Invisible Inheritable Urban Biomimicry: How to Re-discover and Evaluate It

Qualitative and quantitative combined nonlinear dynamics model and its application in analysis of price, supply–demand ratio and selling rate

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time.BackgroundThere exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature.ResultsHere, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions.ConclusionsApplications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.

Jochen Gönsch and Jens Martin Gurr focus on the role of models in understanding and managing urban complexity. A primer on quantitative models from the operations research community shows their advantages and disadvantages. By contrast, from the perspective of literary urban studies, texts are discussed as qualitative models, and a novel on spatial and temporal layers in the Ruhr region serves as a case study on modelling complexities in a literary text. Finally, the role of models in both approaches is compared. The essay thus highlights intersections, transfers and complementarities between quantitative and qualitative models.

Software process simulation models hold out the promise of improving project planning and control. However, quantitative models require a very detailed understanding of the software process. In particular, process knowledge needs to be represented quantitatively which requires extensive, reliable software project data. When such data is lacking, quantitative models must impose severe constraints, restricting the value of the models. In contrast qualitative models are able to cope with imprecise knowledge by reasoning at a more abstract level. This paper illustrates the value and flexibility of qualitative models by developing a model of the software staffing process and comparing it with other quantitative staffing models. We show that the qualitative model provides more insights into the staffing process than the quantitative models because it requires fewer constraints and can thus simulate more behaviors. In particular, the qualitative model produces three possible outcomes: adding staff can increases project duration (i.e. Brooks' Law), adding staff may not affect duration, or adding staff may decrease duration. The qualitative model allows us to determine the conditions under which the different outcomes can occur

Due to highly competitive market environment, the supply chain network of business organization are not only efficient but also flexible. The flexible supply chain of business organization is greatly influenced by the suppliers. Hence, the selection of suppliers has to be flexible which not only take into account the quantitative factors but also take into account qualitative factors. In this paper, a novel attempt is conceptualized to model a flexible supplier selection (FSS) problem by integrating the qualitative and quantitative models for supplier selection problem. A MINLP, a quantitative model, is considered in the proposed FSS framework where factors such as lead time, quality, supplier’s capacity and transportation cost are considered. Similarly, in qualitative model factors such as loyalty, technology adaptability, CSR and environmental factors are considered. AHP and IRP, qualitative models, have been applied and compared for supplier’s ranking. An integrated ranking of AHP and IRP is used for modeling FSS problem. Finally, Integration of quantitative and qualitative model provides the set of deviation which measure the level of flexibility from pure solution of supplier selection problem by quantitative and qualitative model. The methodology of FSS problem is presented and demonstrated through an illustrative example of multi-product, multi-source and multi-period case.

Rapid quality identification of the whole wine-steamed process of Polygonati Rhizome by chromaticity and near-infrared spectroscopy

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy. Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing. Experimental results indicate that our proposed integrative framework is feasible to learn the relationships between biochemical reactants qualitatively and to make the model replicate the behaviours of the target system by optimising the kinetic rates quantitatively. Moreover, potential reactants of a target biochemical system can be discovered by hypothesising complex reactants in the synthetic models. Based on the biochemical models learned from the proposed framework, biologists can further perform experimental study in wet laboratory. In this way, natural biochemical systems can be better understood.

The new European chemicals regulation Registration, Evaluation, Authorization and Restriction of Chemicals entered into force in June 2007 and accelerated the development of quantitative structure-activity relationship (QSAR) models for a variety of endpoints, including carcinogenicity. Here, we would like to present quantitative (continuous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screening of findings of rodent carcinogenicity for 1,481 chemicals accessible via Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected using Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and predicted carcinogenic potency expressed as a tumorgenic dose TD(50) for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good predicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for modeling and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for other toxicological endpoints.

