Mixed anhydrides at the intersection between peptide and RNA autocatalytic sets: evolution of biological coding.

We present a scenario for the origin of biological coding, a semiotic relationship between chemical information stored in one location that links to chemical information stored in a separate location. Coding originated from cooperation between two, originally separate, collectively autocatalytic sets (CASs), one for nucleic acids and one for peptides. Upon interaction, a series of RNA folding-directed processes led to their joint cooperativity. The aminoacyl adenylate was the first covalent association made by these two CASs and solidified their interdependence, and is a palimpsest of this era, a relic of the original semiotic relationship between RNA and proteins. Coding was driven by selection pressure to eliminate waste in CASs. Eventually a 1 : 1 relationship between single amino acids and short RNA pieces was established, i.e. the 'genetic code'. The two classes of aaRS enzymes are remnants of the complementary information in two RNA strands, as postulated by Rodin and Ohno. Every stage in the evolution of coding was driven by the downward selection on the components of a system to satisfy the Kantian whole. Coding was engendered because there were two chemically distinct classes of polymers needed for open-ended evolution; systems with only one polymer cannot exhibit this characteristic. Coding is thus synonymous with life as we know it.

Human cervicovaginal mucus contains an activity that hinders HIV-1 movement.

Cervical and vaginal epithelia are primary barriers against HIV type I (HIV-1) entry during male-to-female transmission. Cervical mucus (CM) is produced by the endocervix and forms a layer locally as well as in the vaginal compartment in the form of cervicovaginal mucus (CVM). To study the potential barrier function of each mucus type during HIV-1 transmission, we quantified HIV-1 mobility in CM and CVM ex vivo using fluorescent microscopy. Virions and 200-nm PEGylated beads were digitally tracked and mean-squared displacement was calculated. The mobility of beads increased significantly in CVM compared with CM, consistent with the known decreased mucin concentration of CVM. Unexpectedly, HIV-1 diffusion was significantly hindered in the same CVM samples in which bead diffusion was unhindered. Inhibition of virus transport was envelope-independent. Our results reveal a previously unknown activity in CVM that is capable of impeding HIV-1 mobility to enhance mucosal barrier function.

Quantitative simulation tools to analyze up- and downstream interactions of soil and water conservation measures: supporting policy making in the Green Water Credits program of Kenya.

Upstream soil and water conservation measures in catchments can have positive impact both upstream in terms of less erosion and higher crop yields, but also downstream by less sediment flow into reservoirs and increased groundwater recharge. Green Water Credits (GWC) schemes are being developed to encourage upstream farmers to invest in soil and water conservation practices which will positively effect upstream and downstream water availability. Quantitative information on water and sediment fluxes is crucial as a basis for such financial schemes. A pilot design project in the large and strategically important Upper-Tana Basin in Kenya has the objective to develop a methodological framework for this purpose. The essence of the methodology is the integration and use of a collection of public domain tools and datasets: the so-called Green water and Blue water Assessment Toolkit (GBAT). This toolkit was applied in order to study different options to implement GWC in agricultural rainfed land for the pilot study. Impact of vegetative contour strips, mulching, and tied ridges were determined for: (i) three upstream key indicators: soil loss, crop transpiration and soil evaporation, and (ii) two downstream indicators: sediment inflow in reservoirs and groundwater recharge. All effects were compared with a baseline scenario of average conditions. Thus, not only actual land management was considered but also potential benefits of changed land use practices. Results of the simulations indicate that especially applying contour strips or tied ridges significantly reduces soil losses and increases groundwater recharge in the catchment. The model was used to build spatial expressions of the proposed management practices in order to assess their effectiveness. The developed procedure allows exploring the effects of soil conservation measures in a catchment to support the implementation of GWC.

Cell-cell interaction and diversity of emergent behaviours.

Despite myriads of possible gene expression profiles, cells tend to be found in a confined number of expression patterns. The dynamics of Boolean models of gene regulatory networks has proven to be a likely candidate for the description of such self-organisation phenomena. Because cells do not live in isolation, but they constantly shape their functions to adapt to signals from other cells, this raises the question of whether the cooperation among cells entails an expansion or a reduction of their possible steady states. Multi random Boolean networks are introduced here as a model for interaction among cells that might be suitable for the investigation of some generic properties regarding the influence of communication on the diversity of cell behaviours. In spite of its simplicity, the model exhibits a non-obvious phenomenon according to which a moderate exchange of products among adjacent cells fosters the variety of their possible behaviours, which on the other hand are more similar to one another. On the contrary, a more invasive coupling would lead cells towards homogeneity.

