Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks

Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described.     

Study of statistical correlations in DNA sequences

Here we present a study of statistical correlations among different positions in DNA sequences and their implications by directly using the autocorrelation function. Such an analysis is possible now because of the availability of large sequences or even complete genomes of many organisms. After describing the way in which the autocorrelation function can be applied to DNA-sequence analysis, we show that long-range correlations, implying scale independence, appear in several bacterial genomes as well as in long human chromosome contigs. The source for such correlations in bacteria, which may extend up to 60 kb in Bacillus subtilis, may be related to massive lateral transfer of compositionally biased genes from other genomes. In the human genome, correlations extend for more than five decades and may be related to the evolution of the ’neogenome’, a modern evolutionary acquisition composed by GC-rich isochores displaying long-range correlations and scale invariance.     

A strategy to optimize the oligo-probes for microarray-based detection of viruses

DNA microarrays have been acknowledged to represent a promising approach for the detection of viral pathogens. However, the probes designed for current arrays could cover only part of the given viral variants, that could result in false-negative or ambiguous data. If all the variants are to be covered, the requirement for more probes would render much higher spot density and thus higher cost of the arrays. Here we have developed a new strategy for oligonucleotide probe design. Using type I human immunodeficiency virus (HIV-1) tat gene as an example, we designed the array probes and validated the optimized parameters in silico. Results show that the oligo number is significantly reduced comparing with the existing methods, while specificity and hybridization efficiency remain intact. The adoption of this method in reducing the oligo numbers could increase the detection capacity for DNA microarrays, and would significantly lower the manufacturing cost for making array chips. These authors contribute equally to the work.     

Thermodynamic criteria for high hit rate antisense oligonucleotide design

Antisense oligonucleotides are used for therapeutic applications and in functional genomic studies. In practice, however, many of the oligonucleotides complementary to an mRNA have little or no antisense activity. Theoretical strategies to improve the ‘hit rate’ in antisense screens will reduce the cost of discovery and may lead to identification of antisense oligonucleotides with increased potency. Statistical analysis performed on data collected from more than 1000 experiments with phosphorothioate-modified oligonucleotides revealed that the oligo-probes, which form stable duplexes with RNA (ΔG^o₃₇ ≤ –30 kcal/mol) and have small self-interaction potential, are more frequently efficient than molecules that form less stable oligonucleotide–RNA hybrids or more stable self-structures. To achieve optimal statistical preference, the values for self-interaction should be (ΔG^o₃₇) ≥ –8 kcal/mol for inter-oligonucleotide pairing and (ΔG^o₃₇) ≥ –1.1 kcal/mol for intra-molecular pairing. Selection of oligonucleotides with these thermodynamic values in the analyzed experiments would have increased the ‘hit rate’ by as much as 6-fold.     

Selection for environmental variation: a statistical analysis and power calculations to detect response

Data from uterine capacity in rabbits (litter size) were analyzed to determine whether the environmental variance was partly genetically determined. The fit of a classical homogeneous variance mixed linear (HOM) model and that of a genetically structured heterogeneous variance mixed linear (HET) model were compared. Various methods to assess the quality of fit favor the HET model. The posterior mean (95% posterior interval) of the additive genetic variance affecting the environmental variance was 0.16 (0.10; 0.25) and the corresponding number for the coefficient of correlation between genes affecting mean and variance was -0.74 (-0.90;-0.52). It is argued that stronger support for the HET model than that derived from statistical analysis of data would be provided by a successful selection experiment designed to modify the environmental variance. A simple selection criterion is suggested (average squared deviation from the mean of repeated records within individuals) and its predicted response and variance under the HET model are derived. This is used to determine the appropriate size and length of a selection experiment designed to change the environmental variance. Results from the analytical expressions are compared with those obtained using simulation. There is good agreement provided selection intensity is not intense.     

Thermodynamic cycle analysis and inhibitor design against beta-lactamase

Beta-lactamases are the most widespread resistance mechanism to beta-lactam antibiotics, such as the penicillins and cephalosporins. Transition-state analogues that bind to the enzymes with nanomolar affinities have been introduced in an effort to reverse the resistance conferred by these enzymes. To understand the origins of this affinity, and to guide design of future inhibitors, double-mutant thermodynamic cycle experiments were undertaken. An unexpected hydrogen bond between the nonconserved Asn289 and a key inhibitor carboxylate was observed in the X-ray crystal structure of a 1 nM inhibitor (compound 1) in complex with AmpC beta-lactamase. To investigate the energy of this hydrogen bond, the mutant enzyme N289A was made, as was an analogue of 1 that lacked the carboxylate (compound 2). The differential affinity of the four different protein and analogue complexes indicates that the carboxylate-amide hydrogen bond contributes 1.7 kcal/mol to overall binding affinity. Synthesis of an analogue of 1 where the carboxylate was replaced with an aldehyde led to an inhibitor that lost all this hydrogen bond energy, consistent with the importance of the ionic nature of this hydrogen bond. To investigate the structural bases of these energies, X-ray crystal structures of N289A/1 and N289A/2 were determined to 1.49 and 1.39 A, respectively. These structures suggest that no significant rearrangement occurs in the mutant versus the wild-type complexes with both compounds. The mutant enzymes L119A and L293A were made to investigate the interaction between a phenyl ring in 1 and these residues. Whereas deletion of the phenyl itself diminishes affinity by 5-fold, the double-mutant cycles suggest that this energy does not come through interaction with the leucines, despite the close contact in the structure. The energies of these interactions provide key information for the design of improved inhibitors against beta-lactamases. The high magnitude of the ion-dipole interaction between Asn289 and the carboxylate of 1 is consistent with the idea that ionic interactions can provide significant net affinity in inhibitor complexes.     