Colin Rowe and Fred Koetter's book Collage City has been one of the most inspiring works in the field of architecture with its elaborate and stimulating critique of Modernist and Post-war architecture and city planning. Published first as an article in 1975 and later as a book in 1978, Collage City has been one of the cornerstones of postmodern architectural and urban theory since. Philosopher Karl Popper's ideas on historicism, utopia, tradition, liberal society, etc. had a great influence in shaping the urban architectural theory and design model of Colin Rowe and his pedagogical approach. Karl Popper's impact is very obvious in the book and at its preceding Rowe's Cornell urban design studio. However, little attention has been paid to his legacy on Collage City. This paper traces Karl Popper's legacy on Rowe's urban design theories and methods through an in-depth comparative reading of Collage City and Popper's seminal publications. I argue that a thorough understanding of the context and the content of the collage city argument, and therefore this specific episode in architectural thinking and its contemporary remnants, can only be grasped truly through this intertextual reading. Hence, the intertextual reading in this paper reveals the social, political, and philosophical basis of the collage city argument, which has been approached mainly as a formalist premise so far. In conclusion, the paper aims to reveal the difficult, ambiguous, even blurred, but also productive relationship between the ideas of Colin Rowe and Karl Popper, between architecture and philosophy.

Mathematical modeling of biological regulatory networks provides valuable insights into the structural and dynamical properties of the underlying systems. While dynamic models based on differential equations provide quantitative information on the biological systems, qualitative models that rely on the logical interactions among the components provide coarse-grained descriptions useful for systems whose mechanistic underpinnings remain incompletely understood. The middle ground class of piecewise affine differential equation models was proven informative for systems with partial knowledge of kinetic parameters. In this work we provide a comparison of the dynamic characteristics of these three approaches applied on several biological regulatory network motifs. Specifically, we compare the attractors and state transitions in asynchronous Boolean, piecewise affine and Hill-type continuous models. Our study shows that while the fixed points of asynchronous Boolean models are observed in continuous Hill-type and piecewise affine models, these models may exhibit different attractors under certain conditions. Overall, qualitative models are suitable for systems with limited knowledge of quantitative information. On the other hand, when practical, using quantitative models can provide detailed information about additional real-valued attractors not present in the qualitative models.

Qualitative modeling and monitoring of selected ecosystem functions

MotivationEfficient assessment of the blood–brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate.ResultsHere, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29–0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates.Availability and implementationWeb server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

Ecosystem models have been developed to help support Ecosystem-Based Management and to help provide better management advice that can account for ecosystem impacts (e.g., climate, species interactions, fishing behaviour). Quantitative end-to-end models have proven to be very useful strategically for exploring future scenarios, but are data intensive, time consuming, and require considerable expertise and training. Conversely, qualitative models have different benefits: they are less dependent on data, relatively faster to develop, can incorporate different types of information that are difficult to measure or combine, and can be co-developed with a variety of audiences. There has been an increase in the use of qualitative models for marine management, however questions have arisen about how well qualitative models perform in comparison to quantitative models, and how they can be used to inform management. Here we compare results from quantitative and qualitative ecosystem models for the same region at differing levels of model complexity to explore their relative utility for EBM. We conclude that the number of linkages between model elements and trophic position of the perturbed model were influential factors in the qualitative model behaviour. When perturbing lower trophic level groups, higher complexity models performed closer to the quantitative model. Lower complexity models were recommended when estimating scenarios with perturbations to mid-trophic groups. Careful consideration among these issues is required to develop the "sweet spot" of model complexity for qualitative ecosystem models to reflect similar results to quantitative models. In addition, utilizing multiple models to determine the strongest impacts from perturbations is recommended to avoid spurious conclusions.

Abstract. The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

Recent research often suggests ideas about quantitative or qualitative causal models of performance measurement systems. We also rely on some works that develop ideas on causal models of SMP. This research has highlighted two approaches in the study of causal models of performance measurement systems: the quantitative and qualitative approach. Indeed, the qualitative models lack precision and the qualitative models are confronted with problems of data collection in hierarchical level deployment. Therefore, it should be noted that the combination of these two methods is very rare or non-existent for the SMPs. Hence the idea of proposing a model that combines the two, because these approach also have certain limits. According to our studies we note a complementarity because to combine these two methods reinforces the richness and the validity of the results.