Robustness analysis of a Boolean model of gene regulatory network with memory.

The response to different kinds of perturbations of a discrete model of gene regulatory network, which is a generalization of the random Boolean network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.

Phase transition in a class of nonlinear random networks.

We discuss the complex dynamics of a nonlinear random networks model as a function of the connectivity k between the elements of the network. We show that this class of networks exhibits an order-chaos phase transition for a critical connectivity k{c}=2 . Also, we show that both pairwise correlation and complexity measures are maximized in dynamically critical networks. These results are in good agreement with the previously reported studies on random Boolean networks and random threshold networks, and show once again that critical networks provide an optimal coordination of diverse behavior.

On the dynamics of random Boolean networks subject to noise: attractors, ergodic sets and cell types.

The asymptotic dynamics of random Boolean networks subject to random fluctuations is investigated. Under the influence of noise, the system can escape from the attractors of the deterministic model, and a thorough study of these transitions is presented. We show that the dynamics is more properly described by sets of attractors rather than single ones. We generalize here a previous notion of ergodic sets, and we show that the Threshold Ergodic Sets so defined are robust with respect to noise and, at the same time, that they do not suffer from a major drawback of ergodic sets. The system jumps from one attractor to another of the same Threshold Ergodic Set under the influence of noise, never leaving it. By interpreting random Boolean networks as models of genetic regulatory networks, we also propose to associate cell types to Threshold Ergodic Sets rather than to deterministic attractors or to ergodic sets, as it had been previously suggested. We also propose to associate cell differentiation to the process whereby a Threshold Ergodic Set composed by several attractors gives rise to another one composed by a smaller number of attractors. We show that this approach accounts for several interesting experimental facts about cell differentiation, including the possibility to obtain an induced pluripotent stem cell from a fully differentiated one by overexpressing some of its genes.

Maximal information transfer and behavior diversity in Random Threshold Networks.

Random Threshold Networks (RTNs) are an idealized model of diluted, non-symmetric spin glasses, neural networks or gene regulatory networks. RTNs also serve as an interesting general example of any coordinated causal system. Here we study the conditions for maximal information transfer and behavior diversity in RTNs. These conditions are likely to play a major role in physical and biological systems, perhaps serving as important selective traits in biological systems. We show that the pairwise mutual information is maximized in dynamically critical networks. Also, we show that the correlated behavior diversity is maximized for slightly chaotic networks, close to the critical region. Importantly, critical networks maximize coordinated, diverse dynamical behavior across the network and across time: the information transmission between source and receiver nodes and the diversity of dynamical behaviors, when measured with a time delay between the source and receiver, are maximized for critical networks.

Enrichment and immunoprecipitation of 22 kDa human growth hormone spiked into human urine.

Approaches to detect whether an athlete has used growth hormone have been intensely investigated by sport organizations for 20 years. This effort has led to a human growth hormone (hGH) isoform ratio test in serum that has been approved by WADA and deployed at three Olympic Games, although a positive case has yet to be reported. We set out to determine whether the ratio test could be applied to urine. First we investigated various ways to extract hGH from spiked urine. We were able to recover 95% using selective centrifugal concentration. This fraction was then subjected to four different commercially available immunoprecipitation kits. The highest yield was obtained with the Invitrogen Dynabeads Protein G kit. Nevertheless it is apparent that these methods do not recover enough hGH for subsequent analysis by mass spectrometry. With further effort greater recovery of the 22 kDa isoform might be achieved, however it is very unlikely that the 20 kDa isoform could be detected. This method may be significantly improved by the application of both nanoparticle and aptamer technology.

Heuristic approach to sparse approximation of gene regulatory networks.

Determining the structure of the gene regulatory network using the information in genomewide profiles of mRNA abundance, such as microarray data, poses several challenges. Typically, "static" rather than dynamical profile measurements, such as those taken from steady state tissues in various conditions, are the starting point. This makes the inference of causal relationships between genes difficult. Moreover, the paucity of samples relative to the gene number leads to problems such as overfitting and underconstrained regression analysis. Here we present a novel method for the sparse approximation of gene regulatory networks that addresses these issues. It is formulated as a sparse combinatorial optimization problem which has a globally optimal solution in terms of l(0) norm error. In order to seek an approximate solution of the l(0) optimization problem, we consider a heuristic approach based on iterative greedy algorithms. We apply our method to a set of gene expression profiles comprising of 24,102 genes measured over 79 human tissues. The inferred network is a signed directed graph, hence predicts causal relationships. It exhibits typical characteristics of regulatory networks organism with partially known network topology, such as the average number of inputs per gene as well as the in-degree and out-degree distribution.