Top 10 Biorxiv Papers Today in Systems Biology


2.075 Mikeys
#1. The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks
Ching-Hao Wang, Caleb J. Bashor, Pankaj Mehta
Eukaryotic cells transmit information by signaling through complex networks of interacting proteins. Here we develop a theoretical and computational framework that relates the biophysics of protein-protein interactions (PPIs) within a signaling network to its information processing properties. To do so, we generalize statistical physics-inspired models for protein binding to account for interactions that depend on post-translational state (e.g. phosphorylation). By combining these models with information-theoretic methods, we find that PPIs are a key determinant of information transmission within a signaling network, with weak interactions giving rise to "noise" that diminishes information transmission. While noise can be mitigated by increasing interaction strength, the accompanying increase in transmission comes at the expense of a slower dynamical response. This suggests that the biophysics of signaling protein interactions give rise to a fundamental "speed-information" trade-off. Surprisingly, we find that cross-talk between...
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biorxivpreprint: The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks https://t.co/IKIkrgSgmZ #bioRxiv
Jeew333T: We conclude by showing how our framework "InfoMax" can be used to design synthetic biochemical networks that maximize information transmission https://t.co/2D1nfoQhCH
biorxiv_sysbio: The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks https://t.co/zYMVqkV2cd #biorxiv_sysbio
Github
Repository: InfomaxDesign
User: chinghao0703
Language: Matlab
Stargazers: 0
Subscribers: 0
Forks: 0
Open Issues: 0
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Sample Sizes : [2, 5, 2, 5, 2, 3, 4, 5, 2, 5]
Authors: 3
Total Words: 13426
Unqiue Words: 3129

2.018 Mikeys
#2. Lineage tracing on transcriptional landscapes links state to fate during differentiation
Caleb Weinreb, Alejo E Rodriguez-Fraticelli, Fernando D Camargo, Allon M Klein
A challenge in stem cell biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Though the development of single cell assays allows for the capture of progenitor cell states in great detail, these assays cannot definitively link those molecular states to their long-term fate. Here, we use expressed DNA barcodes to clonally trace single cell transcriptomes dynamically during differentiation and apply this approach to the study of hematopoiesis. Our analysis identifies functional boundaries of cell potential early in the hematopoietic hierarchy and locates them on a continuous transcriptional landscape. Additionally, we find that the monocyte lineage differentiates through two distinct transcriptional and clonal routes, leaving a persistent imprint on mature cells. Finally, we use our approach to reflect on current methods of dynamics inference from single-cell snapshots. We find that for in vitro hematopoiesis, published fate prediction algorithms do not detect...
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IdoAmitLab: Lineage tracing on transcriptional landscapes links state to fate during differentiation | bioRxiv || expressed DNA barcodes to clonally trace single cell transcriptomes dynamically during hematopoiesis - really cool stuff! https://t.co/4UH1JzJyVm
KSusztak: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/yE9GXjuqmW Super COOL ! (Including the accidental visual from the suppl, under) https://t.co/eXPAABOZIw
dominic_grun: Very nice work from Allon Klein's lab relating hematopoietic progenitor state and fate bias with the help of DNA barcoding. Current algorithms inferring fate bias from snapshot data do not capture earliest fate bias! https://t.co/e4uuLn0ky1 https://t.co/gEsXgGCin0
BrackLab: Lineage tracing on transcriptional landscapes links state to fate during differentiation | bioRxiv https://t.co/xAwHleFcNp
PromPreprint: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/V3Sj8rVTUD
PierreJFabre: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/p6qbsDWmpY
SingleCellNews: https://t.co/Lx2FWXaAKe
pharmacologyman: RT @biorxivpreprint: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/Bla3woqs0S #bio…
quarkyle: RT @PierreJFabre: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/p6qbsDWmpY
AvellinoRob: RT @biorxivpreprint: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/Bla3woqs0S #bio…
planlab_bcn: RT @biorxivpreprint: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/Bla3woqs0S #bio…
KlinglerEsther: RT @PierreJFabre: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/p6qbsDWmpY
In_AnkitSingla: RT @biorxivpreprint: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/Bla3woqs0S #bio…
CmcginnisU: RT @biorxiv_sysbio: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/92pQuAGm57 #bior…
MaxSchoenung: RT @biorxivpreprint: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/Bla3woqs0S #bio…
EliaMagrinelli: RT @PierreJFabre: Lineage tracing on transcriptional landscapes links state to fate during differentiation https://t.co/p6qbsDWmpY
Github
Repository: SPRING_dev
User: AllonKleinLab
Language: JavaScript
Stargazers: 15
Subscribers: 6
Forks: 7
Open Issues: 5
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Sample Sizes : [104, 502, 63149, 3844, 20, 50, 20, 250, 50, 15, 300, 20, 100]
Authors: 4
Total Words: 15701
Unqiue Words: 4145

