Mutated hilltop inflation revisited

In this work we re-investigate pros and cons of mutated hilltop inflation. Applying Hamilton-Jacobi formalism we solve inflationary dynamics and find that inflation goes on along the ${\cal W}_{-1}$ branch of the Lambert function. Depending on the model parameter mutated hilltop model renders two types of inflationary solutions: one corresponds to small inflaton excursion during observable inflation and the other describes large field inflation. The inflationary observables from curvature perturbation are in tune with the current data for a wide range of the model parameter. The small field branch predicts negligible amount of tensor to scalar ratio $r\sim \mathcal{O}(10^{-4})$, while the large field sector is capable of generating high amplitude for tensor perturbations, $r\sim \mathcal{O}(10^{-1})$. Also, the spectral index is almost independent of the model parameter along with a very small negative amount of scalar running. Finally we find that the mutated hilltop inflation closely resembles the $\alpha$-attractor class of inflationary models in the limit of $\alpha\phi\gg 1$.Comment: 17 pages, 13 figures. Accepted for publication in EPJ

Modification of Aminoacyl tRNA Synthetase in Order to Incorporate an Unnatural Amino Acid

Proteins allow daily processes in the cell to occur. A protein consists of amino acids. There are twenty natural amino acids coded for in the DNA of organisms. The natural amino acids can be modified to form unnatural amino acids (UAAs). UAAs have useful characteristics when inserted into a protein of a cell, like the ability of fluoresce, which makes their incorporation important in research. For an UAA to be incorporated into a protein, it must be bound to a transport RNA molecule by an enzyme called aminoacyl tRNA synthetase (aaRS). An existing aaRS was modified in E. Coli bacterial cells to incorporate 3-(2-pyridyl)-L-Alanine since it has metal-binding capabilities. Once incorporated, the UAA acts as a sensor for a metal, making it useful to environmental fields. The aaRS was mutated using saturation mutagenesis at sites L32, V65, W108, G158, A159. The cells were run through a positive screen to determine if the mutated aaRS incorporated the UAA into a green fluorescent protein, which glowed if the UAA was inserted. The results of the positive screen showed mutated aaRSs 2, 4, 7, and 8 incorporated 3-(2-pyridyl)-L-Alanine, while mutated aaRSs 2, 5, 6, 7, 8, and 9 incorporated p-cyanophenylalanine. A negative screen to test if the mutated aaRS only incorporate an UAA, not natural amino acids still present in the cell, will be run on the mutated aaRSs passing the positive screen

PathExpand: Extending biological pathways using molecular interaction networks

We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyze their enrichment in cancer mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes

Effects of local mutations in quadratic iterations

We introduce mutations in replication systems, in which the intact copying mechanism is performed by discrete iterations of a complex quadratic map. More specifically, we consider a "correct" function acting on the complex plane (representing the space of genes to be copied). A "mutation" is a different ("erroneous") map acting on a complex locus of given radius r around a mutation focal point. The effect of the mutation is interpolated radially to eventually recover the original map when reaching an outer radius R. We call the resulting map a "mutated" map. In the theoretical framework of mutated iterations, we study how a mutation (replication error) affects the temporal evolution of the system, on both a local and global scale (from cell diffetentiation to tumor formation). We use the Julia set of the system to quantify simultaneously the long-term behavior of the entire space under mutated maps. We analyze how the position, timing and size of the mutation can alter the topology of the Julia set, hence the system's long-term evolution, its progression into disease, but also its ability to recover or heal. In the context of genetics, mutated iterations may help shed some light on aspects such as the importance of location, size and type of mutation when evaluating a system's prognosis, and of customizing intervention.Comment: 15 pages, 15 figures, 7 reference

Faster Mutation Analysis via Equivalence Modulo States

Mutation analysis has many applications, such as asserting the quality of test suites and localizing faults. One important bottleneck of mutation analysis is scalability. The latest work explores the possibility of reducing the redundant execution via split-stream execution. However, split-stream execution is only able to remove redundant execution before the first mutated statement. In this paper we try to also reduce some of the redundant execution after the execution of the first mutated statement. We observe that, although many mutated statements are not equivalent, the execution result of those mutated statements may still be equivalent to the result of the original statement. In other words, the statements are equivalent modulo the current state. In this paper we propose a fast mutation analysis approach, AccMut. AccMut automatically detects the equivalence modulo states among a statement and its mutations, then groups the statements into equivalence classes modulo states, and uses only one process to represent each class. In this way, we can significantly reduce the number of split processes. Our experiments show that our approach can further accelerate mutation analysis on top of split-stream execution with a speedup of 2.56x on average.Comment: Submitted to conferenc

Correlations in the T Cell Response to Altered Peptide Ligands

The vertebrate immune system is a wonder of modern evolution. Occasionally, however, correlations within the immune system lead to inappropriate recruitment of preexisting T cells against novel viral diseases. We present a random energy theory for the correlations in the naive and memory T cell immune responses. The non-linear susceptibility of the random energy model to structural changes captures the correlations in the immune response to mutated antigens. We show how the sequence-level diversity of the T cell repertoire drives the dynamics of the immune response against mutated viral antigens.Comment: 21 pages; 6 figures; to appear in Physica

A semi-analytical approach to perturbations in mutated hilltop inflation

We study cosmological perturbations and observational aspects for mutated hilltop model of inflation. Employing mostly analytical treatment, we evaluate observable parameters during inflation as well as post-inflationary perturbations. This further leads to exploring observational aspects related to Cosmic Microwave Background (CMB) radiation. This semi-analytical treatment reduces complications related to numerical computation to some extent for studying the different phenomena related to CMB angular power spectrum for mutated hilltop inflation.Comment: 7 pages, 2 figures. Improved version to appear in IJMP

Finding Mutated Subnetworks Associated with Survival in Cancer

Next-generation sequencing technologies allow the measurement of somatic mutations in a large number of patients from the same cancer type. One of the main goals in analyzing these mutations is the identification of mutations associated with clinical parameters, such as survival time. This goal is hindered by the genetic heterogeneity of mutations in cancer, due to the fact that genes and mutations act in the context of pathways. To identify mutations associated with survival time it is therefore crucial to study mutations in the context of interaction networks. In this work we study the problem of identifying subnetworks of a large gene-gene interaction network that have mutations associated with survival. We formally define the associated computational problem by using a score for subnetworks based on the test statistic of the log-rank test, a widely used statistical test for comparing the survival of two populations. We show that the computational problem is NP-hard and we propose a novel algorithm, called Network of Mutations Associated with Survival (NoMAS), to solve it. NoMAS is based on the color-coding technique, that has been previously used in other applications to find the highest scoring subnetwork with high probability when the subnetwork score is additive. In our case the score is not additive; nonetheless, we prove that under a reasonable model for mutations in cancer NoMAS does identify the optimal solution with high probability. We test NoMAS on simulated and cancer data, comparing it to approaches based on single gene tests and to various greedy approaches. We show that our method does indeed find the optimal solution and performs better than the other approaches. Moreover, on two cancer datasets our method identifies subnetworks with significant association to survival when none of the genes has significant association with survival when considered in isolation.Comment: This paper was selected for oral presentation at RECOMB 2016 and an abstract is published in the conference proceeding