Top 10 Biorxiv Papers Today in Systems Biology


2.015 Mikeys
#1. A new image segmentation algorithm with applications in confocal microscopy analysis
Maximo Sanchez-Aragon, Fernando Casares
Gene regulatory networks (GRNs) represent the molecular interactions that govern the behavior of cells in tissues during development. The building and analysis of GRNs require quantitative information on gene expression from tissues. Laser Scanning Confocal Microscopy (LSCM) is commonly used to obtain such information, where immunofluorescence signal can be used as a correlate of gene expression or protein levels. However, a critical step for the extraction of this information is the segmentation of LSCM digital images. Popular segmentation algorithms are frequently based on watershed methods. Here we present an algorithm for the 3D segmentation of nuclei from LSCM (x,y,z) image stacks based on regional merging and graph contractions. This algorithm outperforms watershed methods, especially when the density of images along the z-axis is low and there is a high nuclear signal crowding. In addition, it reduces the parameterization since no filter is needed in order to prevent signal noise side effects (e.g. oversegmentation). Based...
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biorxivpreprint: A new image segmentation algorithm with applications in confocal microscopy analysis https://t.co/8BSbmOPx4c #bioRxiv
biorxiv_sysbio: A new image segmentation algorithm with applications in confocal microscopy analysis https://t.co/lrekUFDUSC #biorxiv_sysbio
katelovesneuro: RT @biorxivpreprint: A new image segmentation algorithm with applications in confocal microscopy analysis https://t.co/8BSbmOPx4c #bioRxiv
nmclark91: RT @biorxiv_sysbio: A new image segmentation algorithm with applications in confocal microscopy analysis https://t.co/lrekUFDUSC #biorxiv_…
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Authors: 2
Total Words: 11318
Unqiue Words: 2710

2.001 Mikeys
#2. 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients
Cansu Dincer, Tugba Kaya, Ozlem Keskin, Attila Gursoy, Nurcan Tuncbag
Mutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of...
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biorxivpreprint: 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients https://t.co/BmjyB7dA0H #bioRxiv
biorxiv_sysbio: 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients https://t.co/Pl0ljzTEyu #biorxiv_sysbio
ntuncbag: Our new preprint in collaboration with @ozlemkeskin @attilagursoy is now online! We compare GBM mutation profiles using protein structures together with network-guided analysis https://t.co/XNq4wyTFWM https://t.co/axxD6LUBcA
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Authors: 5
Total Words: 12358
Unqiue Words: 3513

0.0 Mikeys
#3. Transient Probability Distributions of Gene Regulatory Networks with Slow Promoter Kinetics
Muhammad Ali Al-Radhawi
Under a suitable time-scale separation hypothesis, we show that the time evolution of the probability mass function of gene regulatory networks can be written as a time-varying mixture of Poisson distributions.
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biorxivpreprint: Transient Probability Distributions of Gene Regulatory Networks with Slow Promoter Kinetics https://t.co/deWfySakGG #bioRxiv
biorxiv_sysbio: Transient Probability Distributions of Gene Regulatory Networks with Slow Promoter Kinetics https://t.co/8O4LrB8ZjC #biorxiv_sysbio
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Authors: 1
Total Words: 5718
Unqiue Words: 1355

0.0 Mikeys
#4. A Persistence Detector for Metabolic Network Rewiring in an Animal
Jote T Bulcha, Gabrielle E Giese, Md Zulfikar Ali, Yong-Uk Lee, Melissa D Walker, Amy D Holdorf, L Safak Yilmaz, Robert C Brewster, Albertha JM Walhout
Persistence detection is a mechanism that ensures a physiological output is only executed when the relevant input is sustained. Gene regulatory network circuits known as coherent type 1 feed forward loops (FFLs) with an AND-logic gate have been proposed to generate persistence detection. In such circuits two transcription factors (TFs) are both required to activate target genes and one of the two TFs activates the other. While numerous FFLs have been identified, examples of actual persistence detectors have only been described for bacteria. Here, we discover a transcriptional persistence detector in Caenorhabditis elegans involving the nuclear hormone receptors nhr-10 and nhr-68, which activates genes comprising a propionate shunt pathway. This shunt is used only when flux through the canonical, vitamin B12-dependent propionate breakdown pathway is perturbed. We propose that the propionate persistence detector functions to preferentially catabolize propionate through the canonical pathway to avoid spurious production of toxic...
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job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
AmyHoldorf: For the morning crowd, the latest opus form the Walhout lab in @biorxivpreprint: "A Persistence Detector for Metabolic Network Rewiring in an Animal" https://t.co/ykF4rY0vNi
AmyHoldorf: Latest oeuvre from the Walhout Lab now on @biorxivpreprint: A Persistence Detector for Metabolic Network Rewiring in an Animal https://t.co/ykF4rY0vNi
anshulkundaje: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
NeedhiBhalla: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
dacolon: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
BiswapriyaMisra: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
dgermain21: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
UMMSLibrary: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
mary_munson4: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
benjvincent: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
bwardboston: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
EpigeneticTroll: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
TraverHart: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
MondouxLab: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
syntheticmo: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
jhdcate: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
plewczynski: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
carvunis: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
jsgjames: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
akwalker_lab: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
domhelmlinger: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
UMassMedLabIT: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
HainerLab: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
Hanhui_Ma: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
AkshayKakumanu: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
DrAJRose: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
jzahratk: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
Jonnygfrazer: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
YuLiu_Sunny: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
EpiChromatin: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
_bakshay: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
AlexTamburino: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
MoonspiriTYin: RT @job_dekker: Absolutely stunning work from Walhout lab. Any systems biologist should read this! https://t.co/fP4QEC0OBT
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Authors: 9
Total Words: 9172
Unqiue Words: 2495

