## A quantitative description of membrane current and its application to conduction and excitation in nerve. (SBML l3v1)

### Description

This article concludes a series of papers concerned with the flow of electric current through the surface membrane of a giant nerve fibre (Hodgkin,Huxley & Katz, 1952; Hodgkin & Huxley, 1952 a-c). Its general object is to discuss the results of the preceding papers (Part I), to put them into mathematical form (Part II) and to show that they will account for conduction and excitation in quantitative terms (Part III). This SBML model uses the same formalism as the one described in the paper, contrary to modern versions: * V describes the the membrane depolarisation relative to the resting potential of the membrane * opposing to modern practice, depolarization is negative , not positive , so the sign of V is different * inward transmembrane currents are considered positive (inward current positive), contrary to modern use The changeable parameters are the equilibrium potentials( E_R, E_K, E_L, E_Na ), the membrane depolarization ( V ) and the initial sodium and potassium channel activation and inactivation coefficients ( m,h,n ). The initial values of m,h,n for the model were calculated for V = 0 using the equations from the article: n t=0 = α_n V=0 /(α_n V=0 + β_n V=0 ) and equivalent expressions for h and m . For single excitations apply a negative membrane depolarization (V < 0). To achieve oscillatory behavior either change the resting potential to a more positive value or apply a constant negative ionic current (I < 0). Two assignments for parameters in the model, alpha_n and alpha_m, are not defined at V=-10 resp. -25 mV. We did not change this to keep the formulas similar to the original publication and as most integrators seem not to have any problem with it. The limits at V=-10 and -25 mV are 0.1 for alpha_n resp. 1 for alpha_m. We thank Mark W. Johnson for finding a bug in the model and his helpful comments.

## A synthetic Escherichia coli predator–prey ecosystem

### Description

This is the reduced model described in the article:
A synthetic Escherichia coli predator–prey ecosystem
Balagaddé FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, Quake SR, You L.Mol Syst Biol. 2008;4:187. Epub 2008 Apr 15. PMID: 18414488; DOI:10.1038/msb.2008.24

Abstract:
We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator–prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner.

In the article the cell density is given in per 103 cells per microlitre. To evade a conversion factor in the SBML implementation, the unit for the cell densities was just left the same as for the AHLs A and A2 (nM).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.

## A whole-body mathematical model of cholesterol metabolism and its age-associated dysregulation.

### Description

BACKGROUND: Global demographic changes have stimulated marked interest in the process of ageing. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion.