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El anhídrido maleico es una un producto químico con un amplio rango de aplicaciones como compuesto intermedio en la producción de resinas, polímeros, barnices y pinturas. En el siguiente trabajo se llevó a cabo la simulación del proceso de producción del anhídrido maleico a partir de la oxidación catalítica de benceno mediante el simulador ChemCADâ, versión 5.2.0, con el fin de realizar los balances de masa de todas las corrientes de proceso, determinar el consumo de los servicios auxiliares en los intercambiadores de calor y conocer los parámetros de diseño más importantes del equipamiento empleado. Se llevaron a cabo dos estudios de sensibilidad: influencia de la temperatura y presión de operación del absorbedor sobre la cantidad a obtener de anhídrido maleico por la corriente del fondo de este equipo. Se obtienen 5.027,14 kg/h de anhídrido maleico por la corriente del tope de la columna de destilación con una pureza del 97,45 %. El absorbedor debe operar a una temperatura cercana a 20 ºC y una presión de 5 bar para reducir las pérdidas de anhídrido maleico e incrementar las ganancias económicas del proceso

Petani lada di Provinsi Kepulauan Bangka Belitung menghadapi berbagai tantangan dan kendala disaat kondisi perekenomian semakin hari semakin sulit terutama ditengah pandemi yang tengah melanda. Pemerintah telah memberikan banyak program bantuan berupa subsidi dan pemberian secara cuma-cuma berupa imput pertanian kepada petani lada. Meskipun demikian, hal tersebut belum memberikan dampak positif berupa peningkatan kesejahteraan kepada petani. Program pemberdayaan masyarakat bertujuan untuk memberikan alternatif solusi yang mana akar permasalahan berada di sisi hulu. Rendahnya kapasitas petani mengenai praktik pertanian yang baik merupakan isu yang ada di antara petani lada. Metode yang dilakukan pada program pemberdayaan masyarakat ini berupa sosialisasi dan pelatihan. Program ini diharapkan mampu memberikan kontribusi kepada pemecahan masalah yang dihadapi oleh petani lada di Provinsi Kepulauan Bangka Belitung. Program ini menemukan bahwa selama ini petani masih menerapkan pola pertanian konvensional sehingga produksi lada cenderung tidak ideal. Program ini juga menemukan bahwa telah terjadinya peningkatan kapasitas petani tentang budidaya lada yang baik. Diharapkan bahwa dinas pertanian dan pemangku kepetingan yang lain dapat lebih intensif dalam memobilisasi kegiatan penyuluhan di lapangan agar petani dapat secara terus menerus membudidayakan komoditas lada dengan memenuhi tata cara praktik pertanian yang baik.

The average price of white pepper in the Bangka Belitung Islands Province from January 2007 to December 2016 was IDR 72,531 per kg on the producer market, IDR 80,622 per kg on the export market, and IDR 99,017 per kg on the world market. Based on the data, there is a large marketing margin between the producer market and the export market, IDR 8,091 per kg. While, the margin between the export market and the world market was IDR 18,395 per kg. The present research aims to analyze the transmission of white pepper prices in the Bangka Belitung Islands Province among the three markets. The Asymmetric Error Correction Model (AECM) was used as the model to analyze the transmission of white pepper prices in Bangka Belitung Island Province from producers to exporters, from producers to world market, and from exporters to world market. The transmission of white pepper prices from the three markets shows that in the short period it runs asymmetrically, whereas in the long period it runs symmetrically. Conversely, the transmission of white pepper prices from the world market to the exporters market runs symmetrically in the short term but it runs asymmetrically in the long term.

En el presente trabajo se realizó el diseño de una torre de bandejas perforadas para efectuar un proceso de absorción de etanol gaseoso empleando agua como solvente. El resultado de costo obtenido se comparó con el costo de una columna empacada diseñada por (Pérez, Pérez, & Liaño, 2019) con el fin de seleccionar el equipo más adecuado desde el punto de vista económico. La torre tuvo un diámetro de 0,404 m, una caída de presión del gas de 0,783 kPa/bandeja –inferior a la máxima caída de presión establecida por el sistema (1,0 kPa/bandeja)– y un flujo de arrastre de 0,018 kg/s (64,8 kg/h), el cual puede calificarse como aceptable. La eficiencia de la bandeja de Murphree corregida por flujo de arrastre fue igual a 0,829, mientras que la torre presentó 11 bandejas o etapas en total. La torre de bandejas perforadas tuvo un costo aproximado de usd 15.315, siendo 1,44 veces inferior al costo de la columna empacada determinado por (Pérez, Pérez, & Liaño, 2019). Se concluye que resulta más factible, desde el punto de vista económico, adquirir la torre de bandejas perforadas para llevar a cabo el proceso de absorción establecido en este proyecto.

