The hope engendered by the end of the Cold War's great power struggle was quickly dashed by a progression of seemingly disjointed setbacks in the form of economic crises, regional wars based on nationalism and ethnicity, and global crime and terrorism. These varied challenges stem from the transformation of the post-war global political economy of bounded capitalism, in which domestic social welfare policies mitigated the rapid spread of an international liberal economic order. This status quo, created by the United States and ts partners, is in flux as epochal demographic and economic changes remake the international system. Social welfare plays a central role in this transformation, as developed states struggle to pay for their systems and governments' incapacity stokes citizens' discontent in developing states. Any attempt to deal with coming challenges must address the deeper social and economic transformations at work, rather than focusing piecemeal on their symptoms.

: Saddam Hussein's patrimonial coercive rule reshaped major aspects of the Iraqi state and society, providing structures and motivations that have fueled resistance in the wake of regime change. By linking literature describing the effects of Ba'ath rule on the Iraqi state, society, and individual to the characteristics and motivations of the resistance, a more nuanced understanding of the complex landscape of Iraqi transition is possible. Repressive regimes produce a lasting and complex legacy in the structures of state and society that they leave behind. This legacy is often contentious and unpredictable, complicating efforts toward a democratic transition. This thesis concludes that, in the case of Iraq, patrimonial coercive rule produced a set of Sunni sub-state power structures that coveted the state and personal powers enjoyed under the old system. This sub-state landscape has proven to be difficult terrain for a successful transition, producing a network of actors that resist for varied motives. Exploration of the case of Iraqi transition reveals a demand for balanced political and military policies that address the sociopolitical roots of the resistance as well as the violent symptoms. Military initiatives alone cannot produce a solution to the problems in Iraq.

As humans contain a currently active L1 (LINE-1) non-LTR retrotransposon family (Ta-1), the human genome database likely provides only a partial picture of Ta-1-generated diversity. We used a non-biased method to clone Ta-1 retrotransposon-containing loci from representatives of four ethnic populations. We obtained 277 distinct Ta-1 loci and identified an additional 67 loci in the human genome database. This collection represents approximately 90% of the Ta-1 population in the individuals examined and is thus more representative of the insertional history of Ta-1 than the human genome database, which lacked approximately 40% of our cloned Ta-1 elements. As both polymorphic and fixed Ta-1 elements are as abundant in the GC-poor genomic regions as in ancestral L1 elements, the enrichment of L1 elements in GC-poor areas is likely due to insertional bias rather than selection. Although the chromosomal distribution of Ta-1 inserts is generally a function of chromosomal length and gene density, chromosome 4 significantly deviates from this pattern and has been much more hospitable to Ta-1 insertions than any other chromosome. Also, the intra-chromosomal distribution of Ta-1 elements is not uniform. Ta-1 elements tend to cluster, and the maximal gaps between Ta-1 inserts are larger than would be expected from a model of uniform random insertion.

Cancer cells frequently have disease-specific chromosome rearrangements1,2,3,4. It is poorly understood why translocations between chromosomes recur at specific breakpoints in the genome. Here we provide evidence that higher-order spatial genome organization is a contributing factor in the formation of recurrent translocations. We show that MYC, BCL and immunoglobulin loci, which are recurrently translocated in various B-cell lymphomas, are preferentially positioned in close spatial proximity relative to each other in normal B cells. Loci in spatial proximity are non-randomly positioned towards the nuclear interior in normal B cells. This locus proximity is the consequence of higher-order genome structure rather than a property of individual genes. Our results suggest that the formation of specific translocations in human lymphomas, and perhaps other tissues, is determined in part by higher-order spatial organization of the genome.

Chromosomes exist in the interphase nucleus as individual chromosome territories. It is unclear to what extent chromosome territories occupy particular positions with respect to each other and how structural rearrangements, such as translocations, affect chromosome organization within the cell nucleus. Here we analyze the relative interphase positioning of chromosomes in mouse lymphoma cells compared to normal splenocytes. We show that in a lymphoma cell line derived from an ATM(-/-) mouse, two translocated chromosomes are preferentially positioned in close proximity to each other. The relative position of the chromosomes involved in these translocations is conserved in normal splenocytes. Relative positioning of chromosomes in normal splenocytes is not due to their random distribution in the interphase nucleus and persists during mitosis. These observations demonstrate that the relative arrangement of chromosomes in the interphase nucleus can be conserved between normal and cancer cells and our data support the notion that physical proximity facilitates rearrangements between chromosomes.