2.001 Mikeys
#3. Bayesian estimation for stochastic gene expression using multifidelity models
Huy D Vo, Zachary R Fox, Ania Baetica, Brian Munsky
The finite state projection (FSP) approach to solving the chemical master equation (CME) has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. First, we present an adaptive scheme to improve parameter proposals for Metropolis-Hastings sampling using full FSP-based likelihood evaluations. We then formulate and verify an Adaptive Delayed Acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We test and compare both algorithms on three example models and simulated data to show that the ADAMH scheme achieves substantial...
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biorxivpreprint: Bayesian estimation for stochastic gene expression using multifidelity models https://t.co/sfiGb8be40 #bioRxiv
biorxiv_sysbio: Bayesian estimation for stochastic gene expression using multifidelity models https://t.co/0pCcfPdSVa #biorxiv_sysbio
razoralign: ADAMH: Bayesian estimation for stochastic gene expression using multifidelity models: https://t.co/yPlbGTQx2R
mtanichthys: RT @biorxivpreprint: Bayesian estimation for stochastic gene expression using multifidelity models https://t.co/sfiGb8be40 #bioRxiv
DougPShepherd: RT @biorxivpreprint: Bayesian estimation for stochastic gene expression using multifidelity models https://t.co/sfiGb8be40 #bioRxiv
zachrfox: RT @biorxivpreprint: Bayesian estimation for stochastic gene expression using multifidelity models https://t.co/sfiGb8be40 #bioRxiv
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Sample Sizes : None.
Authors: 4
Total Words: 14918
Unqiue Words: 3169

1.996 Mikeys
#4. A Bayesian framework for the analysis of systems biology models of the brain
Joshua Russell-Buckland, Christopher P. Barnes, Ilias Tachtsidis
Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain a more complete understanding of the parameter space as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a \`healthy' and \`impaired' state. By analysing both of these...
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Sample Sizes : None.
Authors: 3
Total Words: 11976
Unqiue Words: 3197

0.0 Mikeys
#5. Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks
Ermin Hodzic, Raunak Shrestha, Kaiyuan Zhu, Kuoyuan Cheng, Colin C Collins, S. Cenk Sahinalp
Advances in large scale tumor sequencing have lead to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful shared alteration patterns, impacting gene/protein interaction subnetworks has proven to be challenging. We introduce a novel combinatorial method, cd-CAP, for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not), and simultaneously detects multiple alteration profile conserved subnetworks. In a number of TCGA data sets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples. Availability: https://github.com/ehodzic/cd-CAP
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biorxivpreprint: Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks https://t.co/65Rp6exxyp #bioRxiv
biorxiv_sysbio: Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks https://t.co/0ltLAfVCvx #biorxiv_sysbio
ProtifiLlc: Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks https://t.co/QmteXHuEHC
raunakms: RT @biorxivpreprint: Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks https://t.co/65Rp6exxyp #…
Github
Repository: cd-CAP
User: ehodzic
Language: C++
Stargazers: 1
Subscribers: 3
Forks: 1
Open Issues: 0
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Sample Sizes : None.
Authors: 6
Total Words: 10138
Unqiue Words: 2697

