Some ad hoc thoughts about theories, models, and graduate school. I had a great opportunity to read and study with a close friend D through his PhD and post doc in biophysics. He was (is) blind and needed a lot of reading, library, and editing work that was beyond his reader and secretarial stipends, and beyond the reach of a lot of his readers.
This work experience and my friendship (conversations, etc) during the work really transformed my understanding of science. I remained a reader and later a lab tech over the course of a post-doc period that tried to empirically demonstrate a mathematical model or some relative of it. That part failed, but again I learned a lot. Since I was doing the computer imaging (from fluorescence microscopy) work, I was there for the failure. A success would have shown a particular change in the false color image time series and that particular change did not appear. We were working in a friend's new lab. This meant the methodology was flawless, with state of the art tools at the time (mid-90s), and his technical help was invaluable. Fluorescence microscopy was all the new rage then. Now it is just standard fair. This failure was a disaster for D's career. But it was one of those half-full, half-empty dilemmas, or so I argued to cheer him up. The failure said this model was wrong. In a more generous intellectual world, that could be considered a success in elimenating a particular direction. But the technical fields are not generous. You can't use a failure to support the next grant unless you are already established and run a large lab where failures can be tolerated as routine blips in the work flow. The above experiences have multiple barings on economics as a discipline. I started thinking about this last week during the Rag-off scandal. The thread on graduate school, Cult Mentality of Academia was another line of thought. Part of the reason why economics is so intolerant of diverse views is that career success depends on positive results derived from known (acceptable) models. Since the public policy apparatus is rigged (via capitalist hegemony over research money) to limit what is `acceptable' there is no (paid and easy) way out of the universe of standard economic theory and its models. This probably explains why a lot of critical work goes directly to the publishers, internet, blogs, and so forth and skips the academic review channels. In another direction, the above mentioned math model was derived from real time series data that showed periodic pulses decreasing over time. The known underlaying reason was essentially a diffusion theory. We could derive the general shape from the real time series. The method of derivation was a set of fourier transforms that produced a big pulse followed by a fast diminishing sin curve. This class of curves has a lot of fluid dynamics applications. In particular they can be linked to extra-cellular flows of substances that travel by diffusion. Diffusion is a heavily modeled phenomenon in physics, chemistry, and biology. Because of its mathematics, it is a rich field for models. Go here to get the general math idea: http://web.unideb.hu/zerdelyi/Diffusion-on-the-nanoscale/node4.html Glance through Fick's law section 1 and go to section 2.2 Boltzmann's transformation, Parabolic law. Ignor the math and watch figure 2.1. The illustration provides a basic model for a periodic time series of diffusion in pulses---just imagine a continuous process that doesn't return to the beginning partition. The oscilloscope image of a beating heart is pretty much the same general shape. This general model is very useful for tracking the diffusion of active substances and the timing of cell and tissue reactions that start with a strong reaction that diminishes in time as the substance triggers a reaction and diffuses away. This general cycle then is repeated with a new wave of a trigger or signal compound. The key feature of 2.2 to note is a homogeneous plane. This can be generalized into a three dimensional cylindrical tube with a homogeneous diffusion direction. We were dealing with the cellular level, but the basic diffusion model for atoms works pretty much the same for small molecules in low viscosity mediums. For the model purposes the extra-cellular fluid was considered to have the physical characteristics of water (low viscosity, laminar flow) at normal room temperatures. The important thing is to write the equations with clearly identified and quantified components most of which are already known and can be plugged in leaving as few variables as possible. Most of these quantified components were found in physical chemistry and or cell physiology and were well established. The model was constructed to produce a differential gradient in the horizontal axis (perpendicular to flow direction producing a heterogeneous distribution) of a small growing section of a plant root, i.e. a cylindrical tube. The alteration of a standard model was this horizontal gradient. It would appear to be a change of color from one side of the root to the other under UV confocal micrscopy using fluorescent Ca2+ (glow in the dark calcium). A transverse gradient did not appear in over 600 runs. In other words there was a predicted event that didn't happen on a reasonably large sample. The most famous experiment in physics that didn't work was the Michelson Morley experiment. My buddy was too distressed over the failure to even think it might not have been a complete loss. I always thought, well hell it mathematically worked, it was just empirically wrong. That should explain something beside the fact the model was wrong. But I had nothing hanging in the balance, no serious ego involvement. The ideas, methodology, and model were not mine in any creative sense at all. I was just along for the ride. It was only much later that I realized I might have got more intellectual background and stimulus out of D's graduate and post-doc days than he did precisely because I was not on the chopping block. Whatever holes in my knowledge were never going to show and if they did, I could alway find out what to fill in without the terror of a credibility crash. I was free to learn as much as I could tolerate before saturation and fill in a lot of blanks in a general science education. CG _______________________________________________ pen-l mailing list [email protected] https://lists.csuchico.edu/mailman/listinfo/pen-l
