

 

I think our problem is with math and statictics. we must increase the count and the level of this courses that are given to biologists.

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The university that I will be going to has two math routes that you select from for the biology program. One is one calculus or calc 1 followed by two courses in statistics, or the other route is calc 1 and 2 followed by one course in statistics.

 

I don’t know if it matters as much as per say what program you happen to be going into. Say for molecular biology it would probably help to know more math then say other fields of biology. One reason biology programs may lack is simply funding. I mean what are the chances that many biology programs in some states have huge areas of biodiversity in which to go and do field studies? On that note how many biology programs have large wings or funding put into areas like genetics or biotechnology/bioinformatics. None of this stuff is cheap. For instance I know at some places you can take an aquatic ecology class and actually get to see first hand the topics, at other places aquatic ecology is limited to freshwater environments or even worse nothing but coursework in a class.

 

Lastly biology programs or the field itself is not just physics or chemistry for a reason, much like a geology program. I think this needs to be taken into consideration. One prime example I think is the state of Montana. For instance they have huge areas of basically uninhabited by human lands that are stocked with local fauna and flora. You can even get to see such animals as a bear if you want in the wild. I think some biology student in say Los Angeles California would be hard pressed for such. Also some universities invest large amounts of cash again into some of the more latest technology for say a molecular program while others don’t. I mean its one thing if your class wants you to mix X with Y and study Z or bounce a ball in various ways or off of various mediums, its another when your topic might be a thousand pound land mammal with dynamic behavior or a 20 mile long string of codons.

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#2; Biologists are notoriously inept with numbers. I don't like statistics, it's boring, still, it's very important for biologists. In fact, statistics was in great part built for biology. Yet, most universities have only one (very easy) mandatory course in statistics. It's not enough. And what about mathematics! How many ecologists can understand the theoretical works of Peter Abrams or Stephen Hubbell ? Getting some info on an ecosystem, or about the structure of the genome of some drosophilids, it's not enough. We have to use these information to improve our theories. It can rarely be done without some maths.

 

I would imagine this is an even bigger problem for people who might approach biology obliquely from a social science, like biological anthropologists or some neuroscientists. A lot of anthropology BA's have probably ended up struggling in graduate school because they couldn't handle the statistics and often advanced geometry (anthropologists love modeling those bones) that real research in biological anthropology mandates but that the mathematics general education requirements of their university and elementary statistics that the department required didn't prepare them for. Neuroscientists from backgrounds in psychology and philosophy might have the same problems; I don't know a whole lot about that field.

 

Of course, that oblique approach addresses to an extent the other two weaknesses of biology education. Again speaking from the perspective of a biological anthropologist, I know that cell biology probably isn't going to be that useful to me, whereas evolutionary theory and population genetics probably are. It helps narrow the field a bit so less broad net-casting is required.

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I'm a biochem major, so my course work is a bit more rigorous then the bio majors as my school. Except the math (biochem majors don't need to take stat) But we do get plenty of math in calculus and physics courses.

 

And I think the idea is that your going to have to take stat classes in grad school, so we don't need it know??

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I'm a biochem major, so my course work is a bit more rigorous then the bio majors as my school. Except the math (biochem majors don't need to take stat) But we do get plenty of math in calculus and physics courses.

 

And I think the idea is that your going to have to take stat classes in grad school, so we don't need it know??

 

That’s a solid point on what you plan to actually study or do. Biochem from what I understand differs greatly from say a traditional biology program. Most undergrad biology programs have a year of general biology, then courses on genetics, cell biology, developmental biology, ecology and evolution. I don’t think much of those are present in a typical biochem program save maybe genetics and general biology as biology orientated classes.

 

Even a general biology degree typically requires anymore almost two years of solid chemistry anymore, such as a general chemistry sequence and then a coupled of courses on organic chemistry. Biology has many different fields though, from more large scale or ecological orientated programs such as environmental biology, to more discrete programs if you will like biochem or molecular biology.

 

Most highly interdisciplinary fields like biophysics or structural biology I don’t happen to think are taught really as programs at an undergrad level typically, nor is ecology for that matter.