The practice of physical activity presumes numerous benefits by virtue of the way in which it is carried out, in addition to the purposes that are desired to achieve, based on the objectives to know and analyze, the reasons and habits with respect to the practice of physical activity in third grade students of a school in Obregon City, Sonora Mexico. The design of this research is mixed. A convenience sampling was used, which was conformed by 96 students of the third grade of high school; all of them with 14 years of age. The questionnaire "Yesterday Activity Cheklist" was used, adapted for young people from 10 to 16 years old, as well as documentary review and observation. Among the results obtained, the frequency with which girls perform physical activity is lower when compared to boys, which is low in both cases. The determining reason why they practice physical activity is because they like it and have fun, and an alarming fact is that part of the population does not practice physical activity due to lack of time and the greater percentage predominates in boys. Based on all these results, the aim is for teachers and parents to promote physical activity with their students and children in order to improve their quality of life.

Penelitian ini bertujuan untuk mengetahui pengaruh pengungkapan Islamic Corporate Governance (ICG)dan Intellectual Capital (IC) terhadap kinerja keuangan UUS . Pengungkapan ICG diukur menggunakan pedoman Good Governance Bisnis Syariah (GGBS) yang dikeluarkan KNKG (2011), Intellectual Capital diukur dengan iB-VAIC (Ulum,2013) dan kinerja keuangan diukur dengan rasio profitabilitas, yaitu ROA. Penelitian ini adalah penelitian deskriptif dengan pendekatan kuantitatif. Sampel penelitian ini terdiri dari 18 UUS dari periode 2013-2017. Data sekunder diambil dari annual reportdan laporan keuangan triwulan ke-IV khusus UUS dari bank konvensional induk yang kemudian dianalis secara statistik dengan menggunakan metode analisis regresi data panel. Hasil penelitian menunjukka bahwa ICG tidak berpengaruh terhadap kinerja keuangan UUS danIntellectual Capital berpengaruh positif signifikan terhadap kinerja keuangan UUS.

En el presente trabajo se llevó a cabo el diseño de una columna empacada para efectuar la absorción de etanol contenido en una corriente gaseosa usando agua como solvente. Como material de empaque se seleccionó anillos plásticos Nor-Pac® de 35 mm. Se obtuvo un diámetro de la columna de 0,334 m; la caída de presión total calculada fue de 321 Pa/m, la cual es inferior a la permitida por el sistema de absorción (350 Pa/m); se obtuvo un coeficiente global volumétrico de transferencia de masa de 0,201 kmol/m3s; la altura de empaque tuvo un valor de 1,97 m; y la altura total de la columna de absorción fue de 4 m. La columna empacada tendrá un costo total aproximado de usd 22.000.

Muntok White Pepper merupakan produk lada Bangka yang sudah terkenal dari sejak dulu dengan kualitas lada yang diakui paling baik diantara lada yang lainnya. Muntok White pepper masih menjadi unggulan hasil pertanian di Bangka Belitung serta merupakan komoditi unggulan yang sudah terkenal di seluruh dunia. Dalam Penelitian ini, penekanan yang hendak diambil adalah pemanfaatan Internet dan aplikasi android sebagai sarana pengelolaan data tentang Muntok White Pepper mulai dari lahan, informasi bibit, produksi, perawatan serta harga dan ekspor Muntok White Pepper. Potensi pengelolaan secara digitalisasi data Muntok White Pepper lebih baik dengan pengelolaan terpusat serta informasi Muntok White Pepper yang lengkap untuk memudahkan mendapatkan produktivitas, luas lahan dan ekspor lada secara lengkap. Dengan menggunakan website yang dapat diakses secara daring dan Aplikasi android untuk membuat akses yang luas dan sistem pengelolaan data Muntok White Pepper apat berbasis web secara lengkap dan update yang bias menjadi rujukan dalam mencari informasi tentang Muntok White Pepper bagi Bangka Belitung maupun orang luar.

Currently, the performance of the pepper Muntok business continues to decline from time to time. This condition needs to be supported by studying the characteristics of farmers as human resources who carry out business planning and decision-making faced with various threats, challenges, and uncertainties. This study aims to 1) determine personal characteristics; 2) analyze the effect of personal characteristics on entrepreneurial behavior, 3) analyze the effect of personal characteristics and entrepreneurial behavior of farmers on business performance; and 4) analyze the effect of entrepreneurial behavior on business performance. The research method uses a survey method involving 90 farmers. Data analysis was carried out using Structural Equation Modeling (SEM) through Smart Partial Least squares (PLS). The results showed that the personal characteristics of the dominant farmer were strongly reflected by indicators of ethnic origin, experience, and age. Personal characteristics positively and significantly contribute to entrepreneurial behavior, and positive entrepreneurial behavior significantly influences business performance. The study results did not directly influence personal characteristics on business performance but through entrepreneurial behavior. Furthermore, the indicator variables of ethnic origin, experience, and age affect discipline, persistence, perseverance, interest in seeking and increasing knowledge, and mastery of technical skills, which can ultimately determine the level of business productivity and the desire to grow through business expansion. Before and during the COVID-19 pandemic, the entrepreneurial behavior of farmers was still strongly shaped by cognitive and affective variables. It is necessary to increase the skills of farmers through training and entrepreneurship development that pays attention to personal characteristics to improve business performance. This study can recommend the Agriculture Office of the Bangka Belitung Islands Province in formulating policies to increase the productivity of pepper plants and the welfare of pepper farmers in the future.

