Qualitative modelling and analysis of regulations in multi-cellular systems using Petri nets and topological collections

In this paper, we aim at modelling and analyzing the regulation processes in multi-cellular biological systems, in particular tissues. The modelling framework is based on interconnected logical regulatory networks a la Rene Thomas equipped with information about their spatial relationships. The semantics of such models is expressed through colored Petri nets to implement regulation rules, combined with topological collections to implement the spatial information. Some constraints are put on the the representation of spatial information in order to preserve the possibility of an enumerative and exhaustive state space exploration. This paper presents the modelling framework, its semantics, as well as a prototype implementation that allowed preliminary experimentation on some applications.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

Bi-simulation Between P Colonies and P Systems with Multi-stable Catalysts

International audienc

Towards new concepts for a biological neuroscience of consciousness

In the search for a sound model of consciousness, we aim at introducing new concepts: closure, compositionality, biobranes and autobranes. This is important to overcome reductionism and to bring life back into the neuroscience of consciousness. Using these definitions, we conjecture that consciousness co-arises with the non-trivial composition of biological closure in the form of biobranes and autobranes: conscious processes generate closed activity at various levels and are, in turn, themselves, supported by biobranes and autobranes. This approach leads to a non-reductionist biological and simultaneously phenomenological theory of conscious experience, giving new perspectives for a science of consciousness. Future works will implement experimental definitions and computational simulations to characterize these dynamical biobranes interacting

A Possible Role for Entropy in Creative Cognition

This paper states the case for applying the conceptual and analytic tools associated with the study of entropy in physical systems to cognition, focusing on creative cognition. It is proposed that minds modify their contents and adapt to their environments to minimize psychological entropy: arousal-provoking uncertainty, which can be experienced negatively as anxiety, or positively as a wellspring for creativity (or both). Thus, intrinsically motivated creativity begins with detection of high psychological entropy material (e.g., a question or inconsistency), which provokes uncertainty and is arousal-inducing. This material is recursively considering from new contexts until it is sufficiently restructured that arousal dissipates and entropy reaches an acceptable level. Restructuring involves neural synchrony and dynamic binding, and may be facilitated by temporarily shifting to a more associative mode of thought. The creative outcome may similarly induce restructuring in others, and thereby contribute to the cultural evolution of more nuanced understandings. Thus, the concept of entropy could play a unifying role in cognitive science as a driver of thought and action, and in cultural studies as the driver of the creative innovations that fuel cultural evolution. The paper concludes with an invitation for cross-disciplinary exploration of this potential new arena of entropy studies.Comment: 6 pages; http://sciforum.net/conference/84/paper/3652; in Proceedings 3rd International Electronic Conf on Entropy and its Applications, 1-10 Nov (2016

Membrane Computing as a Modeling Framework. Cellular Systems Case Studies

Membrane computing is a branch of natural computing aiming to abstract computing models from the structure and functioning of the living cell, and from the way cells cooperate in tissues, organs, or other populations of cells. This research area developed very fast, both at the theoretical level and in what concerns the applications. After a very short description of the domain, we mention here the main areas where membrane computing was used as a framework for devising models (biology and bio-medicine, linguistics, economics, computer science, etc.), then we discuss in a certain detail the possibility of using membrane computing as a high level computational modeling framework for addressing structural and dynamical aspects of cellular systems. We close with a comprehensive bibliography of membrane computing applications

How to Go Beyond Turing with P Automata: Time Travels, Regular Observer !-Languages, and Partial Adult Halting

In this paper we investigate several variants of P automata having in nite runs on nite inputs. By imposing speci c conditions on the in nite evolution of the systems, it is easy to nd ways for going beyond Turing if we are watching the behavior of the systems on in nite runs. As speci c variants we introduce a new halting variant for P automata which we call partial adult halting with the meaning that a speci c prede ned part of the P automaton does not change any more from some moment on during the in nite run. In a more general way, we can assign !-languages as observer languages to the in nite runs of a P automaton. Speci c variants of regular !-languages then, for example, characterize the red-green P automata