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Of course, some biologists will never need maths (outside statistics) in their career, just like many will never used the knowledge they acquired in physiology or in some other courses. The difference is; mathematics is now everywhere in biology, and it takes more time to build a real understanding of mathematics than it takes to understand a few concepts of physiology. It's in part because mathematics is hierarchical in nature, you have to understand A to understand B, then to get to C...

 

According to Sarah Otto, more than 1/3 of all articles in ecology & evolution involve some sort of mathematical models (excluding statistics), most of time differential equations. But even if they wanted to understand these models, most biologists could not. I think it slows down our science because, even if we have very good theories, experimentalists won't use them. There's many example; pop. models in microbiology are understudied, the ratio- v. prey-dependent debate is unresolved because, even though it's a central debate in theoretical population ecology, few empirical tests were made, et cetera... Obviously, if experimentalists can't understand the models, they can't participate in the debates.

 

Another problem is that we get many faulty models. I just sent a "Letter to the Editor" to a journal of ecology, they published a model with a construction flaw. The error wasn't very hard to spot, in reality, my proof was made with only basic algebra. The same model was used in 2 other journals ("PNAS" & "Science"), nobody saw the mistake ? Why ? Because they're not used to this kind of thinking (and to be fair, the model looked fine at first sight).

 

I think every biologist should have enough knowledge in mathematics to understand the basic systems of differential equations used in ecology, the models in population genetics, the optimization models used pretty much everywhere.. It could easily be done with 1-3 courses, and with a little more effort to integrate these models in the basic courses.

 

Here we go again. If it isn't math, it isn't rigorous. Math is a way of thinking...one way. There are others. Try a few.

 

I'm a theoretician, I have to do maths on a daily basis, most biologists don't. I don't want to turn biologists into theoretical biologists or biomathematicians, I'm happy to cooperate with individual with different skills. I would only like to see a more balanced approach to mathematics in biology. Mathematics is a great tool to unify, in my opinion, we miss many great opportunities because, in many situations, mathematical models are not even considered.

 

First, many biology students chose biology because they think they can get away without using maths.

 

They should go in biochemistry (lots of simple calculations, but very little maths). The truth is, even if some students think they can get away with it, mathematical models are increasingly common in biology.

 

Even the specific field of ecology is also very broad, and again, an ecology 101 class will only be able to go into so much detail if they want to achieve good coverage of the field as a whole. Thus why it seems that these classes are relatively easy - it's because they are. And I'm honestly not sure of a good way around it

 

Perhaps that's the problem; they try to cover too much.

 

I give immunology course for undergraduate students and what I decide to teach I will teach.

 

I might be wrong, but I read a couple of immunology articles, and while many mathematical/theoretical models have been developed in the last 20 years (mostly for cancer dynamics, virus dynamics, virulence, antigenic variation...), I never saw a single one of them in a standard article of immunology.

 

I like biophysics.

 

Be careful, "biophysics" is not the application of physics to biology. It should be, but in reality when you hear "biophysics" it generally means "the applications of physics to biochemistry".

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The problem with using a lot of mathmatical models in biology or bio-related subjects is because you start losing the objectivity of the subject. Im not saying the current curriculum and practice of biology is perfect but I think it is doing alright as it is.

 

A lot of times, mathematical models becomes too abstract that you simply lose track of the physical interpretation of it, which is of most important when dealing with biology/medical related subjects. People will also get side tracked into trying to understand and figuring out the equations in the model more so than the actual fundamental response. Also, mathematical models will easily disencourage people away from the course simply due to its mathematical nature in which some do not like.

 

Lets take for example the mathematical model of a pair potential, that is the lennard jones potential. Theres two ways to remember the effect of a pair potential or a bond between 2 molecules if you will. You can choose to remember that as you push 2 atoms closer the force needed gets harder, siminlar to when you try to pull it away from each other, but it is easier to pull it away until eventually you can pull the atoms apart for good. The other is to use a lennard jone's equation which has very abstract terms that people simply try to understand from a physcal interpretation, which is simply a waste of time. Its like trying to understand quantum mechanics from a physical interpreation stantpoint.