Entrepreneurship is one of the human capital variables of pepper farmers that plays an important role in determining the success of pepper agribusiness development in this creative era. To face the challenges of the business climate change, pepper farmers need entrepreneurship. The objectives compiled in this study are to identify the entrepreneurial characteristics of Muntok White Pepper farmers and to analyze the effect of entrepreneurial characteristics on their entrepreneurial behavior in managing to farm. The study was conducted by survey method, involving 115 farmers. Data analysis used Structural Equation Modeling (SEM) with Partial Least Squares (PLS). The results showed that the entrepreneurial characteristics of pepper farmers are risk-taking, responsive to opportunities, innovative, and motivated. Also, the results showed that the entrepreneurial characteristics of pepper farmers positively and significantly influence their entrepreneurial behavior. Furthermore, the most dominant variable reflecting their entrepreneurial characteristics is the courage to take risks. This study recommends muntok pepper farmers that they always dare to take risks, be persistent and disciplined. Additionally, knowledge and skills need to be constantly updated to grow the ability to innovate and be creative in carrying pepper cultivation activities out.

Los cambios sociales y educativos de las últimas décadas han incrementado una serie de responsabilidades en los profesionales de la educación para desarrollar el proceso formativo mediante prácticas pedagógicas de calidad. En este sentido, es necesario que las instituciones de enseñanza superior implementen programas de capacitación docente y cuenten con mecanismos funcionales para atender la diversidad. El objetivo de este artículo de revisión bibliográfica es realizar un análisis descriptivo y crítico sobre la formación del profesorado universitario en el contexto latinoamericano para el abordaje a estudiantes en condición de discapacidad, pueblos indígenas, afrodescendientes, diversidad sexual y emigrantes en la educación superior. La profesionalización docente requiere de políticas gubernamentales e institucionales coherentes para la obtención de competencias básicas en materia de atención a la diversidad en los espacios de aprendizaje. Es necesario llevar a cabo procesos de investigación más profundos para generar una concepción desde otra perspectiva acerca de la diversidad.

Background: Process simulation has been extensively used in recent years to design, evaluate or optimize processes, systems and specific operations of the chemical industry and its related disciplines. Currently, CHEMCAD® constitute one of the most used process simulators because of the great number of chemical and petrochemical processes that can be simulated. Method: The simulation of the production process of styrene via catalytic dehydrogenation of ethyl-benzene is carried out by using the process simulator CHEMCAD® version 5.2.0, in order to determine the composition and mass flow-rate of each process involved in the production, as well as the main operating parameters of the equipment used. Two sensitivity studies were carried out: firstly, the influence of the temperature and pressure values applied at the LLV Separator on the amounts of ethyl-benzene and styrene to be obtained by the intermediate and top currents of this equipment; secondly, the influence of the operating pressure of the Distillation Column No. 1 (benzene-toluene column) on the quantity of ethyl-benzene and styrene obtained at the bottom stream. The simulating software MATLAB® version 7.8.0 was used to process the results obtained. Results: Around 9234.436 kg/h of styrene is obtained in the last distillation column with 99.6% purity. Additionally, it was found that the water is the main impurity found on this stream, which represents 0.35% of the weight. Conclusions: The LLV Separator must operate at a low temperature (5 – 10 ºC) and at a relatively high pressure (10 bar), whereas the Distillation Column No. 1 must work at a pressure near atmospheric (1.0 bar), or preferably under vacuum conditions in order to obtain the highest yields of styrene and ethyl-benzene.

La educación superior en Honduras, presenta un notable avance en los últimos años. Sin embargo, necesita mejores mecanismos de acceso, permanencia y egreso para el estudiantado en condición de vulnerabilidad. Es una prioridad crear políticas nacionales e institucionales de atención a la diversidad, que se originen a través de propuestas de los diferentes colectivos que participan en el sistema educativo. El objetivo de este artículo es describir la situación actual de la atención a la diversidad en la educación superior de Honduras. La propuesta de este trabajo permite dar a conocer el contexto de las personas en situación de discapacidad, pueblos indígenas, afrohondureños y diversidad sexual. La información explorada proviene de la revisión de evidencias bibliográficas. Los resultados permiten exponer que el débil marco normativo en la legislación hondureña es un factor determinante para que existan diversas situaciones de discriminación y exclusión desde el sistema educativo. Por esta razón, el profesorado universitario debe ser consciente de su práctica pedagógica y examinarla constantemente para generar cambios que comiencen desde el tipo de lenguaje utilizado hasta los componentes metodológicos que forman parte del proceso de enseñanza-aprendizaje en la educación superior.

