Should biology be left to the biologists?

[SW VandeCarr]

A recent paper on arXiv claims a new paradigm is needed for biology. In essence the paper seems to recall mathematician David Hilbert's famous quip that physics is too important to be left to the physicist. Authors Goldenfeld and Woese claim that the existing paradigms of biology are not up the issues of complexity and emergent phenomenology that must be dealt with. In particular, the authors suggest that the mechanisms of biological evolution need a new perspective. They point to evidence of the non random signaling between bacteria in reference to the transfer of resistance factors to antimicrobials.

My personal view is if non equilibrium statistical mechanics is the answer, or at least part of it, as the authors suggest, maybe biologically/medically oriented statisticians can help.

In any case, I would like some discussion from students and professional members in biology and physics.

http://arxiv.org/PS_cache/arxiv/pdf/1011/1011.4125v1.pdf

 

[Pythagorean]

Hi, I'm a physics graduate going into mathatical biology.

My complaint about statistics is summarized in a joke I heard: a physicist, a biologist, and a statistician are out hunting. They see a deer. The physicist shoots and misses by a yard. The biologist intentionally misses by a yard the other direction. The statistician jumps up excitedly and screams, "we got it"

Closer to my field, people have found that in cell networks, taking the average of every parameter and giving every cell that averaged set of parameters doesn't work, yet if we go back and include the distribution of parameters throughout the population, then suddenly the dynamics are taking off.

Anyway, I guess my conclusion is that statistics is for experimental science. Of course we theoreticians use some tools from statistics all the time, but I don't think I'd ever need a statistician to help me explore theoretical models so much as help me interpret experimental results.

 

[SW VandeCarr]

Hi, I'm a physics graduate going into mathatical biology.

My complaint about statistics is summarized in a joke I heard: a physicist, a biologist, and a statistician are out hunting. They see a deer. The physicist shoots and misses by a yard. The biologist intentionally misses by a yard the other direction. The statistician jumps up excitedly and screams, "we got it"

Well, on average, the statistician should be right on the third shot if the the deer would just stay still (ie:state of external "equilibrium"). The problem is the deer won't stay still after the first shot. If the hunters had a a very sophisticated model of how the deer would move, they might hit the deer on the second shot. For that you need a typology of deer and a model of how each type moves when being shot at once. You still wouldn't always hit the deer, but you should do better than if you didn't have a model.

 

[Panwasbipolar]

I'm not qualified but am I wrong in that what this guy is getting at is why mountain chains resemble brain surface? Beyond just the fractals.

I'm guessing that sounded nuts.

 

[Pythagorean]

Well, on average, the statistician should be right on the third shot if the the deer would just stay still (ie:state of equilibrium). The problem is the deer won't stay still after the first shot. If the hunters had a a very sophisticated model of how the deer would move, they might hit the deer on the second shot. For that you need a typology of deer and a model of how each type moves when being shot at once. You still wouldn't always hit the deer, but you should do better than if you didn't have a model.

But the point is that averages are misleading. A shot to x = -1, B shot to x = 1. C didn't shoot because he averaged x = [-1 1] to x = 0, giving a false positive.

For fairly complex data set, you can use all kinds of different statistical methods to get any result you want; see the numerous different conclusions from "climate change" (i.e. global warming) data.

 

[SW VandeCarr]

But the point is that averages are misleading. A shot to x = -1, B shot to x = 1. C didn't shoot because he averaged x = [-1 1] to x = 0, giving a false positive.

That's not statistical reasoning. In the unrealistic scenario of the deer not moving, the statistician would note the fact that the two shots bracketed the deer and fire between them. This is the way artillery fire used to be corrected for hitting a stationary or slow moving target (before GPS). Statistical inference is about expectations. The highest expectation of success for the third shot is around the zero point between +1 and -1 for a stationary target.

For fairly complex data set, you can use all kinds of different statistical methods to get any result you want; see the numerous different conclusions from "climate change" (i.e. global warming) data.

Of course the deer does move, and this becomes a more complex problem. Stochastic models are tools and I believe they will be necessary if we are going to make any progress in modeling complex systems with interacting entities at multiple scales. I know you meant the deer problem as joke, bit it's actually a useful basis for discussion for what is fairly elementary but still complex problem.

With the first shot the deer will react. One instinct is to run in the opposite direction of where the shot passed in a direction perpendicular to the line of fire. This maximizes the value of the deer's forward speed. However, it also exposes the flank of the deer which is larger than its profile if it were to turn tail and run away along the line of fire. However, this reduces the value of its forward speed, assuming the hunters have range to spare. Which is better? If there is good cover (woods, plants), the latter might be.

The deer's actions probably do not involve the neocortex very much, but a lot of processing is going on at lower CNS levels. There will be coordinated neuro-muscular and hormonal responses leading to secondary hemodynamic and respiratory responses. At lower scales, this involves ionic transfers across membranes, etc. Since no two animals are exactly alike, any modeling that is done must rely on averages of many experimental observations and a reasonably accurate typology (how well does the data fit the specific phenomenon you're modeling).

