Simple, robust strategies for generating DNA-directed RNA interference constructs

We describe two complementary strategies for preparing DNA-directed RNA interference (ddRNAi) constructs designed to express hpRNA. The first, oligonucleotide assembly (OA), uses a very simple annealing protocol to combine up to 20 short nucleotides. These are then cloned into appropriately designed restriction sites in expression vectors. OA can be used to prepare simple hairpin (hp)-expressing constructs, but we prefer to use the approach to generate longer constructs. The second strategy, long-range cloning (LRC), uses a novel adaptation of long-range PCR protocols. For LRC, entire vectors are amplified with primers that serve to introduce short sequences into plasmids at defined anchor sites during PCR. The LCR strategy has proven highly reliable in our hands for generating simple ddRNAi constructs. Moreover, LCR is likely to prove useful in many situations in which conventional cloning strategies might prove problematic. In combination, OA and LRC can greatly simplify the design and generation of many expression constructs, including constructs for ddRNAi.

Analysis of the stabilizing effect of Rom on the genetic network controlling ColE1 plasmid replication

A stochastic model of ColE1 plasmid replication is presented. It is implemented by using UltraSAN, a simulation tool based on an extension of stochastic Petri nets (SPNs). It allows an exploration of the variation in plasmid number per bacterium, which is not possible using a deterministic model. In particular, the rate at which plasmid-free bacteria arise during bacterial division is explored in some detail since spontaneous plasmid loss is a widely observed empirical phenomenon. The rate of spontaneous plasmid loss provides an evolutionary explanation for the maintainance of Rom protein. The presence of Rom acts to reduce variance in plasmid copy number, thereby reducing the rate of plasmid loss at bacterial division. The ability of stochastic models to link biochemical function with evolutionary considerations is discussed.

Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets

An integrated understanding of molecular and developmental biology must consider the large number of molecular species involved and the low concentrations of many species in vivo. Quantitative stochastic models of molecular interaction networks can be expressed as stochastic Petri nets (SPNs), a mathematical formalism developed in computer science. Existing software can be used to define molecular interaction networks as SPNs and solve such models for the probability distributions of molecular species. This approach allows biologists to focus on the content of models and their interpretation, rather than their implementation. The standardized format of SPNs also facilitates the replication, extension, and transfer of models between researchers. A simple chemical system is presented to demonstrate the link between stochastic models of molecular interactions and SPNs. The approach is illustrated with examples of models of genetic and biochemical phenomena where the ULTRASAN package is used to present results from numerical analysis and the outcome of simulations.

Detecting heterogeneity of substitution along DNA and protein sequences

Regions of differing constraint, mutation rate or combination along a sequence of DNA or amino acids lead to a nonuniform distribution of polymorphism within species or fixed differences between species. The power of five tests to reject the null hypothesis of a uniform distribution is studied for four classes of alternate hypothesis. The tests explored are the variance of interval lengths; a modified variance test, which includes covariance between neighboring intervals; the length of the longest interval; the length of the shortest third-order interval; and a composite test. Although there is no uniformly most powerful test over the range of alternate hypotheses tested, the variance and modified variance tests usually have the highest power. Therefore, we recommend that one of these two tests be used to test departure from uniformity in all circumstances. Tables of critical values for the variance and modified variance tests are given. The critical values depend both on the number of events and the number of positions in the sequence. A computer program is available on request that calculates both the critical values for a specified number of events and number of positions as well as the significance level of a given data set.