Quasi-cellular Systems: Stochastic Simulation Analysis at Nanoscale Range

I complessi sistemi di reazioni biochimiche all’interno della cellula sono altamente compartimentalizzati, conseguenza di un importante fenomeno di macromolecolar crowding (sovraffollamento molecolare). E’ dunque importante determinare il comportamento e le proprietà di un sistema di reazioni in piccoli volumi. Sono stati riprodotti con successo diversi sistemi di semplici reazioni all’interno di vescicole lipidiche (liposomi) nell’ordine del micro/nanometro di diametro, osservando in molti casi una risposta cinetica diversa dalle reazioni in esame rispetto al comportamento in sistemi di grandi volumi. Questo fenomeno di divergenza tra piccoli e grandi volumi è in gran parte dipendente da fenomeni non completamente chiariti, quali l’incapsulamento delle specie e il crowding molecolare, aspetti sempre più importanti man mano che l’attenzione si sposta verso i piccoli volumi. Recenti dati sperimentali dimostrano che il fenomeno dell'intrappolamento sembra non seguire un andamento casuale squisitamente probabilistico, ma un comportamento di tipo power-law (a legge di potenza), in cui solo pochissime vescicole intrappolano tante specie, mentre la maggior parte resta completamente vuota. A tal proposito è stato intrapreso uno studio sui meccanismi generativi delle distribuzioni a legge di potenza calate nel contesto dell’incapsulamento (entrapment) delle specie all'interno di vescicole lipidiche. Utilizzando un sistema cell-free di trascrizione/traduzione (PURESYSTEM™), volto alla produzione di EGFP all’interno di liposomi di POPC, è possibile monitorare la produzione di proteina fluorescente in liposomi di differente grandezza. Tuttavia, è molto difficile osservare la produzione di molecole fluorescenti in singole vescicole di 100 nm di diametro; diventa così importante poter studiare in silico la di produzione di proteina in singole vescicole virtuali, utilizzando un modello formalmente valido del complesso sistema di reazioni del PURESYSTEM™. QDC (Quick Direct-Method Controlled), è un software di simulazione stocastico precedentemente sviluppato in laboratorio, basato sull’algoritmo di simulazione SSA Direct-Method di Gillespie, tra i più usati in biologia computazionale/systems biology. L’argomento della tesi riguarda l’uso di questo software nello studio delle oltre 100 reazioni biochimiche del PURESYSTEM™, comparando i risultati ottenuti in diverse condizioni (volume totale di reazione, concentrazioni delle specie, costanti cinetiche delle singole reazioni). Dopo aver affinato il modello in silico di Trascrizione/traduzione coupled (accoppiato), sono state effettuate delle simulazioni variando alcune variabili macroscopiche (concentrazioni delle specie e costanti cinetiche), mostrando un'importante dipendenza della traduzione dalla trascrizione, soprattutto considerando il grande limite energetico di un sistema che non produce al suo interno nucleotidi trifosfato

Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud \ud \ud KEYWORDS: Emergence of Life and Human Consciousness;\ud Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments. \ud \u

A new P-Lingua toolkit for agile development in membrane computing

Membrane computing is a massively parallel and non-deterministic bioinspired computing paradigm whose models are called P systems. Validating and testing such models is a challenge which is being overcome by developing simulators. Regardless of their heterogeneity, such simulators require to read and interpret the models to be simulated. To this end, P-Lingua is a high-level P system definition language which has been widely used in the last decade. The P-Lingua ecosystem includes not only the language, but also libraries and software tools for parsing and simulating membrane computing models. Each version of P-Lingua supported new types or variants of P systems. This leads to a shortcoming: Only a predefined list of variants can be used, thus making it difficult for researchers to study custom ones. Moreover, derivation modes cannot be user-defined, i.e, the way in which P system computations should be generated is determined by the simulation algorithm in the source code. The main contribution of this paper is a completely new design of the P-Lingua language, called P-Lingua 5, in which the user can define custom variants and derivation modes, among other improvements such as including procedural programming and simulation directives. It is worth mentioning that it has backward-compatibility with previous versions of the language. A completely new set of command-line tools is provided for parsing and simulating P-Lingua 5 files. Finally, several examples are included in this paper covering the most common P system types.Agencia Estatal de Investigación TIN2017-89842-