In your neurophysiological work, you are relying on parameters of both central tendency and variability much more than the physicist when you get into the details of predicting behavior.

 

[Pythagorean]

That's not statistical reasoning. In the unrealistic scenario of the deer not moving, the statistician would note the fact that the two shots bracketed the deer and fire between them. This is the way artillery fire used to be corrected for hitting a stationary or slow moving target (before GPS). Statistical inference is about expectations. The highest expectation of success for the third shot is around the zero point between +1 and -1 for a stationary target.

I understand that a statistician can also use his tools properly, I didn't mean to offend.

Of course the deer does move, and this becomes a more complex problem. Stochastic models are tools and I believe they will be necessary if we are going to make any progress in modeling complex systems with interacting entities at multiple scales. I know you meant the deer problem as joke, bit it's actually a useful basis for discussion for what is fairly elementary but still complex problem.

Yeah, as a joke, but also as something I see in politics a lot, but I'll see if I can play along with your new direction.

The deer's actions probably do not involve the neocortex very much, but a lot of processing is going on at lower CNS levels. There will be coordinated neuro-muscular and hormonal responses leading to secondary hemodynamic and respiratory responses. At lower scales, this involves ionic transfers across membranes, etc. Since no two animals are exactly alike, any modeling that is done must rely on averages of many experimental observations and a reasonably accurate typology (how well does the data fit the specific phenomenon you're modeling).

In your neurophysiological work, you are relying on parameters of both central tendency and variability much more than the physicist when you get into the details of predicting behavior.

It's true, but I don't need much statistics to understand that. I've only seen statistics in thermodynamics. More particularly, I do coincidence testing, which measures how often your model spiking matches the experiment spiking. Part of the equation subtracts from your coincidence score how many coincidences a Poisson process of the same firing rate would score so that you don't get an accidental statistical error.

I guess that's the reason, that really, understanding statistics is important; so you don't make the "hunting error", which is exactly what happens if you don't subtract the Poisson process score from your coincidence score.

 

[Math Is Hard]

Moderator note: Moving this to the biology forum at the OP's request.

 

[Ivan Seeking]

A recent paper on arXiv claims a new paradigm is needed for biology. In essence the paper seems to recall mathematician David Hilbert's famous quip that physics is too important to be left to the physicist. Authors Goldenfeld and Woese claim that the existing paradigms of biology are not up the issues of complexity and emergent phenomenology that must be dealt with. In particular, the authors suggest that the mechanisms of biological evolution need a new perspective. They point to evidence of the non random signaling between bacteria in reference to the transfer of resistance factors to antimicrobials.

My personal view is if non equilibrium statistical mechanics is the answer, or at least part of it, as the authors suggest, maybe biologically/medically oriented statisticians can help.

In any case, I would like some discussion from students and professional members in biology and physics.

http://arxiv.org/PS_cache/arxiv/pdf/1011/1011.4125v1.pdf

I am surprised to see this is news to anyone. Back when I was finishing up my undergrad degree in physics, which was quite a long time ago, we had a guest lecturer who said that in the coming years, the biggest opportunities in physics, would be in biology.

 

[Jack the Stri]

It depends what framework in biology you're thinking of. Whereas ecologists etc. have a more than basic grasp of statistics because they work with large sample sizes, cell biologists usually rely on quantitative and somewhat subjective results (colorimetric assays), with t-tests being a very advanced method to use for significance. There is a trend towards more advanced statistics though, and I think that not supposing homogeneity in cell cultures and tissues will open the field to more advanced analysis (and is probably closer to the truth). A biology-oriented statistician or a statistics-oriented biologist could equally well analyse a data set in this context, but I think the latter would be more adept at making up a relevant experimental set-up.

 

[Andy Resnick]

In any case, I would like some discussion from students and professional members in biology and physics.

http://arxiv.org/PS_cache/arxiv/pdf/1011/1011.4125v1.pdf

There's lots of good stuff in that paper. I think Physicists can contribute meaningfully to Biology. However, the Physicist must first learn some biology.

While I was (essentially) a postdoc in a Physiology department, I saw first hand what happens when a mathematician or physicist with little to no biology training presents their research findings- uniformly, they are ignored. Sometimes they are told directly that their research is 'biological without biology'.

One major barrier to effective cross-disciplinary research is the fact that MDs and Biologists are *terrified* of math. Terrified to the point of denying the utility of mathematical modeling. There are a few exceptions, but they are so sparse they 'prove the rule'.

The biggest mistake a Physicist can make in trying to perform biological research is ignoring the *context*. We are so used to abstracting out the most general characteristics of a system, we forget that it's the specifics that matter most in biology. Cell biologists don't study 'cells'. They study epithelial cells. Or endothelial cells. Or leukocytes. Or osteoclasts. As another example, witness what has happened since the human genome was first sequenced: there is a growing recognition that the genome is a tiny part of the organism: epigenetics, the proteome, metabolome, immunome, biome... all have been invoked to 'explain' why I am different than you.

I've found some success in carrying out a research program with a significant biological component. It's been difficult, but I've been able to make the case that physics matters in biology-specifically, that fluid flow stimulation results in physiological responses in renal epithelial tissue. Hopefully, you can see the amount of context present in that sentence.