Los biofertilizantes se han convertido en una alternativa de bajo costo, efectiva y amigable con el medio ambiente en comparación con los fertilizantes químicos. En el presente trabajo se desarrollaron los modelos de ingeniería de proceso y costo de una planta de biofertilizantes líquidos con una capacidad de 44 toneladas por año (568 kg/lote). Los modelos fueron obtenidos empleando el simulador de procesos SuperPro Designer® versión 8.5 (Intelligen, 2012), mientras que el diseño conceptual en 3D y dimensionamiento de la planta se desarrolló mediante el software OptiPlant (ASD Global, 2015). Se requiere una inversión total de USD $ 3 975 000 para erigir la planta, el costo de producción unitario de una botella de 1,5 L de biofertilizantes líquido es de USD $ 24,009, mientras que los indicadores económicos Valor Actual Neto (VAN) y Tasa Interna de Retorno (TIR) tuvieron valores de USD $ 716 000 y 2,55 %, respectivamente. También se obtienen ganancias totales de USD $ 985 000/año y un valor del Período de Retorno de la Inversión de 6,70 años.

The aim of this study was to evaluate the effect of a sports initiation school program in conditional physical capabilities in children 8 and 9 years of a primary school in Ciudad Obregón. We worked with 20 students who were divided into two groups of 10 students, a control group and an experimental group. A program of sports initiation which was only applied to the experimental group over a period of three months with a frequency of four days a week with duration of 60 minutes per session having an initial evaluation and final, while the control group was made It did not make any formal or directed physical activity. Experimental group significant differences in the ability of speed and strength with p = 0.008 and p = 0.001 respectively were found in the capacity of resistance and flexibility but there was no statistically significant improvements, while the control group showed no improvement. These data can be used for future research related to the subject, which can be enriched through comparisons and / or differences with other authors.

Ecuador (en 1989) y Sri Lanka (en 1990) empezaron sus procesos de Manejo Costero Integrado - MCI utilizando fondos de la misma Cooperación Internacional y asesoramiento y evaluación de la misma universidad extranjera. Tomando en cuenta estas similitudes y que ambos son países tropicales en vía de desarrollo se efectuó un análisis comparativo entre ambos. A través del modelo conceptual de ciclos de MCI sugerido por GESAMP y una amplia revisión bibliográfica y documental sobre la temática, fueron analizados ambos procesos de MCI, a fin de entender sus fortalezas y debilidades para fomentar nuevos procesos de MCI, así como también evaluar el uso del modelo conceptual de GESAMP como herramienta de análisis. Lo que en principio parecían procesos similares, en la práctica fueron muy distintos. Ecuador presentó un proceso más orientado a la participación desde el comienzo, mientras Sri Lanka solamente en la tercera generación logró integrar aproximaciones participativas. De otro lado, Sri Lanka tuvo un proceso más ordenado y centrado en desarrollo institucional, estableciendo primero un marco institucional que sirviera de base al comienzo del proceso de MCI. Ambos países han alcanzado resultados de primer orden (cambios institucionales), de segundo orden (cambios en el comportamiento) y posiblemente de tercer orden (cambios socioeconómicos y ambientales). Se concluye con este estudio de caso que los procesos participativos, la voluntad política, los procesos de evaluación y la financiación fueron fundamentales para el éxito de ambos proceso de MCI. También se encontró que el modelo conceptual propuesto por GESAMP representa una herramienta teórica fundamental para la planificación y análisis de los procesos de MCI, pero podría ser revisado y contrapuesto empíricamente a través de una investigación más profunda, con un mayor número de casos internacionales concretos de implementación de planes de MCI.

Motivation: Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation., Results: To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress., Availability and Implementation: More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website., (© The Author(s) 2024. Published by Oxford University Press.)

The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scientific community. Data from the first phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement. HTAN data can be accessed via the HTAN Portal, explored in visualization tools-including CellxGene, Minerva, and cBioPortal-and analyzed in the cloud through the NCI Cancer Research Data Commons nodes. We have developed a streamlined infrastructure to ingest and disseminate data by leveraging the Synapse platform. Taken together, the HTAN DCC's approach demonstrates a successful model for coordinating, standardizing, and disseminating complex cancer research data via multiple resources in the cancer data ecosystem, offering valuable insights for similar consortia, and researchers looking to leverage HTAN data.

Experimental tools and resources, such as animal models, cell lines, antibodies, genetic reagents and biobanks, are key ingredients in biomedical research. Investigators face multiple challenges when trying to understand the availability, applicability and accessibility of these tools. A major challenge is keeping up with current information about the numerous tools available for a particular research problem. A variety of disease-agnostic projects such as the Mouse Genome Informatics database and the Resource Identification Initiative curate a number of types of research tools. Here, we describe our efforts to build upon these resources to develop a disease-specific research tool resource for the neurofibromatosis (NF) research community. This resource, the NF Research Tools Database, is an open-access database that enables the exploration and discovery of information about NF type 1-relevant animal models, cell lines, antibodies, genetic reagents and biobanks. Users can search and explore tools, obtain detailed information about each tool as well as read and contribute their observations about the performance, reliability and characteristics of tools in the database. NF researchers will be able to use the NF Research Tools Database to promote, discover, share, reuse and characterize research tools, with the goal of advancing NF research. Database URL: https://tools.nf.synapse.org/., (© The Author(s) 2022. Published by Oxford University Press.)

