A stochastic model of autocatalytic reaction networks

Autocatalytic cycles are rather widespread in nature and in several theoretical models of catalytic reaction networks their emergence is hypothesized to be inevitable when the network is or becomes sufficiently complex. Nevertheless, the emergence of autocatalytic cycles has been never observed in wet laboratory experiments. Here, we present a novel model of catalytic reaction networks with the explicit goal of filling the gap between theoretical predictions and experimental findings. The model is based on previous study of Kauffman, with new features in the introduction of a stochastic algorithm to describe the dynamics and in the possibility to increase the number of elements and reactions according to the dynamical evolution of the system. Furthermore, the introduction of a temporal threshold allows the detection of cycles even in our context of a stochastic model with asynchronous update. In this study, we describe the model and present results concerning the effect on the overall dynamics of varying (a) the average residence time of the elements in the reactor, (b) both the composition of the firing disk and the concentration of the molecules belonging to it, (c) the composition of the incoming flux.     

Evolution before genes

Background  

Our current understanding of evolution is so tightly linked to template-dependent replication of DNA and RNA molecules that the old idea from Oparin of a self-reproducing 'garbage bag' ('coacervate') of chemicals that predated fully-fledged cell-like entities seems to be farfetched to most scientists today. However, this is exactly the kind of scheme we propose for how Darwinian evolution could have occurred prior to template replication.     

Co-regulation of the mixed-function oxidation of p-nitroanisole and glucuronidation of p-nitrophenol in the perfused rat liver by carbohydrate reserves

Maximal rates of mixed-function oxidation of p-nitroanisole and the glucuronidation of p-nitrophenol in perfused livers from phenobarbital-treated rats varied directly with the nutritional state of the rat (i.e., fasted < fed < fasted-refed). Rates correlated with intracellular concentrations of NADPH, UDP-glucuronic acid, and glycogen but not with amounts of cytochrome P-450 or glucuronyltransferase activity. These data support the hypothesis that mixed-function oxidation and glucuronidation are coregulated in intact cells by carbohydrate-dependent cofactor synthesis.     

Effect of ions on phospholipid layer structure as indicated by Raman spectroscopy.

Various anions and cations are found to induce changes in the layered structure of phosphatidylcholine-water systems as indicated by Raman Spectroscopy. From the ratio of Raman intensities, I1064/I1089, it is inferred that dipositive ions decrease the proportion of gauche character in the hydrocarbon chains, with the relative influence being: Ba2+ less than Mg2+ less than Ca2+ similar to Cd2+. Unipositive ions (Li+, K+ and Na+) produce no observed changes in the Raman spectrum of the lecithin dispersion. The proportion of gauche character of the hydrocarbon chains is found to be nearly independent of the anion for: Br-, Cl-, acetate-, I-, ClO4-, CNS- and SO42-. Dispersions prepared with a solution of KI+I2 produced Raman spectra in which the 1089cm-1 peak, which is characteristic of random lipid chains, was greatly intensified, presumably because of the presence of I3- which is known to penetrate the lipid lamellae. The observed trends are discussed.     

A physical analogy of the genetic toggle switch.

We show that there is a physical analogy between a stochastic model of a genetic toggle switch system and a thermostated particle moving in a potential field, derived from the probability distribution of the toggle switch. This result suggests that one can actually simulate the dynamics of a more complex gene network by considering an ensemble of thermostated particles moving in a potential field, derived from the stationary distribution of the chemical stochastic model describing the gene network.     

Discovery and development of telaprevir: an NS3-4A protease inhibitor for treating genotype 1 chronic hepatitis C virus

Infection with hepatitis C virus (HCV) is a major medical problem with over 170 million people infected worldwide. Substantial morbidity and mortality are associated with hepatic manifestations (cirrhosis and hepatocellular carcinoma), which develop with increasing frequency in people infected with HCV for more than 20 years. Less well known is the burden of HCV disease associated with extrahepatic manifestations (diabetes, B-cell proliferative disorders, depression, cognitive disorders, arthritis and Sj?gren's syndrome). For patients infected with genotype 1 HCV, treatment with polyethylene glycol decorated interferon (peginterferon) α and ribavirin (PR) is associated with a low (40-50%) success rate, substantial treatment-limiting side effects and a long (48-week) duration of treatment. In the past 15 years, major scientific advances have enabled the development of new classes of HCV therapy, the direct-acting antiviral agents, also known as specifically targeted antiviral therapy for hepatitis C (STAT-C). In combination with PR, the HCV NS3-4A protease inhibitor telaprevir has recently been approved for treatment of genotype 1 chronic HCV in the United States, Canada, European Union and Japan. Compared with PR, telaprevir combination therapy offers significantly improved viral cure rates and the possibility of shortened treatment duration for diverse patient populations. Developers of innovative drugs have to blaze a new path with few validated sign posts to guide the way. Indeed, telaprevir's development was once put on hold because of its performance in a standard IC(50) assay. Data from new hypotheses and novel experiments were required to justify further investment and reduce risk that the drug might fail in the clinic. In addition, the poor drug-like properties of telaprevir were a formidable hurdle, which the manufacturing and formulation teams had to overcome to make the drug. Finally, novel clinical trial designs were developed to improve efficacy and shorten treatment in parallel instead of sequentially. Lessons learned from the development of telaprevir suggest that makers of innovative medicines cannot rely solely on traditional drug discovery metrics, but must develop innovative, scientifically guided pathways for success.     

Patterns of genetic variation in populations of infectious agents

Background  

The analysis of genetic variation in populations of infectious agents may help us understand their epidemiology and evolution. Here we study a model for assessing the levels and patterns of genetic diversity in populations of infectious agents. The population is structured into many small subpopulations, which correspond to their hosts, that are connected according to a specific type of contact network. We considered different types of networks, including fully connected networks and scale free networks, which have been considered as a model that captures some properties of real contact networks. Infectious agents transmit between hosts, through migration, where they grow and mutate until elimination by the host immune system.