 

[SW VandeCarr]

There's lots of good stuff in that paper. I think Physicists can contribute meaningfully to Biology. However, the Physicist must first learn some biology.

In fact, a lot of physics has been utilized in physiology for quite some time: hemodynamics, fluid turbulence models, acid-base equilibria, membrane potentials, biomechanics, diffusion models, pharmacokinetics, connectivity models, and more. Most of this work has been carried out by people whose training is rooted in biology, physiology and medicine, not physics. A detailed knowledge of these fields requires years to acquire. You don't just walk over from physics and take over.

I found this paper from 1989 which involves the application of non-linear statistical models in DNA denaturation. I'm sure this area of research has made a fair bit of progress since then. In other words, it's nothing new. This paper begins with fairly classical approach and moves to a non-linear treatment beginning at about equation 10. There are many papers like this that are easily found on the web.

http://biophysics.physics.brown.edu/BPJC/JC pdf paper files/BPJC Spring 2004/PRL89DNA.pdf

 

[Andy Resnick]

In fact, a lot of physics has been utilized in physiology for quite some time: hemodynamics, fluid turbulence models, acid-base equilibria, membrane potentials, biomechanics, diffusion models, pharmacokinetics, connectivity models, and more. Most of this work has been carried out by people whose training is rooted in biology, physiology and medicine, not physics. A detailed knowledge of these fields requires years to acquire. You don't just walk over from physics and take over.

I agree. Here's some references more in line with my own research:

http://www.citeulike.org/group/4585/article/1485001
J. Lighthill. Mathematical Biofluiddynamics. SIAM, 1975
http://www.paperbackswap.com/Biological-Membranes-Theory-Ove-Sten-Knudsen/book/0521036356/

 

[Ygggdrasil]

As a biophysicist, I of course believe that physics can be of much help to biology. In fact, many physicists have already made important contributions for biology. One major area in which physicists (and chemists) have helped is in the development of new tools for biology. X-ray crystallography, electron microscopy, and many other tools originally developed by physicists/chemists have allowed biologists to study their systems in new ways and measure new quantities with greater precision and accuracy.

Physicists themselves have also made important contributions to biology. For example, Francis Crick, one of the co-discoverers or the strucutre of DNA, was a physicist as well as Max Delbruck, another physicist won a Nobel prize for his work in biology. Delbruck's work (especially the Luria-Delbruck experiment examining the mechanisms by which organisms acquire mutations) reflects his background as a physicist as he approached biology much more quantitatively than his peers and was therefore able to devise much more powerful experiments as a result.

Right now, biology is at a strange point. Currently, biologists can generate data faster than they can interpret it (this is in contrast to many physics experiments, such as those being done at the LHC, and the fact that many physicists are constructing theories, like string theory, that currently cannot be tested experimentally). Furthermore, whereas biology in the past half-century focused on identifying the key molecules involved in biological processes, the next phase of biology will be focused on determining how these molecules interact to create a self-assembling, self-replicating, homeostatic system. Many of these questions seem to require new insight from quantitative theories to help guide new experiments. Currently, physics students, not biology students, seem best suited to tackle these sorts of problems.

 

[Pythagorean]

the next phase of biology will be focused on determining how these molecules interact to create a self-assembling, self-replicating, homeostatic system. Many of these questions seem to require new insight from quantitative theories to help guide new experiments. Currently, physics students, not biology students, seem best suited to tackle these sorts of problems.

I'm curious, has Turing's work on morphogenesis been productive to that end? There's of course, many papers published on the subject, but a lot of the terminology is over my head in how it applies in biology/chemistry.

 

[Andy Resnick]

Furthermore, whereas biology in the past half-century focused on identifying the key molecules involved in biological processes, the next phase of biology will be focused on determining how these molecules interact to create a self-assembling, self-replicating, homeostatic system. Many of these questions seem to require new insight from quantitative theories to help guide new experiments. Currently, physics students, not biology students, seem best suited to tackle these sorts of problems.

I agree with this, too- integration, not reduction, is the trend in biophysics/physiology. Physics brings conceptual tools that biology simply doesn't have (and the converse: physiology has a very nice set of experimental tools)

 

[SW VandeCarr]

I agree with this, too- integration, not reduction, is the trend in biophysics/physiology. Physics brings conceptual tools that biology simply doesn't have (and the converse: physiology has a very nice set of experimental tools)

How easily can a physicist to transfer from one branch of physics to another; say from atmospheric physics to condensed matter physics? How does that compare to. say, going from condensed matter physics to immunology? The common tools of physics are differential equations and linear algebra in the main, but there is still a lot specialized knowledge and tools involved in the different branches. So chaos theory is used in meteorology while statistical applications are big part of quantum mechanics and thermodynamics.

Jumping from condensed matter physics to immunology, I would suspect, is a much bigger transition. Yet immunology is one of the major unifying branches of biology/physiology. How does a background in condensed matter physics help a physicist develop insights into understanding immunology? I'm asking this not be argumentative, but to try to understand the authors' point in concrete terms.

