Modelling the effect of urbanization on the transmission of an infectious disease

This paper models the impact of urbanization on infectious disease transmission by integrating a CA land use development model, population projection matrix model and CA epidemic model in S-Plus. The innovative feature of this model lies in both its explicit treatment of spatial land use development, demographic changes, infectious disease transmission and their combination in a dynamic, stochastic model. Heuristically-defined transition rules in cellular automata (CA) were used to capture the processes of both land use development with urban sprawl and infectious disease transmission. A population surface model and dwelling distribution surface were used to bridge the gap between urbanization and infectious disease transmission. A case study is presented involving modelling influenza transmission in Southampton, a dynamically evolving city in the UK. The simulation results for Southampton over a 30-year period show that the pattern of the average number of infection cases per day can depend on land use and demographic changes. The modelling framework presents a useful tool that may be of use in planning applications.     

Dynamics of HIV infection studied with cellular automata and conformon-P systems

Recently a cellular automaton (CA) has been used to model the dynamics of HIV infection, with interesting results. We replicate and further test this model, and we introduce an alternative model based on conformon-P (cP) systems. We find (in common with other recently published comments) that the CA model is very sensitive to initial conditions and produces appropriate qualitative dynamics only for a narrow range of rule probabilities. In contrast, the cP system model is robust to a wide range of conditions and parameters, with more reproducible qualitative agreement to the overall dynamics and to the densities of healthy and infected cells observed in vivo.     

Sources of cell-to-cell variability in canonical nuclear factor-κB (NF-κB) signaling pathway inferred from single cell dynamic images

The canonical nuclear factor-κB (NF-κB) signaling pathway controls a gene network important in the cellular inflammatory response. Upon activation, NF-κB/RelA is released from cytoplasmic inhibitors, from where it translocates into the nucleus, subsequently activating negative feedback loops producing either monophasic or damped oscillatory nucleo-cytoplasmic dynamics. Although the population behavior of the NF-κB pathway has been extensively modeled, the sources of cell-to-cell variability are not well understood. We describe an integrated experimental-computational analysis of NF-κB/RelA translocation in a validated cell model exhibiting monophasic dynamics. Quantitative measures of cellular geometry and total cytoplasmic concentration and translocated RelA amounts were used as priors in Bayesian inference to estimate biophysically realistic parameter values based on dynamic live cell imaging studies of enhanced GFP-tagged RelA in stable transfectants. Bayesian inference was performed on multiple cells simultaneously, assuming identical reaction rate parameters, whereas cellular geometry and initial and total NF-κB concentration-related parameters were cell-specific. A subpopulation of cells exhibiting distinct kinetic profiles was identified that corresponded to differences in the IκBα translation rate. We conclude that cellular geometry, initial and total NF-κB concentration, IκBα translation, and IκBα degradation rates account for distinct cell-to-cell differences in canonical NF-κB translocation dynamics.     

Cellular automata and its advances to drug therapy for HIV infection

This paper gives an over view of the use of cellular automata (CA) model of drug therapy for HIV infection. Nonuniform CA is employed to simulate drug treatment of HIV infection, where each computational domain may contain different CA rules, in contrast to normal uniform CA models. Ordinary (or partial) differential equation models are insufficient to describe the two extreme time scales involved in HIV infection (days and decades), as well as the implicit spatial heterogeneity. Zorzenon and Coutinho [Phy Rev Lett, 16 (2001) 1] reported a cellular automata approach to simulate three-phase patterns of human immunodeficiency virus (HIV) infection consisting of primary response, clinical latency and onset of acquired immunodeficiency syndrome (AIDS). But here we present a related model, based on non-uniform CA to study the dynamics of drug therapy of HIV infection. The main aim in this model is to simulate the four phases (acute, chronic, drug treatment responds and onset of AIDS). The results shown here indicate that both simulations (with and without treatments) evolve to the relatively same steady state (characteristics of Wolfram's class II behavior). Different kinds of drug therapies can also be simulated in this model, which can be found useful for developing a proper drug therapy.     

