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 PLOS ONE , 2012, DOI: 10.1371/journal.pone.0015456 Abstract: Developing in vitro engineered hepatic tissues that exhibit stable phenotype is a major challenge in the field of hepatic tissue engineering. However, the rapid dedifferentiation of hepatic parenchymal (hepatocytes) and non-parenchymal (liver sinusoidal endothelial, LSEC) cell types when removed from their natural environment in vivo remains a major obstacle. The primary goal of this study was to demonstrate that hepatic cells cultured in layered architectures could preserve or potentially enhance liver-specific behavior of both cell types. Primary rat hepatocytes and rat LSECs (rLSECs) were cultured in a layered three-dimensional (3D) configuration. The cell layers were separated by a chitosan-hyaluronic acid polyelectrolyte multilayer (PEM), which served to mimic the Space of Disse. Hepatocytes and rLSECs exhibited several key phenotypic characteristics over a twelve day culture period. Immunostaining for the sinusoidal endothelial 1 antibody (SE-1) demonstrated that rLSECs cultured in the 3D hepatic model maintained this unique feature over twelve days. In contrast, rLSECs cultured in monolayers lost their phenotype within three days. The unique stratified structure of the 3D culture resulted in enhanced heterotypic cell-cell interactions, which led to improvements in hepatocyte functions. Albumin production increased three to six fold in the rLSEC-PEM-Hepatocyte cultures. Only rLSEC-PEM-Hepatocyte cultures exhibited increasing CYP1A1/2 and CYP3A activity. Well-defined bile canaliculi were observed only in the rLSEC-PEM-Hepatocyte cultures. Together, these data suggest that rLSEC-PEM-Hepatocyte cultures are highly suitable models to monitor the transformation of toxins in the liver and their transport out of this organ. In summary, these results indicate that the layered rLSEC-PEM-hepatocyte model, which recapitulates key features of hepatic sinusoids, is a potentially powerful medium for obtaining comprehensive knowledge on liver metabolism, detoxification and signaling pathways in vitro.
 Journal of Nanobiotechnology , 2010, DOI: 10.1186/1477-3155-8-29 Abstract: The modified sandwich cultures replace collagen with self-assembling peptide, RAD16-I, combined with functional peptide motifs such as the integrin-binding sequence RGD and the laminin receptor binding sequence YIG to create a cell-instructive scaffold. In this work, we show that a plasma-deposited coating can be used to obtain a peptide layer thickness in the nanometric range, which in combination with the incorporation of functional peptide motifs have a positive effect on the expression of adult hepatocyte markers including albumin, CYP3A2 and HNF4-alpha.This study demonstrates the capacity of sandwich cultures with modified instructive self-assembling peptides to promote cell-matrix interaction and the importance of thinner scaffold layers to overcome mass transfer problems. We believe that this bioengineered platform improves the existing hepatocyte culture methods to be used for predictive toxicology and eventually for hepatic assist technologies and future artificial organs.The liver is an important and complex organ that plays a vital role in metabolism and is responsible for many important functions of the body including glycogen storage, plasma protein production, drug detoxification and xenobiotics metabolization. Due to the importance of this organ in many of the body's daily processes, liver malfunction often leads to death. Most of the activity of the liver can be attributed to hepatocytes, which make up 60-80% of the cytoplasmic mass of the liver [1,2]. Loss of hepatocyte function can result in acute or chronic liver disease and, as a result, substantially compromise the rest of the organ and the body. Many previous strategies have been implemented to maintain these hepatocyte functions in vitro, including the use of extracellular matrices such as the current standard, collagen [3-6], Matrigel [7] or liver derived basement membrane matrix [8]. However, the liver carries out and regulates numerous biochemical reactions that require the combined effort
 Computer Science , 2012, Abstract: Computational methods for discovering patterns of local correlations in sequences are important in computational biology. Here we show how to determine the optimal partitioning of aligned sequences into non-overlapping segments such that positions in the same segment are strongly correlated while positions in different segments are not. Our approach involves discovering the hidden variables of a Bayesian network that interact with observed sequences so as to form a set of independent mixture models. We introduce a dynamic program to efficiently discover the optimal segmentation, or equivalently the optimal set of hidden variables. We evaluate our approach on two computational biology tasks. One task is related to the design of vaccines against polymorphic pathogens and the other task involves analysis of single nucleotide polymorphisms (SNPs) in human DNA. We show how common tasks in these problems naturally correspond to inference procedures in the learned models. Error rates of our learned models for the prediction of missing SNPs are up to 1/3 less than the error rates of a state-of-the-art SNP prediction method. Source code is available at www.uwm.edu/~joebock/segmentation.
 Quantitative Biology , 2007, DOI: 10.1103/PhysRevE.76.011917 Abstract: We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g. the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy $H$ and the perturbation size $\Delta$, a quantity that reflects the distance between the set of fixed points of the perturbed network to that of the unperturbed network. Applying our approach to the yeast-cell cycle network introduced by Li \textit{et al.} provides a low dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoints functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.