The effect of noise on dynamics and the influence of biochemical systems

Understanding a complex system requires integration and collective analysis of data from many levels of organisation. Predictive modelling of biochemical systems is particularly challenging because of the nature of data being plagued by noise operating at each and every level. Inevitably we have to decide whether we can reliably infer the structure and dynamics of biochemical systems from present data. Here we approach this problem from many fronts by analysing the interplay between deterministic and stochastic dynamics in a broad collection of biochemical models. In a classical mathematical model we first illustrate how this interplay can be described in surprisingly simple terms; we furthermore demonstrate the advantages of a statistical point of view also for more complex systems. We then investigate strategies for the integrated analysis of models characterised by different organisational levels, and trace the propagation of noise through such systems. We use this approach to uncover, for the first time, the dynamics of metabolic adaptation of a plant pathogen throughout its life cycle and discuss the ecological implications. Finally, we investigate how reliably we can infer model parameters of biochemical models. We develop a novel sensitivity/inferability analysis framework that is generally applicable to a large fraction of current mathematical models of biochemical systems. By using this framework to quantify the effect of parametric variation on system dynamics, we provide practical guidelines as to when and why certain parameters are easily estimated while others are much harder to infer. We highlight the limitations on parameter inference due to model structure and qualitative dynamical behaviour, and identify candidate elements of control in biochemical pathways most likely of being subjected to regulation

Measuring Solvency In The Turkish Public Transportation Industry Cem

Financial analysis is a combined effort of knowledge and experience to obtain relevant ratios and elaborate on the results. The study is an application of financial analysis on Istanbul Public Transportation Company. As seen in the literature review section of the study public transportation companies require high subsidies by governments which is a factor to consider in the research. The research period is 2014- 2016. It is a comparative study with the inclusion of Izmir – ESHOT and Ankara – EGO public transportation companies. The results are therefore compared with industry averages computed by the average of public transportation companies of three big cities in Turkey. The research is based on 7 selected ratios, 3 for measuring liquidity and the remaining 4 for measuring solvency. The ratios used in this paper are current ratio, acid test ratio, cash ratio, total debt ratio, debt equity ratio, equity multiplier, and long term debt ratio. According to the results, the companies have a very low current ratio which means they operate with low liquidity. Solvency analysis also reveal that, there are high degrees of leverage and negative shareholders’ equity. The research shows that companies might struggle to repay debts without government support

Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse)

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations

Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival

Climate change and global warming are emerging as important threats to human health, particularly through the potential increase in vector- and water-borne diseases. Environmental variables are known to affect substantially the population dynamics and abundance of the poikilothermic vectors of disease, but the exact extent of this sensitivity is not well established. Focusing on malaria and its main vector in Africa, Anopheles gambiae sensu stricto, we present a set of novel mathematical models of climate-driven mosquito population dynamics motivated by experimental data suggesting that in An. gambiae, mortality is temperature and age dependent. We compared the performance of these models to that of a “standard” model ignoring age dependence. We used a longitudinal dataset of vector abundance over 36 months in sub-Saharan Africa for comparison between models that incorporate age dependence and one that does not, and observe that age-dependent models consistently fitted the data better than the reference model. This highlights that including age dependence in the vector component of mosquito-borne disease models may be important to predict more reliably disease transmission dynamics. Further data and studies are needed to enable improved fitting, leading to more accurate and informative model predictions for the An. gambiae malaria vector as well as for other disease vectors

A subsystems approach for parameter estimation of ODE models of hybrid systems

We present a new method for parameter identification of ODE system descriptions based on data measurements. Our method works by splitting the system into a number of subsystems and working on each of them separately, thereby being easily parallelisable, and can also deal with noise in the observations.Comment: In Proceedings HSB 2012, arXiv:1208.315

A Key Recovery Attack on Error Correcting Code Based a Lightweight Security Protocol

One of the interesting types of RFID application is RFID searching which aims to hear a specific RFID tag from a large group of tags, i.e. ability of detecting whether a target RFID tag is nearby. Very recently, a lightweight protocol using error-correcting codes has been proposed by Chen et al. to provide a solution to needs in this field. The authors give a detailed analysis of their protocol in terms of security, privacy, communication overhead, hardware cost and they claim that it is a realizable scheme with fulfilling security and privacy requirements. In this study, however, we investigate security of this protocol and clearly demonstrate its security flaws that completely allow an adversary to exploit the system. In particular, by using linear properties of error correcting coding we firstly describe a tag tracing attack that undermines untraceability property which is one its design objectives. Then along with its implementation details we present a key recovery attack that reduces dramatically search space of a tag\u27s secret key and show that an adversary can compromise it in practical time by only querying this tag for several times. As an illustrative example we retrieve the secret key of the protocol in two hours for the adopted linear block code C(47,24,11) which is one of the suggested codes

sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 2 approved]

This article describes the sPop packages implementing the deterministic and stochastic versions of an age-structured discrete-time population dynamics model. The packages enable mechanistic modelling of a population by monitoring the age and development stage of each individual. Survival and development are included as the main effectors and they progress at a user-defined pace: follow a fixed-rate, delay for a given time, or progress at an age-dependent manner. The model is implemented in C, Python, and R with a uniform design to ease usage and facilitate adoption. Early versions of the model were previously employed for investigating climate-driven population dynamics of the tiger mosquito and the chikungunya disease spread by this vector. The sPop packages presented in this article enable the use of the model in a range of applications extending from vector-borne diseases towards any age-structured population including plant and animal populations, microbial dynamics, host-pathogen interactions, infectious diseases, and other time-delayed epidemiological processes

Scalability and Security Conflict for RFID Authentication Protocols

Many RFID authentication protocols have been proposed to preserve security and privacy. Nevertheless, most of these protocols are analyzed and it is shown that they can not provide security against some RFID attacks. Moreover, some of the secure ones are criticized, because they suffer from scalability at the reader/server side as in tag identification or authentication phase they require a linear search depending on number of tags in the system. Recently, new authentication protocols have been presented to solve scalability issue, i.e. they require constant time for tag identification with providing security. In this paper, we analyze two of these new RFID authentication protocols SSM (very recently proposed by Song and Mitchell) and LRMAP (proposed by Ha et al.) and to the best of our knowledge, they have received no attacks yet. These schemes take O(1) work to authenticate a tag and are designed to meet the privacy and security requirements. The common point of these protocols is that normal and abnormal states are defined for tags. In the normal state, server authenticates the tag in constant time, while in the abnormal state, occurs rarely, authentication is realized with linear search. We show that, however, these authentication protocols do not provide untraceability which is one of their design objectives. We also discover that the SSM protocol is vulnerable to a desynchronization attack, that prevents a legitimate reader/server from authenticating a legitimate tag. Furthermore, in the light of these attacks, we conclude that allowing tags to be in different states may give clue to an adversary in tracing the tags, although such a design is preferred to achieve scalability and efficiency at the server side

ABC-SysBio-approximate Bayesian computation in Python with GPU support

MOTIVATION: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions. RESULTS: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio. AVAILABILITY: http://abc-sysbio.sourceforge.ne

ABC-SysBio-approximate Bayesian computation in Python with GPU support.

Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions. Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio