Biochemical analysis of cellular target of S-trityl-l-cysteine derivatives using affinity matrix

Biochemical analysis of the cellular target of S-trityl-l-cysteine (STLC) derivatives was performed by using the newly synthesized STLC derivative-immobilized affinity beads (3d). The affinity beads efficiently captured KSP in HCT116 cytoplasmic cell lysate. The results obtained from pull-down and competition experiments using 3d with STLC derivatives provided the first evidence for direct interaction of these derivatives with KSP in cancer cells. Design, synthesis and application of 3d were reported.     

Implementing false discovery rate control: increasing your power

Popular procedures to control the chance of making type I errors when multiple statistical tests are performed come at a high cost: a reduction in power. As the number of tests increases, power for an individual test may become unacceptably low. This is a consequence of minimizing the chance of making even a single type I error, which is the aim of, for instance, the Bonferroni and sequential Bonferroni procedures. An alternative approach, control of the false discovery rate (FDR), has recently been advocated for ecological studies. This approach aims at controlling the proportion of significant results that are in fact type I errors. Keeping the proportion of type I errors low among all significant results is a sensible, powerful, and easy-to-interpret way of addressing the multiple testing issue. To encourage practical use of the approach, in this note we illustrate how the proposed procedure works, we compare it to more traditional methods that control the familywise error rate, and we discuss some recent useful developments in FDR control.     

Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology

Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.     

Chemical potential derivatives and preferential interaction parameters in biological systems from Kirkwood-Buff theory

New expressions for chemical potential derivatives and preferential interaction parameters for ternary mixtures are derived for open, semiopen, and closed ensembles in terms of Kirkwood-Buff integrals, where all three components are present at finite concentrations. This is achieved using a simple approach that avoids the use of the general matrix formulation of Kirkwood-Buff theory. The resulting expressions provide a rigorous foundation for the analysis of experimental and simulation data. Using the results, a simple model is developed and used to investigate the possible effects of finite protein concentrations on the corresponding cosolvent dependent chemical potential and denaturation thermodynamics.     

Sustainable ecosystem management using optimal control theory: part 1 (deterministic systems)

The concept of sustainability, an abstract one by its nature, has been given a mathematical representation through the use of Fisher information as a measure. It is used to propose the sustainability hypotheses for dynamical systems, which has paved the way to achieve sustainable development through externally enforced control schemes. For natural systems, this refers to the task of ecosystem management, which is complicated due the lack of clear objectives. This work attempts to incorporate the idea of sustainability in ecosystem management. The natural regulation of ecosystems suggests two possible control options, top-down control and bottom-up control. A comparison of these two control philosophies is made on generic food chain models using the objectives derived from the sustainability hypotheses. Optimal control theory is used to derive the control profiles to handle the complex nature of the models and the objectives. The results indicate a strong relationship between the hypotheses and the dynamic behavior of the models, supporting the use of Fisher information as a measure. As regards to ecosystem management, it has been observed that top-down control is more aggressive but can result in instability, while bottom-up control is guaranteed to give a stable and improved dynamic response. The results also indicate that bottom-up control is a better option to affect shifts in the dynamic regimes of a system, which may be required to recover the system from a natural disaster like the hurricane Katrina.     

Sustainable ecosystem management using optimal control theory: part 2 (stochastic systems)

Sustainable development of ecosystems through external ecosystem management is assuming importance for the environmentalists. To that effect, previous work by the authors looked at the option of manipulating population dynamics of the species in an ecosystem to achieve sustainability. Fisher information is used as the quantifying measure of sustainability and optimal control theory is used to derive the control profiles. However, that work considered only deterministic systems. Uncertainty being prevalent in all systems, particularly in natural systems, this paper extends that work to analyse uncertain systems. Predator-prey models are used to model the species populations and different control philosophies are compared. Ito mean reverting process is used to model the stochastic process, and stochastic maximum principle is used to derive the control profiles. The results for the objective of FI variance minimization qualitatively agree with those for the deterministic system, while the results for the FI maximization objective differ. It is observed that the instability associated with the FI maximization objective for deterministic systems is absorbed by the noise introduced by the uncertainty. Quantitatively, it is observed that the degree of uncertainty, along with its presence, is also important to identify the most appropriate management strategy.     