Agent-Based Analysis of Biomass Feedstock Production Dynamics

The success of the bioenergy sector based on lignocellulosic feedstock will require a sustainable and resilient transition from the current agricultural system focused on food crops to one also producing energy crops. The dynamics of this transition are not well understood. It will be driven significantly by the collective participation, behavior, and interaction of various stakeholders such as farmers within the production system. The objective of this work is to study the system dynamics through the development and application of an agent-based model using the theory of complex adaptive systems. Farmers and biorefinery, two key stakeholders in the system, are modeled as independent agents. The decision making of each agent as well as its interaction with other agents is modeled using a set of rules reflecting the economic, social, and personal attributes of the agent. These rules and model parameters are adapted from literature. Regulatory mechanisms such as Biomass Crop Assistance Program are embedded in the decision-making process. The model is then used to simulate the production of Miscanthus as an energy crop in Illinois. Particular focus has been given on understanding the dynamics of Miscanthus adaptation as an agricultural crop and its impact on biorefinery capacity and contractual agreements. Results showed that only 60% of the maximum regional production capacity could be reached, and it took up to 15 years to establish that capacity. A 25% reduction in the land opportunity cost led to a 63% increase in the steady- state productivity. Sensitivity analysis showed that higher initial conversion of land by farmers to grow energy crop led to faster growth in regional productivity.     

Oscillations in an epidemiological model based on asynchronous probabilistic cellular automaton

Consider a contagious disease affecting a host population composed of two groups with distinct habits. At each time step, each individual of this population can be in one of two states: susceptible (S) or infective (I). Here, a SIS epidemic model based on cellular automaton (CA) is proposed to study the disease spreading in such a population. In this model, the state transitions are described by probabilistic rules and each group has its own schedule to update the states of its individuals. We also propose a set of difference equations (DE) to analyze this population dynamics and we show how these two approaches (CA and DE) can be equivalent. We noticed that oscillations can be found in the composition of the group with more active social life, but not in the composition of the other group.     

模拟青霉素发酵过程中菌体生长动态的细胞自动机模型

在青霉素发酵生产机理及其动力学微分方程模型的基础上,建立了模拟青霉素分批发酵过程中菌体生长动态的细胞自动机模型(CABGM)。CABGM采用三维细胞自动机作为菌体生长空间,采用Moore型邻域作为细胞邻域,其演化规则根据青霉素分批发酵过程中菌体生长机理和动力学微分方程模型设计。CABGM中的每一个细胞既可代表单个的青霉素产生菌,又可代表特定数量的青霉素产生菌,它具有不同的状态。对CABGM进行了统计特性的理论分析和仿真实验,理论分析和仿真实验结果均证明了CABGM能一致地复现动力学微分方程模型所描述的青霉素分批发酵菌体生长过程。最后,对所建模型在实际生产过程中的应用问题进行了分析,指出了需要进一步研究的问题。     

Two dynamic processes for the induction of long-term potentiation in hippocampal CA1 neurons

This work sets out to investigate fast and slow dynamic processes and how they effect the induction of long-term potentiation (LTP). Functionally, the fast process will work as a time window to take a spatial coincidence among various inputs projected to the hippocampus, and the slow process will work as a temporal integrator of a sequence of dynamic events. Firstly, the two factors were studied using a “burst” stimulus and a “long-interval patterns” stimulus. Secondly, we propose that, for the induction of LTP, there are two dynamic processes, fast and slow, which are productively activated by bursts and long-interval patterns. The model parameters, a time constant of short dynamics and one of long dynamics, were determined by fitting the values obtained from model simulation to the experimental data. A molecular factor or cellular factors with these two time constants are likely to be induced in LTP induction. Received: 3 November 1997 / Accepted in revised form: 18 August 1999     

Comparing costs associated with risk stratification rules for t-year survival

Accurate risk prediction is an important step in developing optimal strategies for disease prevention and treatment. Based on the predicted risks, patients can be stratified to different risk categories where each category corresponds to a particular clinical intervention. Incorrect or suboptimal interventions are likely to result in unnecessary financial and medical consequences. It is thus essential to account for the costs associated with the clinical interventions when developing and evaluating risk stratification (RS) rules for clinical use. In this article, we propose to quantify the value of an RS rule based on the total expected cost attributed to incorrect assignment of risk groups due to the rule. We have established the relationship between cost parameters and optimal threshold values used in the stratification rule that minimizes the total expected cost over the entire population of interest. Statistical inference procedures are developed for evaluating and comparing given RS rules and examined through simulation studies. The proposed procedures are illustrated with an example from the Cardiovascular Health Study.     

