Predestination and freedom in Milton's 'Paradise lost'

John Milton's epic poem Paradise Lost (1667) offers a highly creative seventeenth-century reconstruction of the doctrine of predestination, a reconstruction which both anticipates modern theological developments and sheds important light on the history of predestinarian thought. Moving beyond the framework of post-Reformation controversies, the poem emphasises both the freedom and the universality of electing grace, and the eternally decisive role of human freedom in salvation. The poem erases the distinction between an eternal election of some human beings and an eternal rejection of others, portraying reprobation instead as the temporal self-condemnation of those who wilfully reject their own election and so exclude themselves from salvation. While election is grounded in the gracious will of God, reprobation is thus grounded in the fluid sphere of human decision. Highlighting this sphere of human decision, the poem depicts the freedom of human beings to actualise the future as itself the object of divine predestination. While presenting its own unique vision of predestination, Paradise Lost thus moves towards the influential and distinctively modern formulations of later thinkers like Schleiermacher and Barth.

Inflammation suppressor genes: please switch out all the lights

An effective immune system requires rapid and appropriate activation of inflammatory mechanisms but equally rapid and effective resolution of the inflammatory state. A review of the canonical host response to gram-negative bacteria, the lipopolysaccharide-Toll-like receptor 4 signaling cascade, highlights the induction of repressors that act at each step of the activation process. These inflammation suppressor genes are characterized by their induction in response to pathogen, typically late in the macrophage activation program, and include an expanding class of dominant-negative proteins derived from alternate splicing of common signaling components. Despite the expanse of anti-inflammatory mechanisms available to an activated macrophage, the frailty of this system is apparent in the large numbers of genes implicated in chronic inflammatory diseases. This apparent lack of redundancy between inflammation suppressor genes is discussed with regard to evolutionary benefits in generating a heterogeneous population of immune cells and consequential robustness in defense against new and evolving pathogens.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.