0.0 Mikeys
#6. Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico
Thomas D Sherman, Luciane T Kagohara, Raymon Cao, Raymond Cheng, Matthew Satriano, Michael Considine, Gabriel Krigsfeld, Ruchira Ranaweera, Yong Tang, Sandra Jablonski, Genevieve Stein-O'Brien, Daria Gaykalova, Louis M Weiner, Christine Chung, Elana Fertig
Motivation: Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. Results: We develop an R/Bioconductor package CancerInSilico to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for cell-based mathematical model, implemented for an off-lattice, cell-center Monte Carlo mathematical model. We also adapt this model to simulate the impact of growth suppression by targeted therapeutics in cancer and benchmark simulations against bulk in vitro experimental data. Sensitivity to parameters is evaluated and used to predict the relative impact of variation in cellular growth parameters and cell types on tumor...
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Github
Repository: CancerInSilico-Figures
User: FertigLab
Language: R
Stargazers: 0
Subscribers: 2
Forks: 1
Open Issues: 3
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Authors: 15
Total Words: 9022
Unqiue Words: 2241

0.0 Mikeys
#7. Stochastic temporal timing for intracellular events to cross dynamically fluctuating thresholds
Baohua Qiu, Jiajun Zhang, Tianshou Zhou
Timing of events, which is essential for many cellular processes, depends on regulatory proteins reaching a critical threshold that in general is dynamically fluctuating due to molecular interactions. Increasing evidence shows considerable cell-to-cell variation in the timing of key intracellular events among isogenic cells, but how expression noise and threshold fluctuations impact both threshold crossing and timing precision remain elusive. Here we first formulate stochastic temporal timing of events as a problem of the first passage time (FPT) to a fluctuating threshold and then transform this problem into a higher-dimensional FPT problem with some fixed threshold. Using a stochastic model of gene regulation, we show that (1) in contrast to the case of fixed threshold, threshold fluctuations can both accelerate response (i.e., shorten the time of threshold crossing) and raise timing precision (i.e., reduce timing variability), (2) there is an optimal mean burst size such that the timing precision is best, and (3) fast threshold...
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biorxiv_sysbio: Stochastic temporal timing for intracellular events to cross dynamically fluctuating thresholds https://t.co/0knnIUeRnP #biorxiv_sysbio
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Sample Sizes : [1]
Authors: 3
Total Words: 13268
Unqiue Words: 3213

0.0 Mikeys
#8. Guiding the refinement of biochemical knowledgebases with ensembles of metabolic networks and semi-supervised learning
Gregory L Medlock, Jason A Papin
Mechanistic models are becoming common in biology and medicine.1 These models are often more generalizable than data-driven models because they explicitly represent known interactions between components. While this generalizability has advantages, it also creates a dilemma: how should efforts be focused to improve model performance?2 We present a semi-supervised machine learning approach to this problem and apply it to genome-scale metabolic network reconstructions. We generate an ensemble of candidate models consistent with experimental data, then perform in silico simulations for which improved predictiveness is desired. We apply semi-supervised learning to these simulation results to identify structural variation in ensemble members that maximally influences variance in simulation outcomes across the ensemble. These structural variants are high priority candidates for curation through targeted experimentation. We demonstrate this approach by applying it to 29 bacterial species to identify curation targets that improve gene...
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medlockgreg: New preprint from me and @papinsysbio on guiding curation of metabolic networks using ensembles and machine learning https://t.co/g6TJdyiOrF 1/n
MaureenACarey: Awesome work by @medlockgreg & @papinsysbio is out on @biorxivpreprint 'Guiding the refinement of biochemical knowledgebases with ensembles of metabolic networks and semi-supervised learning' wish we had the data to do this curation for parasites! https://t.co/Pltk7GJlfr
Github

Analysis of ensembles of metabolic network reconstructions

Repository: Medusa
User: gregmedlock
Language: Python
Stargazers: 3
Subscribers: 2
Forks: 2
Open Issues: 10
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Authors: 2
Total Words: 1350
Unqiue Words: 656

