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)

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

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)

Real experimental data between 0 to 5 days.

<p>Real experimental data between 0 to 5 days.</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>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

Nickel-based ethylene oligomerization catalysts supported by PNSiP ligands

<p>A series of nickel (II) complexes bearing silicon bridged diphosphines ligands (PNSiP) have been synthesized and characterized. All nickel precatalysts, activated with ethylaluminum dichloride (EtAlCl₂), exhibited moderate to high activities for ethylene dimerization to butylene. The <i>in situ</i> nickel precatalysts formed by mixing N-cyclopentyl-N-((diphenylphosphanyl)dimethylsilyl)-1,1-diphenylphosphanamine (<b>L2</b>) with NiBr₂(DME) showed high catalytic activity (2.40 × 10⁸ g/(mol_Ni<b>·</b>h)) and high product selectivity (88.6%) towards butene using methylcyclohexane as solvent at 1.0 MPa ethylene pressure and 45°C temperature, no polyethylene(PE) was observed. Ligand backbone tuning of PNSiP-based catalytic systems help in precise understanding of steric bulk variation effects on catalytic performance.</p