Volumetric measurements in binary solvents: theory to experiment

Interactions of proteins and protein groups with water-soluble cosolvents have been studied for the last 50 years with a variety of theoretical and experimental methods. The contribution of volumetric techniques to these studies is relatively modest, although volumetric properties of solutes are sensitive to the entire spectrum of solute-solvent and solute-cosolvent interactions. This deficiency is partly related to formidable experimental difficulties related to conducting volumetric measurements at high cosolvent concentrations and partly to the lack of the theoretical framework within which volumetric results can be rationalized in terms of solute-solvent and solute-cosolvent interactions. However, recent years have witnessed a revival of interest in application of the volumetric approach to characterization of solute-solvent interactions in protein solutions in binary mixtures. This review presents an overview of recent advances in the field, focusing on both the theoretical and the experimental developments. While presenting the current state of the art, it also outlines the strategy for future volumetric studies that will result in new insights into the old problem of interactions of proteins with protecting and denaturing osmolytes.     

Coevolution-driven predator-prey cycles: predicting the characteristics of eco-coevolutionary cycles using fast-slow dynamical systems theory

Eco-coevolutionary theory predicts that predator-prey coevolution occurring on the time scale of ecological dynamics (e.g., changes in population abundances) can drive novel kinds of predator-prey cycles, e.g., cryptic cycles where one species cycles while the other remains effectively constant and clockwise cycles where peaks in predator density precede peaks in prey density. However, because this body of theory has focused on particular models and studied the different cycle types in isolation, it is unclear what biological characteristics (e.g., costs for offense or defense) determine when a particular cycle type will arise. In this study, I explore the kinds of predator-prey cycles that arise in a general eco-coevolutionary model where there is disruptive selection and the coevolutionary dynamics are fast relative to the ecological dynamics of the system. With a graphical tool created using the theory of fast-slow dynamical systems, I predict what kinds of cycles can arise in the model and how cycle type depends on the costs for prey defense and predator offense. Fast-slow dynamical systems theory requires a separation of time scales between the ecological and evolutionary processes; however, numerical simulations show that this tool can help predict how coevolution drives populations cycles in systems where the speeds of ecological and evolutionary dynamics are comparable. Thus, this work is a step forward in building a general eco-coevolutionary theory.     

From NMR chemical shifts to amino acid types: Investigation of the predictive power carried by nuclei

An approach to automatic prediction of the amino acid type from NMR chemical shift values of its nuclei is presented here, in the frame of a model to calculate the probability of an amino acid type given the set of chemical shifts. The method relies on systematic use of all chemical shift values contained in the BioMagResBank (BMRB). Two programs were designed, one (BMRB stats) for extracting statistical chemical shift parameters from the BMRB and another one (RESCUE2) for computing the probabilities of each amino acid type, given a set of chemical shifts. The Bayesian prediction scheme presented here is compared to other methods already proposed: PROTYP RESCUE and PLATON and is found to be more sensitive and more specific. Using this scheme, we tested various sets of nuclei. The two nuclei carrying the most information are C(beta) and H(beta), in agreement with observations made in Grzesiek and Bax, 1993. Based on four nuclei: H(beta), C(beta), C(alpha) and C', it is possible to increase correct predictions to a rate of more than 75%. Taking into account the correlations between the nuclei chemical shifts has only a slight impact on the percentage of correct predictions: indeed, the largest correlation coefficients display similar features on all amino acids.     