An overview of statistical approaches for adaptive designs and design modifications

With adaptive design methods for clinical trials, design elements such as sample size or further interim sample sizes may be changed during the course of the trial depending on all previously collected data. The focus of the overview is on group sequential approaches where the types of adaptations need not be specified in advance. An overview of the different statistical approaches for adaptive design methods proposed in the literature is given, relations between these methods are described and some perspectives of application and for future research are discussed.     

Information theory reveals large-scale synchronisation of statistical correlations in eukaryote genomes

We study short-range correlations in DNA sequences with methods from information theory and statistics. We find a persisting degree of identity between the correlation patterns of different chromosomes of a species. Except for the case of human and chimpanzee inter-species differences in this correlation pattern allow robust species distinction: in a clustering tree based upon the correlation curves on the level of individual chromosomes distinct clusters for the individual species are found. This capacity of distinguishing species persists, even when the length of the underlying sequences is drastically reduced. In comparison to the standard tool for studying symbol correlations in DNA sequences, namely the mutual information function, we find that an autoregressive model for higher order Markov processes significantly improves species distinction due to an implicit subtraction of random background.     

Geometrical correlations useful for design of sequence-specific DNA narrow groove binding ligands

Isohelical geometry of sequence-specific DNA narrow groove binding ligands was analyzed in terms of H-bond donor/acceptor complementarity between the base pair atoms facing into the narrow groove and the corresponding H-bond donating atoms regularly disposed along the ligand molecule. Spatial correlations found in analytical form were applied to analysis of naturally occurring and hypothetical drug molecule structures. For the case of B-like isohelices the permitted values of the distance L0 between each two neighboring H-bond donating atoms of the ligand as well as the bending angle tau 0 of the line subsequently connecting these atoms were estimated as follows: L0 congruent to (5.0 +/- 0.4) A; tau 0 congruent to (26 +/- 2) degrees.     

Thermodynamic integration calculations of binding free energy difference for Gly-169 mutation in subtilisin BPN′

The binding free energy difference for the Gly-169 → Ala-169 (G169A) mutation in subtilisin BPN′ complexed with a tripeptide substrate analogue is explored using the thermodynamic integration approach. The structure of the mutant enzyme–substrate complex obtained from free energy simulation is in good agreement with experimental X-ray refinement. The near perfect reversibility is obtained in the present work for ensuring the correctness of the free energy calculations. The results of the binding free energy difference are close to similar experimental data. © 1993 Wiley-Liss, Inc.     

Guidelines for the design and statistical analysis of experiments in papers submitted to ATLA

In vitro experiments need to be well designed and correctly analysed if they are to achieve their full potential to replace the use of animals in research. An "experiment" is a procedure for collecting scientific data in order to answer a hypothesis, or to provide material for generating new hypotheses, and differs from a survey because the scientist has control over the treatments that can be applied. Most experiments can be classified into one of a few formal designs, the most common being completely randomised, and randomised block designs. These are quite common with in vitro experiments, which are often replicated in time. Some experiments involve a single independent (treatment) variable, while other "factorial" designs simultaneously vary two or more independent variables, such as drug treatment and cell line. Factorial designs often provide additional information at little extra cost. Experiments need to be carefully planned to avoid bias, be powerful yet simple, provide for a valid statistical analysis and, in some cases, have a wide range of applicability. Virtually all experiments need some sort of statistical analysis in order to take account of biological variation among the experimental subjects. Parametric methods using the t test or analysis of variance are usually more powerful than non-parametric methods, provided the underlying assumptions of normality of the residuals and equal variances are approximately valid. The statistical analyses of data from a completely randomised design, and from a randomised-block design are demonstrated in Appendices 1 and 2, and methods of determining sample size are discussed in Appendix 3. Appendix 4 gives a checklist for authors submitting papers to ATLA.     

