Computational Approaches to Linguistic Chronology and Subgrouping

Precision oncology informatics for anticancer drug combination responses: A systematic review.

Arrhythmia models: in vivo, in vitro and in silico

Synthetic biology is an emerging field that strives to build increasingly complex biological networks through the integration of molecular biology and engineering. The growth of the field has been supported by progress in the design and construction of synthetic genetic and protein networks. This has led to the possibility of assembling modular components to attain novel biological functions and tools. In addition, these synthetic networks give rise to insights that facilitate the investigation of interactions and phenomena in naturally-occurring networks. Integration of well-characterized biological components into higher order networks requires computational modeling approaches to rationally construct systems that are directed towards a desired outcome. A computational approach would improve the predictability of the underlying mechanisms that would otherwise be difficult to deduce through experimentation alone. The analysis and interpretation of both qualitative and quantitative models also becomes increasingly important towards taking a systems-level perspective on synthetic genetic and protein networks. This review will first discuss the analogy of synthetic networks to circuit engineering. It will then look at computational modeling approaches that can be applied to biological systems and how synthetic biology will help to develop more accurate in silico representations of these systems. Keywords: Synthetic biology, genetic circuits, protein circuits, computational modeling, networks, biological complexity

ConspectusLaboratory-based experimental astrochemistry regularly entails simulation of astrophysical environments whereby low-temperature condensed ices are exposed to radiation from ultraviolet (UV) photons or energetic charged particles. Here, excited atoms/radicals are generated that are not in thermal equilibrium with their surroundings (i.e., they are nonthermal, or suprathermal). These species can surpass typical reaction barriers and partake in unusual chemical processes leading to novel molecular species. Often, these are uniquely observable under low-temperature conditions where the surrounding ice matrix can stabilize excited intermediates that would otherwise fall apart. Fourier-transform infrared (FTIR) spectroscopy is traditionally utilized to monitor the evolution of chemical species within ices in situ during radiolysis. Yet, the characterization and quantification of novel species and radicals formed within astrophysical ices is often hindered since many of these cannot be synthesized by traditional synthetic chemistry. Computational approaches can provide fundamental vibrational frequencies and isotopic shifts to help aid in assignments alongside infrared intensities and Raman activities to quantify levels of production. In this Account, we begin with a brief history and background regarding the composition and radiation of interstellar ices. We review details of some of the early work on carbon oxides produced during the radiolysis of pure carbon dioxide ices and contention around the carrier of an absorption feature that could potentially be a product of radiation. We then provide an overview of current and emerging experimental methodologies and some of the chemistries that occur via nonthermal processes during radiolysis of low-temperature ices. Next, we detail computational approaches to reliably predict vibrational frequencies, infrared intensities, and Raman activities based on our recent work. Our focus then turns to studies on the formation of complex organics and carbon oxides, highlighting those aided by computational approaches and their role in astrochemistry. Some recent controversies regarding assignments alongside our recent results on the characterization of novel carbon oxide species are discussed. We present an argument for the potential role of carbon oxides within cometary ices as parent molecular species for small volatiles. We provide an overview of some of the complex organic species that can be formed within interstellar and cometary ices that contain either carbon monoxide or carbon dioxide. We examine how Raman spectroscopy could potentially be leveraged to help determine and characterize carbon oxides in future experiments as well as how computational approaches can aid in these assignments. We conclude with brief remarks on future directions our research group is taking to unravel astrochemically relevant carbon oxides using combined computational and experimental approaches.