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Eddy JA et al.)

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Plant biomass degradation by fungal-derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction., Results: To expand this knowledge base, a detailed network of reactions important for deconstruction of plant cell wall polysaccharides into simple sugars was constructed for the filamentous fungus Neurospora crassa . To reconstruct this network, information was integrated from five heterogeneous data types: functional genomics, transcriptomics, proteomics, genetics, and biochemical characterizations. The combined information was encapsulated into a feature matrix and the evidence weighted to assign annotation confidence scores for each gene within the network. Comparative analyses of RNA-seq and ChIP-seq data shed light on the regulation of the plant cell wall degradation network, leading to a novel hypothesis for degradation of the hemicellulose mannan. The transcription factor CLR-2 was subsequently experimentally shown to play a key role in the mannan degradation pathway of N. crassa ., Conclusions: Here we built a network that serves as a scaffold for integration of diverse experimental datasets. This approach led to the elucidation of regulatory design principles for plant cell wall deconstruction by filamentous fungi and a novel function for the transcription factor CLR-2. This expanding network will aid in efforts to rationally engineer industrially relevant hyper-production strains.

The significance of islet Ag-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects is unclear, partly because similar cells are also found in healthy control (HC) subjects. We hypothesized that key disease-associated cells would show evidence of prior Ag exposure, inferred from expanded TCR clonotypes, and essential phenotypic properties in their transcriptomes. To test this, we developed single-cell RNA sequencing procedures for identifying TCR clonotypes and transcript phenotypes in individual T cells. We applied these procedures to analysis of islet Ag-reactive CD4 + memory T cells from the blood of T1D and HC individuals after activation with pooled immunodominant islet peptides. We found extensive TCR clonotype sharing in Ag-activated cells, especially from individual T1D subjects, consistent with in vivo T cell expansion during disease progression. The expanded clonotype from one T1D subject was detected at repeat visits spanning >15 mo, demonstrating clonotype stability. Notably, we found no clonotype sharing between subjects, indicating a predominance of "private" TCR specificities. Expanded clones from two T1D subjects recognized distinct IGRP peptides, implicating this molecule as a trigger for CD4 + T cell expansion. Although overall transcript profiles of cells from HC and T1D subjects were similar, profiles from the most expanded clones were distinctive. Our findings demonstrate that islet Ag-reactive CD4 + memory T cells with unique Ag specificities and phenotypes are expanded during disease progression and can be detected by single-cell analysis of peripheral blood., (Copyright © 2017 by The American Association of Immunologists, Inc.)

Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases., Competing Interests: Below are the disclosures for R.C.P.: Pfizer, Inc., and Janssen Alzheimer Immunotherapy: Chair, Data Monitoring Committee. Hoffman-La Roche, Inc.: Consultant. Merck, Inc.: Consultant. Genentech, Inc.: Consultant. Biogen, Inc.: Consultant. Eli Lilly & Co.: Consultant. N.R.G.-R. has multicenter treatment study grants from Lilly and TauRx and consulted for Cytox. N.E.-T. has consulted for Cytox. The remaining authors declare no competing financial interests.

Young men who have sex with men (YMSM) may be at greater risk for body dissatisfaction, compared to their heterosexual peers. However, differences within YMSM populations are understudied, precluding the identification of YMSM who are at greatest risk. This study examined body dissatisfaction in a racially/ethnically diverse sample of YMSM ages 18-19 in New York City. Using cross-sectional data from the baseline visit of a longitudinal cohort study of YMSM (N = 591), body dissatisfaction was assessed using the Male Body Attitudes Scale. Three outcomes were modeled using linear regression: (1) overall body dissatisfaction, (2) muscularity dissatisfaction, and (3) body fat dissatisfaction. Covariates in the models included race/ethnicity, sexual orientation, BMI, gay community affiliation, and internalized homonegativity. White YMSM experienced greater body dissatisfaction across the three models. Internalized homonegativity was a statistically significant predictor of dissatisfaction across the three models, though its association with body dissatisfaction was relatively small. The findings point to future avenues of research, particularly qualitative research to explore demographic and cultural nuances in body attitudes among YMSM.

In the shared pursuit of elucidating detailed mechanisms of cell function, systems biology presents a natural complement to ongoing efforts in cell biology. Systems biology aims to characterize biological systems through integrated and quantitative modeling of cellular information. The process of model building and analysis provides value through synthesizing and cataloging information about cells and molecules, predicting mechanisms and identifying generalizable themes, generating hypotheses and guiding experimental design, and highlighting knowledge gaps and refining understanding. In turn, incorporating domain expertise and experimental data is crucial for building towards whole cell models. An iterative cycle of interaction between cell and systems biologists advances the goals of both fields and establishes a framework for mechanistic understanding of the genome-to-phenome relationship., (Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.)

