Top 8 Biorxiv Papers Today in Systems Biology


2.027 Mikeys
#1. Molecular Inverse Comorbidity between Alzheimer's disease and Lung Cancer: new insights from Matrix Factorization
Alessandro Greco, Jon Sanchez-Valle, Vera Pancaldi, Anais Baudot, Emmanuel Barillot, Michele Caselle, Alfonso Valencia, Amdrei Zinovyev, Laura Cantini
Matrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology. We here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimer's disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC described in AD patients. To the day, the molecular mechanisms underlying DD relationships remain poorly characterized and their better characterization might offer unprecedented clinical opportunities. To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which the previously identified immune system and mitochondrial metabolism. We then discriminate mechanisms specific to LC from those shared with other cancers through...
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biorxivpreprint: Molecular Inverse Comorbidity between Alzheimer's disease and Lung Cancer: new insights from Matrix Factorization https://t.co/5V9IEg72DE #bioRxiv
biorxiv_sysbio: Molecular Inverse Comorbidity between Alzheimer's disease and Lung Cancer: new insights from Matrix Factorization https://t.co/4HAk71SK8B #biorxiv_sysbio
LauCan88: πŸ‘‰New preprint "Molecular Inverse Comorbidity between Alzheimer's disease and Lung Cancer: new insights from Matrix Factorization " Great collaboration with @BaudotAnais @VeraPancaldi @jonsv89 @grecademic @SysBioCurie @Alfons_Valencia #biorxiv_sysbio https://t.co/W0YHahYlAt
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Authors: 9
Total Words: 0
Unqiue Words: 0

2.025 Mikeys
#2. Do signaling networks and whole-transcriptome gene expression profiles orchestrate the same symphony?
Mehran Piran, Reza Karbalaee, Mehrdad Piran, Mehdi Mirzaie, Naser Ansari-Pour, Jing Tang, Mohieddin Jafari
Studying relationships among gene-product expression profiles is a common approach in systems biology. Many studies have generalized this subject to different levels of the central dogma information flow and assumed correlation of transcript and protein expression levels. All these efforts have updated the signaling network models and expanded the current signaling databases, which include interactions among the gene-products extracted based on either the literature or direct and indirect experiments. In fact, due to unavailability or high-cost of the experiments, most of the studies do not look for the direct interactions (gene-protein or protein-protein) and some of them are contradictory. In addition, it is now a standard practice to undertake enrichment analysis on biological annotations especially in omics research to make claims about the potentially implicated biological pathways in disease. Specifically, upon identifying differentially expressed genes, molecular mechanistic insights are proposed based on statistically...
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biorxivpreprint: Do signaling networks and whole-transcriptome gene expression profiles orchestrate the same symphony? https://t.co/i5nPWeMZqI #bioRxiv
biorxiv_sysbio: Do signaling networks and whole-transcriptome gene expression profiles orchestrate the same symphony? https://t.co/3xyTaDcrUj #biorxiv_sysbio
razoralign: Do signaling networks and whole-transcriptome gene expression profiles orchestrate the same symphony? https://t.co/osfzTZrLGb
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Authors: 7
Total Words: 5841
Unqiue Words: 1662

2.017 Mikeys
#3. Nested Active Learning for Efficient Model Contextualization and Parameterization
R Chase Cockrell, Jonathan Ozik, Nick Collier, Gary An
The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free-parameters; given the wide range of variable combinations, along with the intractability of ab initio modeling techniques which could be used to constrain these combinations, an astronomical number of simulations would be required to achieve this goal. In this work, we utilize a nested active-learning workflow to efficiently parameterize and contextualize an agent-based model of sepsis. Methods: Billions of microbial sepsis patients were simulated using a previously validated agent-based model (ABM) of sepsis, the Innate Immune Response Agent-Based Model (IIRABM). Contextual parameter space was examined using the following parameters: cardio-respiratory-metabolic resilience; two properties of microbial virulence, invasiveness and toxigenesis; and degree of contamination from the environment. The model's internal...
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biorxivpreprint: Nested Active Learning for Efficient Model Contextualization and Parameterization https://t.co/3ceDuM23By #bioRxiv
biorxiv_sysbio: Nested Active Learning for Efficient Model Contextualization and Parameterization https://t.co/gILRcAHHn7 #biorxiv_sysbio
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Authors: 4
Total Words: 3620
Unqiue Words: 1353

