Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Use of Petri Nets to Manage Civil Engineering Infrastructures

Over the last years there has been a shift, in the most developed countries, in investment and efforts within the construction sector. On the one hand, these countries have built infrastructures able to respond to current needs over the last decades, reducing the need for investments in new infrastructures now and in the near future. On the other hand, most of the infrastructures present clear signs of deterioration, making it fundamental to invest correctly in their recovery. The ageing of infrastructure together with the scarce budgets available for maintenance and rehabilitation are the main reasons for the development of decision support tools, as a mean to maximize the impact of investments. The objective of the present work is to develop a methodology for optimizing maintenance strategies, considering the available information on infrastructure degradation and the impact of maintenance in economic terms and loss of functionality, making possible the implementation of a management system transversal to different types of civil engineering infrastructures. The methodology used in the deterioration model is based on the concept of timed Petri nets. The maintenance model was built from the deterioration model, including the inspection, maintenance and renewal processes. The optimization of maintenance is performed through genetic algorithms. The deterioration and maintenance model was applied to components of two types of infrastructure: bridges (pre-stressed concrete decks and bearings) and buildings (ceramic claddings). The complete management system was used to analyse a section of a road network. All examples are based on Portuguese data

Modelling polymer electrolyte membrane fuel cells for dynamic reliability assessment

Tackling climate change is arguably the biggest challenge humanity faces in the 21st century. Rising average global temperatures threaten to destabilize the fragile ecosystem of the Earth and bring unprecedented changes to human lives if nothing is done to prevent it. This phenomenon is caused by the anthropogenic greenhouse effect due to the increasing atmospheric concentrations of carbon dioxide (CO2). One way to avert the disaster is to drastically reduce the consumption of fossil fuels in all spheres of human activities, including transportation. To do this, research and development of electric vehicles (EVs) to make them more efficient, reliable and accessible is essential. [Continues.

From qualitative data to quantitative models: analysis of the phage shock protein stress response in Escherichia coli

Background Bacteria have evolved a rich set of mechanisms for sensing and adapting to adverse conditions in their environment. These are crucial for their survival, which requires them to react to extracellular stresses such as heat shock, ethanol treatment or phage infection. Here we focus on studying the phage shock protein (Psp) stress response in Escherichia coli induced by a phage infection or other damage to the bacterial membrane. This system has not yet been theoretically modelled or analysed in silico. Results We develop a model of the Psp response system, and illustrate how such models can be constructed and analyzed in light of available sparse and qualitative information in order to generate novel biological hypotheses about their dynamical behaviour. We analyze this model using tools from Petri-net theory and study its dynamical range that is consistent with currently available knowledge by conditioning model parameters on the available data in an approximate Bayesian computation (ABC) framework. Within this ABC approach we analyze stochastic and deterministic dynamics. This analysis allows us to identify different types of behaviour and these mechanistic insights can in turn be used to design new, more detailed and time-resolved experiments. Conclusions We have developed the first mechanistic model of the Psp response in E. coli. This model allows us to predict the possible qualitative stochastic and deterministic dynamic behaviours of key molecular players in the stress response. Our inferential approach can be applied to stress response and signalling systems more generally: in the ABC framework we can condition mathematical models on qualitative data in order to delimit e.g. parameter ranges or the qualitative system dynamics in light of available end-point or qualitative information.Medical Research Council (Great Britain)Biotechnology and Biological Sciences Research Council (Great Britain)Wellcome Trust (London, England)Royal Society (Great Britain) (Wolfson Research Merit Award

PREDICTING AVAILABILITY AND RESPONSE TIMES OF IT SERVICES

When IT service providers adapt their IT system landscapes because of new technologies or changing business requirements, the effects of changes to the quality of service must be considered to fulfill service level agreements. Analytical prediction models can support this process in the service design stages, but dependencies between quality aspects are not taken into account. In this paper, a novel approach for predicting availability and response time of an IT service is developed, which is simulation-based to support dynamic analysis of service quality. The correctness of the model as well as its applicability in a real case can be evaluated. Therefore, this work presents a step towards an analytical framework for predicting IT service quality aspects

Design for dependability: A simulation-based approach

This research addresses issues in simulation-based system level dependability analysis of fault-tolerant computer systems. The issues and difficulties of providing a general simulation-based approach for system level analysis are discussed and a methodology that address and tackle these issues is presented. The proposed methodology is designed to permit the study of a wide variety of architectures under various fault conditions. It permits detailed functional modeling of architectural features such as sparing policies, repair schemes, routing algorithms as well as other fault-tolerant mechanisms, and it allows the execution of actual application software. One key benefit of this approach is that the behavior of a system under faults does not have to be pre-defined as it is normally done. Instead, a system can be simulated in detail and injected with faults to determine its failure modes. The thesis describes how object-oriented design is used to incorporate this methodology into a general purpose design and fault injection package called DEPEND. A software model is presented that uses abstractions of application programs to study the behavior and effect of software on hardware faults in the early design stage when actual code is not available. Finally, an acceleration technique that combines hierarchical simulation, time acceleration algorithms and hybrid simulation to reduce simulation time is introduced