Nonlinear deterministic equations in biological evolution

We review models of biological evolution in which the population frequency changes deterministically with time. If the population is self-replicating, although the equations for simple prototypes can be linearised, nonlinear equations arise in many complex situations. For sexual populations, even in the simplest setting, the equations are necessarily nonlinear due to the mixing of the parental genetic material. The solutions of such nonlinear equations display interesting features such as multiple equilibria and phase transitions. We mainly discuss those models for which an analytical understanding of such nonlinear equations is available.Comment: Invited review for J. Nonlin. Math. Phy

Comparison of Gene-Transfer Efficiency in Human Embryonic Stem Cells

Technologies designed to allow manipulation and modification of human embryonic stem (hES) cells are numerous and vary in the complexity of their methods, efficiency, reliability, and safety. The most commonly studied and practiced of these methods include electroporation, lipofection, nucleofection, and lentiviral transduction. However, at present, it is unclear which protocol offers the most efficient and reliable method of gene transfer to hES cells. In this study, a bi-fusion construct with ubiquitin promoter driving enhanced green fluorescent protein reporter and the firefly luciferase (pUb-eGFP-Fluc) along with neomycin selection marker was used for in vitro and in vivo studies. In vitro studies examined the transfection efficiency and viability of each technique using two hES cell lines (male H1 and female H9 cells). Lentiviral transduction demonstrated the highest efficiency (H1: 25.3 ± 4.8%; H9: 22.4 ± 6.5%) with >95% cell viability. Nucleofection demonstrated transfection efficiency of 16.1 ± 3.6% (H1) and 5.8 ± 3.2% (H9). However, minimal transfection efficiency was observed with electroporation (2.1 ± 0.4% (H1) and 1.9 ± 0.3% (H9)) and lipofection (1.5 ± 0.5% (H1) and 1.3 ± 0.2% (H9); P < 0.05 vs. lentiviral transduction). Electroporation also demonstrated the highest cell death (62 ± 11% (H1) and 42 ± 10% (H9)) followed by nucleofection (25 ± 9% (H1) and 30 ± 15 (H9)). Importantly, lentiviral transduction generated a greater number of hES cell lines stably expressing the double-fusion reporter gene (hES-DF) compared to other transfection techniques. Finally, following subcutaneous transplantation into immunodeficient nude mice, the hES-eGFP-Fluc cells showed robust proliferation as determined by longitudinal bioluminescence imaging. In summary, this study demonstrates that lentiviral transduction and nucleofection are efficient, simple, and safe techniques for reliable gene transfer in hES cells. The double-fusion construct provides an attractive approach for generating stable hES cell lines and monitoring engraftment and proliferation in vitro and in vivo

Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

BACKGROUND: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. RESULTS: To address this problem, we developed eRank(PPI), an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank(PPI) employs multiple features including interface probability estimates calculated by eFindSite(PPI) and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank(PPI) consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. CONCLUSIONS: eRank(PPI) was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi

Can general practice help address youth mental health? A retrospective cross-sectional study in Dublin\u27s south inner city

Aims: With general practice potentially having an important role in early intervention of mental and substance use disorders among young people, we aim to explore this issue by determining the prevalence of psychological problems and general practice/health service utilization among young people attending general practice. Methods: A retrospective cross-sectional study of patients attending three general practices in Dublin city. Results: Among a sample of young people (mostly women, 44% general medical services (GMS) eligible), we observed considerable contact with general practice, both lifetime and for the 2 years of the study. The mean consultation rate was 3.9 consultations in 2 years and psychosocial issues (most commonly stress/anxiety and depression) were documented in 35% of cases. Identification of psychosocial issues was associated with GMS eligibility, three or more doctor consultations, and documentation of smoking and drinking status. Conclusions: Psychosocial issues are common among young people attending general practice and more work on their epidemiology and further identification in general practice are advocated.ACCEPTEDpeer-reviewe