 

[Andy Resnick]

How easily can a physicist to transfer from one branch of physics to another; say from atmospheric physics to condensed matter physics? How does that compare to. say, going from condensed matter physics to immunology? The common tools of physics are differential equations and linear algebra in the main, but there is still a lot specialized knowledge and tools involved in the different branches. So chaos theory is used in meteorology while statistical applications are big part of quantum mechanics and thermodynamics.

Jumping from condensed matter physics to immunology, I would suspect, is a much bigger transition. Yet immunology is one of the major unifying branches of biology/physiology. How does a background in condensed matter physics help a physicist develop insights into understanding immunology? I'm asking this not be argumentative, but to try to understand the authors' point in concrete terms.

I'm a single data point, but I know a few people who did something similar to me- PhD in Physics, postdoc etc. in Physiology. I/we have grants from NIH that are part of a loose program to specifically train physicists to perform biomedical research- the K25 award mechanism.

Speaking personally, it was hard work- I took some graduate classes (think 'boot camp' style coursework), but it was a major cultural gap. Beyond the factual knowledge required, there's a totally different research philosophy: so-called "hypothesis-driven research". Learning how to perform biomedical research took a while.

As an aside- immunology is not a "unifying branch" of biology/physiology. Genetics may be, but that only goes so far- plant genes are different than yeast genes are different than bacterial genes are different than mammalian genes- homology is the mechanism by which some sort of unification is reached. IMO, there currently are *no* unifying concepts in biology- the field is, conceptually, in a pre-Newtonian state.

My point is, there are a lot of rewards for a physicist who is interested in taking the plunge to biology/physiology/biophysics. There are also substantive barriers- one of which is *time*- the time required to gain mastery of a body of work that is *significantly* larger than physics. The average age of someone getting their first 'real' research grant (the R01) has been increasing steadily, and IIRC is about 42 (years old) now.

 

[Pythagorean]

I'm a single data point, but I know a few people who did something similar to me- PhD in Physics, postdoc etc. in Physiology. I/we have grants from NIH that are part of a loose program to specifically train physicists to perform biomedical research- the K25 award mechanism.

Speaking personally, it was hard work- I took some graduate classes (think 'boot camp' style coursework), but it was a major cultural gap. Beyond the factual knowledge required, there's a totally different research philosophy: so-called "hypothesis-driven research". Learning how to perform biomedical research took a while.

As an aside- immunology is not a "unifying branch" of biology/physiology. Genetics may be, but that only goes so far- plant genes are different than yeast genes are different than bacterial genes are different than mammalian genes- homology is the mechanism by which some sort of unification is reached. IMO, there currently are *no* unifying concepts in biology- the field is, conceptually, in a pre-Newtonian state.

My point is, there are a lot of rewards for a physicist who is interested in taking the plunge to biology/physiology/biophysics. There are also substantive barriers- one of which is *time*- the time required to gain mastery of a body of work that is *significantly* larger than physics. The average age of someone getting their first 'real' research grant (the R01) has been increasing steadily, and IIRC is about 42 (years old) now.

So what do you think are some good fundamental biology courses for a physics grad to take?

I'm considering macromolecules (protein) as I thought they were kind of the smallest "unit"; (the thing that I would have guessed as a unifying aspect of biology). But I often wonder if I should take same Organic Chem.

 

[Ygggdrasil]

I'm curious, has Turing's work on morphogenesis been productive to that end? There's of course, many papers published on the subject, but a lot of the terminology is over my head in how it applies in biology/chemistry.

I don't know a lot about developmental biology, but there have been some evidence that Turing's reaction-diffusion model can explain some phenomena in developmental biology although the extent to which such a simple model can accurately explain a very complex biological system is still a mater of controversy. A landmark paper in this area was a work done by Thomas Schlake's group where they showed that a reaction-diffusion system consisting of the signaling molecule WNT and its inhibitor DKK regulates hair follicle spacing in mice (Sick et al. (2005) WNT and DKK Determine Hair Follicle Spacing Through a Reaction-Diffusion Mechanism. Science 314: 1447. http://dx.doi.org/10.1126/science.1130088):

Abstract:
"Mathematical reaction-diffusion models have been suggested to describe formation of animal pigmentation patterns and distribution of epidermal appendages. However, the crucial signals and in vivo mechanisms are still elusive. Here we identify WNT and its inhibitor DKK as primary determinants of murine hair follicle spacing, using a combined experimental and computational modeling approach. Transgenic DKK overexpression reduces overall appendage density. Moderate suppression of endogenous WNT signaling forces follicles to form clusters during an otherwise normal morphogenetic program. These results confirm predictions of a WNT/DKK-specific mathematical model and provide in vivo corroboration of the reaction-diffusion mechanism for epidermal appendage formation."​

Here it's important to note that the project was done in collaboration between a group of immunologists and a group of physicists, which I think is typical for these types of studies (even papers containing purely biology are often collaborations between two or more biology groups). This may provide a good model for how these types of studies can be done: between a group of biologists who are experts in the biology and know a lot about the molecules involved and a group of physicists who are experts in the modeling and know a lot about the model and its parameters. The physicists can help interpret the data from the biologists and guide the experiments while the biologists can provide sanity checks on the model to make sure the model makes biological sense.

