Predicting species diversity in tropical forests

A fundamental question in ecology is how many species occur within a given area. Despite the complexity and diversity of different ecosystems, there exists a surprisingly simple, approximate answer: the number of species is proportional to the size of the area raised to some exponent. The exponent often turns out to be roughly 1/4. This power law can be derived from assumptions about the relative abundances of species or from notions of self-similarity. Here we analyze the largest existing data set of location-mapped species: over one million, individually identified trees from five tropical forests on three continents. Although the power law is a reasonable, zeroth-order approximation of our data, we find consistent deviations from it on all spatial scales. Furthermore, tropical forests are not self-similar at areas ≤50 hectares. We develop an extended model of the species-area relationship, which enables us to predict large-scale species diversity from small-scale data samples more accurately than any other available method.

Predicting crossover generation in DNA shuffling

We introduce a quantitative framework for assessing the generation of crossovers in DNA shuffling experiments. The approach uses free energy calculations and complete sequence information to model the annealing process. Statistics obtained for the annealing events then are combined with a reassembly algorithm to infer crossover allocation in the reassembled sequences. The fraction of reassembled sequences containing zero, one, two, or more crossovers and the probability that a given nucleotide position in a reassembled sequence is the site of a crossover event are estimated. Comparisons of the predictions against experimental data for five example systems demonstrate good agreement despite the fact that no adjustable parameters are used. An in silico case study of a set of 12 subtilases examines the effect of fragmentation length, annealing temperature, sequence identity and number of shuffled sequences on the number, type, and distribution of crossovers. A computational verification of crossover aggregation in regions of near-perfect sequence identity and the presence of synergistic reassembly in family DNA shuffling is obtained.

Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes.

A computational model is presented that can be used as a tool in the design of safer chemicals. This model predicts the rate of hydrogen-atom abstraction by cytochrome P450 enzymes. Excellent correlations between biotransformation rates and the calculated activation energies (delta Hact) of the cytochrome P450-mediated hydrogen-atom abstractions were obtained for the in vitro biotransformation of six halogenated alkanes (1-fluoro-1,1,2,2-tetrachloroethane, 1,1-difluoro-1,2,2-trichloroethane, 1,1,1-trifluro-2,2-dichloroethane, 1,1,1,2-tetrafluoro-2-chloroethane, 1,1,1,2,2,-pentafluoroethane, and 2-bromo-2-chloro-1,1,1-trifluoroethane) with both rat and human enzyme preparations: In(rate, rat liver microsomes) = 44.99 - 1.79(delta Hact), r2 = 0.86; In(rate, human CYP2E1) = 46.99 - 1.77(delta Hact), r2 = 0.97 (rates are in nmol of product per min per nmol of cytochrome P450 and energies are in kcal/mol). Correlations were also obtained for five inhalation anesthetics (enflurane, sevoflurane, desflurane, methoxyflurane, and isoflurane) for both in vivo and in vitro metabolism by humans: In[F(-)]peak plasma = 42.87 - 1.57(delta Hact), r2 = 0.86. To our knowledge, these are the first in vivo human metabolic rates to be quantitatively predicted. Furthermore, this is one of the first examples where computational predictions and in vivo and in vitro data have been shown to agree in any species. The model presented herein provides an archetype for the methodology that may be used in the future design of safer chemicals, particularly hydrochlorofluorocarbons and inhalation anesthetics.