A statistical design for testing transgenerational genomic imprinting in natural human populations

Genomic imprinting is a phenomenon in which the same allele is expressed differently, depending on its parental origin. Such a phenomenon, also called the parent-of-origin effect, has been recognized to play a pivotal role in embryological development and pathogenesis in many species. Here we propose a statistical design for detecting imprinted loci that control quantitative traits based on a random set of three-generation families from a natural population in humans. This design provides a pathway for characterizing the effects of imprinted genes on a complex trait or disease at different generations and testing transgenerational changes of imprinted effects. The design is integrated with population and cytogenetic principles of gene segregation and transmission from a previous generation to next. The implementation of the EM algorithm within the design framework leads to the estimation of genetic parameters that define imprinted effects. A simulation study is used to investigate the statistical properties of the model and validate its utilization. This new design, coupled with increasingly used genome-wide association studies, should have an immediate implication for studying the genetic architecture of complex traits in humans.     

Optimization of fermentation media for nitrite oxidizing bacteria using sequential statistical design

The sequential statistical experimental design (Plackett-Burman, factorial, response surface and steepest ascent experiment) was applied to optimize the culture medium of nitrite oxidizing bacteria for improving the nitrite oxidizing rate. Estimated optimum medium composition of the nitrite oxidizing rate was as follows: NaHCO(3), 1.86gl(-1); NaNO(2), 2.04gl(-1); Na(2)CO(3), 0.2gl(-1); NaCl, 0.2gl(-1); KH(2)PO(4), 0.1gl(-1); MgSO(4).7H(2)O, 0.1gl(-1); and FeSO(4).7H(2)O, 0.01gl(-1). The nitrite oxidizing rate was increased by 48.0% and reached a maximum at 859.5+/-8.4mgNO(2)-N/gMLSS.d as compared to 580.7+/-25.8mgNO(2)-N/gMLSS.d. In the field trial, 50L of nitrite oxidizing bacteria concentrate (1.99gVSS/L) with 850mgNO(2)-N/gMLSS.d were added to 0.6ha of the aquaculture water. Nitrite level in all treated ponds remained very low compared to the steady increase observed in all of the control ponds during 7 days.     

A parsimonious statistical design and breeding procedure for evaluating and selecting desirable characteristics over environments

The concept of stability as described in the literature does not meet all of the desirable criteria required by growers of cultivars. Various types of possible responses are discussed, and these are divided into those desirable from a grower's viewpoint and those not. Measures of stability appearing in the literature are based on variances, linear regression slopes, and/or deviations from regression. The most desirable response type would be denoted as unstable by current concepts of stability. It is shown how to simulate environments that exceed the ranges found in practice. A statistical design is described which is the height of parsimony and has the advantage that the conditions varied are known. The experimental results can then be interpreted in light of the known conditions. The design is optimally cost effective in terms of funds, material, and personnel. A breeding procedure is presented for such characteristics as desired response, stability under current definitions, tolerance (to pests, cold, drought, etc.), protein, quality, fiber, etc.Technical Report Series No. Bu-960-MA of the Biometrics Unit, Cornell University, Ithaca, N.Y.Liberty Hyde Bailey Professor Emeritus, Biometrics Unit, Department of Plant Breeding and Biometry, Cornell University, Ithaca, NY; Assistant Professor of Vegetable Crops, IFAS, University of Florida, Everglades Research Center, Belle Glade, Fla.     

Primer design and marker clustering for multiplex SNP-IT primer extension genotyping assay using statistical modeling

MOTIVATION: The optimization of the primer design is critical for the development of high-throughput SNP genotyping methods. Recently developed statistical models of the SNP-IT primer extension genotyping reaction allow further improvement of primer quality for the assay. RESULTS: Here we describe how the statistical models can be used to improve primer design for the assay. We also show how to optimize clustering of the SNP markers into multiplex panels using statistical model for multiplex SNP-IT. The primer set failure probability calculated by a model is used as a minimization function for both primer selection and primers clustering. Three clustering algorithms for the multiplex genotyping SNP-IT assay are described and their relative performance is evaluated. We also describe the approaches to improve the speed of primer design and clustering calculations when using the statistical models. Our clustering decreases the average failure probability of the marker set by 7-25%. The experimental marker failure rate in the multiplex reaction was reduced dramatically and success rate can be achieved as high as 96%. AVAILABILITY: The primer design using statistical models is freely available from www.autoprimer.com.     

Genetic design and statistical power of nested association mapping in maize

We investigated the genetic and statistical properties of the nested association mapping (NAM) design currently being implemented in maize (26 diverse founders and 5000 distinct immortal genotypes) to dissect the genetic basis of complex quantitative traits. The NAM design simultaneously exploits the advantages of both linkage analysis and association mapping. We demonstrated the power of NAM for high-power cost-effective genome scans through computer simulations based on empirical marker data and simulated traits with different complexities. With common-parent-specific (CPS) markers genotyped for the founders and the progenies, the inheritance of chromosome segments nested within two adjacent CPS markers was inferred through linkage. Genotyping the founders with additional high-density markers enabled the projection of genetic information, capturing linkage disequilibrium information, from founders to progenies. With 5000 genotypes, 30-79% of the simulated quantitative trait loci (QTL) were precisely identified. By integrating genetic design, natural diversity, and genomics technologies, this new complex trait dissection strategy should greatly facilitate endeavors to link molecular variation with phenotypic variation for various complex traits.