 

[bobze]

As an aside- immunology is not a "unifying branch" of biology/physiology. Genetics may be, but that only goes so far- plant genes are different than yeast genes are different than bacterial genes are different than mammalian genes- homology is the mechanism by which some sort of unification is reached. IMO, there currently are *no* unifying concepts in biology- the field is, conceptually, in a pre-Newtonian state.

Sorry not to off topic this, but I totally disagree with you here, evolutionary theory (rather the modern synthesis) certainly is a unifying concept in biology. Really the unifying concept.

Dobzhansky wasn't just having a go at pulling your leg when he said; "Nothing in Biology Makes Sense Except in the Light of Evolution". Those "different genes" that fit so nicely under words like "yeast", "bacteria", etc do so because of the unifying concept in biology.

It unites the 5 most foundational principles of biology; cell theory, genetics, homeostasis, transformation of energy (metabolism), descent (reproduction with fidelity).

 

[Pythagorean]

Thank you for your general reply Ygggdrasil

Here it's important to note that the project was done in collaboration between a group of immunologists and a group of physicists, which I think is typical for these types of studies (even papers containing purely biology are often collaborations between two or more biology groups). This may provide a good model for how these types of studies can be done: between a group of biologists who are experts in the biology and know a lot about the molecules involved and a group of physicists who are experts in the modeling and know a lot about the model and its parameters. The physicists can help interpret the data from the biologists and guide the experiments while the biologists can provide sanity checks on the model to make sure the model makes biological sense.

I agree! I'm lucky enough to have an experimental biophysicists on my interdisciplinary committee who appreciates the mathematics and so far our collaboration has been beneficial. It's always nice to have help with the biological motivation.

Of course, I hope to someday be able to motivate the biology myself, but I have a lot of learning left before I can do that.

If you have any input on my academic question above, I'd be interested to hear it.

 

[Andy Resnick]

So what do you think are some good fundamental biology courses for a physics grad to take?

I'm considering macromolecules (protein) as I thought they were kind of the smallest "unit"; (the thing that I would have guessed as a unifying aspect of biology). But I often wonder if I should take same Organic Chem.

It depends on what you are interested in. Definitely take a class based on "Molecular biology of the Cell" (Alberts' book)- it may be called molecular biology, cell biology, or something else. O-chem doesn't hurt (I took it and hated it).

After that, it really depends on what you want to study.

 

[Andy Resnick]

Sorry not to off topic this, but I totally disagree with you here, evolutionary theory (rather the modern synthesis) certainly is a unifying concept in biology. Really the unifying concept.

Dobzhansky wasn't just having a go at pulling your leg when he said; "Nothing in Biology Makes Sense Except in the Light of Evolution". Those "different genes" that fit so nicely under words like "yeast", "bacteria", etc do so because of the unifying concept in biology.

It unites the 5 most foundational principles of biology; cell theory, genetics, homeostasis, transformation of energy (metabolism), descent (reproduction with fidelity).

I hear what you are saying, but "evolution" does not really answer any questions: what, for example, is the evolutionary advantage to have 5 fingers on each hand instead of 4 or 6? Why does the cytosol have high sodium, low potassium, while the extracellular milieu have low sodium, high potassium? Why do we use ATP for energy and not GTP?

Evolution is not a *predictive* mechanism, it is a *postdictive* mechanism.

 

[Ygggdrasil]

For classes, I would agree with Andy that a course based on Alberts' book is a must (this type of course is sometimes also taught using Lodish's "Molecular Cell Biology").

The basics to cover (probably in two classes) would be what I would classify as a molecular biology course and a cell biology course. The molecular biology course would cover the "central dogma" of biology: DNA replication, transcription (DNA->RNA) and translation (RNA->protein). It would discuss the types of molecules involved, the general enzymatic mechanisms, and some of the ways in which the cell regulates these processes. The cell biology course would cover some the basic organization of the cell and cover some of the basic processes of the cell (transport processes, cell cycle and cell division, signaling, etc.). Taking two courses with this information would give a good foundation to begin learning more advanced topics in biology.

Organic chemistry is useful for biology, but not absolutely essential. If you really want to delve into biochemistry to understand how enzymes work, how biological macromolecules adopt their structures and assemble into complexes, and other fundamental concepts in molecular biology, organic chemistry is essential. If you are not so interested in the molecular details of biology and would rather focus on the cell or systems level, organic chemistry is less important. So, as Andy said, it depends on your interests.

 

[SW VandeCarr]

Why does the cytosol have high sodium, low potassium, while the extracellular milieu have low sodium, high potassium? Why do we use ATP for energy and not GTP?

Evolution is not a *predictive* mechanism, it is a *postdictive* mechanism.

Sorry, but intracellular potassium is high relative to extracellular potassium and the reverse for sodium.

I agree evolution is postdictive, but it's also adaptive. If you could specify the future environment, we might be able to anticipate how living systems adapt by having a better comprehensive understanding of the general mechanisms of adaptation beyond the broad concept of natural selection. At least this is a major point made by the authors.