0.0 Mikeys
#5. Cellular responses to reactive oxygen species can be predicted on multiple biological scales from molecular mechanisms
Laurence Yang, Nathan Mih, Amitesh Anand, Joon Ho Park, Justin Tan, James T. Yurkovich, Jonathan M. Monk, Colton J. Lloyd, Troy E. Sandberg, Sang Woo Seo, Donghyuk Kim, Anand V. Sastry, Patrick Phaneuf, Ye Gao, Jared T. Broddrick, Ke Chen, David Heckmann, Richard Szubin, Ying Hefner, Adam M. Feist, Bernhard O. Palsson
Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can completely inhibit cell growth when unmanaged and thus elicits an essential stress response that is universal and fundamental in biology. We develop a computable multi-scale description of the ROS stress response in Escherichia coli. We show that this quantitative framework allows for the understanding and prediction of ROS stress responses at three levels: 1) pathways: amino acid auxotrophies, 2) networks: the systemic response to ROS stress, and 3) genetic basis: adaptation to ROS stress during laboratory evolution. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.
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Authors: 21
Total Words: 3551
Unqiue Words: 1369

0.0 Mikeys
#6. iOmicsPASS: a novel method for integration of multi-omics data over biological networks and discovery of predictive subnetworks
Hiromi WL Koh, Damian Fermin, Kwok Pui Choi, Rob Ewing, Hyungwon Choi
We developed iOmicsPASS, an intuitive method for network-based multi-omics data integration and detection of biological subnetworks for phenotype prediction. The method converts abundance measurements into co-expression scores of biological networks and uses a powerful phenotype prediction method adapted for network-wise analysis. Simulation studies show that the proposed data integration approach considerably improves the quality of predictions. We illustrate iOmicsPASS through the integration of quantitative multi-omics data using transcription factor regulatory network and protein-protein interaction network for cancer subtype prediction. Our analysis of breast cancer data identifies network signatures surrounding established markers of molecular subtypes. The analysis of colorectal cancer data highlights a protein interactome surrounding key proto-oncogenes as predictive features of subtypes,rendering them more biologically interpretable than the approaches integrating data without a priori relational information. However, the...
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Data Integration tool utilizing network information for predictive analyses

Repository: iOmicsPASS
User: cssblab
Language: C++
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Subscribers: 2
Forks: 0
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Authors: 5
Total Words: 14792
Unqiue Words: 3781

0.0 Mikeys
#7. An synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions
Marco Fondi, Francesca Di Patti
Marine ecosystems are characterized by an intricate set of interactions among their representatives. One of the most important occurs through the exchange of dissolved organic matter (DOM) provided by phototrophs and used by heterotrophic bacteria as their main carbon and energy source. This metabolic interaction represents the foundation of the entire ocean food-web. Here we have assembled a synthetic ecosystem to assist the systems-level investigation of this biological association. This was achieved building an integrated, genome-scale metabolic reconstruction using two model organisms (a diatom Phaeodactylum tricornutum and an heterotrophic bacterium, Pseudoalteromonas haloplanktis) to explore and predict their metabolic interdependencies. The model was initially analysed using a constraint-based approach (Flux Balance Analysis, FBA) and then turned into a dynamic (dFBA) model to simulate a diatom-bacteria co-culture and to study the effect of changes in growth parameters on such a system. Finally, we developed a simpler...
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jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
mrcfnd: We have constructed and modelled a synthetic ecosystem composed of a diatom and a bacterium. Very much looking forward to experimentally replicating it and adjusting/correcting/rewriting/dropping the model. https://t.co/PcfmF94rA0
brochlorococcus: interesting! A synthetic microbial loop for modeling heterotroph-phototroph metabolic interactions https://t.co/rxC1unOu33
ctitusbrown: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
msmjetten: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
jpbacteria: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
amlinz16: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
sarilog: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
Raphael_micro: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
pmberube: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
willigo09: RT @biorxiv_sysbio: An synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/q3oWQNPzDJ #biorx…
DanielSGregoire: RT @jcamthrash: A synthetic microbial loop for modelling heterotroph-phototroph metabolic interactions https://t.co/VDHYdD07Io
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Authors: 2
Total Words: 9855
Unqiue Words: 2752