 

So really, biology is much more effective just remembering the rules of the game, than simply remembering mathamtical models. Also even if fundamental mathamtical models were developable for biology, they are themselves not fundamental anyways. There really is no point into doing this unless you can concretely have a very good theory which will ultimately be very limited, because a lot of biological responses and topics relating to it, can either be described by existing biochemistry rules and laws, or if some magical relationship you are tyring to describe were developed, they were really the phenomenon arising from more complexed multilevel, nonlinear interactions in which case your model itself will be very limited in its applicability.

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Originally Posted by Phil

I might be wrong, but I read a couple of immunology articles, and while many mathematical/theoretical models have been developed in the last 20 years (mostly for cancer dynamics, virus dynamics, virulence, antigenic variation...), I never saw a single one of them in a standard article of immunology.

 

and probably you won't see..

IMO there is two major reasons for that:

 

1# Biologists as most of us agree are very poor in math. They don't get needed background in math in their undergrad. So when I (for example) have to decide what to teach, I prefer to give my students minimum math formulas and models, which they will find hard to understand, will loss me, and will miss the point in trying to grasp the meaning of the given math formula.

 

2# Most of immunologists, virologists and bacteriologists are practical and not theoretical, so they prefer to do a test instead of trying some known math model which will not replace practical examination anyway.

For example: when I have to determine virus replication time, I prefer to do very simple test instead of using suit model for that, which will not as I said will ''free'' me from experiment, and for just seeing if this particular model works in my virus and my system as well as in HIV for example I don't see as necessary..

virology like immunology theories changes all the time, so for a middle researcher who wasn't ''grown'' on math, very hard to relay on known math models which for today don't replace practical examinations that you need when you try to publish an article.

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I wanted to start a new topic based on the question whether biology training of biologists is worse than that of other natural sciences. Generally I would argue that it is largely dependent on the university/institute, however the question arises, whether there are intrinsic features common to all biology student courses.

 

I think so. And I am a biology student. IMO, the teachers tend to adjust to the level of thinking of the students, how well they could understand certain concepts. While some of my professors (oops!), i'd say, are pretty lazy and lack the enthusiasm in imparting knowledge and skills. An upperclass biology student, who is my friend, even commented that my professor doesn't have the what it takes to teach us a certain subject (oops again!).

 

Regarding math, i graduated from a high school that offers math subjects higher compared to the other high schools in my country. Well, when i got to college i told myself that i am so tired of math and decided to take biology. there were a few math subjects that i passed with flying colors. and to my surprise, i missed solving math problems.

 

Biology in our university focuses in ecology, IMO. We have been trained doing some field works and we are glad of the availability and proximity of areas we could get data from. However, I think we still lack mastery of significant concepts that one should learn to be a good biologist.

 

I took a career competency test. On how confident I am of my knowledge and skills to be successful in my chosen career. I was rated poorly.

 

This feeling is compensated for I think I have grown socially. I joined some organizations, which I think would give me challenges and would allow me to make use of my time wisely.

 

I still hope I could be trained as well as I want to be trained.

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The problem with using a lot of mathmatical models in biology or bio-related subjects is because you start losing the objectivity of the subject. Im not saying the current curriculum and practice of biology is perfect but I think it is doing alright as it is.

 

A lot of times, mathematical models becomes too abstract that you simply lose track of the physical interpretation of it, which is of most important when dealing with biology/medical related subjects. People will also get side tracked into trying to understand and figuring out the equations in the model more so than the actual fundamental response. Also, mathematical models will easily disencourage people away from the course simply due to its mathematical nature in which some do not like.

 

Lets take for example the mathematical model of a pair potential, that is the lennard jones potential. Theres two ways to remember the effect of a pair potential or a bond between 2 molecules if you will. You can choose to remember that as you push 2 atoms closer the force needed gets harder, siminlar to when you try to pull it away from each other, but it is easier to pull it away until eventually you can pull the atoms apart for good. The other is to use a lennard jone's equation which has very abstract terms that people simply try to understand from a physcal interpretation, which is simply a waste of time. Its like trying to understand quantum mechanics from a physical interpreation stantpoint.

