Evolutionary epistemology as an overlapping,interlevel theory

I examine the branch of evolutionary epistemology which tries to account for the character of cognitive mechanisms in animals and humans by extending the biological theory of evolution to the neurophysiological substrates of cognition. Like Plotkin, I construe this branch as a struggling science, and attempt to characterize the sort of theory one might expect to find this truly interdisciplinary endeavor, an endeavor which encompasses not only evolutionary biology, cognitive psychology, and developmental neuroscience, but also and especially, the computational modeling of artificial life programming; I suggest that extending Schaffner's notion of interlevel theories to include both horizontal and vertical levels of abstraction best fits the theories currently being developed in cognitive science. Finally, I support this claim with examples drawn from computational modeling data using the genetic algorithm.     

FINGAR: A new genetic algorithm-based method for fitting NMR data

Summary A new NMR refinement method, FINGAR (FIt NMR using a Genetic AlgoRithm), has been developed, which allows one to determine a weighted set of structures that best fits measured NMR-derived data. This method shows appreciable advantages over commonly used refinement methods. FINGAR generates an ensemble of conformations whose average reproduces the experimental NMR-derived restraints. In addition, a statistical importance weight is assigned to each of the conformations in the ensemble. As a result, one is not limited to simply presenting an envelope of sampled conformers. Instead, one can subsequently focus on a select few conformers of high weight. This is critical, because many structural analyses depend on using discrete conformations, not simply averages or ensembles. The genetic algorithm used by FINGAR allows one to simultaneously and reliably fit against many restraints, and to generate solutions which include as many conformations with non-zero weights as are necessary to generate the best fit. An added benefit of FINGAR is that because the time-consuming step in this method needs only to be performed once, in the beginning of the first run, numerous FINGAR simulations can be performed rapidly.     

Clustering of fatty acids in phospholipid bilayers

From electrophoresis experiments it is concluded that acidic phospholipids incorporated in liquid crystalline phosphatidylcholine bilayers at neutral pH are randomly distributed. The same is true for spin-labelled fatty acids. In contrast, long chain fatty acids are not fully ionized at neutral pH and appear to be clustered, i.e. they segregate out into patches. Only at $\text{pH}\text{>11}$ is the fatty acid-COOH group fully ionized and charge repulsion leads to a random distribution of the fatty acid within the plane of the bilayer.     

Biolistic mediated site-specific integration in rice

Cre-lox mediated site-specific integration in tobacco or Arabidopsis used polyethylene glycol or Agrobacterium, respectively, to deliver the integrating DNA. The polyethylene glycol method is inconvenient since it requires the use of protoplasts. The Agrobacterium method is inefficient as the single-stranded T-DNA is not a substrate for Cre-lox recombination. In this study, we tested the biolistic method for the site-specific insertion of DNA into the rice genome. Two target callus lines, each harboring a single genomic lox target, were generated by Agrobacterium-mediated transformation. The target callus lines were subjected to a second round of transformation by particle bombardment with a construct designed to excise the plasmid backbone from the integrating DNA, followed by the recombination of the integrating DNA into the genomic lox target. Site-specific integration was obtained from both target callus lines. Three integrant plants were regenerated from one target line and were found to have a precise copy of the integrating DNA at the target site, although only one plant has the integrating DNA as the sole copy in the genome. Site-specific integration through the biolistic delivery of DNA can be considered for other plants that are transformable via particle bombardment.     

De novo design of the hydrophobic core of ubiquitin.

We have previously reported the development and evaluation of a computational program to assist in the design of hydrophobic cores of proteins. In an effort to investigate the role of core packing in protein structure, we have used this program, referred to as Repacking of Cores (ROC), to design several variants of the protein ubiquitin. Nine ubiquitin variants containing from three to eight hydrophobic core mutations were constructed, purified, and characterized in terms of their stability and their ability to adopt a uniquely folded native-like conformation. In general, designed ubiquitin variants are more stable than control variants in which the hydrophobic core was chosen randomly. However, in contrast to previous results with 434 cro, all designs are destabilized relative to the wild-type (WT) protein. This raises the possibility that beta-sheet structures have more stringent packing requirements than alpha-helical proteins. A more striking observation is that all variants, including random controls, adopt fairly well-defined conformations, regardless of their stability. This result supports conclusions from the cro studies that non-core residues contribute significantly to the conformational uniqueness of these proteins while core packing largely affects protein stability and has less impact on the nature or uniqueness of the fold. Concurrent with the above work, we used stability data on the nine ubiquitin variants to evaluate and improve the predictive ability of our core packing algorithm. Additional versions of the program were generated that differ in potential function parameters and sampling of side chain conformers. Reasonable correlations between experimental and predicted stabilities suggest the program will be useful in future studies to design variants with stabilities closer to that of the native protein. Taken together, the present study provides further clarification of the role of specific packing interactions in protein structure and stability, and demonstrates the benefit of using systematic computational methods to predict core packing arrangements for the design of proteins.     

Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization

The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results were obtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R²) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.