0.0 Mikeys
#9. Cheminformatics tools for analyzing and designing optimized small molecule libraries
Nienke Moret, Nicholas Clark, Marc Hafner, Yuan Wang, Eugen Lounkine, Mario Medvedovic, Jinhua Wang, Nathanael Gray, Jeremy Jenkins, Peter Sorger
Libraries of highly annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools,...
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biorxivpreprint: Cheminformatics tools for analyzing and designing optimized small molecule libraries https://t.co/KrtnoiuVaZ #bioRxiv
tonets: 論文はこちら Cheminformatics tools for analyzing and designing optimized small molecule libraries | bioRxiv https://t.co/b29ar7eSdb https://t.co/tFo1SHQ4m8
BiswapriyaMisra: #Cheminformatics tools for analyzing and designing optimized #small molecule #libraries @biorxivpreprint https://t.co/r0LJKgAPdt #bioRxiv #preprints #chemistry #metabolomics #chembio #tools #resources https://t.co/aWPkE3x25J
rguha: https://t.co/rm7DGYJBvE is interesting writeup on tools for screening library design and some #rstats #shinyapp such as https://t.co/yGjZfzN0NP (though restricted to LINCS dataset) #hts #kinase #chemicallibrary
biorxiv_sysbio: Cheminformatics tools for analyzing and designing optimized small molecule libraries https://t.co/203PUhEJ3G #biorxiv_sysbio
probesanddrugs: P&D ver. 08.2018 is out (https://t.co/3SMOgKWyqy) with two new compound sets extracted from preprint Cheminformatics tools for analyzing and designing optimized small molecule libraries (https://t.co/Y1nQDISZu6).
zervanto: Cheminformatics tools for analyzing and designing optimized small molecule libraries https://t.co/yH5zcbaNmb
irileniaN: Cheminformatics tools for analyzing and designing optimized small molecule libraries https://t.co/gINxL1fTYj
kshameer: RT @rguha: https://t.co/rm7DGYJBvE is interesting writeup on tools for screening library design and some #rstats #shinyapp such as https://…
MilkaKostic: RT @biorxiv_sysbio: Cheminformatics tools for analyzing and designing optimized small molecule libraries https://t.co/203PUhEJ3G #biorxiv_…
cispt2: RT @rguha: https://t.co/rm7DGYJBvE is interesting writeup on tools for screening library design and some #rstats #shinyapp such as https://…
abhik1368: RT @rguha: https://t.co/rm7DGYJBvE is interesting writeup on tools for screening library design and some #rstats #shinyapp such as https://…
rkakamilan: RT @tonets: 論文はこちら Cheminformatics tools for analyzing and designing optimized small molecule libraries | bioRxiv https://t.co/b29ar7eSdb h…
AurijitSarkar: RT @rguha: https://t.co/rm7DGYJBvE is interesting writeup on tools for screening library design and some #rstats #shinyapp such as https://…
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Authors: 10
Total Words: 10321
Unqiue Words: 2884

0.0 Mikeys
#10. DECODER: A probabilistic approach to integrate big data reveals mitochondrial Complex I as a potential therapeutic target for Alzheimer's disease
Safiye Celik, Josh C Russell, Cezar R Pestana, Ting-I Lee, Shubhabrata Mukherjee, Paul K Crane, Dirk Keene, Jennifer F Bobb, Matt Kaeberlein, Su-In Lee
Identifying gene expression markers for Alzheimer's disease (AD) neuropathology through meta-analysis is a complex undertaking because available data are often from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. Here, we developed a probabilistic model-based framework, DECODER, leveraging these discrepancies to identify robust biomarkers for complex phenotypes. Our experiments present: (1) DECODER's potential as a general meta-analysis framework widely applicable to various diseases (e.g., AD and cancer) and phenotypes (e.g., Amyloid-beta (Abeta) pathology, tau pathology, and survival), (2) our results from a meta-analysis using 1,746 human brain tissue samples from nine brain regions in three studies -- the largest expression meta-analysis for AD, to our knowledge --, and (3) in vivo validation of identified modifiers of Abeta toxicity in a transgenic Caenorhabditis elegans model expressing AD-associated Abeta, which pinpoints mitochondrial Complex I as a...
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ProtifiLlc: DECODER: A probabilistic approach to integrate big data reveals mitochondrial Complex I as a potential therapeutic target for Alzheimer's disease https://t.co/0vrpnezd42
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Authors: 10
Total Words: 16054
Unqiue Words: 3903

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