Psychological Factors Determining Individual Compliance with Rules for Common Pool Resource Management: The Case of a Cuban Community Sharing a Solar Energy System

This study focuses on individuals’ subjective reasons for complying with rules for common pool resource management. We examine the topic of individual rule compliance, which the commons literature has addressed only marginally, and outline recent empirical findings. Hypotheses are derived based on rule compliance theory and explored using data gathered in a Cuban community sharing a solar energy system. The statistical analyses reveal that compliance with rules for energy management is influenced by various factors. Depending on the particular rule, factors such as sanctioning, legitimacy, and compatibility, among others, influence the frequency of individual rule compliant behavior to differing extents.     

Effects of time limitation and egg limitation on lifetime reproductive success of a parasitoid in the field

We used field observations of freely foraging Aphytis aonidiae parasitoids in conjunction with results of laboratory studies of A. aonidiae and other Aphytis species to simulate lifetime patterns of behavior and reproduction. Field observations provided estimates of encounter rates with three classes of hosts, the mortality rate from predation on adult parasitoids, and host-handling times for oviposition and host feeding by adult wasps. A series of physiological parameters, including the egg maturation rate and the value of host-feeding meals, were estimated from previously published studies. Plasticity in parasitoid behavior was incorporated in two ways. For one set of simulations we used a behavioral rule derived empirically from observations of parasitoids made in the field, and for another we used a dynamic state-variable model to generate a set of behavioral rules that maximize lifetime reproductive success. As was expected, the empirically derived rule led to better matches with field observations than did simulations using the output of the dynamic model. Projections of lifetime reproductive success in the field ranged between three and 37 eggs within the 95% confidence intervals of the mortality rate and host encounter rate and depending on which behavioral rule was used. Lifetime reproductive success from the simulation with central estimates of the mortality and host encounter rates that incorporated the empirical rule was 6.25 eggs. Using the empirical versus the theoretical rule in the simulations led to a 10%-30% decline in projections of lifetime reproductive success, depending on mortality and host encounter rates. Regardless of the behavioral rule, the simulations underscored the observation that the host encounter rate was greater than the egg maturation rate. The overall oviposition rate was sufficiently high to lead to daily episodes of temporary egg limitation during which parasitoids must mature an egg before being able to oviposit.     

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process (CAPFM) was established in this study, based on a morphologically structured dynamic penicillin production model, that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation. CAPFM uses the three-dimensional cellular automata as a growth space, and a Moore-type neighborhood as the cellular neighborhood. The transition rules of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes. Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms, and has various state. The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly. __________ Translated from ACTA BIOPHYSICA, 2005, 21(2) [译自: 生物物理学报, 2005,21(2)]     

Information processing mechanisms in microtubules at physiological temperature: Model predictions for experimental tests

Both direct and indirect experimental evidence has shown signaling, communication and conductivity in microtubules (MTs). Theoretical models have predicted that MTs can be potentially used for both classical and quantum information processing although controversies arose in regard to physiological temperature effects on these capabilities. In this paper, MTs have been studied using well-established principles of classical statistical physics as applied to information processing, information storage and signal propagation. To investigate the existence of information processing in MTs we used cellular automata (CA) models with neighbor rules based on the electrostatic properties of the molecular structure of tubulin, and both synchronous and asynchronous updating methods. We obtained a phase diagram of possible dynamic behaviors in MTs that depend on the values of characteristic physical parameters that can be experimentally verified.     