Pre-exposure prophylaxis (PrEP) is considered an effective biomedical approach for HIV prevention. However, there is limited understanding of PrEP uptake among racially/ethnically and socioeconomically diverse young men who have sex with men (YMSM). This study examined attitudes and perceptions toward PrEP uptake among YMSM by conducting semistructured interviews with a sample (N = 100) of YMSM in New York City. Thematic analysis was employed to explore key issues related to attitudes and perceptions toward PrEP utilization. Findings suggest that self-perceived risk for HIV transmission, enjoying unprotected sex, and being in a romantic relationship were associated with PrEP uptake. The most prominent barriers to PrEP uptake included costs, adherence regimen, and access. In summary, these findings underscore the importance of addressing behavioral and structural factors in maximizing the effectiveness of PrEP. In addition, PrEP implementation programs ought to consider the role of social and structural challenges to PrEP uptake and adherence among YMSM.

Anti-inflammatory strategies are proposed to have beneficial effects in Alzheimer's disease. To explore how anti-inflammatory cytokine signaling affects Aβ pathology, we investigated the effects of adeno-associated virus (AAV2/1)-mediated expression of Interleukin (IL)-10 in the brains of APP transgenic mouse models. IL-10 expression resulted in increased Aβ accumulation and impaired memory in APP mice. A focused transcriptome analysis revealed changes consistent with enhanced IL-10 signaling and increased ApoE expression in IL-10-expressing APP mice. ApoE protein was selectively increased in the plaque-associated insoluble cellular fraction, likely because of direct interaction with aggregated Aβ in the IL-10-expressing APP mice. Ex vivo studies also show that IL-10 and ApoE can individually impair glial Aβ phagocytosis. Our observations that IL-10 has an unexpected negative effect on Aβ proteostasis and cognition in APP mouse models demonstrate the complex interplay between innate immunity and proteostasis in neurodegenerative diseases, an interaction we call immunoproteostasis., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Astrocytoma is the most common glioma, accounting for half of all primary brain and spinal cord tumors. Late detection and the aggressive nature of high-grade astrocytomas contribute to high mortality rates. Though many studies identify candidate biomarkers using high-throughput transcriptomic profiling to stratify grades and subtypes, few have resulted in clinically actionable results. This shortcoming can be attributed, in part, to pronounced lab effects that reduce signature robustness and varied individual gene expression among patients with the same tumor. We addressed these issues by uniformly preprocessing publicly available transcriptomic data, comprising 306 tumor samples from three astrocytoma grades (Grade 2, 3, and 4) and 30 non-tumor samples (normal brain as control tissues). Utilizing Differential Rank Conservation (DIRAC), a network-based classification approach, we examined the global and individual patterns of network regulation across tumor grades. Additionally, we applied gene-based approaches to identify genes whose expression changed consistently with increasing tumor grade and evaluated their robustness across multiple studies using statistical sampling. Applying DIRAC, we observed a global trend of greater network dysregulation with increasing tumor aggressiveness. Individual networks displaying greater differences in regulation between adjacent grades play well-known roles in calcium/PKC, EGF, and transcription signaling. Interestingly, many of the 90 individual genes found to monotonically increase or decrease with astrocytoma grade are implicated in cancer-affected processes such as calcium signaling, mitochondrial metabolism, and apoptosis. The fact that specific genes monotonically increase or decrease with increasing astrocytoma grade may reflect shared oncogenic mechanisms among phenotypically similar tumors. This work presents statistically significant results that enable better characterization of different human astrocytoma grades and hopefully can contribute towards improvements in diagnosis and therapy choices. Our results also identify a number of testable hypotheses relating to astrocytoma etiology that may prove helpful in developing much-needed biomarkers for earlier disease detection.

Background: Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)-a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery., Results: Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets., Conclusions: We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis 'point-and-click' simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both 'in silico' and 'bench' scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.

To characterize gene expression patterns in the regional subdivisions of the mammalian brain, we integrated spatial gene expression patterns from the Allen Brain Atlas for the adult mouse with panels of cell type-specific genes for neurons, astrocytes, and oligodendrocytes from previously published transcriptome profiling experiments. We found that the combined spatial expression patterns of 170 neuron-specific transcripts revealed strikingly clear and symmetrical signatures for most of the brain's major subdivisions. Moreover, the brain expression spatial signatures correspond to anatomical structures and may even reflect developmental ontogeny. Spatial expression profiles of astrocyte- and oligodendrocyte-specific genes also revealed regional differences; these defined fewer regions and were less distinct but still symmetrical in the coronal plane. Follow-up analysis suggested that region-based clustering of neuron-specific genes was related to (i) a combination of individual genes with restricted expression patterns, (ii) region-specific differences in the relative expression of functional groups of genes, and (iii) regional differences in neuronal density. Products from some of these neuron-specific genes are present in peripheral blood, raising the possibility that they could reflect the activities of disease- or injury-perturbed networks and collectively function as biomarkers for clinical disease diagnostics.

