Biology subjects

Kim, S.-Y.

Publications and source records attributed to Kim, S.-Y..

6 recordsLinked to original sources

bioRxiv2018

Cluster Burst Synchronization in A Scale-Free Network of Inhibitory Bursting Neurons

We consider a scale-free network of inhibitory Hindmarsh-Rose (HR) bursting neurons, and investigate coupling-induced burst synchronization by varying the average coupling strength J0. When passing a lower threshold [Formula], a transition from desynchronization to 3-cluster burst synchronization occurs due to a constructive role of inhibition to favor the cluster burst synchronization. For this case, the whole population is segregated into 3 clusters, and HR neurons in each cluster exhibit burst synchronization. However, as J0 is increased and passes an intermediate threshold [Formula], HR neurons begin to make intermittent hoppings between the 3 clusters. Due to the intermittent intercluster hoppings, the 3 clusters are integrated into a single one. In spite of break-up of the 3 clusters, burst synchronization persists in the whole population, which is well visualized in the raster plot of burst onset times where bursting stripes (composed of burst onset times and indicating burst synchronization) appear successively. With further increase in J0, intercluster hoppings are intensified, and bursting stripes also become smeared more and more due to a destructive role of inhibition to spoil the burst synchronization. Eventually, when passing a higher threshold [Formula] a transition to desynchronization occurs via complete overlap between the bursting stripes. Finally, we also investigate the effects of stochastic noise on both 3-cluster burst synchronization and intercluster hoppings.\n\nPACS numbers: 87.19.lm, 87.19.lc

neuroscience↗

bioRxiv2018

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

The scientific literature is vast, growing, and increasingly specialized, making it difficult to connect disparate observations across subfields. To address this problem, we sought to develop automated hypothesis generation by networking at scale the MeSH terms curated by the National Library of Medicine. The result is a Mesh Term Objective Reasoning (MeTeOR) approach that tallies associations among genes, drugs and diseases from PubMed and predicts new ones.\n\nComparisons to reference databases and algorithms show MeTeOR tends to be more reliable. We also show that many predictions based on the literature prior to 2014 were published subsequently. In a practical application, we validated experimentally a surprising new association found by MeTeOR between novel Epidermal Growth Factor Receptor (EGFR) associations and CDK2. We conclude that MeTeOR generates useful hypotheses from the literature (http://meteor.lichtargelab.org/).\n\nAUTHOR SUMMARYThe large size and exponential expansion of the scientific literature forms a bottleneck to accessing and understanding published findings. Manual curation and Natural Language Processing (NLP) aim to address this bottleneck by summarizing and disseminating the knowledge within articles as key relationships (e.g. TP53 relates to Cancer). However, these methods compromise on either coverage or accuracy, respectively. To mitigate this compromise, we proposed using manually-assigned keywords (MeSH terms) to extract relationships from the publications and demonstrated a comparable coverage but higher accuracy than current NLP methods. Furthermore, we combined the extracted knowledge with semi-supervised machine learning to create hypotheses to guide future work and discovered a direct interaction between two important cancer genes.

bioinformatics↗

bioRxiv2018

Vascular defects of DYRK1A knockouts are ameliorated by modulating calcium signaling in zebrafish

DYRK1A is a major causative gene in Down syndrome (DS). Reduced incidence of solid tumors and vascular anomalies in DS patients suggests a role of DYRK1A in angiogenesis, but in vivo evidence is lacking. Here, we used zebrafish dyrk1aa mutant embryos to understand DYRK1A function in the cerebral vasculature formation. Zebrafish dyrk1aa mutants exhibited cerebral hemorrhage and defects in angiogenesis of central arteries in the developing hindbrain. Such phenotypes were rescued by wild-type dyrk1aa mRNA, but not by a kinase-dead form, indicating the importance of DYRK1A kinase activity. Chemical screening using a bioactive small molecule library identified a calcium chelator, EGTA, as one of the hits that most robustly rescued the hemorrhage. Vascular defects of mutants were also rescued by independent modulation of calcium signaling by FK506. Furthermore, the transcriptomic analyses supported the alterations of calcium signaling networks in dyrk1aa mutants. Together, our results suggest that dyrk1aa plays an essential role in angiogenesis and in maintenance of the developing cerebral vasculature via regulation of calcium signaling, which may have therapeutic potential for DYRK1A-related vascular diseases.

developmental biology↗

bioRxiv2018

Effect of Inhibitory Spike-Timing-Dependent Plasticity on Fast Sparsely Synchronized Rhythms in A Small-World Neuronal Network

We consider the Watts-Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity D. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of D, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of D. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good synchronization (with higher spiking measure) is found to get better via LTD, while bad synchronization (with lower spiking measure) is found to get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability p of the SWN in the presence of iSTDP.

neuroscience↗

bioRxiv2018

Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory Spike-Timing-Dependent Plasticity

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabasi-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the preand the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l* and the asymmetry parameter {Delta}l in the SFN.

neuroscience↗

bioRxiv2017

Dr.Paso: Drug response prediction and analysis system for oncology research

The prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.

bioinformatics↗