A New Visualization and Analysis Method for a Convolved Representation of Mass Computational Experiments with Biological Models

Nonlinear dynamics and chaotic systems have been of great interest to many scientists and engineers over the past two decades. Many nonlinear systems are a result of mathematical models of physical and biological systems that possess inherent nonlinear properties. In this paper, we focus our attention on a second-degree nonlinear differential equation with a sinusoidal forcing function and constant coefficients; such equations, which are commonly employed for mathematical modeling of biological systems usually possess inherent nonlinear properties. We show that the second-degree nonlinear differential equation system could be aperiodic and therefore the long-term behavior is not predictable. However, in the case of the increased frequency of the sinusoidal input, the system shows somewhat periodic, convergent behavior. The simulations demonstrate how small, seemingly insignificant changes to the parameters (e.g., initial conditions) can dramatically alter the system response.

The data-driven modeling of dynamical systems is an important scientific activity, and many studies have applied genetic programming (GP) to the task of automatically constructing such models in the form of systems of ordinary differential equations (ODEs). These previous studies assumed that data measurements were available for all variables in the system, whereas in real-world settings, it is typically the case that one or more variables are unmeasured or "hidden." Here, we investigate the prospect of automatically constructing ODE models of dynamical systems from time series data with GP in the presence of hidden variables. Several examples with both synthetic and physical systems demonstrate the unique challenges of this problem and the circumstances under which it is possible to reverse-engineer both the form and parameters of ODE models with hidden variables.

Sleep plays a significant role in human’ smental and physical health. Recently, the associations between lack of sleep and weight gain, development of cancer and many other health problems have been recognized. Then monitoring the sleep and wake state all night is becoming a hotspot issue. Traditionally it classified by a PSG recording which is very costly and uncomfortable. Nowadays, with the advance of internet of things, many convenient wearable devices are being used for medical use, like measuring the heart rate (HR), blood pressure and other signals. With the sleep quality monitor problem, the key question is how to discriminate the sleep and weak stage from these signals. This paper proposed a Bayesian approach based on dynamic time warping (DTW) method for sleep and wake classification. It used HR and surplus pulse O2 (SPO2) signals to analyze the sleep states and the occurrence of some sleep-related problems. DTW is an algorithm that searches an optimal alignment between time series with scaling and shifting and Bayesian methods have been successfully used for object classification in many study. In this paper, a three-step process is used for sleep and wake classification. In the first step, the DTW is used to extract features of the original HR and SPO2 signals. Then a probabilistic model is introduced for using the Bayesian classification for uncertain data. And in the classification step, the DTW features are used as the training dataset in the Bayesian approach for sleep and wake classification. Finally, a case study form a real-word applications, collected from the website of the Sleep Heart Health Study, is presented to shown the feasibility and advantages of the DTW-based Bayesian approach.

Global sensitivity analysis of biological multiscale models

Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop an iterative procedure for combining DSM and ABM to leverage their strengths and gain insights that surpass insights obtained by each approach separately. The procedure uses results of an ABM as inputs for a DSM development. In the following steps, results of the DSM analyses guide future analysis of the ABM and vice versa. This dialogue, more than having a tight connection between the models, enables pushing the research frontier, expanding the set of research questions and insights. We illustrate our method with the example of poverty traps and innovation in agricultural systems, but our conclusions are general and can be applied to other DSM-ABM combinations.