Young men who have sex with men (YMSM) may be at increased risk for mental health problems including depression, post-traumatic stress (PTSD), and suicidality. The overriding goal of the current investigation was to examine mental health and mental health services in a diverse sample of YMSM. We analyzed cross-sectional data from a cohort study of 598 YMSM, including sociodemographics, mental health, and mental health care. We then tested for bivariate associations, and used multivariable modeling to predict depression, PTSD, suicidality and mental health care utilization. Lower socioeconomic status, unstable housing, and school non-enrollment predicted depression and PTSD scores, while unstable housing and school non-enrollment predicted recent suicide attempt(s). These recent suicide attempt(s) also predicted current utilization of counseling or treatment, any history of psychiatric hospitalization, and any history of psychiatric diagnosis. Black and API men were less likely to have ever accessed mental health counseling or treatment. There were significant class-based differences with regard to mental health outcomes, but not mental health services. Further, recent crises (i.e., suicide attempt, hospitalization) were strong predictors of accessing mental health services. Improving the mental health of YMSM requires addressing the underlying structural factors that influence mental health outcomes and service access.

Background: Human tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (Human Recon 1) coupled with abundant high-throughput data now makes possible the reconstruction of tissue-specific metabolic models. However, the number of available tissue-specific models remains incomplete compared with the large diversity of human tissues., Results: We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues., Conclusions: This large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.

The theory of syndemics has been widely applied in HIV prevention studies of gay, bisexual, and other MSM over the last decade. Our investigation is the first to consider the applicability of the theory in a sample of aging (ages 50 and over) HIV-positive MSM, which is a growing population in the United States. A sample of 199 men were actively recruited and assessed in terms of mental health and drug use burden, as well as sexual risk behaviors. Bivariate and multivariable analyses indicate a high level of association between psychosocial burdens (i.e., drug use and mental health) and same-sex unprotected sexual behaviors, providing initial support for the applicability of the theory of syndemics to this population. Further support can be seen in participants' narratives. Findings suggest the mutually reinforcing nature of drug use, psychiatric disorders, and unprotected sexual behavior in older, HIV-positive, gay, bisexual, and other MSM, highlighting the need for holistic strategies to prevention and care among this population of older and sexually active individuals. In short, the generation of gay men who came of age in the late 1970s and 1980s, "the AIDS Generation," are continuing to mature such that further efforts must be enacted to meet the multidimensional nature of these men's physical, mental, and sexual health needs.

Dysfunction in energy metabolism-including in pathways localized to the mitochondria-has been implicated in the pathogenesis of a wide array of disorders, ranging from cancer to neurodegenerative diseases to type II diabetes. The inherent complexities of energy and mitochondrial metabolism present a significant obstacle in the effort to understand the role that these molecular processes play in the development of disease. To help unravel these complexities, systems biology methods have been applied to develop an array of computational metabolic models, ranging from mitochondria-specific processes to genome-scale cellular networks. These constraint-based (CB) models can efficiently simulate aspects of normal and aberrant metabolism in various genetic and environmental conditions. Development of these models leverages-and also provides a powerful means to integrate and interpret-information from a wide range of sources including genomics, proteomics, metabolomics, and enzyme kinetics. Here, we review a variety of mechanistic modeling studies that explore metabolic functions, deficiency disorders, and aberrant biochemical pathways in mitochondria and related regions in the cell.

Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g., NF-κB (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior.

Background: Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications., Results: We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed., Conclusions: Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.

Summary: The top-scoring pair (TSP) and top-scoring triplet (TST) algorithms are powerful methods for classification from expression data, but analysis of all combinations across thousands of human transcriptome samples is computationally intensive, and has not yet been achieved for TST. Implementation of these algorithms for the graphics processing unit results in dramatic speedup of two orders of magnitude, greatly increasing the searchable combinations and accelerating the pace of discovery., Availability: http://www.igb.illinois.edu/labs/price/downloads/.

