Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

Polyspecific malaria antibodies present at the time of infection inhibit the development of immunity to malaria but antibodies specific for the malaria merozoite surface protein, MSP1, facilitate immunity

Serum taken from mice immune to malaria as a result of infection and drug cure, or from mice immunized with a recombinant form of the merozoite surface protein, MSP1, can provide passive protection of recipient mice against the lethal parasite, Plasmodium yoelii YM. However, recipients of MSP1-immune serum go on to develop long-term immunity, whereas recipients of serum from mice naturally immune to malaria rapidly lose their resistance to infection. We demonstrate that 'infection/cure' serum suppresses the development of both antibody and cell-mediated parasite-specific responses in recipients, whereas these develop in recipients of MSP1-specific antibodies. These data have profound implications for our understanding of the development of malaria immunity in babies who passively acquire antibodies from their mothers.

Finite difference time domain (FDTD) method for modeling the effect of switched gradients on the human body in MRI

Numerical modeling of the eddy currents induced in the human body by the pulsed field gradients in MRI presents a difficult computational problem. It requires an efficient and accurate computational method for high spatial resolution analyses with a relatively low input frequency. In this article, a new technique is described which allows the finite difference time domain (FDTD) method to be efficiently applied over a very large frequency range, including low frequencies. This is not the case in conventional FDTD-based methods. A method of implementing streamline gradients in FDTD is presented, as well as comparative analyses which show that the correct source injection in the FDTD simulation plays a crucial rule in obtaining accurate solutions. In particular, making use of the derivative of the input source waveform is shown to provide distinct benefits in accuracy over direct source injection. In the method, no alterations to the properties of either the source or the transmission media are required. The method is essentially frequency independent and the source injection method has been verified against examples with analytical solutions. Results are presented showing the spatial distribution of gradient-induced electric fields and eddy currents in a complete body model.

Composition and energy density of eggs from two species of freshwater turtle with twofold ranges in egg size

Lipid, protein, ash, carbohydrate and water content and energy density of eggs were measured from different clutches over a range of egg size in two species of freshwater turtle. Dry egg contents consisted of protein (54-60%), lipid (25-31%) and ash (5-6%) while carbohydrate was found to be negligible (

Evolution of additive and nonadditive genetic variance in development time along a cline in Drosophila serrata

Latitudinal clines provide natural systems that may allow the effect of natural selection on the genetic variance to be determined. Ten clinal populations of Drosophila serrata collected from the eastern coast of Australia were used to examine clinal patterns in the trait mean and genetic variance of the life-history trait egg-to-adult development time. Development time significantly lengthened from tropical areas to temperate areas. The additive genetic variance for development time in each population was not associated with latitude but was associated with the population mean development time. Additive genetic variance tended to be larger in populations with more extreme development times and appeared to be consistent with allele frequency change. In contrast, the nonadditive genetic variance was not associated with the population mean but was associated with latitude. Levels of nonadditive genetic variance were greatest in the region of the cline where the gradient in the change in mean was greatest, consistent with Barton's (1999) conjecture that the generation of linkage disequilibrium may become an important component of the genetic variance in systems with a spatially varying optimum.

The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.

A new method for analysing the equilibrium and time-dependent behaviour of Markovian models

Many large-scale stochastic systems, such as telecommunications networks, can be modelled using a continuous-time Markov chain. However, it is frequently the case that a satisfactory analysis of their time-dependent, or even equilibrium, behaviour is impossible. In this paper, we propose a new method of analyzing Markovian models, whereby the existing transition structure is replaced by a more amenable one. Using rates of transition given by the equilibrium expected rates of the corresponding transitions of the original chain, we are able to approximate its behaviour. We present two formulations of the idea of expected rates. The first provides a method for analysing time-dependent behaviour, while the second provides a highly accurate means of analysing equilibrium behaviour. We shall illustrate our approach with reference to a variety of models, giving particular attention to queueing and loss networks. (C) 2003 Elsevier Ltd. All rights reserved.

The variety of variables in automated real-time refinement

The refinement calculus is a well-established theory for deriving program code from specifications. Recent research has extended the theory to handle timing requirements, as well as functional ones, and we have developed an interactive programming tool based on these extensions. Through a number of case studies completed using the tool, this paper explains how the tool helps the programmer by supporting the many forms of variables needed in the theory. These include simple state variables as in the untimed calculus, trace variables that model the evolution of properties over time, auxiliary variables that exist only to support formal reasoning, subroutine parameters, and variables shared between parallel processes.

A high definition, finite difference time domain method

A high definition, finite difference time domain (HD-FDTD) method is presented in this paper. This new method allows the FDTD method to be efficiently applied over a very large frequency range including low frequencies, which are problematic for conventional FDTD methods. In the method, no alterations to the properties of either the source or the transmission media are required. The method is essentially frequency independent and has been verified against analytical solutions within the frequency range 50 Hz-1 GHz. As an example of the lower frequency range, the method has been applied to the problem of induced eddy currents in the human body resulting from the pulsed magnetic field gradients of an MRI system. The new method only requires approximately 0.3% of the source period to obtain an accurate solution. (C) 2003 Elsevier Science Inc. All rights reserved.

Fast, scalable master equation solution algorithms. III. Direct time propagation accelerated by a diffusion approximation preconditioned iterative solver

In this paper we propose a novel fast and linearly scalable method for solving master equations arising in the context of gas-phase reactive systems, based on an existent stiff ordinary differential equation integrator. The required solution of a linear system involving the Jacobian matrix is achieved using the GMRES iteration preconditioned using the diffusion approximation to the master equation. In this way we avoid the cubic scaling of traditional master equation solution methods and maintain the low temperature robustness of numerical integration. The method is tested using a master equation modelling the formation of propargyl from the reaction of singlet methylene with acetylene, proceeding through long lived isomerizing intermediates. (C) 2003 American Institute of Physics.