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Mathematical Biology Is Our Secret Weapon In the Fight Against Disease (scientificamerican.com)
An anonymous reader shares excerpts from a Scientific American article: In recent years, increasingly detailed experimental procedures have lead to a huge influx in the biological data available to scientists. This data is being used to generate hypotheses about the complexity of previously abstruse biological systems. In order to test these hypotheses, they must be written down in the form of a model which can be interrogated to determine whether it correctly mimics the biological observations. Mathematics is the natural language in which to do this. In addition, the advent of, and subsequent increase in, computational ability over the last 60 years has enabled us to suggest and then interrogate complex mathematical models of biological systems. The realisation that biological systems can be treated mathematically, coupled with the computational ability to build and investigate detailed biological models, has led to the dramatic increase in the popularity of mathematical biology. Maths has become a vital weapon in the scientific armoury we have to tackle some of the most pressing questions in medical, biological and ecological science in the 21st century. By describing biological systems mathematically and then using the resulting models, we can gain insights that are impossible to access though experiments and verbal reasoning alone. Mathematical biology is incredibly important if we want to change biology from a descriptive into a predictive science -- giving us power, for example, to avert pandemics or to alter the effects of debilitating diseases.
Are they suggesting that the characteristics of physical systems can be represented by mathematical equations? And that these mathematical models may be used to predict the characteristics of physical systems that may not even exist yet?
My God, who would have ever thought this possible or valuable? They are leading biological science boldly into the 17th century!
If only there was some device and a standardized language with which to create these mathematical models without having to completely defined the models in closed form. Perhaps to "simulate" (to coin a phrase) these systems in an abstract way.
I'm a physicist working on tools that quantitatively measure biologic interactions. A common use for my tools is for a pharma company to take a set of molecules that they have determined through modeling will interact (inhibit activity, enhance activity, etc.) with a target protein and we measure whether that actually happens directly at the molecular level.
So why hasn't this been done before?
Biology is not just complex, it's also very often overgeneralized and poorly understood. It's dogma (literally) in biology that a cell using a particular sequence of DNA to build a protein will always build the same protein. This is not true. Two "identical" proteins are almost certainly chemically different. Biology to date has dealt with collections and statistical averages of protein function and genetic activity. The measurements used to work in biology, to date, use simplified environments (gels, buffers, simplified model organisms) and reporting methods that interfere with the molecular function of the biological system (fluorescence, dye binding), but these methods and approaches have been necessary to make progress.
At this point, most biologists either have forgotten, or never appreciated, that these systems are measuring secondary effects and generalizations of true biological function. Convincing them that a quantitative, direct, math-and-physics based approach to biology can produce helpful information is an enormous cultural challenge. When presented with data showing that a correlative measurement (i.e. fluorescence) disagrees with a direct physics based measurement (i.e. an electronic biosensor), biologists will tend to believe the secondary measurement over the direct measurement.
As direct measurement tools and simulations become more widely used, there will need to be a change in the way biologists think about their measurements, and that's not going to be a comfortable thing for them. There is an opportunity for folks in computational and physical science to lead the way and show them that our approaches to problem solving are valid and helpful.