Leiomyosarcoma is one of the most common mesenchymal tumors. Proteomics profiling analysis by reverse-phase protein lysate array surprisingly revealed that expression of the epithelial marker E-cadherin (encoded by CDH1) was significantly elevated in a subset of leiomyosarcomas. In contrast, E-cadherin was rarely expressed in the gastrointestinal stromal tumors, another major mesenchymal tumor type. We further sought to 1) validate this finding, 2) determine whether there is a mesenchymal to epithelial reverting transition (MErT) in leiomyosarcoma, and if so 3) elucidate the regulatory mechanism responsible for this MErT. Our data showed that the epithelial cell markers E-cadherin, epithelial membrane antigen, cytokeratin AE1/AE3, and pan-cytokeratin were often detected immunohistochemically in leiomyosarcoma tumor cells on tissue microarray. Interestingly, the E-cadherin protein expression was correlated with better survival in leiomyosarcoma patients. Whole genome microarray was used for transcriptomics analysis, and the epithelial gene expression signature was also associated with better survival. Bioinformatics analysis of transcriptome data showed an inverse correlation between E-cadherin and E-cadherin repressor Slug (SNAI2) expression in leiomyosarcoma, and this inverse correlation was validated on tissue microarray by immunohistochemical staining of E-cadherin and Slug. Knockdown of Slug expression in SK-LMS-1 leiomyosarcoma cells by siRNA significantly increased E-cadherin; decreased the mesenchymal markers vimentin and N-cadherin (encoded by CDH2); and significantly decreased cell proliferation, invasion, and migration. An increase in Slug expression by pCMV6-XL5-Slug transfection decreased E-cadherin and increased vimentin and N-cadherin. Thus, MErT, which is mediated through regulation of Slug, is a clinically significant phenotype in leiomyosarcoma.

Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.

A powerful way to separate signal from noise in biology is to convert the molecular data from individual genes or proteins into an analysis of comparative biological network behaviors. One of the limitations of previous network analyses is that they do not take into account the combinatorial nature of gene interactions within the network. We report here a new technique, Differential Rank Conservation (DIRAC), which permits one to assess these combinatorial interactions to quantify various biological pathways or networks in a comparative sense, and to determine how they change in different individuals experiencing the same disease process. This approach is based on the relative expression values of participating genes-i.e., the ordering of expression within network profiles. DIRAC provides quantitative measures of how network rankings differ either among networks for a selected phenotype or among phenotypes for a selected network. We examined disease phenotypes including cancer subtypes and neurological disorders and identified networks that are tightly regulated, as defined by high conservation of transcript ordering. Interestingly, we observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. At a sample level, DIRAC can detect a change in ranking between phenotypes for any selected network. Variably expressed networks represent statistically robust differences between disease states and serve as signatures for accurate molecular classification, validating the information about expression patterns captured by DIRAC. Importantly, DIRAC can be applied not only to transcriptomic data, but to any ordinal data type.

The enormous amount of biomolecule measurement data generated from high-throughput technologies has brought an increased need for computational tools in biological analyses. Such tools can enhance our understanding of human health and genetic diseases, such as cancer, by accurately classifying phenotypes, detecting the presence of disease, discriminating among cancer sub-types, predicting clinical outcomes, and characterizing disease progression. In the case of gene expression microarray data, standard statistical learning methods have been used to identify classifiers that can accurately distinguish disease phenotypes. However, these mathematical prediction rules are often highly complex, and they lack the convenience and simplicity desired for extracting underlying biological meaning or transitioning into the clinic. In this review, we survey a powerful collection of computational methods for analyzing transcriptomic microarray data that address these limitations. Relative Expression Analysis (RXA) is based only on the relative orderings among the expressions of a small number of genes. Specifically, we provide a description of the first and simplest example of RXA, the K-TSP classifier, which is based on _ pairs of genes; the case K = 1 is the TSP classifier. Given their simplicity and ease of biological interpretation, as well as their invariance to data normalization and parameter-fitting, these classifiers have been widely applied in aiding molecular diagnostics in a broad range of human cancers. We review several studies which demonstrate accurate classification of disease phenotypes (e.g., cancer vs. normal), cancer subclasses (e.g., AML vs. ALL, GIST vs. LMS), disease outcomes (e.g., metastasis, survival), and diverse human pathologies assayed through blood-borne leukocytes. The studies presented demonstrate that RXA-specifically the TSP and K-TSP classifiers-is a promising new class of computational methods for analyzing high-throughput data, and has the potential to significantly contribute to molecular cancer diagnosis and prognosis.

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.

The computational identification from global data sets of stable and predictive patterns of gene and protein relative expression reversals offers a simple, yet powerful approach to target therapies for personalized medicine and to identify pathways that are disease-perturbed. We previously utilized this approach to identify a molecular classifier with near 100% accuracy for differentiating gastrointestinal stromal tumor (GIST) and leiomyosarcoma (LMS), two cancers that have very similar histopathology, but require very different treatments. Differential Rank Conservation (DIRAC) is a novel approach for studying gene ordering within pathways and is based on the relative expression ranks of participating genes. DIRAC provides quantitative measures of how pathway rankings differ both within and between phenotypes. DIRAC between pathways in a selected phenotype contrasts the scenarios where either (i) pathways are ranked similarly in all samples; or (ii) the ordering of pathway genes is highly varied. We examined gene expression in GIST and LMS tumor profiles and identified pathways that appear to be tightly regulated based on high conservation of gene ordering. The second form of DIRAC manifests as a change in ranking (i.e., shuffling) between phenotypes for a selected pathway. These variably expressed pathways serve as signatures for molecular classification, and the ability to accurately classify microarray samples provided strong validation for the pathway-level expression differences identified by DIRAC.

Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)-based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for "fast" reactions and incorporates "slow" reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporated kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here.