EDIT: Re immunology. I agree it's not the unifying conceptual frame for all biology, but it an example of a protein based kinetic system of some complexity which is involved in the self/non self recognition that distinguishes the system of the individual organism from others and which acts across organ systems in higher metazoa.

 

[Pythagorean]

Ygggdrasil, Andy:

Thank you for your replies. I am particularly interested in systems/cell biology (my research is in biophysical neural networks and I have just recently used a genetic algorithm to tune the parameters of a single Morris-Lecar neuron model to behave like an experimental rat cortical neuron from an INCF competition). I did this after the competition was over, inspired by the results published by Gerstner:

http://www.sciencemag.org/content/326/5951/379.summary

Knowing the foundation, the breadth courses as you have presented them is essentially where the gap was. I am happy that the consensus is not favoring O-chem as a requirement.

 

[bobze]

I hear what you are saying, but "evolution" does not really answer any questions: what, for example, is the evolutionary advantage to have 5 fingers on each hand instead of 4 or 6?

But this is a mistake even many a student of biology make trying to understand evolution. In my study of it, I believe this to be due to our easy ability (probably thanks to evolution! )anthropomorphize everything.


The question "what is the evolutionary advantage" may not be a good or even valid question regarding evolution. We expect that everything has "it's evolutionary benefit" because, as I suspect, a product of our brains ordering our worlds.

Consider this though Andy, consider a population where a lineage arises with 6 fingered individuals and those 6 fingers confer a greater fitness than only 5. But, consider that the phenotype that comes with 5 fingers has a greater net fitness than does the ones associated with 6 fingers. What then?

Obviously, the 6 fingers will be lost to time and the fossil record, despite its advantage over 5 fingers.

It is net fitness that matters in the grandscheme of things, which means questions like "why is this trait better than that trait" are often going to be invalid questions. Genes "hitch" rides, because of proximity to other genes, because of the luck being in a particularly "fit" phenotype or because they are "neutral".

Evolutionary theory (MS, short for modern synthesis from here on), does then answer the question; why 5 fingers?

Because at some point in our evolutionary history, the phenotypes with 5 phalanges out competed those with different numbers (and there were many variations on number of phalanges early in history) and when we reach a "point of no return" this (5 phalanges) phenotype stuck and here we (and a great deal of vertebrates) are.

Why does the cytosol have high sodium, low potassium, while the extracellular milieu have low sodium, high potassium?

Other way around ECF is high in Na low in K, ICF is low Na high K, but I know what you mean. Again, same thing. MS does explain why we (the descendants of those before us) have ion concentrations the way we do. Because our ancestors utilized pumps and channels which led to those concentrations, as did their ancestors, as did theirs, etc.

Possibly there was a stage in life with "reversed Na and K" chemistry, but for whatever that may or may not be lost to time, the Na/K chemistry we have today prevailed.

Why do we use ATP for energy and not GTP?

Well we do use GTP in some circumstances, ATP just happens to be the energy carrier for most (but certainly not all) metabolic reactions. But again, this falls under the same premise as before. Because of descent and because at some point in time, ancestors who predominantly used ATP out competed those who did not. Again, the MS answers that.

Evolution is not a *predictive* mechanism, it is a *postdictive* mechanism.

Disagree. Evolutionary theory (as put forth by Darwin) predicted one of the greatest areas of biology today; genetics.

Some examples of the predictive prowess of evolutionary theory;

http://ncse.com/rncse/17/4/predictive-power-evolutionary-biology-discovery-eusociality-"

http://www.genetics.org/cgi/content/full/163/4/1237"

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T3Y-49S7YW4-2&_user=10&_coverDate=04%2F30%2F2004&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=693df2069a189dc8c7b3c64bc6e21c8e&searchtype=a"

http://www.pbs.org/wgbh/evolution/sex/guppy/low_bandwidth.html"

http://www.newscientist.com/article/mg19125681.500-meet-your-ancestor--the-fish-that-crawled.html"

http://www.evcforum.net/RefLib/EvidencesMacroevolution4.html#pred20"

http://books.google.com/books?id=XK...6AEwAA#v=onepage&q=marsupial fossils&f=false" (I know this is a preview of Jerry Conyne's book , he just says it so well though )


Its okay though, honest mistake, there are many a professional full time biologist that makes that mistake and an untold number of students of biology There obviously many more

Some more can be found http://chem.tufts.edu/answersinscience/evo_science.html" , which includes predictions from island biogeography to evolutionary developmental biology (evodevo).

 

[bobze]

Ygggdrasil, Andy:

Thank you for your replies. I am particularly interested in systems/cell biology (my research is in biophysical neural networks and I have just recently used a genetic algorithm to tune the parameters of a single Morris-Lecar neuron model to behave like an experimental rat cortical neuron from an INCF competition). I did this after the competition was over, inspired by the results published by Gerstner:

http://www.sciencemag.org/content/326/5951/379.summary

Knowing the foundation, the breadth courses as you have presented them is essentially where the gap was. I am happy that the consensus is not favoring O-chem as a requirement.