Phospholipase D was first described in plant tissue but has recently been shown to occur in mammalian cells where it is activated by cell surface receptors. Its mode of activation by receptors in unclear. Biochemical studies suggest that it may occur downstream of other effector proteins and that small GTP-dependent regulatory proteins may be involved. The sequence in a non-designated region of mammalian phospholipase D1 and 2 shows similarity to a structural domain that is present in signalling proteins that are regulated by protein kinases or heterotrimeric G-proteins. Mammalian phospholipase D has structural similarities with other lipid signalling phospholipases and thus may be regulated by receptors in an analogous fashion.

We have compared a novel sequence-structure matching technique, FORESST, for detecting remote homologs to three existing sequence based methods, including local amino acid sequence similarity by BLASTP, hidden Markov models (HMMs) of sequences of protein families using SAM, HMMs based on sequence motifs identified using meta-MEME. FORESST compares predicted secondary structures to a library of structural families of proteins, using HMMs. Altogether 45 proteins from nine structural families in the database CATH were used in a cross-validated test of the fold assignment accuracy of each method. Local sequence similarity of a query sequence to a protein family is measured by the highest segment pair (HSP) score. Each of the HMM-based approaches (FORESST, MEME, amino acid sequence-based HMM) yielded log-odds score for the query sequence. In order to make a fair comparison among these methods, the scores for each method were converted to Z-scores in a uniform way by comparing the raw scores of a query protein with the corresponding scores for a set of unrelated proteins. Z-Scores were analyzed as a function of the maximum pairwise sequence identity (MPSID) of the query sequence to sequences used in training the model. For MPSID above 20%, the Z-scores increase linearly with MPSID for the sequence-based methods but remain roughly constant for FORESST. Below 15%, average Z-scores are close to zero for the sequence-based methods, whereas the FORESST method yielded average Z-scores of 1.8 and 1.1, using observed and predicted secondary structures, respectively. This demonstrates the advantage of the sequence-structure method for detecting remote homologs.

Here we propose an approach to include global structural information in the secondary structure prediction procedure based on hidden Markov models (HMMs) of protein folds. We first identify the correct fold or 'topology' of a protein by means of the HMMs of topology families of proteins. Then the most likely structural model for that protein is used to modify the sequence of secondary structure states previously obtained with a prediction algorithm. Our goal is to investigate the effect on the prediction accuracy of including global structural information in the secondary structure prediction scheme, by means of the HMMs. We find that when the HMM of the predicted topology of a protein is used to adjust the secondary structure sequence predicted originally with the Quadratic-Logistic method, the cross-validated prediction accuracy (Q3) improves by 3%. The topology is correctly predicted in 68% of the cases. We conclude that this HMM based approach is a promising tool for effectively incorporating global structural information in the secondary structure prediction scheme.

Statistical potentials based on pairwise interactions between C-alpha atoms are commonly used in protein threading/fold-recognition attempts. Inclusion of higher order interaction is a possible means of improving the specificity of these potentials. Delaunay tessellation of the C-alpha-atom representation of protein structure has been suggested as a means of defining multi-body interactions.A large number of parameters are required to define all four-body interactions of 20 amino acid types (20(4) = 160,000). Assuming that residue order within a four-body contact is irrelevant reduces this to a manageable 8,855 parameters, using a nonredundant dataset of 608 protein structures.Three Lines of evidence support the significance and utility of the four-body potential for sequence-structure matching. First, compared to the four-body model, all lower-order interaction models (three-body, two-body, one-body) are found statistically inadequate to explain the frequency distribution of residue contacts.Second, coherent patterns of interaction are seen in a graphic presentation of the four-body potential. Many patterns have plausible biophysical explanations and are consistent across sets of residues sharing certain properties (e.g., size, hydrophobicity, or charge).Third, the utility of the multi-body potential is tested on a test set of 12 same-length pairs of proteins of known structure for two protocols: Sequence-recognizes-structure, where a query sequence is threaded (without gap) through the native and a non-native structure; and structure-recognizes-sequence, where a query structure is threaded by its native and another non-native sequence. Using cross-validated training, protein sequences correctly recognized their native structure in all 24 cases. Conversely, structures recognized the native sequence in 23 of 24 cases. Further, the score differences between correct and decoy structures increased significantly using the three- or four-body potential compared to potentials of lower order.