 

So really, biology is much more effective just remembering the rules of the game, than simply remembering mathamtical models. Also even if fundamental mathamtical models were developable for biology, they are themselves not fundamental anyways. There really is no point into doing this unless you can concretely have a very good theory which will ultimately be very limited, because a lot of biological responses and topics relating to it, can either be described by existing biochemistry rules and laws, or if some magical relationship you are tyring to describe were developed, they were really the phenomenon arising from more complexed multilevel, nonlinear interactions in which case your model itself will be very limited in its applicability.

 

You definitely bring up a good point in that the danger of using too much math can be to loose track of the actual physical mechanisms going on. Though judging by the aversion to math most biologists here have mentioned, I don't think any of us are in danger of using it too much. ;P Seriously though, mathematical models can still be powerful tools, as long as they are used properly. The relationship of the variables to actual, physical phenomenon has to be well understood in order to be accurately applied. If you're trying to understand the effect of a complex system - say, how weather patterns affect the population demographics of a certain species of animal - there can be so many factors involved that it becomes difficult to visualize it ourselves. This is where mathematical models can help us, but again, as long as the variables are accurately mapped to the environmental factors they represent - which in itself can become a whole research project.

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Some of the recent B.S. (Biology and biotech) and a few M.S. graduates we have hired required being taught:

-how to make a 1:10 dilution

-serial dilutions

-to manually calculate the viable cell count from a hemocytometer

-significant digits/reporting results to scientific form (X.XXe+0X)

-to make it a common practice to properly label the value (90.6+e08 µL...say what?)

Not calculus, not statistics, just basic application is either absent from the curriculum or lacking.

 

I do wonder if it is caused by the individual’s ability to recall the knowledge and not the quality of the education received.

 

Besides math issues:

-inability to properly use a serological pipette, pipet-aid, or other common instruments

-aseptic techniques

-importance of proper documentation

-understanding of cGMP, cGTP, and clean room regulations and standards

 

What makes it most frustrating for me is the attitude that these are minor issues and when they are brought to attention it is corrected (if possible) and forgotten.

 

Has anyone else in industry seen this behavior or are my expectations too high?

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With engineering, one is trained to solve problems using basic principles of science.

So are biologists.

 

One is not encouraged to use ingenuity to speculate possible solutions to problems. One is obliged to play by the experimental rules of the game, which is often based on the blackbox of statistics. For example, whether we have a bow-arrow, hand-ball, gun or cannon, the engineering sees projectile motion. The biologist sees each as a separate phenomena requiring empirical experiments. The engineering can see the similarity in the compound bow and crossbow, but the biologists sees these as further sub-categories for investigation. The engineer can solve them all on the back of an envelop. The biologist is required, by training, to put in hard labor with the proper resources.

 

This is because much of physics is deterministic, whereas biology deals with populations of individuals: individuals that vary between themselves. Therefore the deterministic equations that works for physics do NOT work for biology. Biology deals with probabilistic outcomes, not deterministic ones. Of course, this means that your desire to turn biology into engineering/physics is doomed. Simply because you don't understand the fundamental differences in the subject matter.

 

The reason biology is memory intensive, and procedure orientated, is the state of the art has not be able to get beyond observational empiricsm.

 

I would say that the reason there is so much memorization is that evolution is not emphasized enough or taught enough. As Dobzhansky noted, evolution is the central organizing principle of biology. Without it you are left with unrelated facts.

 

The biologist does not yet have a good set of practical compact relationships.

 

Yes, he does. Evolution. Chemistry. And there are mathematical (altho complicated) models for everything from protein folding to ecological systems.

 

When I was young I was always attracted to biology. But I always avoided it because there was just too much memory work for my tastes. I always felt it would be nice, if biology was like engineering where basic principles could be taught, that would allow one to rationally solve problems without needing experiments up front. After the rational prediction, then you run one or two quickie experiments, since you know what to expect.