2.014 Mikeys
#4. Guided proposals for efficient weighted stochastic simulation
Colin S Gillespie, Andrew Golightly
Rare event probabilities play an important role in the understanding of the behaviour of biochemical systems. Due to the intractability of the most natural Markov jump process representation of a system of interest, rare event probabilities are typically estimated using importance sampling. While the resulting algorithm is reasonably well developed, the problem of choosing a suitable importance density is far from straightforward. We, therefore, leverage recent developments on simulation of conditioned jump processes to propose an importance density that is simple to implement and requires no tuning. Our results demonstrate superior performance over some existing approaches.
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biorxivpreprint: Guided proposals for efficient weighted stochastic simulation https://t.co/Qd0tUcRcsJ #bioRxiv
biorxiv_sysbio: Guided proposals for efficient weighted stochastic simulation https://t.co/p9B5pzI71e #biorxiv_sysbio
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Authors: 2
Total Words: 0
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1.997 Mikeys
#5. Gut microbiome activity contributes to individual variation in glycemic response in adults
Hal Tily, Ally Perlina, Eric Patridge, Stephanie Gline, Matvey Genkin, Vishakh Gopu, Haely Lindau, Alisson Sjue, Niels Klitgord, Momchilo Vuyisich, Helen Messier, Guruduth S Banavar
Limiting postprandial glycemic response is an important factor in reducing the risk of chronic metabolic diseases, and contributes to significant health benefits in people with elevated levels of blood sugar. In this study, we collected gut microbiome activity (i.e., metatranscriptomic) data and measured the glycemic responses of 550 adults who consumed more than 27,000 meals from omnivore or vegetarian/gluten-free diets. We demonstrate that gut microbiome activity makes a statistically significant contribution to individual variation in glycemic response, in addition to anthropometric factors and the nutritional composition of foods. We describe a multilevel mixed-effects regression model of variation in glycemic response among individuals ingesting the same foods. We introduce functional features aggregated from microbial activity data as candidates for association with mechanisms of glycemic control. In summary, we demonstrate for the first time that metatranscriptomic activity of the gut microbiome is correlated with glycemic...
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biorxivpreprint: Gut microbiome activity contributes to individual variation in glycemic response in adults https://t.co/EQLEpN58Sz #bioRxiv
biorxiv_sysbio: Gut microbiome activity contributes to individual variation in glycemic response in adults https://t.co/Jm2FFTyC10 #biorxiv_sysbio
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Authors: 12
Total Words: 7915
Unqiue Words: 2804

1.996 Mikeys
#6. SAFEPPP: a Simple And Fast method to Find and analyze Extreme Points of a metabolic Phenotypic Phase Plane
Mohammad Hossein Moteallehi-Ardakani, Sayed-Amir Marashi
There are many algorithms that help us understand how genome-scale metabolic networks work and what are their capabilities. But unfortunately, the majority of these methods are based on integer linear programming suffer from long run times and high instrumental demand. Optimal solutions in any constraint-based modeling as genome-scale metabolic networks models are on the extreme points of the solution space. We introduce a fast and simple toolbox that reveals extreme characters of metabolic networks in desired situations which can unmask the hidden potentials of metabolic networks. Determining the possibility of coupling between two desired reaction and the capability of synergic substrate consuming are examples of the applications of this method. Fast enumeration of elementary flux modes that exist in extreme points of phase plane of any two reactions is another achievement of this study.
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Authors: 2
Total Words: 3738
Unqiue Words: 1148

1.993 Mikeys
#7. Metabolic response to Parkinson's disease recapitulated by the haploinsufficient phenotype of diploid yeast cells hemizygous for the adrenodoxin reductase gene
Duygu Dikicioglu, James Coxon, Steve Oliver
Adrenodoxin reductase, a widely conserved mitochondrial P450 protein, catalyses essential steps in steroid hormone biosynthesis and is highly expressed in the adrenal cortex. The yeast adrenodoxin reductase homolog, Arh1p, is involved in cytoplasmic and mitochondrial iron homeostasis and is required for activity of enzymes containing an Fe-S cluster. In this paper, we investigated the response of yeast to the loss of a single copy of ARH1, an oxidoreductase of the mitochondrial inner membrane, which is among the few mitochondrial proteins that is essential for viability in yeast. The phenotypic, transcriptional, proteomic, and metabolic landscape indicated that Saccharomyces cerevisiae successfully adapted to this loss, displaying an apparently dosage-insensitive cellular response. However, a considered investigation of transcriptional regulation in ARH1-impaired yeast highlighted that a significant hierarchical reorganisation occurred, involving the iron assimilation and tyrosine biosynthetic processes. The interconnected roles...
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Authors: 3
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1.993 Mikeys
#8. Multiplexed measurement of protein biomarkers in high-frequency longitudinal dried blood spot (DBS) samples: characterization of inflammatory responses
Norman Leigh Anderson, Morteza Razavi, Matthew E Pope, Richard Yip, Terry W Pearson
A detailed understanding of changes in blood protein biomarkers occuring in individuals over time would enable truly personalized approaches to health and disease monitoring. Such measurements could reveal smaller, earlier departures from normal baseline levels of biomarkers thus allowing better disease detection and treatment monitoring. Current practice, however, generally involves infrequent, sporadic biomarker testing, and this undersampling likely fails to capture important biological phenomena. Here we report the use of a robust multiplex immuno-mass spectrometric method (SISCAPA) to measure a panel of clinically-relevant proteins in a unique collection of 1,522 dried blood spots collected longitudinally by 8 individuals over periods of up to 9 years, with daily sampling during some intervals. Analytical workflow CVs of 2-6% for most assays were achieved by normalizing DBS plasma volume using a set of 3 minimally varying proteins, facilitating temporal analysis of both high- and low-amplitude biomarker changes compared to...
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Authors: 5
Total Words: 19041
Unqiue Words: 4826

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