9,815 research outputs found
System-theoretic trends in econometrics
Economics;Estimation;econometrics
Computational Investigations on Polymerase Actions in Gene Transcription and Replication Combining Physical Modeling and Atomistic Simulations
Polymerases are protein enzymes that move along nucleic acid chains and
catalyze template-based polymerization reactions during gene transcription and
replication. The polymerases also substantially improve transcription or
replication fidelity through the non-equilibrium enzymatic cycles. We briefly
review computational efforts that have been made toward understanding
mechano-chemical coupling and fidelity control mechanisms of the polymerase
elongation. The polymerases are regarded as molecular information motors during
the elongation process. It requires a full spectrum of computational approaches
from multiple time and length scales to understand the full polymerase
functional cycle. We keep away from quantum mechanics based approaches to the
polymerase catalysis due to abundant former surveys, while address only
statistical physics modeling approach and all-atom molecular dynamics
simulation approach. We organize this review around our own modeling and
simulation practices on a single-subunit T7 RNA polymerase, and summarize
commensurate studies on structurally similar DNA polymerases. For multi-subunit
RNA polymerases that have been intensively studied in recent years, we leave
detailed discussions on the simulation achievements to other computational
chemical surveys, while only introduce very recently published representative
studies, including our own preliminary work on structure-based modeling on
yeast RNA polymerase II. In the end, we quickly go through kinetic modeling on
elongation pauses and backtracking activities. We emphasize the fluctuation and
control mechanisms of the polymerase actions, highlight the non-equilibrium
physical nature of the system, and try to bring some perspectives toward
understanding replication and transcription regulation from single molecular
details to a genome-wide scale
Kernel Density Estimation with Linked Boundary Conditions
Kernel density estimation on a finite interval poses an outstanding challenge
because of the well-recognized bias at the boundaries of the interval.
Motivated by an application in cancer research, we consider a boundary
constraint linking the values of the unknown target density function at the
boundaries. We provide a kernel density estimator (KDE) that successfully
incorporates this linked boundary condition, leading to a non-self-adjoint
diffusion process and expansions in non-separable generalized eigenfunctions.
The solution is rigorously analyzed through an integral representation given by
the unified transform (or Fokas method). The new KDE possesses many desirable
properties, such as consistency, asymptotically negligible bias at the
boundaries, and an increased rate of approximation, as measured by the AMISE.
We apply our method to the motivating example in biology and provide numerical
experiments with synthetic data, including comparisons with state-of-the-art
KDEs (which currently cannot handle linked boundary constraints). Results
suggest that the new method is fast and accurate. Furthermore, we demonstrate
how to build statistical estimators of the boundary conditions satisfied by the
target function without apriori knowledge. Our analysis can also be extended to
more general boundary conditions that may be encountered in applications
Coding Theory and Algebraic Combinatorics
This chapter introduces and elaborates on the fruitful interplay of coding
theory and algebraic combinatorics, with most of the focus on the interaction
of codes with combinatorial designs, finite geometries, simple groups, sphere
packings, kissing numbers, lattices, and association schemes. In particular,
special interest is devoted to the relationship between codes and combinatorial
designs. We describe and recapitulate important results in the development of
the state of the art. In addition, we give illustrative examples and
constructions, and highlight recent advances. Finally, we provide a collection
of significant open problems and challenges concerning future research.Comment: 33 pages; handbook chapter, to appear in: "Selected Topics in
Information and Coding Theory", ed. by I. Woungang et al., World Scientific,
Singapore, 201
High-throughput sequencing of the T-cell receptor repertoire: pitfalls and opportunities.
T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information. The latest generation of bioinformatics tools allows millions of DNA sequences to be accurately and rapidly assigned to their respective variable V and J gene segments, and to reconstruct an almost error-free representation of the non-templated additions and deletions that occur. High-level processing can measure the diversity of the repertoire in different samples, quantify V and J usage and identify private and public T-cell receptors. Finally, we discuss the major challenge of linking T-cell receptor sequence to function, and specifically to antigen recognition. Sophisticated machine learning algorithms are being developed that can combine the paradoxical degeneracy and cross-reactivity of individual T-cell receptors with the specificity of the overall T-cell immune response. Computational analysis will provide the key to unlock the potential of the T-cell receptor repertoire to give insight into the fundamental biology of the adaptive immune system and to provide powerful biomarkers of disease
Computational Intelligence for Life Sciences
Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences
Model-based Cognitive Neuroscience: Multifield Mechanistic Integration in Practice
Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration
- …