 

I invented the H-potential analysis for the cell, so I could have a compact backpack for moving around the living state. That way I could avoid having to put on the huge memory backpack, and still enjoy biology.

 

So NOW we know why you did this: not to explain data but because you are lazy. You simply ignored the basic difference between biology and engineering/physics and tried to make biology into engineering for your convenience! No wonder it's such a flawed theory.

 

What makes it most frustrating for me is the attitude that these are minor issues and when they are brought to attention it is corrected (if possible) and forgotten.

 

Has anyone else in industry seen this behavior or are my expectations too high?

 

Your expections are a bit too high. I would think that a BS should be able to do your first list:

"-how to make a 1:10 dilution

-serial dilutions

-to manually calculate the viable cell count from a hemocytometer

-significant digits/reporting results to scientific form (X.XXe+0X)

-to make it a common practice to properly label the value (90.6+e08 µL...say what?)"

 

The second list is too high:

"-inability to properly use a serological pipette, pipet-aid, or other common instruments

-aseptic techniques

-importance of proper documentation

-understanding of cGMP, cGTP, and clean room regulations and standards"

 

Most schools aren't going to do bench work for cell culture or necessarily involve experiments with a pipet-aid or the regulations and standards. These are taught in Ph.D. courses, not the master's courses. And they are learned in the lab as you do them. Many/most master's programs do not require lab bench work.

 

You're going to have to train them, just as I do.

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Math and simulation is often important to research but it can actually be unnecessary to get the job done. I used to be a development engineer and I preferred doing old time science. One does some basic research, formulates their theories, narrows down the experiments, rund a few to see if the predictions were correct or whether the theory needs revision, then start working on scale-up. That is about 2-3 weeks of work. This was ideal during emergenices, but if it was not an emergency, the project gets done too fast. The math would come in handy to stretch things out so it could meet a longer time requirements. Many biologists have techniques that allow faster turn-around. The math could come in handy, if things were better rationalized and subject to the cause and affect of math.

 

Let me give an example, say we have enzyme A, which is one of several standards, which are well characterized, in terms of form and function. We have unknown enzyme B, that has various sectors of amino acids that both parallel, and are different than the standard. Based on the properties of these animo acid blocks, as a function of relative geometry, one is able to calculate the expected reactivity of the unknown enzyme. After these calculations, one goes to the lab runs a few experiments, done. But this is beyond, the current capability, such that math may not be handy. To include math would require assumptions beyond current proof. At least hard data is easier to intellectually transfer, allowing faster turn-around.

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Your expections are a bit too high. I would think that a BS should be able to do your first list:

"-how to make a 1:10 dilution

-serial dilutions

-to manually calculate the viable cell count from a hemocytometer

-significant digits/reporting results to scientific form (X.XXe+0X)

-to make it a common practice to properly label the value (90.6+e08 µL...say what?)"

 

The second list is too high:

"-inability to properly use a serological pipette, pipet-aid, or other common instruments

-aseptic techniques

-importance of proper documentation

-understanding of cGMP, cGTP, and clean room regulations and standards"

 

Most schools aren't going to do bench work for cell culture or necessarily involve experiments with a pipet-aid or the regulations and standards. These are taught in Ph.D. courses, not the master's courses. And they are learned in the lab as you do them. Many/most master's programs do not require lab bench work.

 

You're going to have to train them, just as I do.

 

"Too high".

I completely, respectfully disagree.

 

A BS biology or biotech graduate should absolutely know sterile techniques, how to use the basic tools of the trade, and proper documentation. They also need a basic understanding of statistics. In my opinion, I believe that this (stats) is probably the most important thing most programs are missing.

They need to understand how to work in the trade, have a hands-on basic understanding of the tools of the trade, and certainly know how to properly document and analyze their own or others' data.... no matter what they are planning to do after the degree.

These are basic necessities.

 

What kind of programs are graduating students without these basic skills? I know they exist, but I'd like to know in order to avoid their grads.

