Molecular dynamics recipes for genome research

Molecular dynamics (MD) simulation allows one to predict the time evolution of a system of interacting particles. It is widely used in physics, chemistry and biology to address specific questions about the structural properties and dynamical mechanisms of model systems. MD earned a great success in genome research, as it proved to be beneficial in sorting pathogenic from neutral genomic mutations. Considering their computational requirements, simulations are commonly performed on HPC computing devices, which are generally expensive and hard to administer. However, variables like the software tool used for modeling and simulation or the size of the molecule under investigation might make one hardware type or configuration more advantageous than another or even make the commodity hardware definitely suitable for MD studies. This work aims to shed lights on this aspect

Empowering precision medicine through high performance computing clusters

The role of High Performance Computing (HPC) in Medicine is greatly increase in these last years, moving from basic research to the clinics. With the advent of Next Generation Sequencing (NGS) technologies, diverse areas of human health have been investigated through different omics techniques. The extensive use of these NGS platforms to high throughput profile human health issues in a cost-efficient manner, is generating huge amount of sequencing data pushing " (https://www.facebook.com/pages/Oatext/1439466783004774) # $ % (https://www.youtube.com/user/users/oatext) ○ ○ ○ Article Article Info Author Info Figures & Data bioinformatic research in the big-data field. Speed, accuracy and reproducibility of massively sequencing analysis have allowed to transfer molecular biology knowledge into precision medicine. Furthermore, Molecular Dynamics (MD) earned a great importance in aiding genome research. Sequencing studies of cancer have allowed to detect and characterize mutated genes that drive tumorigenesis. As a complementary approach, from a biophysical perspective, MD simulations, executed on HPC architectures, have permitted to investigate the role played by pathological mutations on the molecular mechanism of activation

Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity

Using a large database (~ 215 000 records) of relevant articles, we empirically study the "complex systems" field and its claims to find universal principles applying to systems in general. The study of references shared by the papers allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific "trading zones", ie sub-communities that create an interface around specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and Technology (2012) 10.1002/asi.2264

Ribosome traffic on mRNAs maps to gene ontology : genome-wide quantification of translation initiation rates and polysome size regulation

Peer reviewedPublisher PD

Joint scaling laws in functional and evolutionary categories in prokaryotic genomes

We propose and study a class-expansion/innovation/loss model of genome evolution taking into account biological roles of genes and their constituent domains. In our model numbers of genes in different functional categories are coupled to each other. For example, an increase in the number of metabolic enzymes in a genome is usually accompanied by addition of new transcription factors regulating these enzymes. Such coupling can be thought of as a proportional "recipe" for genome composition of the type "a spoonful of sugar for each egg yolk". The model jointly reproduces two known empirical laws: the distribution of family sizes and the nonlinear scaling of the number of genes in certain functional categories (e.g. transcription factors) with genome size. In addition, it allows us to derive a novel relation between the exponents characterising these two scaling laws, establishing a direct quantitative connection between evolutionary and functional categories. It predicts that functional categories that grow faster-than-linearly with genome size to be characterised by flatter-than-average family size distributions. This relation is confirmed by our bioinformatics analysis of prokaryotic genomes. This proves that the joint quantitative trends of functional and evolutionary classes can be understood in terms of evolutionary growth with proportional recipes.Comment: 39 pages, 21 figure

Association of a homozygous GCK missense mutation with mild diabetes

Background: Homozygous inactivating GCK mutations have been repeatedly reported to cause severe hyperglycemia, presenting as permanent neonatal diabetes mellitus (PNDM). Conversely, only two cases of GCK homozygous mutations causing mild hyperglycemia have been so far described. We here report a novel GCK mutation (c.1116G>C, p.E372D), in a family with one homozygous member showing mild hyperglycemia. Methods: GCK mutational screening was carried out by Sanger sequencing. Computational analyses to investigate pathogenicity and molecular dynamics (MD) were performed for GCK-E372D and for previously described homozygous mutations associated with mild (n = 2) or severe (n = 1) hyperglycemia, used as references. Results: Of four mildly hyperglycemic family-members, three were heterozygous and one, diagnosed in the adulthood, was homozygous for GCK-E372D. Two nondiabetic family members carried no mutations. Fasting glucose (p = 0.016) and HbA1c (p = 0.035) correlated with the number of mutated alleles (0–2). In-silico predicted pathogenicity was not correlated with the four mutations’ severity. At MD, GCK-E372D conferred protein structure flexibility intermediate between mild and severe GCK mutations. Conclusions: We present the third case of homozygous GCK mutations associated with mild hyperglycemia, rather than PNDM. Our in-silico analyses support previous evidences suggesting that protein stability plays a role in determining clinical severity of GCK mutations

Family-specific scaling laws in bacterial genomes

Among several quantitative invariants found in evolutionary genomics, one of the most striking is the scaling of the overall abundance of proteins, or protein domains, sharing a specific functional annotation across genomes of given size. The size of these functional categories change, on average, as power-laws in the total number of protein-coding genes. Here, we show that such regularities are not restricted to the overall behavior of high-level functional categories, but also exist systematically at the level of single evolutionary families of protein domains. Specifically, the number of proteins within each family follows family-specific scaling laws with genome size. Functionally similar sets of families tend to follow similar scaling laws, but this is not always the case. To understand this systematically, we provide a comprehensive classification of families based on their scaling properties. Additionally, we develop a quantitative score for the heterogeneity of the scaling of families belonging to a given category or predefined group. Under the common reasonable assumption that selection is driven solely or mainly by biological function, these findings point to fine-tuned and interdependent functional roles of specific protein domains, beyond our current functional annotations. This analysis provides a deeper view on the links between evolutionary expansion of protein families and the functional constraints shaping the gene repertoire of bacterial genomes.Comment: 41 pages, 16 figure

Reduction of dynamical biochemical reaction networks in computational biology

Biochemical networks are used in computational biology, to model the static and dynamical details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multi-scaleness is another property of these networks, that can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler networks, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state and quasi-equilibrium approximations, and provide practical recipes for model reduction of linear and nonlinear networks. We also discuss the application of model reduction to backward pruning machine learning techniques