Functional Translational Readthrough: A Systems Biology Perspective

Translational readthrough (TR) has come into renewed focus because systems biology approaches have identified the first human genes undergoing functional translational readthrough (FTR). FTR creates functional extensions to proteins by continuing translation of the mRNA downstream of the stop codon. Here we review recent developments in TR research with a focus on the identification of FTR in humans and the systems biology methods that have spurred these discoveries.     

Products and Ecological Models: A Population Ecology Perspective

Industrial ecology has used the systems ecology model, with its emphasis on the flows of energy and nutrients, as a tool to find ways to minimize the adverse environmental effects of industrial activity. A second ecosystem model, the population ecology model, emphasizes intra-and inter-specific interactions of many types. When applied to industrial systems, it suggests an increased focus on products. It therefore can provide a useful complement to the systems ecology approach. If industrial processes that are less harmful to the environment are to be successfully implemented, they will have to produce products that can successfully penetrate the marketplace. A number of historical examples are used to illustrate the many product interactions discussed.     

Modeling Imatinib-Treated Chronic Myelogenous Leukemia: Reducing the Complexity of Agent-Based Models

We develop a model for describing the dynamics of imatinib-treated chronic myelogenous leukemia. Our model is based on replacing the recent agent-based model of Roeder et al. (Nat. Med. 12(10):1181–1184, 2006) by a system of deterministic difference equations. These difference equations describe the time-evolution of clusters of individual agents that are grouped by discretizing the state space. Hence, unlike standard agent-base models, the complexity of our model is independent of the number of agents, which allows to conduct simulation studies with a realistic number of cells. This approach also allows to directly evaluate the expected steady states of the system. The results of our numerical simulations show that our model replicates the averaged behavior of the original Roeder model with a significantly reduced computational cost. Our general approach can be used to simplify other similar agent-based models. In particular, due to the reduced computational complexity of our technique, one can use it to conduct sensitivity studies of the parameters in large agent-based systems.     

Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement

Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise. It demonstrates that agreement systems are motivated by the need to minimize combinatorial search and semantic ambiguity, and it shows, for the first time, that once a population of agents adopts a strategy to invent, acquire and coordinate meaningful markers through social learning, linguistic self-organization leads to the spontaneous emergence and cultural transmission of an agreement system. The article also demonstrates how attested grammaticalization phenomena, such as phonetic reduction and conventionalized use of agreement markers, happens as a side effect of additional economizing principles, in particular minimization of articulatory effort and reduction of the marker inventory. More generally, the article illustrates a novel approach for studying how key features of human languages might emerge.     

系统生物学中的数学模型方法及其应用

综述了当前在系统生物学研究领域中常用数学模型的建立和研究方法,介绍了通量平衡分析、基元通量模式、生化系统理论,以及代谢控制分析等模型的理论基础和发展背景,讨论了这些模型之间的联系、区别,以及适用范围,并总结了这些模型在分析代谢网络结构、优化代谢途径、指导菌种改进以提高琥珀酸、色氨酸、乙醇等重要化工品生产率和转化率中的实际应用。     

Emergent Features Due to Grid-Cell Biology: Synchronisation in Biophysical Models

Modelling studies of upper ocean phenomena, such as that of the spatial and temporal patchiness in plankton distributions, typically employ coupled biophysical models, with biology in each grid-cell represented by a plankton ecosystem model. It has not generally been considered what impact the choice of grid-cell ecosystem model, from the many developed in the literature, might have upon the results of such a study. We use the methods of synchronisation theory, which is concerned with ensembles of interacting oscillators, to address this question, considering the simplest possible case of a chain of identically represented interacting plankton grid-cells. It is shown that the ability of the system to exhibit stably homogeneous (fully synchronised) dynamics depends crucially upon the choice of biological model and number of grid-cells, with dynamics changing dramatically at a threshold strength of mixing between grid-cells. Consequently, for modelling studies of the ocean the resolution chosen, and therefore number of grid-cells used, could drastically alter the emergent features of the model. It is shown that chaotic ecosystem dynamics, in particular, should be used with care.     

A Perspective on Epistasis: Limits of Models Displaying No Main Effect

The completion of a draft sequence of the human genome and the promise of rapid single-nucleotide-polymorphism-genotyping technologies have resulted in a call for the abandonment of linkage studies in favor of genome scans for association. However, there exists a large class of genetic models for which this approach will fail: purely epistatic models with no additive or dominance variation at any of the susceptibility loci. As a result, traditional association methods (such as case/control, measured genotype, and transmission/disequilibrium test [TDT]) will have no power if the loci are examined individually. In this article, we examine this class of models, delimiting the range of genetic determination and recurrence risks for two-, three-, and four-locus purely epistatic models. Our study reveals that these models, although giving rise to no additive or dominance variation, do give rise to increased allele sharing between affected sibs. Thus, a genome scan for linkage could detect genomic subregions harboring susceptibility loci. We also discuss some simple multilocus extensions of single-locus analysis methods, including a conditional form of the TDT.     

Spider Mites of Japan: Their Biology and Control

Spider mite biology and control in Japan were reviewed. Seventy-eight spider mite species of 16 genera (Family Tetranychidae) have been recorded in Japan. Several of the species recently described were separated from a species complex comprising strains with different ecological performance such as host range. These separations were first supported by crossing experiments and then confirmed by molecular genetic studies. Spider mite control in Japan is still dependent on heavy acaricide spraying in order to attain products of extremely high quality. The commercial use of natural enemies in spider mite management has just started.     

Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.