An analysis of the cooperative mechano-sensitive feedback between intracellular signaling, focal adhesion development, and stress fiber contractility

Cells communicate with their external environment via focal adhesions and generate activation signals that in turn trigger the activity of the intracellular contractile machinery. These signals can be triggered by mechanical loading that gives rise to a cooperative feedback loop among signaling, focal adhesion formation, and cytoskeletal contractility, which in turn equilibrates with the applied mechanical loads. We devise a signaling model that couples stress fiber contractility and mechano-sensitive focal adhesion models to complete this above mentioned feedback loop. The signaling model is based on a biochemical pathway where IP3 molecules are generated when focal adhesions grow. These IP3 molecules diffuse through the cytosol leading to the opening of ion channels that disgorge Ca2+ from the endoplasmic reticulum leading to the activation of the actin/myosin contractile machinery. A simple numerical example is presented where a one-dimensional cell adhered to a rigid substrate is pulled at one end, and the evolution of the stress fiber activation signal, stress fiber concentrations, and focal adhesion distributions are investigated. We demonstrate that while it is sufficient to approximate the activation signal as spatially uniform due to the rapid diffusion of the IP3 through the cytosol, the level of the activation signal is sensitive to the rate of application of the mechanical loads. This suggests that ad hoc signaling models may not be able to capture the mechanical response of cells to a wide range of mechanical loading events. © 2011 American Society of Mechanical Engineers.

An evaluation of a new product data model

Engineering companies face many challenges today such as increased competition, higher expectations from consumers and decreasing product lifecycle times. This means that product development times must be reduced to meet these challenges. Concurrent engineering, reuse of engineering knowledge and the use of advanced methods and tools are among the ways of reducing product development times. Concurrent engineering is crucial in making sure that the products are designed with all issues considered simultaneously. The reuse of engineering knowledge allows existing solutions to be reused. It can also help to avoid the mistakes made in previous designs. Computer-based tools are used to store information, automate tasks, distribute work, perform simulation and so forth. This research concerns the evaluation of tools that can be used to support the design process. These tools are evaluated in terms of the capture of information generated during the design process. This information is vital to allow the reuse of knowledge. Present CAD systems store only information on the final definition of the product such as geometry, materials and manufacturing processes. Product Data Management (PDM) systems can manage all this CAD information along with other product related information. The research includes the evaluation of two PDM systems, Windchill and Metaphase, using the design of a single-handed water tap as a case study. The two PDMs were then compared to PROSUS/DDM. PROSUS is the Process-Based Support System proposed by [Blessing 94] using the same case study. The Design Data Model is the product data model that includes PROSUS. The results look promising. PROSUS/DDM is able to capture most design information and structure and present it logically. The design process and product information is related and stored within the DDM structure. The PDMs can capture most design information, but information from early stages of design is stored only as unstructured documentation. Some problems were found with PROSUS/DDM. A proposal is made that may make it possible to resolve these problems, but this will require further research.

An investigation of the kinetics of CO2 uptake by a synthetic calcium based sorbent

This study examines the kinetics of carbonation by CO2 at temperatures of ca. 750 °C of a synthetic sorbent composed of 15 wt% mayenite (Ca12Al14O33) and CaO, designated HA-85-850, and draws comparisons with the carbonation of a calcined limestone. In-situ XRD has verified the inertness of mayenite, which neither interacts with the active CaO nor does it significantly alter the CaO carbonation–calcination equilibrium. An overlapping grain model was developed to predict the rate and extent of carbonation of HA-85-850 and limestone. In the model, the initial microstructure of the sorbent was defined by a discretised grain size distribution, assuming spherical grains. The initial input to the model – the size distribution of grains – was a fitted parameter, which was in good agreement with measurements made with mercury porosimetry and by the analysis of SEM images of sectioned particles. It was found that the randomly overlapping spherical grain assumption offered great simplicity to the model, despite its approximation to the actual porous structure within a particle. The model was able to predict the performance of the materials well and, particularly, was able to account for changes in rate and extent of reaction as the structure evolved after various numbers of cycles of calcination and carbonation.

An investigation of the kinetics of CO 2 uptake by a synthetic calcium based sorbent

This study examines the kinetics of carbonation by CO 2 at temperatures of ca. 750°C of a synthetic sorbent composed of 15wt% mayenite (Ca 12Al 14O 33) and CaO, designated HA-85-850, and draws comparisons with the carbonation of a calcined limestone. In-situ XRD has verified the inertness of mayenite, which neither interacts with the active CaO nor does it significantly alter the CaO carbonation-calcination equilibrium. An overlapping grain model was developed to predict the rate and extent of carbonation of HA-85-850 and limestone. In the model, the initial microstructure of the sorbent was defined by a discretised grain size distribution, assuming spherical grains. The initial input to the model - the size distribution of grains - was a fitted parameter, which was in good agreement with measurements made with mercury porosimetry and by the analysis of SEM images of sectioned particles. It was found that the randomly overlapping spherical grain assumption offered great simplicity to the model, despite its approximation to the actual porous structure within a particle. The model was able to predict the performance of the materials well and, particularly, was able to account for changes in rate and extent of reaction as the structure evolved after various numbers of cycles of calcination and carbonation. © 2011 Elsevier Ltd.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.