A Multi-Paradigm Modeling Framework to Simulate Dynamic Reciprocity in a Bioreactor

Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications.     

A gradual update method for simulating the steady-state solution of stiff differential equations in metabolic circuits

Numerical simulation of differential equation systems plays a major role in the understanding of how metabolic network models generate particular cellular functions. On the other hand, the classical and technical problems for stiff differential equations still remain to be solved, while many elegant algorithms have been presented. To relax the stiffness problem, we propose new practical methods: the gradual update of differential-algebraic equations based on gradual application of the steady-state approximation to stiff differential equations, and the gradual update of the initial values in differential-algebraic equations. These empirical methods show a high efficiency for simulating the steady-state solutions for the stiff differential equations that existing solvers alone cannot solve. They are effective in extending the applicability of dynamic simulation to biochemical network models. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Metabolic modeling of Saccharomyces cerevisiae using the optimal control of homeostasis: a cybernetic model definition

A model is presented to describe the observed behavior of microorganisms that aim at metabolic homeostasis while growing and adapting to their environment in an optimal way. The cellular metabolism is seen as a network with a multiple controller system with both feedback and feedforward control, i.e., a model based on a dynamic optimal metabolic control. The dynamic network consists of aggregated pathways, each having a control setpoint for the metabolic states at a given growth rate. This set of strategies of the cell forms a true cybernetic model with a minimal number of assumptions. The cellular strategies and constraints were derived from metabolic flux analysis using an identified, biochemically relevant, stoichiometry matrix derived from experimental data on the cellular composition of continuous cultures of Saccharomyces cerevisiae. Based on these data a cybernetic model was developed to study its dynamic behavior. The growth rate of the cell is determined by the structural compounds and fluxes of compounds related to central metabolism. In contrast to many other cybernetic models, the minimal model does not consist of any assumed internal kinetic parameters or interactions. This necessitates the use of a stepwise integration with an optimization of the fluxes at every time interval. Some examples of the behavior of this model are given with respect to steady states and pulse responses. This model is very suitable for describing semiquantitatively dynamics of global cellular metabolism and may form a useful framework for including structured and more detailed kinetic models.     

Computational modeling of cellular signaling processes embedded into dynamic spatial contexts

Cellular signaling processes depend on spatiotemporal distributions of molecular components. Multicolor, high-resolution microscopy permits detailed assessment of such distributions, providing input for fine-grained computational models that explore mechanisms governing dynamic assembly of multimolecular complexes and their role in shaping cellular behavior. However, it is challenging to incorporate into such models both complex molecular reaction cascades and the spatial localization of signaling components in dynamic cellular morphologies. Here we introduce an approach to address these challenges by automatically generating computational representations of complex reaction networks based on simple bimolecular interaction rules embedded into detailed, adaptive models of cellular morphology. Using examples of receptor-mediated cellular adhesion and signal-induced localized mitogen-activated protein kinase (MAPK) activation in yeast, we illustrate the capacity of this simulation technique to provide insights into cell biological processes. The modeling algorithms, implemented in a new version of the Simmune toolset, are accessible through intuitive graphical interfaces and programming libraries.     

A mixed dispatching rule approach in FMS scheduling

Short-term scheduling in flexible manufacturing systems (FMSs) is a difficult problem because of the complexities and dynamic behavior of FMSs. To solve this problem, a dispatching rule approach is widely used. In this approach, however, a single dispatching rule is usually assigned for all machines in a system during a given scheduling interval. In this paper, a mixed dispatching rule which can assign a different dispatching rule for each machine is proposed. A search algorithm which selects an appropriate mixed dispatching rule using predictions based on discrete event simulation is developed for this approach. The search algorithm for the mixed dispatching rule is described in detail. The effectiveness (in meeting performance criteria) of the mixed dispatching rule and the efficiency of the search algorithm relative to exhaustive search (complete enumeration) is demonstrated on an FMS model. The mixed dispatching rule approach performs up to 15.9% better than the conventional approach, and is 4% better on average. The statistical significance of the results is dicussed.