The three-dimensional fold of a protein is described by the organization of its secondary structure elements in 3D space, i.e. its "topology". We find that the protein topology can be recognized from the ID sequence of secondary structure states of the residues alone. Automated recognition is facilitated by use of hidden Markov models (HMMs) to represent topology families of proteins. Such models can be trained on the experimentally observed secondary structure sequences of family members using well established algorithms. Here, we model various topology groups in the alpha class of proteins and identify, from a large database, those proteins having the topology described by each model. The correct topology family for protein secondary structure sequences could be recognized 12 out of 14 times. When the observed secondary structure sequences are replaced with predicted sequences recognition is still achievable 8 out of 14 times. The success rate for observed sequences indicates that our approach will become increasingly useful as the accuracy of secondary prediction algorithms is improved. Our study indicates that the HMMs are useful for protein topology recognition even when no detectable primary amino acid sequence similarity is present. To illustrate the potential utility of our method, protein topology recognition is attempted on leptin, the obese gene product, and the human interleukin-6 sequence, for which fold predictions have been previously published.

We present an analysis of the blind predictions submitted to the fold recognition category for the second meeting on the Critical Assessment of techniques for protein Structure Prediction. Our method achieves fold recognition from predicted secondary structure sequences using hidden Markov models (HMMs) of protein folds. HMMs are trained only with experimentally derived secondary structure sequences of proteins having similar fold, therefore protein structures are described by the models at a remarkably simplified level. We submitted predictions for five target sequences, of which four were later found to be suitable for threading. Our approach correctly predicted the fold for three of them. For a fourth sequence the fold could have been correctly predicted if a better model for its structure was available. We conclude that we have additional evidence that secondary structure information represents an important factor for achieving fold recognition.

We construct a graphical representation of protein structure based on the 3D C-alpha carbon point set, using the Delaunay tessellation to define interacting quadruples of amino acid residues. The tessellation is filtered by two criteria: interaction distance less than 9.5 angstroms and circumsphere radius less than 8.0 angstroms using dataset of 608 protein structures of low mutual sequence identity and a likelihood ratio test, we show that 3-body and 4-body interactions are indeed significant. We identify particular significant three-body interactions by first reducing the dataset to interacting triples, and classifying amino acid residues in a reduced alphabet. Although cystein was previously shown to be a dominant source of 3-body interactions, we now identify additional significant 3-body interactions of charged, hydrophobic and small residues.

Most recent protein secondary structure prediction methods use sequence alignments to improve the prediction quality. We investigate the relationship between the location of secondary structural elements, gaps, and variable residue positions in multiple sequence alignments. We further investigate how these relationships compare with those found in structurally aligned protein families. We show how such associations may be used to improve the quality of prediction of the secondary structure elements, using the Quadratic-Logistic method with profiles. Furthermore, we analyze the extent to which the number of homologous sequences influences the quality of prediction. The analysis of variable residue positions shows that surprisingly, helical regions exhibit greater variability than do coil regions, which are generally thought to be the most common secondary structure elements in loops. However, the correlation between variability and the presence of helices does not significantly improve prediction quality. Gaps are a distinct signal for coil regions. Increasing the coil propensity for those residues occurring in gap regions enhances the overall prediction quality. Prediction accuracy increases initially with the number of homologues, but changes negligibly as the number of homologues exceeds about 14. The alignment quality affects the prediction more than other factors, hence a careful selection and alignment of even a small number of homologues can lead to significant improvements in prediction accuracy.

Many different methods have been presented for aligning multiple biological sequences. These methods can be classified into three categories: rigorous, tree-based, and iterative. The rigorous method, which always generates the optimal alignment, requires memory space and computation time proportional to the product of the sequence lengths. Even for a modest number of sequences, this method becomes impractical. As a result, the other two methods were introduced. The iterative methods were shown to generate better alignments than the tree-based methods. However, these methods require as much as 100 times longer computation time. A number of days may be required to align a large number of sequences (e.g., over 100 sequences) sequentially. We present a parallel speculative computational method which reduces the computation time of the iterative methods from days to minutes. To evaluate our parallel method, we implemented a speculative computation version of the iterative improvement method of Berger and Munson on an Intel iPSC/860 parallel computer. The empirical results demonstrate that our parallel method obtained a significant speed up in comparison to the sequential method.

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We extend logistic discriminant function methodology to compete effectively with neural networks and "information theory" methods in prediction of protein secondary structure. Unlike "black-box" methods, our model produces 400 pairwise interaction parameters which are interpretable from a molecular standpoint. Under optimal conditions, our model can produce up to 65.9% crossvalidated prediction accuracy on three states. A broad family of models is searched using a semi-parametric (penalized) approach combined with stepwise parameter selection. We show that optimal models have about 800 effective parameters for this data set. The highest prediction accuracy is concentrated in a fraction of the total residues, and the confidence of a prediction can be easily calculated. Such high-confidence predictions may be useful as the basis for prediction of the complete structure of the protein.<>