The purpose of this research was to determine (1) the difference effect between the use of module with an unplugged computational thinking approach and module with a scientific approach to students' cognitive abilities on the subject of temperature and heat transfer, (2) the differences effect of students' critical thinking abilities in the high and low category on students' cognitive abilities on the subject of temperature and heat transfer, (3) the interaction between the effect of using module with a computational thinking unplugged approach and module with a scientific approach and students' critical thinking skills on students' cognitive abilities on the subject of temperature and heat transfer. This research used a quasi-experimental method with a 2x2 factorial design. The population in this research were all students of the first semester of physics education at Universitas Sebelas Maret. Data collection techniques used are tests and questionnaires. Data were analyzed using a two-way ANOVA with different cell contents. The results of the study show that: (1) there is a difference in the effect between the use of module with a computational thinking unplugged approach and module with a scientific approach to students' cognitive abilities on the subject of temperature and heat transfer ( 2) there is a difference in the effect of students' critical thinking skills in the high and low categories on students' cognitive abilities on the subject of temperature and heat transfer, and (3) there is no interaction between the effect of using module with a computational approach thinking unplugged and module with a scientific approach and students' critical thinking skills on students' cognitive abilities on the subject of temperature and heat transfer.

Charactering ionizable residue networks at SH2 interfaces in pH-sensitive proteins.

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era.

The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways – Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

BackgroundDementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making.Main bodyDespite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.ConclusionThe data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Molecular crowding plays a crucial role in biological and medicinal systems, impacting the structure, behavior, and function of biomolecules within the densely packed environments of cells. This chapter provides an overview of the implications of molecular crowding, exploring how the high concentration of macromoleculessuch as proteins, nucleic acids, and other biological entitiesimpacts biochemical reactions and cellular processes. The discussion highlights the challenges associated with experimental studies of molecular crowding, including challenges in creating accurate in vitro models, controlling concentrations, and isolating crowding effects from other interactions. To address these challenges, the chapter emphasizes the importance of computational techniques. Various computational approaches, including molecular dynamics simulations, Monte Carlo simulations, Brownian dynamics, lattice-models, finite element analysis, coarse-grained modeling, quantum mechanics/molecular mechanics simulations, and multi-scale modeling, are discussed in detail. Each of these techniques contributes unique insights into the molecular-level impacts of crowding, enhancing our understanding of biophysical processes critical for therapeutic development and biological function. The chapter discusses also quantum computing, machine learning and classical simulations hybrid approaches for future directions around molecular crowding studies.

Diffuse axonal injury (DAI) is the pathological consequence of traumatic brain injury (TBI) that most of all requires a multiscale approach in order to be, first, understood and then possibly prevented. While in fact the mechanical insult usually happens at the head (or macro) level, the consequences affect structures at the cellular (or microlevel). The quest for axonal injury tolerances has so far been addressed both with experimental and computational approaches. On one hand, the experimental approach presents challenges connected to both temporal and spatial resolution in the identification of a clear axonal injury trigger after the application of a mechanical load. On the other hand, computational approaches usually consider axons as homogeneous entities and therefore are unable to make inferences about their viability, which is thought to depend on subcellular damages. Here, we propose a computational multiscale approach to investigate the onset of axonal injury in two typical experimental scenarios. We simulated single-cell and tissue stretch injury using a composite finite element axonal model in isolation and embedded in a matrix, respectively. Inferences on axonal damage are based on the comparison between axolemma strains and previously established mechanoporation thresholds. Our results show that, axons embedded in a tissue could withstand higher deformations than isolated axons before mechanoporation occurred and this is exacerbated by the increase in strain rate from 1/s to 10/s.

Binding of small molecules to their targets triggers complex pathways. Computational approaches are keys for predictions of the molecular events involved in such cascades. Here we review current efforts at characterizing the molecular determinants in the largest membrane-bound receptor family, the G-protein-coupled receptors (GPCRs). We focus on odorant receptors, which constitute more than half GPCRs. The work presented in this review uncovers structural and energetic aspects of components of the cellular cascade. Finally, a computational approach in the context of radioactive boron-based antitumoral therapies is briefly described.

physiological genomics is currently entering its eighth year of publication. The formation of this new journal was considered a high risk venture when first discussed in 1998. The American Physiological Society was convinced that this journal would advance and consolidate the information emerging

A computational approach for predicting microstructure and mechanical properties of plasma sprayed ceramic coatings from powder to bulk

Opportunities and limitations of integrating computational and collaborative approaches to scenario planning