Pollination mode in fig wasps: the predictive power of correlated traits

The over 700 species of Ficus are thought to have co-speciated with their obligate pollinators (family Agaonidae). Some of these wasp species pollinate figs actively, while others are passive pollinators. Based on direct observations of mode of pollination in 88 species, we show that mode of pollination can confidently be predicted from fig traits only (anther-to-ovule ratio) or from wasp traits only (presence of coxal combs). The presence of pollen pockets is not a predictor of mode of pollination. Data, direct and indirect, on 142 species, demonstrate numerous cases of the loss of active pollination and suggest one or few origins of active pollination. Hence, active pollination, an impressive example of the sophisticated traits that may result from mutualistic coevolution, depends on selective forces that can be overcome in some species, allowing reversions. Despite frequent loss, active pollination remains the predominant mode of pollination in Ficus.     

Quality coding by neural populations in the early olfactory pathway: analysis using information theory and lessons for artificial olfactory systems

In this article, we analyze the ability of the early olfactory system to detect and discriminate different odors by means of information theory measurements applied to olfactory bulb activity images. We have studied the role that the diversity and number of receptor neuron types play in encoding chemical information. Our results show that the olfactory receptors of the biological system are low correlated and present good coverage of the input space. The coding capacity of ensembles of olfactory receptors with the same receptive range is maximized when the receptors cover half of the odor input space - a configuration that corresponds to receptors that are not particularly selective. However, the ensemble's performance slightly increases when mixing uncorrelated receptors of different receptive ranges. Our results confirm that the low correlation between sensors could be more significant than the sensor selectivity for general purpose chemo-sensory systems, whether these are biological or biomimetic.     

New oxandrolone derivatives by biotransformation using Rhizopus stolonifer

Structural transformation of the steroidal lactone, oxandrolone (1), by suspended-cell cultures of the plant pathogen fungus Rhizopus stolonifer, resulted in the production of three new metabolites. These metabolites were identified as 11α-hydroxyoxandrolone (2), 6α-hydroxyoxandrolone (3) and 9α-hydroxyoxandrolone (4), by different spectroscopic methods and single-crystal X-ray diffraction analysis for metabolite 2. Compounds 1 and 3 showed a significant β-glucuronidase inhibitory activity.     

Biochemical polymorphic systems in inbred lines of chickens: A survey

Biochemical polymorphic gene frequency profiles from blood samples of two outbred and seven inbred lines of chickens were studied for hemoglobin, albumin, transferrin, alkaline phosphatase, esterase II, and leucine aminopeptidase, and from egg samples of these lines for ovoalbumen, ovoglobulin (G₂ and G₃), and conalbumen. Complete gene fixation was found for hemoglobin, albumin, transferrin, ovalbumin, ovoglobulin, and conalbumin. The same alleles were fixed in each system in each line. For four systems, a particular allele within a system predominated in seven populations; gene frequencies ranged from 0.60 to 0.98. For esterases I and II, the genes Es-I ^B and Es-II ^S ranged in frequency between 0.82 and 0.97, and between 0.64 and 0.93, respectively. For ovoglobulin, G ₂ B ranged between 0.75 and 1.00 with four lines fixed for this allele. The rather remarkable similarity of gene frequency profiles among lines, several of which are only remotely related, suggests that certain characteristic polymorphic frequencies for these biochemical polymorphisms possess higher adaptive values in an evolutionary sense.     

On the predictive power of numerical and Braun-Blanquet classification: an example from beechwoods

Abstract. A numerical classification method and the Braun-Blanquet method, based on external criteria, were compared with the aim of clarifying the differences in predictive power. The numerical analysis leads to a changed perspective on the floristic data and produces, as a whole, an ecologically more differentiated classification. The groups produced by numerical analysis are almost identical with the Braun-Blanquet classification with respect to marginal site conditions but they may differ in cases of intermediate site conditions. Nevertheless, a classification emerges which is on the whole, both floristically and as to site, very similar to the Braun-Blanquet classification. The discriminatory importance of the external variables is, to a large extent, the same in both methods, but discriminant analysis shows that the numerical classification is somewhat more predictive.     

Some new directions in control theory inspired by systems biology

This paper, addressed primarily to engineers and mathematicians with an interest in control theory, argues that entirely new theoretical problems arise naturally when addressing questions in the field of systems biology. Examples from the author's recent work are used to illustrate this point.