I don't know what classes you've taken yet, so some of these suggestions maybe redundant, but here they are for the topics you've listed interest in;

Cellular/developmental biology classes
Biodiversity and systematics classes
Physiology/Comparative physiology/Animal form and function classes
Anatomy/Comparative anatomy
Molecular genetics (though if you don't want to get into orgo look for one without the orgo prereq)
Lots of schools offer integrated molecular and cellular biology classes (at the graduate level) for non-biologists
Neurobiology
Developmental neurobiology
Neurophysiology

I'm sure there are a lot more you'd find interesting

 

[Andy Resnick]

Sorry, but intracellular potassium is high relative to extracellular potassium and the reverse for sodium.

Oopsy! You are quite correct...must not have had my coffee yet...

 

[Andy Resnick]

But this is a mistake even many a student of biology make trying to understand evolution. In my study of it, I believe this to be due to our easy ability (probably thanks to evolution! )anthropomorphize everything.

Fair enough- I don't claim to be an expert in modern theories of evolution. I am familiar (in passing only) with the idea of a 'fitness landscape'- I particularly enjoyed Kauffman's "Origins of Order" book.

Some examples of the predictive prowess of evolutionary theory;
<snip>

I only had a chance to look at the antibiotics paper, and it linked to their previos work here:

http://www.genetics.org/cgi/content...e6e0cef6ea851836a298ba0e&keytype2=tf_ipsecsha

I don't think we mean 'predictive' in the same way. For example, using in vitro (forced?) evolution as a predictive model for 'natural' evolution is fine. But surely, the researchers did not predict *which substitutions* would be made by the evolving/mutating bacteria.

As another example- let's say I send you and a <ahem> harem out into space for several generations. We know there are genes that respond to microgravity conditions, and we even know which ones (in a few model organisms). However, I can't predict how your genome will evolve in response to your new environment.

 

[bobze]

Fair enough- I don't claim to be an expert in modern theories of evolution. I am familiar (in passing only) with the idea of a 'fitness landscape'- I particularly enjoyed Kauffman's "Origins of Order" book.
I only had a chance to look at the antibiotics paper, and it linked to their previos work here:

http://www.genetics.org/cgi/content...e6e0cef6ea851836a298ba0e&keytype2=tf_ipsecsha

I don't think we mean 'predictive' in the same way. For example, using in vitro (forced?) evolution as a predictive model for 'natural' evolution is fine. But surely, the researchers did not predict *which substitutions* would be made by the evolving/mutating bacteria.

I see what you mean by "predictive" now. I don't think its that simple though, because mutations are certainly chaotic (I don't use the word random here since certain areas of the genome are more prone to mutation than others, which in itself makes part of the process of mutation "non-random"). However, I don't think because the MS has chaotic elements it cannot be "predictive", it only means you cannot take a reductionist approach.

Similarly, your local weathermen/women are pretty good at predicting weather--So long as we are only talking a few days out, because the inherit chaos in the system (weather systems or over the long term, climate systems).

What I think that a great many biologist have realized over the last half century, is that biology (and its children disciplines) are not reductionist to the degree we can appreciate in physics or chemistry and never will be because of the complexity and chaos of biological systems (not all systems, but the more we "zoom out" the more complex these systems become)--That's the problem with letting all 'you physicists' in on biology, I kid, I kid! There are a seemingly endless supply of variables that make long term predictions (because of inflections and new set points to systems) hard. In the short term though, such predictions to evolutionary change are "doable" (see John Endler's guppies), when we can account for the strongest variables.

That also isn't to say that long term evolutionary change couldn't be predictable, I'd just feel rather sorry for the poor chap that has to write the algorithm

As another example- let's say I send you and a <ahem> harem out into space for several generations. We know there are genes that respond to microgravity conditions, and we even know which ones (in a few model organisms). However, I can't predict how your genome will evolve in response to your new environment.

Right, we don't necessarily "know" how the genome will evolve, but in some cases (short term let's say) we can certainly predict the outcome of the change. Let's consider a more down to Earth example (pun intended . Suppose we took a bunch of white beach mice and transplanted their populations to gradually darker and darker backgrounds while introducing a strong selective pressure introduced by a visual hunter.

We could predict that those mice will get darker and darker coats matching the background and we might even try and predict the changes in the genome that accompany those phenotypical changes. It could be an inactivating mutation in the melanin gene, or a inactivating mutation in the receptor for melanin vacuoles, or the gene for the enzyme which cleaves promelanin, or the gene for the enzyme which activates the promelanin cleavage enzyme, or an even more radical change like the down-regulation of melanin sequestering cells, or or or etc. And if so, where in the gene? Is it a mutation that leads to altered splice sequence, a frame shift, a base deletion?

The problem is then, that evolution has many more solutions to environmental problems than we have the ability to imagine. So the question becomes, which is the more "important" prediction. From the reductionists view probably that "change in the gene", however biology is a "systems game" and the larger "system level" prediction is the one I'd argue is more important here for understanding the biology.