Without these basic skills, they are good for flipping burgers maybe....but not holding down jobs as associate research scientists (or even technicians).

I definitely won't hire them....never have...never will. They would need to take some more hands-on labs or regular classes and/or work out of a temp agency for a while first.

 

"Too high"? How can you make this statement? Why would you settle for hiring people with these deficincies? As long as they are being hired, the programs won't change.

 

BTW: I agree that cGMP, cGTP, and clean room regulations and standards can be learned on the job......or in electrive programs...but htey would certainly be a positive factor in a hiring decision.

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I just started college and I'm taking biology of cells. I've discovered that the math is all tied in. Right now I'm researching how to make a tertiary model of an enzyme that I get to make up. It's really confusing how to put it together. There is no way you could do it without having basic math skills. Also in a recent lab we're learning how to estimate sizes of bacteria. The professor stresses the practice of estimation, so that we can give acurate acounts of what we see. However, I do agree that biology is way less involved in math than physics.

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The living state appears to have too many variables to make the math easy. Say there were twelve forces of nature instead of four, the physics math would get extremely complex, such that experimental empiricsm might be a faster approach. Those able to follow the mathematics would be few in number, making it harder to teach and transfer this skill.

 

Statistical math is useful when there are too many variables to express the math with cause and affect equations. For example, say we had a crowd of people. The person at one end has story to tell to the person at the other end, but the crowd is very dense. So he passes the story to the first adjacent person, who then passes it on, etc., until it reaches the final person. By then the story has been modified. To mathamatically model this, every person in the chain needs to be one of the variables. But each person is a complex system with their own unknown variables. Statistics compares input to output to get an average result, with relative ease.

 

But the problem with statistical math, it doesn't really tell one how the data was massaged along the way, but only the final result of all the massaging. Some people could be embellishing the story, while others are downplaying the embellishment. If the final result comes out only slightly embellished, one may not know, at one point, it was more embellished. Not knowing this internal data, can cause one to draw conclusions, which are perfect with respect to input-output data, but not with respect to the path of the migration. If this input-output conclusion becomes standardized, with the premse a little off, other rational extrapolations don't fully add up, creating the impression things are far more chaotic. One gets pigeon holed into statisical analysis, since the apparent number of variables increases due to the input-output assumptions.

 

Biology needs is an approach that can reduce the number of variables so the analysis is simple enough, to where more people are able to use math beyond statistics. It takes an attitude of what things have in common instead of how they are different. How things differ adds more variables. Whereas how they are the same, reduces the number of variables.

 

If we had a group of people, one could narrow this group down, to what they have in common. Or one can assess how each person is unique. If our need is to determine how this group, will act as a group, common features make the analysis easier. If one is trying to model the cell, looking for a common thread, or two, makes it easier to rationally explain bulk dynamics over a wide range. If we focus, instead, on the uniqueness of each individual thing, the variables get too numerous.

 

For example, our crowd are all Red Sox fans. Now I can predict how they will react to the ebb and flow of the baseball game. If I focused on uniqueness, I would feel the need to include that fact that Jane has brown hair, a cell phone, and is wearing a green blouse. So if someone predicts she will cheer for a homerun; what about her brown hair? Brown hair girls may be less flamboyant, than a blond, and this will have somewhat of an impact. This variable appears necessary even though it is not essential. But to factor that out, we might have to go back and use statistics since we just made the analysis way to complicated for causual math.

 

I see this contrast between commonness-differences, the philosophical problem, faced by the H-potential model. This is a common thread approach, but the analysis is a conceptual framework at this point. It can't take into account, the observation that Jane has a tatoo on her right shoulder and a toe ring on the left foot. That would be possible later, after the framework is scientifically set and it is time to start fluffing it up.

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A BS biology or biotech graduate should absolutely know sterile techniques, how to use the basic tools of the trade, and proper documentation. They also need a basic understanding of statistics. In my opinion, I believe that this (stats) is probably the most important thing most programs are missing..

I am currently working to teach myself the variables that determine what is "statistically significant".

There are bio-statistians that handle this for clinical data but it seems a valuable skill in design of process and analytical validation projects.

