Table2_PCGIMA: developing the web server for human position-defined CpG islands methylation analysis.DOCX

Introduction: CpG island (CGI) methylation is one of the key epigenomic mechanisms for gene expression regulation and chromosomal integrity. However, classical CGI prediction methods are neither easy to locate those short and position-sensitive CGIs (CpG islets), nor investigate genetic and expression pattern for CGIs under different CpG position- and interval- sensitive parameters in a genome-wide perspective. Therefore, it is urgent for us to develop such a bioinformatic algorithm that not only can locate CpG islets, but also provide CGI methylation site annotation and functional analysis to investigate the regulatory mechanisms for CGI methylation.Methods: This study develops Human position-defined CGI prediction method to locate CpG islets using high performance computing, and then builds up a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate these functions into PCGIMA to provide relevant online computing and visualization service.Results: The main results include: (1) Human position-defined CGI prediction method is more efficient to predict position-defined CGIs with multiple consecutive (d) values and locate more potential short CGIs than previous CGI prediction methods. (2) Our annotation and analysis method not only can investigate the connections between position-defined CGI methylation and gene expression specificity from a genome-wide perspective, but also can analysis the potential association of position-defined CGIs with gene functions. (3) PCGIMA (http://www.combio-lezhang.online/pcgima/home.html) provides an easy-to-use analysis and visualization platform for human CGI prediction and methylation.Discussion: This study not only develops Human position-defined CGI prediction method to locate short and position-sensitive CGIs (CpG islets) using high performance computing to construct MR-CpGCluster algorithm, but also a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate them into PCGIMA for online computing and visualization.</p

Table1_PCGIMA: developing the web server for human position-defined CpG islands methylation analysis.DOCX

Introduction: CpG island (CGI) methylation is one of the key epigenomic mechanisms for gene expression regulation and chromosomal integrity. However, classical CGI prediction methods are neither easy to locate those short and position-sensitive CGIs (CpG islets), nor investigate genetic and expression pattern for CGIs under different CpG position- and interval- sensitive parameters in a genome-wide perspective. Therefore, it is urgent for us to develop such a bioinformatic algorithm that not only can locate CpG islets, but also provide CGI methylation site annotation and functional analysis to investigate the regulatory mechanisms for CGI methylation.Methods: This study develops Human position-defined CGI prediction method to locate CpG islets using high performance computing, and then builds up a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate these functions into PCGIMA to provide relevant online computing and visualization service.Results: The main results include: (1) Human position-defined CGI prediction method is more efficient to predict position-defined CGIs with multiple consecutive (d) values and locate more potential short CGIs than previous CGI prediction methods. (2) Our annotation and analysis method not only can investigate the connections between position-defined CGI methylation and gene expression specificity from a genome-wide perspective, but also can analysis the potential association of position-defined CGIs with gene functions. (3) PCGIMA (http://www.combio-lezhang.online/pcgima/home.html) provides an easy-to-use analysis and visualization platform for human CGI prediction and methylation.Discussion: This study not only develops Human position-defined CGI prediction method to locate short and position-sensitive CGIs (CpG islets) using high performance computing to construct MR-CpGCluster algorithm, but also a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate them into PCGIMA for online computing and visualization.</p

Schematic of the multi-scale modeling of OBs, OCs and MMs.

<p><b>Intracellular scale</b>: describes the communication among myeloma cells, osteoclasts and osteoblasts and their ‘phenotypic’ switches. <b>Intercellular scale</b>: describes the dynamics of molecules in signaling pathways for each cell after receiving cytokine stimulation from other cells and the specific migration rules for cells. <b>Tissue scale</b>: describes the diffusion of drugs and cytokines.</p

Simulating non-small cell lung cancer with a multiscale agent-based model-2

<p><b>Copyright information:</b></p><p>Taken from "Simulating non-small cell lung cancer with a multiscale agent-based model"</p><p>http://www.tbiomed.com/content/4/1/50</p><p>Theoretical Biology & Medical Modelling 2007;4():50-50.</p><p>Published online 21 Dec 2007</p><p>PMCID:PMC2259313.</p><p></p>increases from 2.65 × 1.0 to 2.65 × 31.1, 2.65 × 31.2, and finally, to 2.65 × 50.0 nM. (From to ) plotted are the absolute change of PLC, rate of change of PLC, and rate of change of ERK. Note that the number of proliferations is decreasing gradually and finally disappears at a phase transition between the EGF concentrations of 2.65 × 31.1 and 2.65 × 31.2 nM. (For phenotype labeling see Fig. 4)

Shows the multicellular patterns that emerge through rule A and rule B, respectively

<p><b>Copyright information:</b></p><p>Taken from "Simulating non-small cell lung cancer with a multiscale agent-based model"</p><p>http://www.tbiomed.com/content/4/1/50</p><p>Theoretical Biology & Medical Modelling 2007;4():50-50.</p><p>Published online 21 Dec 2007</p><p>PMCID:PMC2259313.</p><p></p> Describes the numeric evolution () of each cell phenotype as well as of the [total] cell population () over time () for rule A () and rule B (), respectively. Note: proliferative tumor cells are labeled in , migratory cells in , quiescent cells in and dead cells in

Simulating non-small cell lung cancer with a multiscale agent-based model-1

<p><b>Copyright information:</b></p><p>Taken from "Simulating non-small cell lung cancer with a multiscale agent-based model"</p><p>http://www.tbiomed.com/content/4/1/50</p><p>Theoretical Biology & Medical Modelling 2007;4():50-50.</p><p>Published online 21 Dec 2007</p><p>PMCID:PMC2259313.</p><p></p>g the corresponding rule (see Fig. 3). The line indicates rule A-mediated migrations in , while the line denotes rule B-mediated proliferations in Fitting curves in are calculated using a standard linear least squares method. Slopes of the fitting curves are 1.40 cells/step in and 0.03 cells/step in , respectively. Note: The drop of the dashed red line in the of is caused by the termination of the simulation when a cell reached the source (in this case, no further computation on remaining cells will be performed)

Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data

<div><p>Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.</p></div

3D snapshots of the tumor system with single-agent Lidamycin treatment at (a) time step 65, (b) time step 70 and (c) time step 85.

<p>3D snapshots of the tumor system with single-agent Lidamycin treatment at (a) time step 65, (b) time step 70 and (c) time step 85.</p

A comparison of the experimental[17] and simulated data after GC treatment.

<p>A comparison of the experimental[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143206#pone.0143206.ref017" target="_blank">17</a>] and simulated data after GC treatment.</p

A comparison of the experimental and simulated data after Lidamycin treatment.

<p>A comparison of the experimental and simulated data after Lidamycin treatment.</p