1,721,049 research outputs found
Preface In : Lecca P, Re A editors. Computational Systems Biology – Inference and Modeling
No full textIdentifying necessary and sufficient conditions for the observability of models of biochemical processes
No full textThe notions of observability and controllability of non-linear systems are a cornerstone of mathematical control theory and cover a wide scope of applications including process design, characterization, monitoring and control. Synthetic biology - which aims to (re)-program living functionalities - and bio-based process engineering - which aims to develop biotechnological manufacturing processes based on industrial and natural living agents - remarkably benefit of methodological improvements inspired to control theory for countless reasons including the huge variety of control mechanisms in living organisms, experimental limitations in terms of measurement feasibility, design of controllers - at single cell or population level - of synthetic production processes and process optimization purposes. Many fundamental problems of control theory such as stabilisability of unstable systems and optimal control may be solved under the assumption that the system is observable/controllable. Observability and controllability are mathematical duals, that means that the observability property can be determined analysing the controllability of the dual system and vice versa. Given this duality, we focus on observability. In this work, we revisit a generalization of the Fujisawa and Kuh theorem as a tool to explore the possibility that a system is observable. We show that the theorem, when applicable, is a sufficient but not necessary condition for observability. We revisit the theorem to propose a necessary and sufficient condition for observability for non-linear systems. Finally, we show how it is possible to identify regions of the phase space of the model in which the model is observable
Detecting modules in biological networks by edge weights clustering and entropy significance
Full text linkDetection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be successfully used in the differential analysis of physical protein-protein interaction (PPI) networks. Specifically, applying WG-Cluster to a PPI network weighted by measurements of differential gene expression permits to explore the changes in network topology under two distinct (normal vs. tumor) conditions. WG-Cluster code is available at https://sites.google.com/site/paolaleccapersonalpage/
A reaction-based model of the state space of chemical reaction systems enables efficient simulations
No full textThe choice of the state space representation of a system can turn into a prominent advantage or burden in any endeavour to mathematically model dynamical systems since it entails the analytical tractability of the related modelling formalism and the efficiency of the numerical computation. The Reaction-Based Model (RBM) of the state space, which is presented in this article, is a novel formalization of the kinetics of a system of interacting molecules. According to our representation, the state Sμ of a system of M reactions and N molecular species, is identified with the occurrence of the reaction Rμ ( μ = 1, ..., M). The transition between any two states Sμ and Sν is modelled as a first-order reaction Sμ → Sν and described by mass action-like equation for the partial time derivative of the variables P(Sμ, t) and P(Sν, t), which denote the probabilities that the system lies in the two states, respectively. The rate equations for the state probabilities are coupled with those for the abundance of molecular species. Altogether, the rate equations along with the specification of the initial conditions define the Cauchy problem whose solution describes the time-evolution of the system. The RBM has been applied to a typical severely stiff biological case study. The numerical solutions of the system's dynamics turned out to be computationally more efficient and in agreement with the results of the stochastic and hybrid stochastic/deterministic simulation algorithms
The Impact of Regional R&D Subsidy in a Computable General Equilibrium Model
No full textThis article presents a computable general equilibrium model for the region of
Sardinia (Italy) with the purpose of investigating the macroeconomic impact of
research and development (R&D) policies. The model incorporates induced technical
change obtained through knowledge accumulation and external knowledge
spillovers. It turns out that the cost of R&D policies may change according to wage
setting in the region. Indeed, the likely size of the optimal subsidy that is required to
reach a given target growth is lower when wages are bargained locally compared to
the case where wages are bargained nationally. Furthermore, the capacity of such a
policy to generate knowledge spillovers from international and interregional trade is
quite modest. Indeed, the capacity of the regional system to internalize innovations
embedded in imported goods is partially offset by an increase in internal efficiency
that lowers the spillover intensity through a reduction in the share of imports
On TD-WGcluster - theoretical foundations and guidelines for the user
No full textWe review the TD-WGcluster (time delayed weighted edge clustering) software integrating static interaction networks with time series data in order to detect modules of nodes between which the information flows at similar time delays and intensities. The software has represented an advancement of the state of the art in the software for the identification of connected components due to its peculiarity of dealing with direct and weighted graphs, where the attributes of the physical entities represented by nodes vary over time. This chapter aims to deepen those theoretical aspects of the clustering model implemented by TD-WGcluster that may be of greater interest to the user. We show the instructions necessary to run the software through some exploratory cases and comment on the results obtained
Observability of bacterial growth models in bubble column bioreactors
No full textObservability being the dual of controllability is an advisable property for any dynamic model of bio-based chemical production processes encompassing substrate consumption, bacterial growth and products formation. In this study we show a mathematical model of these processes and present a novel observability analysis. The invertibility properties of the observability mapping of this model in a space-time domain are analysed independently of the discretization of such domains and indicate the existence of subdomains where the measurement of the output may not be sufficiently accurate to allow reconstructing the states of the system
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