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.     

Limited proteolysis of native proteins: the interaction between avidin and proteinase K.

Avidin is a tetramer of 16-kDa subunits that have a high affinity for biotin. Proteolysis of native apoavidin by proteinase K results in a limited attack at the loop between beta-strands 3 and 4, involving amino acids 38-43. Specifically, sites of proteolysis are at Thr 40-Ser 41 and Asn 42-Glu 43. The limited proteolysis results in an avidin product that remains otherwise intact and which has enhanced binding for 4'-hydroxyazobenzene-2-benzoic acid (HABA), a chromogenic reporter that can occupy the biotin-binding site. Saturation of the biotin-binding site with the natural ligand protects avidin from proteolysis, but saturation with HABA enhances the rate of proteolysis of the same site. Analysis of the three-dimensional structures of apoavidin and holoavidin reveals that the 3-4 loop is accessible to solvent and scores highly in an algorithm developed to identify sites of proteolytic attack. The structure of holoavidin is almost identical to the apoprotein. In particular, the 3-4 loop has the same structure in the apo and holo forms, yet there are marked differences in proteolytic susceptibility of this region. Evidence suggests that the 3-4 loop is rather mobile and flexible in the apoprotein, and that it becomes constrained upon ligand binding. In one crystal structure of the apoprotein, this loop appears constrained by contacts with symmetry-related molecules. Structural analyses suggest that the "lid" to the biotin-binding site, formed by the 3-4 loop, is displaced and made more accessible by HABA binding, thereby enhancing its proteolytic susceptibility.     

单纯形加速法拟合生态学中的非线性模型

本文以Logistic模型,Taylor幂法则模型,Holling功能反应模型,以及种群内禀增长力Rm等模型的拟合和参数估计为例,探讨单纯形加速法在生态模型优化拟合和参数估计中的应用.结果表明,单纯形加速法拟合生态学中的非线性模型不仅适用广泛,而且拟合过程是直接求原来非线性模型的最优拟合,因而优于生态学中通常使用的将原模型“线性化后再拟合”的方法,而与其它一些最优化方法,如:麦夸方法、枚举选优法等比较,由于单纯形法不需计算目标函数的偏导数,因而计算不受目标函数及其偏导函数复杂程度的限制,而且对于各种模型其求优计算过程十分相似,可以编制统一的计算程序.本研究所编制的计算机程序对于本文未提到的其它一些模型也是完全适用的,在应用时仅需修改定义目标函数的自定义函数语句即可.研究也发现,在求优过程中,只要搜索系数选择适当和实际数据合理,是可以保证寻优成功的.