Multiplexing Biochemical Signals

In this paper we show that living cells can multiplex biochemical signals, i.e. transmit multiple signals through the same signaling pathway simultaneously, and yet respond to them very specifically. We demonstrate how two binary input signals can be encoded in the concentration of a common signaling protein, which is then decoded such that each of the two output signals provides reliable information about one corresponding input. Under biologically relevant conditions the network can reach the maximum amount of information that can be transmitted, which is 2 bits.Comment: 4 pages, 4 figure

Anisotropic coarse-grained statistical potentials improve the ability to identify native-like protein structures

We present a new method to extract distance and orientation dependent potentials between amino acid side chains using a database of protein structures and the standard Boltzmann device. The importance of orientation dependent interactions is first established by computing orientational order parameters for proteins with alpha-helical and beta-sheet architecture. Extraction of the anisotropic interactions requires defining local reference frames for each amino acid that uniquely determine the coordinates of the neighboring residues. Using the local reference frames and histograms of the radial and angular correlation functions for a standard set of non-homologue protein structures, we construct the anisotropic pair potentials. The performance of the orientation dependent potentials was studied using a large database of decoy proteins. The results demonstrate that the new distance and orientation dependent residue-residue potentials present a significantly improved ability to recognize native folds from a set of native and decoy protein structures.Comment: Submitted to "The Journal of Chemical Physics

Symbiotic interactions between chickpea (Cicer arietinum L.) genotypes and Mesorhizobium strains

Legume genotype (GL) x rhizobium genotype (GR) interaction in chickpea was studied using a genetically diverse set of accessions and rhizobium strains in modified Leonard Jars. A subset of effective GL x GR combinations was subsequently evaluated in a pot experiment to identify combinations of chickpea genotypes and rhizobium strains with stable and superior symbiotic performance. A linear mixed model was employed to analyse the occurrence of GL x GR interaction and an additive main effects and multiplicative interaction (AMMI) model was used to study patterns in the performance of genotype-strain combinations.We found statistically significant interaction in jars in terms of symbiotic effectiveness that was entirely due to the inclusion of one of the genotypes, ICC6263. No interaction was found in a subsequent pot experiment. The presence of two genetic groups (Kabuli and Desi genepools) did not affect interaction with Mesorhizobium strains. With the exception of a negative interaction with genotype ICC6263 in the jar experiment, the type strain Mesorhizobium ciceri LMG 14989 outperformed or equalled other strains on all chickpea genotypes in both jar and pot experiments. Similar to earlier reports in common bean, our results suggest that efforts to findmore effective strains may be more rewarding than aiming for identification of superior combinations of strains and genotypes

Phylogeography and Symbiotic Effectiveness of Rhizobia Nodulating Chickpea (Cicer arietinum L.) in Ethiopia

Chickpea (Cicer arietinum L.) used to be considered a restrictive host that nodulated and fixed nitrogen only with Mesorhizobium ciceri and M.mediterraneum. Recent analysis revealed that chickpea can also establish effective symbioseswith strains of several other Mesorhizobium species such as M. loti, M. haukuii, M. amorphae, M. muleiense, etc. These strains vary in their nitrogen fixation potential inviting further exploration. We characterized newly collected mesorhizobial strains isolated from various locations in Ethiopia to evaluate genetic diversity, biogeographic structure and symbiotic effectiveness. Symbiotic effectiveness was evaluated in Leonard Jars using a locally released chickpea cultivar “Nattoli”. Most of the new isolates belonged to a clade related to M. plurifarium, with very few sequence differences, while the total collection of strains contained three additional mesorhizobial genospecies associated with M. ciceri, M. abyssinicae and an unidentified Mesorhizobium species isolated from a wild host in Eritrea. The four genospecies identified represented a subset of the eight major Mesorhizobium clades recently reported for Ethiopia based on metagenomic data. All Ethiopian strains had nearly identical symbiotic genes that grouped them in a single cluster with M. ciceri, M. mediterraneum and M. muleiense, but not with M. plurifarium. Some phylogeographic structure was observed, with elevation and geography explaining some of the genetic differences among strains, but the relation between genetic identity and symbiotic effectiveness was observed to be weak

Regulatory control and the costs and benefits of biochemical noise

Experiments in recent years have vividly demonstrated that gene expression can be highly stochastic. How protein concentration fluctuations affect the growth rate of a population of cells, is, however, a wide open question. We present a mathematical model that makes it possible to quantify the effect of protein concentration fluctuations on the growth rate of a population of genetically identical cells. The model predicts that the population's growth rate depends on how the growth rate of a single cell varies with protein concentration, the variance of the protein concentration fluctuations, and the correlation time of these fluctuations. The model also predicts that when the average concentration of a protein is close to the value that maximizes the growth rate, fluctuations in its concentration always reduce the growth rate. However, when the average protein concentration deviates sufficiently from the optimal level, fluctuations can enhance the growth rate of the population, even when the growth rate of a cell depends linearly on the protein concentration. The model also shows that the ensemble or population average of a quantity, such as the average protein expression level or its variance, is in general not equal to its time average as obtained from tracing a single cell and its descendants. We apply our model to perform a cost-benefit analysis of gene regulatory control. Our analysis predicts that the optimal expression level of a gene regulatory protein is determined by the trade-off between the cost of synthesizing the regulatory protein and the benefit of minimizing the fluctuations in the expression of its target gene. We discuss possible experiments that could test our predictions.Comment: Revised manuscript;35 pages, 4 figures, REVTeX4; to appear in PLoS Computational Biolog

