Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

<p>Abstract</p> <p>Background</p> <p>With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.</p> <p>Results</p> <p>Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.</p> <p>Conclusion</p> <p>SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.</p

An ink-jet printed electrical stimulation platform for muscle tissue regeneration

Conducting polymeric materials have been used to modulate response of cells seeded on their surfaces. However, there is still major improvement to be made related to their biocompatibility, conductivity, stability in biological milieu, and processability toward truly tissue engineered functional device. In this work, conductive polymer, poly(3,4-ethylene-dioxythiophene):polystyrene-sulfonate (PEDOT:PSS), and its possible applications in tissue engineering were explored. In particular PEDOT:PSS solution was inkjet printed onto a gelatin substrate for obtaining a conductive structure. Mechanical and electrical characterizations, structural stability by swelling and degradation tests were carried out on different PEDOT-based samples obtained by varying the number of printed PEDOT layers from 5 to 50 on gelatin substrate. Biocompatibility of substrates was investigated on C2C12 myoblasts, through metabolic activity assay and imaging analysis during a 7-days culture period, to assess cell morphology, differentiation and alignment. The results of this first part allowed to proceed with the second part of the study in which these substrates were used for the design of an electrical stimulation device, with the aim of providing the external stimulus (3 V amplitude square wave at 1 and 2 Hz frequency) to guide myotubes alignment and enhance differentiation, having in this way promising applications in the field of muscle tissue engineering