Network modeling in systems biology

A key aim of current systems biology research is to understand biology at the system level, to systematically catalogue all molecules and their interactions within a living cell, rather than the characteristics of isolated parts of a cell or organism. Network modeling is characterized by viewing cells in terms of their underlying network structure at many different levels of detail is a cornerstone of systems biology. Two emerging methodologies in network modeling provide invaluable insights into biological systems: static large-scale biological network modeling and dynamic quantitative modeling. Static large-scale biological network modeling focuses on integrating, visualizing and topologically modeling To facilitate application of these methods in biological research and improve existing network modeling software, this work presents: i) OmicsViz and OmicsAnalyzer, software tools, dedicated to integrating and analyzing omics data sets in network context. ii) CytoModeler, software tool, dedicated to providing a bridge between static large-scale biological network modeling and dynamic quantitative modeling methods. It not only facilitates network design, model creation, and computational simulation but provides advanced visualization for simulation results. iii) Comparative network modeling application in the systems biology of the SM-SNARE protein regulation in exocytotic membrane fusion. This work presents applications of biological network modeling methods to understand regulation mechanisms in complex biological systems. all kinds of omics data sets which are produced by innovative high throughput screening biotechnologies. Dynamic quantitative modeling focuses on exploring dynamics of biological systems by applying computational simulation and mathematical modeling

Modeling carbon black reinforcement in rubber compounds

One of the advocated reinforcement mechanisms is the formation by the filler of a network interpenetrating the polymer network. The deformation and reformation of the filler network allows the explanation of low strain dynamic physical properties of the composite. The present model relies on a statistical study of a collection of elementary mechanical systems, This leads to a mathematical approach of the complex modulus G* = G' + iG". The storage and loss modulus (G' and G", respectively), are expressed in the form of two integrals capable of modeling their Variation with respect to strain

A complex network approach to urban growth

The economic geography can be viewed as a large and growing network of interacting activities. This fundamental network structure and the large size of such systems makes complex networks an attractive model for its analysis. In this paper we propose the use of complex networks for geographical modeling and demonstrate how such an application can be combined with a cellular model to produce output that is consistent with large scale regularities such as power laws and fractality. Complex networks can provide a stringent framework for growth dynamic modeling where concepts from e.g. spatial interaction models and multiplicative growth models can be combined with the flexible representation of land and behavior found in cellular automata and agent-based models. In addition, there exists a large body of theory for the analysis of complex networks that have direct applications for urban geographic problems. The intended use of such models is twofold: i) to address the problem of how the empirically observed hierarchical structure of settlements can be explained as a stationary property of a stochastic evolutionary process rather than as equilibrium points in a dynamics, and, ii) to improve the prediction quality of applied urban modeling.evolutionary economics, complex networks, urban growth

Statecharts for Gene Network Modeling

State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks

Cluster-based network modeling: From snapshots to complex dynamical systems

We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics. CNM describes short- and long-term behavior and is fully automatable, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields

The innovation network as a complex adaptive system: flexible multi-agent based modeling, simulation and evolutionary decision making

The literature rarely considers an innovation network as a complex adaptive system. In this paper, theories of complex adaptive systems research are employed to model and analyze intra-organization networks, inter-organization networks as well as their interaction mechanisms in the whole innovation context, with a conceptual framework proposed and presented. Flexible multi-agent based modeling, smart simulation, self-survival and adaptive intelligent software agents, expert systems, analytic hierarchy process, hybrid decision support approach, and statistical methods are integrated to deal with the innovation network problem and support evolutionary decision making in the open and dynamic environments

Quantifying the Dynamics of Coupled Networks of Switches and Oscillators

Complex network dynamics have been analyzed with models of systems of coupled switches or systems of coupled oscillators. However, many complex systems are composed of components with diverse dynamics whose interactions drive the system's evolution. We, therefore, introduce a new modeling framework that describes the dynamics of networks composed of both oscillators and switches. Both oscillator synchronization and switch stability are preserved in these heterogeneous, coupled networks. Furthermore, this model recapitulates the qualitative dynamics for the yeast cell cycle consistent with the hypothesized dynamics resulting from decomposition of the regulatory network into dynamic motifs. Introducing feedback into the cell-cycle network induces qualitative dynamics analogous to limitless replicative potential that is a hallmark of cancer. As a result, the proposed model of switch and oscillator coupling provides the ability to incorporate mechanisms that underlie the synchronized stimulus response ubiquitous in biochemical systems