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Pulmonary surfactant, a complex mixture of phospholipids and proteins, secreted by the type II epithelial cells in the lungs, is crucial to reducing the effort required for breathing. A lack of adequate amounts of pulmonary surfactant in underdeveloped lungs of premature infants results in infant respiratory distress syndrome (RDS). Surfactant replacement therapy (SRT) is the approved method of mitigating the effects of RDS. The development of new SRT regimens requires a fundamental understanding of the links between surfactant biochemistry and functionality. We use a coarse-grained (CG) model to predict the surface pressure-surface concentration relationship (equation of state) for pure DPPC, which is a major component of endogenous and synthetic pulmonary surfactant mixtures. We show that the model can be efficiently used to predict the equation of state in excellent agreement with experimental results. We also study the structure of the monolayer as a function of the surface tension of the system. We show that a decrease in the applied surface tension results in an increase in order in the head group region and a decrease in order in the tail region of DPPC. This technique may be useful in the prediction of equations of state for surfactant replacements.

Enveloped viruses are viewed as an opportunity to understand how highly organized and functional biosystems can emerge from a collection of millions of chaotically moving atoms. They are an intermediate level of complexity between macromolecules and bacteria. They are a natural system for testing theories of self-assembly and structural transitions, and for demonstrating the derivation of principles of microbiology from laws of molecular physics. As some constitute threats to human health, a computer-aided vaccine and drug design strategy that would follow from a quantitative model would be an important contribution. However, current molecular dynamics simulation approaches are not practical for modeling such systems. Our multiscale approach simultaneously accounts for the outer protein net and inner protein/genomic core, and their less structured membranous material and host fluid. It follows from a rigorous multiscale deductive analysis of laws of molecular physics. Two types of order parameters are introduced: (1) those for structures wherein constituent molecules retain long-lived connectivity (they specify the nanoscale structure as a deformation from a reference configuration) and (2) those for which there is no connectivity but organization is maintained on the average (they are field variables such as mass density or measures of preferred orientation). Rigorous multiscale techniques are used to derive equations for the order parameters dynamics. The equations account for thermal-average forces, diffusion coefficients, and effects of random forces. Statistical properties of the atomic-scale fluctuations and the order parameters are co-evolved. By combining rigorous multiscale techniques and modern supercomputing, systems of extreme complexity can be modeled.