Abstrak Kondisi permukaan jalan merupakan faktor yang memengaruhi kenyamanan dan keselamatan pengendara. Sehingga perlu dilakukan pemeriksaan kondisi jalan secara berkala. Pemantauan kondisi permukaan jalan di Indonesia pada umumnya masih masih dilakukan secara konvensional. Oleh karena itu, penelitian ini bertujuan untuk menerapkan pendekatan yang lebih efisien dalam mengukur kondisi permukaan jalan dengan menggunakan sensor accelerometer dan global positioning sistem (GPS) yang terinstall pada smartphone. Penelitian ini menggunakan metode Dynamic Time Warping (DTW) untuk mengevaluasi kondisi permukaan jalan secara umum dan metode K-means clustering untuk mengidentifikasi lokasi anomali yang terdapat pada permukaan jalan. Data hasil perekaman kondisi permukaan jalan USAID dari SMA Negeri 1 Lhoknga sampai Pantai Lhokseudu di Provinsi Aceh (lebih kurang 16 kilometer) dianalisis pada penelitian ini. Hasil analisis menunjukkan bahwa bentuk topografi jalan menurun dan menanjak cenderung memiliki kondisi yang lebih buruk dibandingkan segmen jalan yang mendatar. Selain itu, hasil penelitian juga menunjukkan bahwa kawasan dengan kepadatan penduduk yang lebih rendah memiliki kondisi jalan yang kurang baik dibandingkan kawasan dengan kepadatan penduduk yang lebih tinggi. Metode DTW dan K-means clustering berpotensi untuk digunakan sebagai metode alternatif dalam mengukur kondisi permukaan jalan yang lebih hemat tenaga, waktu dan biaya jika dibandingkan dengan pemeriksaan secara konvensional. Kata-kata Kunci: DTW, k-means clustering, permukaan jalan, smartphone Abstract The condition of the road surface is a factor that affects the comfort and safety of the driver. It is necessary to examine the condition of the road on a regular basis. Monitoring of road surface conditions in Indonesia is generally still done conventionally. Therefore, this study aims to apply a more efficient approach in measuring road surface conditions using accelerometer sensors and a global positioning system (GPS) installed on a smartphone. This study employs the Dynamic Time Warping (DTW) method to evaluate general road surface conditions and the K-means clustering method to identify the location of anomalies on the road surface. Recording data of USAID road surface condition from SMA Negeri 1 Lhoknga to Lhok Seudu Beach in Aceh Province (approximately 16 kilometers) were analyzed in this study. The results of the analysis show that the topography of downhill and uphill roads tends to possess worse conditions than the horizontal road segments. In addition, the results also show that areas with lower population densities have adverse road conditions than areas with higher population densities. The DTW method and K-means clustering demonstrate the potential to be used as an alternative method in measuring road surface conditions which is more efficient in energy, time and cost when compared to conventional inspection. Keywords: DTW, k-means clustering, road surface, smartphone

Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

Population models with singular equilibrium

This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive Control (MPC) algorithm in which such a network is used for prediction. The Jordan network is a simple recurrent neural structure in which only one value of the process input signal (from the previous sampling instant) and only one value of the delayed output signal of the model (from the previous sampling instant) are used as the inputs of the network. In order to obtain a computationally simple MPC algorithm, the nonlinear Jordan neural model is repeatedly linearised on-line around an operating point, which leads to a quadratic optimisation problem. Effectiveness of the described MPC algorithm is compared with that of the truly nonlinear MPC scheme with on-line nonlinear optimisation performed at each sampling instant.

Multi-timescale systems and fast-slow analysis

Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals.

With the advent and popularization of technologies such as CellML and SBML it has become relatively easy to share and exchange mathematical models of biological systems. However, as such technology evolves it is inevitable that not all tools will correctly interpret the full range of models able to be described. It is also necessary to completely describe the mathematical models allowing model users to appropriately use each model. To address these issues we have previously developed methods and tools based on community defined standards which allow for the specification of a reference description of a mathematical model. Such reference descriptions provide a comprehensive description of both the mathematical model and the outcomes expected when performing specific numerical simulations using the model. Thus providing for quantitative validation of tool interpretation of a given model as well as informing the model user as to all aspects of the model definition. Further development of this work has led to the conclusion that presenting such reference descriptions in a user friendly and interactive manner provides a powerful tool for the dissemination and understanding of the mathematical model. Furthermore, such a tool is seen as an invaluable teaching aid in the education of biomedical engineers and scientists allowing for anything from a superficial to a very comprehensive interaction with the mathematical model.

Gene regulatory network inference: Data integration in dynamic models—A review

Systems biology develops mathematical models of biological systems that seek to explain, or better still predict, how the system behaves. In bottom-up systems biology, systematic quantitative experimentation is carried out to obtain the data required to parameterize models, which can then be analyzed and simulated. This paper describes an approach to integrated information management that supports bottom-up systems biology, with a view to automating, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models.Keywordscomputationalsystems biologyworkflow