Pressure and temperature dependence of growth and morphology of Escherichia coli: Experiments and Stochastic Model

We have investigated the growth of Escherichia coli E.coli, a mesophilic bacterium, as a function of pressure $P$ and temperature $T$. E.coli can grow and divide in a wide range of pressure (1-400atm) and temperature ($23-40^{\circ}$C). For $T>30^{\circ}$ C, the division time of E.coli increases exponentially with pressure and exhibit a departure from exponential behavior at pressures between 250-400 atm for all the temperatures studied in our experiments. For $T<30^{\circ}$ C, the division time shows an anomalous dependence on pressure -- first decreases with increasing pressure and then increases upon further increase of pressure. The sharp change in division time is followed by a sharp change in phenotypic transition of E. Coli at high pressures where bacterial cells switch to an elongating cell type. We propose a model that this phenotypic changes in bacteria at high pressures is an irreversible stochastic process whereas the switching probability to elongating cell type increases with increasing pressure. The model fits well the experimental data. We discuss our experimental results in the light of structural and thus functional changes in proteins and membranes.Comment: 28 pages, 12 figure

On growth and form of Bacillus subtilis biofilms

International audienceA general feature of mature biofilms is their highly heterogeneous architecture that partitions the microbial city into sectors with specific micro-environments. To understand how this heterogeneity arises, we have investigated the for- mation of a microbial community of the model organism Bacillus subtilis. We first show that the growth of macroscopic colonies is inhibited by the accumulation of ammoniacal by-products. By constraining biofilms to grow approximately as two-dimensional layers, we then find that the bacteria which differentiate to produce extracellular polymeric substances form tightly packed bacterial chains. In addition to the process of cellular chaining, the bio- mass stickiness also strongly hinders the reorganization of cells within the biofilm. Based on these observations, we then write a biomechanical model for the growth of the biofilm where the cell density is constant and the physical mechanism responsible for the spreading of the biomass is the pressure gener- ated by the division of the bacteria. Besides reproducing the velocity field of the biomass across the biofilm, the model predicts that, although bacteria divide everywhere in the biofilm, fluctuations in the growth rates of the bacteria lead to a coarsening of the growing bacterial layer. This process of kin- etic roughening ultimately leads to the formation of a rough biofilm surface exhibiting self-similar properties. Experimental measurements of the biofilm texture confirm these predictions

Signal and noise in bridging PCR

BACKGROUND: In a variant of the standard PCR reaction termed bridging, or jumping, PCR the primer-bound sequences are originally on separate template molecules. Bridging can occur if, and only if, the templates contain a region of sequence similarity. A 3' end of synthesis in one round of synthesis that terminates in this region of similarity can prime on the other. In principle, Bridging PCR (BPCR) can detect a subpopulation of one template that terminates synthesis in the region of sequence shared by the other template. This study considers the sensitivity and noise of BPCR as a quantitative assay for backbone interruptions. Bridging synthesis is also important to some methods for computing with DNA. RESULTS: In this study, BPCR was tested over a 328 base pair segment of the E. coli lac operon and a signal to noise ratio (S/N) of approximately 10 was obtained under normal PCR conditions with Taq polymerase. With special precautions in the case of Taq or by using the Stoffel fragment the S/N was improved to 100, i.e. 1 part of cut input DNA yielded the same output as 100 parts of intact input DNA. CONCLUSIONS: In the E. coli lac operator region studied here, depending on details of protocol, between 3 and 30% per kilobase of final PCR product resulted from bridging. Other systems are expected to differ in the proportion of product that is bridged consequent to PCR protocol and the sequence analyzed. In many cases physical bridging during PCR will have no informational consequence because the bridged templates are of identical sequence, but in a number of special cases bridging creates, or, destroys, information

Physical Model of the Genotype-to-Phenotype Map of Proteins

How DNA is mapped to functional proteins is a basic question of living matter. We introduce and study a physical model of protein evolution which suggests a mechanical basis for this map. Many proteins rely on large-scale motion to function. We therefore treat protein as learning amorphous matter that evolves towards such a mechanical function: Genes are binary sequences that encode the connectivity of the amino acid network that makes a protein. The gene is evolved until the network forms a shear band across the protein, which allows for long-range, soft modes required for protein function. The evolution reduces the high-dimensional sequence space to a low-dimensional space of mechanical modes, in accord with the observed dimensional reduction between genotype and phenotype of proteins. Spectral analysis of the space of 10(6) solutions shows a strong correspondence between localization around the shear band of both mechanical modes and the sequence structure. Specifically, our model shows how mutations are correlated among amino acids whose interactions determine the functional mode

Protein-DNA computation by stochastic assembly cascade

The assembly of RecA on single-stranded DNA is measured and interpreted as a stochastic finite-state machine that is able to discriminate fine differences between sequences, a basic computational operation. RecA filaments efficiently scan DNA sequence through a cascade of random nucleation and disassembly events that is mechanistically similar to the dynamic instability of microtubules. This iterative cascade is a multistage kinetic proofreading process that amplifies minute differences, even a single base change. Our measurements suggest that this stochastic Turing-like machine can compute certain integral transforms.Comment: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC129313/ http://www.pnas.org/content/99/18/11589.abstrac