From compact to fractal crystalline clusters in concentrated systems of monodisperse hard spheres

We address the crystallization of monodisperse hard spheres in terms of the properties of finite- size crystalline clusters. By means of large scale event-driven Molecular Dynamics simulations, we study systems at different packing fractions {\phi} ranging from weakly supersaturated state points to glassy ones, covering different nucleation regimes. We find that such regimes also result in different properties of the crystalline clusters: compact clusters are formed in the classical-nucleation-theory regime ({\phi} \leq 0.54), while a crossover to fractal, ramified clusters is encountered upon increasing packing fraction ({\phi} \geq 0.56), where nucleation is more spinodal-like. We draw an analogy between macroscopic crystallization of our clusters and percolation of attractive systems to provide ideas on how the packing fraction influences the final structure of the macroscopic crystals. In our previous work (Phys. Rev. Lett., 106, 215701, 2011), we have demonstrated how crystallization from a glass (at {\phi} > 0.58) happens via a gradual (many-step) mechanism: in this paper we show how the mechanism of gradual growth seems to hold also in super-saturated systems just above freezing showing that static properties of clusters are not much affected by dynamics.Comment: Soft Matter, 201

Consistency, variability, and predictability of on-farm nutrient responses in four grain legumes across east and west Africa

Open Access Article; Published online: 26 May 2023Grain legumes are key components of sustainable production systems in sub-Saharan Africa, but wide-spread nutrient deficiencies severely restrict yields. Whereas legumes can meet a large part of their nitrogen (N) requirement through symbiosis with N2-fixing bacteria, elements such as phosphorus (P), potassium (K) and secondary and micronutrients may still be limiting and require supplementation. Responses to P are generally strong but variable, while evidence for other nutrients tends to show weak or highly localised effects. Here we present the results of a joint statistical analysis of a series of on-farm nutrient addition trials, implemented across four legumes in four countries over two years. Linear mixed models were used to quantify both mean nutrient responses and their variability, followed by a random forest analysis to determine the extent to which such variability can be explained or predicted by geographic, environmental or farm survey data. Legume response to P was indeed variable, but consistently positive and we predicted application to be profitable for 67% of farms in any given year, based on prevailing input costs and grain prices. Other nutrients did not show significant mean effects, but considerable response variation was found. This response heterogeneity was mostly associated with local or temporary factors and could not be explained or predicted by spatial, biophysical or management factors. An exception was K response, which displayed appreciable spatial variation that could be partly accounted for by spatial and environmental covariables. While of apparent relevance for targeted recommendations, the minor amplitude of expected response, the large proportion of unexplained variation and the unreliability of the predicted spatial patterns suggests that such data-driven targeting is unlikely to be effective with current data

Generic mechanism for generating a liquid-liquid phase transition

Recent experimental results indicate that phosphorus, a single-component system, can have two liquid phases: a high-density liquid (HDL) and a low-density liquid (LDL) phase. A first-order transition between two liquids of different densities is consistent with experimental data for a variety of materials, including single-component systems such as water, silica and carbon. Molecular dynamics simulations of very specific models for supercooled water, liquid carbon and supercooled silica, predict a LDL-HDL critical point, but a coherent and general interpretation of the LDL-HDL transition is lacking. Here we show that the presence of a LDL and a HDL can be directly related to an interaction potential with an attractive part and two characteristic short-range repulsive distances. This kind of interaction is common to other single-component materials in the liquid state (in particular liquid metals), and such potentials are often used to decribe systems that exhibit a density anomaly. However, our results show that the LDL and HDL phases can occur in systems with no density anomaly. Our results therefore present an experimental challenge to uncover a liquid-liquid transition in systems like liquid metals, regardless of the presence of the density anomaly.Comment: 5 pages, 3 ps Fig

Homogeneous Bubble Nucleation driven by local hot spots: a Molecular Dynamics Study

We report a Molecular Dynamics study of homogenous bubble nucleation in a Lennard-Jones fluid. The rate of bubble nucleation is estimated using forward-flux sampling (FFS). We find that cavitation starts with compact bubbles rather than with ramified structures as had been suggested by Shen and Debenedetti (J. Chem. Phys. 111:3581, 1999). Our estimate of the bubble-nucleation rate is higher than predicted on the basis of Classical Nucleation Theory (CNT). Our simulations show that local temperature fluctuations correlate strongly with subsequent bubble formation - this mechanism is not taken into account in CNT