0.0 Mikeys
#8. Overdosage of balanced protein complexes reduces proliferation rate in aneuploid cells
Ying Chen, Siyu Chen, Ke Li, Yuliang Zhang, Xiahe Huang, Ting Li, Shaohuan Wu, Yingchun Wang, Lucas B. Carey, Wenfeng Qian
Cells with complex aneuploidies, such as tumor cells, display a wide range of phenotypic abnormalities. However, molecular basis for this has been mainly studied in trisomic (2n+1) and disomic (n+1) cells. To determine how karyotype affects proliferation rate in cells with complex aneuploidies we generated forty 2n+x yeast strains in which each diploid cell has an extra 5 to 12 chromosomes and found that these strains exhibited abnormal cell-cycle progression. Proliferation rate was negatively correlated with the number of protein complexes in which all subunits were at the 3-copy level, but not with the number of imbalanced complexes made up of a mixture of 2-copy and 3-copy genes. Proteomics revealed that most 3-copy members of imbalanced complexes were expressed at only 2n protein levels whereas members of complexes in which all subunits are stoichiometrically balanced at 3 copies per cell had 3n protein levels. We identified individual protein complexes for which overdosage reduces proliferation rate, and found that deleting...
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LucasBCarey: Why are anueploid cells different? A new hypothesis, with data. Collab w/Wenfeng Qian. https://t.co/1fp0XLXNtv https://t.co/7Snry74rt9
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Authors: 10
Total Words: 10270
Unqiue Words: 2756

0.0 Mikeys
#9. Quantitative modelling explains distinct STAT1 and STAT3 activation dynamics in response to both IFNγ and IL-10 stimuli and predicts emergence of reciprocal signalling at the level of single cells
Uddipan Sarma, Debasri Mukherjee, Moitrayee Maiti, Sagar Bhadange, Arathi Nair, Ankita Srivastava, Bhaskar Saha
Cells use IFNγ-STAT1 and IL-10-STAT3 pathways primarily to elicit pro and anti-inflammatory responses, respectively. However, activation of STAT1 by IL-10 and STAT3 by IFNγ is also observed. The regulatory mechanisms controlling the amplitude and dynamics of both the STATs in response to these functionally opposing stimuli remains less understood. Here, our experiments at cell population level show distinct early signalling dynamics of both STAT1 and STAT3(S/1/3) in responses to IFNγ and IL-10 stimulation. We built a mathematical model comprising both the pathways that quantitatively captured the dose-dependent dynamics of S/1/3 in response both IFNγ and IL-10 stimulation. As both the functionally opposing cues activate S/1/3 we next predicted a co-stimulation scenario (IL-10 and IFNγ applied simultaneously) which suggests signalling dynamics of STAT3 would remain robustly IL-10 driven which it ensures through strong induction of SOCS1, a negative regulator of IFNγ pathway; the prediction was subsequently validated. Next, to...
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Authors: 7
Total Words: 10877
Unqiue Words: 3110

0.0 Mikeys
#10. Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks
Natalie M Clark, Eli Buckner, Adam P Fisher, Emily C Nelson, Thomas T Nguyen, Abigail R Simmons, Maria A de Luis Balaguer, Tiara Butler-Smith, Parnell J Sheldon, Dominique C Bergmann, Cranos M Williams, Rosangela Sozzani
Stem cells are responsible for generating all of the differentiated cells, tissues, and organs in a multicellular organism and, thus, play a crucial role in cell renewal, regeneration, and organization. A number of stem cell type-specific genes have a known role in stem cell maintenance, identity, and/or division. Yet, how genes expressed across different stem cell types, referred here as stem-cell-ubiquitous genes, contribute to stem cell regulation is less understood. Here, we find that, in the Arabidopsis root, a stem-cell-ubiquitous gene, TESMIN-LIKE CXC2 (TCX2), controls stem cell division by regulating stem cell-type specific networks. Development of a mathematical model of TCX2 expression allowed us to show that TCX2 orchestrates the coordinated division of different stem cell types. Our results highlight that genes expressed across different stem cell types ensure cross-communication among cells, allowing them to divide and develop harmonically together.
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biorxivpreprint: Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks https://t.co/ouLp04Rfhi #bioRxiv
stanfordstomata: What might be at the center of plant stem cell behaviors? Intriguing computational analysis of seven different cell-type lineages in Arabidopsis roots from @nmclark91 and @RossSozzani https://t.co/hLHXUNthet https://t.co/IvNGGgkqLu
carolynplants: This looks interesting! Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks | bioRxiv https://t.co/4f4TdMgpd8
biorxiv_sysbio: Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks https://t.co/TMWGsOigce #biorxiv_sysbio
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Authors: 12
Total Words: 7834
Unqiue Words: 2699

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Assert is a website where the best academic papers on arXiv (computer science, math, physics), bioRxiv (biology), BITSS (reproducibility), EarthArXiv (earth science), engrXiv (engineering), LawArXiv (law), PsyArXiv (psychology), SocArXiv (social science), and SportRxiv (sport research) bubble to the top each day.

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