By Daniel A. Beard

ISBN-10: 0521768233

ISBN-13: 9780521768238

This functional advisor to biosimulation offers the hands-on event had to devise, layout and learn simulations of biophysical tactics for functions in organic and biomedical sciences. via real-world case experiences and labored examples, scholars will advance and follow simple operations via to complicated innovations, overlaying a variety of biophysical themes together with chemical kinetics and thermodynamics, delivery phenomena, and mobile electrophysiology. every one bankruptcy is outfitted round case reports in a given program quarter, with simulations of actual organic structures built to research and interpret facts. Open-ended project-based workouts are supplied on the finish of every bankruptcy, and with all info and machine codes to be had on-line (www.cambridge.org/biosim) scholars can quick and simply run, control, discover and extend at the examples within. This hands-on advisor is perfect to be used on senior undergraduate/graduate classes and in addition as a self-study advisor for an individual who must enhance computational versions of organic structures.

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Extra resources for Biosimulation: Simulation of Living Systems

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41) where k2 , k3 , k−2 , and k−3 are adjustable parameters. These equations assume that the fluxes through reactions 2 and 3 are slowed through buildup of xylose 10 By “trial and error” I am referring to the process of trying different model formulations in an attempt to find a model that can fit the data. The model presented here is the simplest one (in terms of the complexity of the kinetics and number of adjustable parameters) that I was able to find to match the data. That does not mean the model is unique.

Next, add noise to the simulated time course data to obtain “pseudo-data” to compare with model predictions. Vary the model parameters (by hand or using a computer algorithm) to try to obtain a “best fit” of the model to the pseudo-data. Here you are free to define “best fit” in any reasonable way (perhaps as the least-square difference between the data and the simulated variables, or perhaps as the most visually satisfying plot of the predictions along with the data). How close are your parameter estimates to the original values used to obtain the pseudo-data?

Here note that Jv is measured in volume per unit time; Js in moles per unit time; and I in coulombs per unit time. Hence μw = p − , μs = RT ln c1ν1 c2ν2 , and E = μ2 /(z2 F ). In certain applications (including the example below) electrical current is not significant, and we seek a reduced form of Eq. 13) with I = 0. With I = 0, Eq. 13) becomes Jv Js = L11 − L213 /L33 (L12 − L13 L23 /L33 ) (L12 − L13 L23 /L33 ) L22 − L223 /L33 RT p− ln c1ν1 c2ν2 . 14) Often these equations are expressed using the following definitions: s = RT (c1(I ) + c2(I ) − c1(I I ) − c2(I I ) ), and cs = s RT ln c1ν1 c2ν2 .

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Biosimulation: Simulation of Living Systems by Daniel A. Beard


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