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  • Health Reference Center Academic (Gale)  (2)
  • SwePub (National Library of Sweden)  (2)
  • 1
    Language: English
    In: BMC bioinformatics, 22 July 2014, Vol.15, pp.250
    Description: Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system's response after systematic perturbations are available. We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway. Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.
    Keywords: Algorithms ; Signal Transduction ; Systems Biology -- Methods
    E-ISSN: 1471-2105
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  • 2
    In: PLoS ONE, 2016, Vol.11(12)
    Description: The development of high-throughput sequencing technologies have allowed the possibility to investigate and characterise the entire microbiome of individuals, providing better insight to the complex interaction between different microorganisms. This will help to understand how the microbiome influence the susceptibility of secondary agents and development of disease. We have applied viral metagenomics to investigate the virome of lymph nodes from Swedish pigs suffering from the multifactorial disease postweaning multisystemic wasting syndrome (PMWS) as well as from healthy pigs. The aim is to increase knowledge of potential viruses, apart from porcine circovirus type 2 (PCV2), involved in PMWS development as well as to increase knowledge on the virome of healthy individuals. In healthy individuals, a diverse viral flora was seen with several different viruses present simultaneously. The majority of the identified viruses were small linear and circular DNA viruses, such as different circoviruses, anelloviruses and bocaviruses. In the pigs suffering from PMWS, PCV2 sequences were, as expected, detected to a high extent but other viruses were also identified in the background of PCV2. Apart from DNA viruses also RNA viruses were identified, among them were a porcine pestivirus showing high similarity to a recently (in 2015) discovered atypical porcine pestivirus in the US. Majority of the viruses identified in the background of PCV2 in PMWS pigs could also be identified in the healthy pigs. PCV2 sequences were also identified in the healthy pigs but to a much lower extent than in PMWS affected pigs. Although the method used here is not quantitative the very clear difference in amount of PCV2 sequences in PMWS affected pigs and healthy pigs most likely reflect the very strong replication of PCV2 known to be a hallmark of PMWS. Taken together, these findings illustrate that pigs appear to have a considerable viral flora consisting to a large extent of small single-stranded and circular DNA viruses. Future research on these types of viruses will help to better understand the role that these ubiquitous viruses may have on health and disease of pigs. We also demonstrate for the first time, in Europe, the presence of a novel porcine pestivirus.
    Keywords: Research Article ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Research And Analysis Methods ; Research And Analysis Methods ; Biology And Life Sciences ; Research And Analysis Methods
    E-ISSN: 1932-6203
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