 

[Pythagorean]

I think what Andy was saying is that environment has no formal theoretical framework in the context of biology, so evolution is really only half complete (we have the reductionist/genetic side already going strong)

 

[SW VandeCarr]

In another thread (Are Robots Living Things?) a definition of life was offered which had as one criterion, that living things are made of (biological) cells. I think this is a good example of what the authors (Goldenfeld and Woese) meant regarding in the tendency of biologists to focus on the cell rather than living systems as systems.

Systems in general may be thought of as:

1. Having structure consisting of identifiable components.

2. Having behavior based on inputs, processing and outputs

3. Having interconnectivity of components on which behavior is based.

I would like to hear ideas on how living systems can be defined in these terms. That is, what are the essential (minimal) components of a living system, how to these components interact in terms of inputs, outputs and processing, and what essential interconnectivity is necessary to support these behaviors.

 

[Andy Resnick]

Living systems can repair themselves, at least to some degree.

Living systems also seem to require constant turnover of components- cells, proteins, etc.

 

[SW VandeCarr]

Living systems can repair themselves, at least to some degree.

Living systems also seem to require constant turnover of components- cells, proteins, etc.

OK. That's seems to be a good start. Both repair and maintenance can be thought of in terms of a system's ability to remain in a stable operating state. Both metabolism and replication at smaller scales are involved. Metabolism provides the energy for this. So we can have a system that maintains itself internally and makes copies of some of its components, but may not be able to copy itself in its entirety. So these systems would not qualify as life if we require living systems to make copies of themselves and evolve. Nevertheless such systems would be interesting, especially if they were able to maintain themselves indefinitely within a range of environmental parameters.

Interestingly, hurricanes are simple systems which show limited abilities to repair themselves. They maintain themselves over a short "lifetime" of up to two weeks by extracting energy from the environment, and they exhibit very identifiable structure and behavioral patterns. No one would suggest these things are alive, but they part of a class of systems known as dissipative structures in which some scientists also include living systems.

Individual organisms die, but by making copies of themselves individual living systems are in a sense operating as components in a larger system. That larger system might be a community of similar organisms or an even larger system which we might call an ecology. At this point, it becomes a matter of taste whether you want to call these larger systems "dissipative" if they can last for millions or even billions of years.

My point is that what we might call a living system might be better thought of as a component of a larger sustaining ecology and may be itself a sustaining ecology for a complex of subsystems which could also be called living systems such as individual cells in metazoa.

 

[Ygggdrasil]

I just read a nice essay that's somewhat relevant to the topic of the future of biology and how biologists should be doing research in the future. Here's a short snippet:

Conventionally, biologists have sought to understand life as it exists. Increasingly, however, from stem-cell reprogramming to microbial factories, researchers are describing what is and exploring what could be. An analogous shift occurred in physics and chemistry, especially in the nineteenth century. Like biology, these fields once focused on explaining observed natural processes or material, such as planetary motion or 'organic' molecules. Now they study physical and chemical principles that govern what can or cannot be, in natural and artificial systems, such as semiconductors and synthetic organic molecules.

The expansion of biology from a discipline that focuses on natural organisms to one that includes potential organisms will have three long-term effects. First, it will enlarge the community of biologists to include researchers with different assumptions and goals, such as engineers. Second, it will alter the way in which scientists address the fundamental problem of how biological systems work. Integrating reverse and forward engineering approaches will free biologists to uncover fundamental principles that explain, unify and extrapolate beyond mechanisms observed in specific model systems. Third, it will provide a new conceptual basis for teaching biology — one founded on stimulating inquiry from students as to how biological components and modules could be used to implement complex functions.

Elowitz and Lim. (2010) Build life to understand it. Nature 468: 889. http://dx.doi.org/10.1038/468889a

 

[Andy Resnick]

Elowitz and Lim. (2010) Build life to understand it. Nature 468: 889. http://dx.doi.org/10.1038/468889a

Thanks for the link- nice lego dude,also...

 

[SW VandeCarr]

Here's an interesting short article re a communication out of the U of Illinois,Urbana discussing self assembling charged latex spheres in a saline solution. They're called "Janus" spheres because they carry opposite charges on each side that alternately attract or repel their neighbors. Under the right conditions of salinity they will self assemble into complex structures including helices.

http://www.kurzweilai.net/self-asse...paign=97c0672014-UA-946742-1&utm_medium=email

 

[Andy Resnick]

There are a lot of groups doing cool stuff like that, with all kinds of wacky particles- diblock copolymers, colloidosomes, lithographically patterned nanoparticles, and even DNA (http://www.nature.com/nature/journal/v459/n7245/abs/nature08016.html)

 

[SW VandeCarr]

There are a lot of groups doing cool stuff like that, with all kinds of wacky particles- diblock copolymers, colloidosomes, lithographically patterned nanoparticles, and even DNA (http://www.nature.com/nature/journal/v459/n7245/abs/nature08016.html)

Thanks Andy. I'm not familiar with DNA's uses in nanotechnology. However, I have read about self-replicating nanorobots. I passed it off as interesting but not likely in the near term. Perhaps I'm wrong. What seems to be becoming clear is that the basic components of possible bio-like systems can be quite diverse.