Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Fundamenta Informaticae

2 2020

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: flux balance analysis

Sortuj według:

Ogranicz wyniki do:

The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System

Maspero Davide, Damiani Chiara, Antoniotti Marco, Graudenzi Alex, Di Filippo Marzia, Vanoni Marco, Caravagna Giulio, Colombo Riccardo, Ramazzotti Daniele, Pescini Dario

Fundamenta Informaticae

2020

Vol. 171, nr 1-4

279--295

The metabolic processes related to the synthesis of the molecules needed for a new round of cell division underlie the complex behaviour of cell populations in multi-cellular systems, such as tissues and organs, whereas their deregulation can lead to pathological states, such as cancer. Even within genetically homogeneous populations, complex dynamics, such as population oscillations or the emergence of specific metabolic and/or proliferative patterns, may arise, and this aspect is highly amplified in systems characterized by extreme heterogeneity. To investigate the conditions and mechanisms that link metabolic processes to cell population dynamics, we here employ a previously introduced multi-scale model of multi-cellular system, named FBCA (Flux Balance Analysis with Cellular Automata), which couples biomass accumulation, simulated via Flux Balance Analysis of a metabolic network, with the simulation of population and spatial dynamics via Cellular Potts Models. In this work, we investigate the influence that different modes of nutrients diffusion within the system may have on the emerging behaviour of cell populations. In our model, metabolic communication among cells is allowed by letting secreted metabolites to diffuse over the lattice, in addition to diffusion of nutrients from given sources. The inclusion of the diffusion processes in the model proved its effectiveness in characterizing plausible biological scenarios.

Integrating Petri Nets and Flux Balance Methods in Computational Biology Models : a Methodological and Computational Practice

Pernice Simone, Follia Laura, Balbo Gianfranco, Milanesi Luciano, Sartini Giulia, Totis Niccoló, Lió Pietro, Merelli Ivan, Cordero Francesca, Beccuti Marco

Fundamenta Informaticae

2020

Vol. 171, nr 1-4

367--392

Computational Biology is a fast-growing field that is enriched by different data-driven methodological approaches and by findings and applications in a broad range of biological areas. Fundamental to these approaches are the mathematical and computational models used to describe the different states at microscopic (for example a biochemical reaction), mesoscopic (the signalling effects at tissue level), and macroscopic levels (physiological and pathological effects) of biological processes. In this paper we address the problem of combining two powerful classes of methodologies: Flux Balance Analysis (FBA) methods which are now producing a revolution in biotechnology and medicine, and Petri Nets (PNs) which allow system generalisation and are central to various mathematical treatments, for example Ordinary Differential Equation (ODE) specification of the biosystem under study. While the former is limited to modelling metabolic networks, i.e. does not account for intermittent dynamical signalling events, the latter is hampered by the need for a large amount of metabolic data. A first result presented in this paper is the identification of three types of cross-talks between PNs and FBA methods and their dependencies on available data. We exemplify our insights with the analysis of a pancreatic cancer model. We discuss how our reasoning framework provides a biologically and mathematically grounded decision making setting for the integration of regulatory, signalling, and metabolic networks and greatly increases model interpretability and reusability. We discuss how the parameters of PN and FBA models can be tuned and combined together so to highlight the computational effort needed to perform this task. We conclude with speculations and suggestions on this new promising research direction.