 

"Too high"? How can you make this statement? Why would you settle for hiring people with these deficincies? As long as they are being hired, the programs won't change..

I don't hire them, this fact only adds to my frustration. I usually give a list of suggestions/additional teaching to the AS biotech program each year after the students complete their internships with us.

It may end up in the wastepaper basket unread but it’s all I have time to do right now.

 

BTW: I agree that cGMP, cGTP, and clean room regulations and standards can be learned on the job......or in electrive programs...but htey would certainly be a positive factor in a hiring decision.

I do understand that these are industry specific and would not normally be in a general biology curriculum.

The cGMP, cGTP, and clean room standards are a major component of working with my/their current employer. Never the less, they maintain this attitude that these are minor issues and when they are brought to attention it is corrected and forgotten.

I have suggested that these employees should have an audit of their work, skills and practices to get a better understanding. I have to do it in my lab, it’s unpleasant but if it is done properly it is a learning tool and not an act of torture.

Any advice for training such aloof employees?

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The cGMP, cGTP, and clean room standards are a major component of working with my/their current employer. Never the less, they maintain this attitude that these are minor issues and when they are brought to attention it is corrected and forgotten.

I have suggested that these employees should have an audit of their work, skills and practices to get a better understanding. I have to do it in my lab, it’s unpleasant but if it is done properly it is a learning tool and not an act of torture.

Any advice for training such aloof employees?

 

Sounds like a job for your quality manager and your ops manager. Have you had a chance to discuss it with them?

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So apparently one consensus is that overall there are serious deficits in mathematical knowledge in most biologists (a point that I agree to). In most biological fields one requires at least basic statistical training (which imo is quite simple if you only need to apply it and not develop new models). Though at least in the postgenomics field (keyword: bioinformatics) more advanced forms of modellings are common.

 

I wonder whether it is just a lack of training possibilities (e.g. courses, teachers) or does it come from the student's side (lack of interest) or both? I have the feeling that it might be that the foundations (e.g. in basic stochastics) are often lacking, despite the fact that they are taught (at least when I studied). Many just learn it to pass the exam and then promptly forget about it. Later on this knowledge is lacking when advanced forms of statistics and modellings are taught. Maybe there is a too large time span between the time when one learns the basics and the time when one actually has to use it for ones own work (during diploma/master thesis)?

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A BS biology or biotech graduate should absolutely know sterile techniques, how to use the basic tools of the trade, and proper documentation. They also need a basic understanding of statistics. In my opinion, I believe that this (stats) is probably the most important thing most programs are missing.

They need to understand how to work in the trade, have a hands-on basic understanding of the tools of the trade, and certainly know how to properly document and analyze their own or others' data.... no matter what they are planning to do after the degree.

These are basic necessities.

 

What kind of programs are graduating students without these basic skills? I know they exist, but I'd like to know in order to avoid their grads.

Without these basic skills, they are good for flipping burgers maybe....but not holding down jobs as associate research scientists (or even technicians).

I definitely won't hire them....never have...never will. They would need to take some more hands-on labs or regular classes and/or work out of a temp agency for a while first.

 

I personally don't see what's so mortifying about not knowing how to use a pipette or pipet-aid because you've never had to before. It's not a very complicated thing to learn. When I got my first job as a research assistant during my junior year of undergrad, I most certainly hadn't done any of those things before (it had taken me a while to settle on biology as a major, so I hadn't taken a lot of practical labs) - but neither did it take much effort to learn them. And even from that first list, until I got my current job, I didn't even know what a hemacytometer was. But with about 1 minute's worth of someone showing me how to use it, I was fine.

 

Also, temp lab jobs? I tried to find such a thing, but had no luck whatsoever. And somehow I don't think a temp-er would get a lot of training, anyway. I do think that undergrad education should try to be a little more hands-on when it comes to lab work, but the fact of the matter is that a decent amount of people fresh out of undergrad will have to be taught some specific skills on the job, even if that job is just volunteering in a research lab at their alma mater. You have to start somewhere.

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