Network thermodynamic simulation of biological systems: An overview

Abstract

Abstract Network Thermodynamics is a formalism which uses the concepts of network theory to solve problems generated by thermodynamic and/or kinetic descriptions of systems (generally by simulation, for complex, highly organized, dynamic systems). It is particularly useful in Biology due to the organizational complexity in living systems. Network thermodynamic simulation now utilizes pre-existing circuit simulation programs, such as NET2, SPICE2, and ASTEC. This provides a cheap and efficient way to achieve simulations on a digital computer which previously were best done by analog or hybrid computation due to the complexity of the systems being simulated (1). A variety of non-linear, dynamic physiological systems has been successfully simulated including coupled flows through epithelial membranes, reaction diffusion systems (including those with multiple steady states), whole body pharmacokinetics and the cellular pharmacokinetics of anticancer agents, kidney and general microcirculation, active transport of calcium in the sarcoplasmic reticulum in muscle, and others. In the future, the principles of network simulation are to be incorporated into a network simulation package written in biological language and with a parameter estimation capability. In the mean time, network simulation by means of circuit simulation programs has become widely used by ordinary biomedical researchers because it allows them to design and modify their own models without the aid of computer or mathematical modeling experts. It is anticipated that the analysis of experiments in biomedical research ranging from the most fundamental to the highly clinical will become more quantitative and less descriptive as the network approach is made known on a wider scale. At the level of fundamentals, new insight into basic thermodynamic concepts is being obtained by looking at both classical and nonequilibrium thermodynamics from the perspective of network theory (2)-(7). Among these new results are the proof of Onsager Reciprocity through Tellegen's Theorem, and the use of networks to generate Legendre transforms and other classical results (2).