Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

Towards a Boolean network-based Computational Model for Cell Differentiation and its applications to Robotics

Living organisms are the ultimate product of a series of complex processes that take place within—and among—biological cells. Most of these processes, such as cell differentiation, are currently poorly understood. Cell differentiation is the process by which cells progressively specialise. Being a fundamental process within cells, its dysregulations have dramatic implications in biological organisms ranging from developmental issues to cancer formation. The thesis objective is to contribute to the progress in the understanding of cell differentiation and explore the applications of its properties for designing artificial systems. The proposed approach, which relies on Boolean networks based modelling and on the theory of dynamical systems, aims at investigating the general mechanisms underlying cell differentiation. The results obtained contribute to taking a further step towards the formulation of a general theoretical framework—so far missing—for cellular differentiation. We conducted an in-depth analysis of the impact of self-loops in random Boolean networks ensembles. We proposed a new model of differentiation driven by a simplified bio-inspired methylation mechanism in Boolean models of genetic regulatory networks. On the artificial side, by introducing the conceptual metaphor of the “attractor landscape” and related proofs of concept that support its potential, we paved the way for a new research direction in robotics called behavioural differentiation robotics: a branch of robotics dealing with the designing of robots capable of expressing different behaviours in a way similar to that of biological cells that undergo differentiation. The implications of the results achieved may have beneficial effects on medical research. Indeed, the proposed approach can foster new questions, experiments and in turn, models that hopefully in the next future will take us to cure differentiation-related diseases such as cancer. Our work may also contribute to address questions concerning the evolution of complex behaviours and to help design robust and adaptive robots

On the Criticality of Adaptive Boolean Network Robots

Systems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states

A simplified model of chromatin dynamics drives differentiation process in Boolean models of GRN

Cellular types of multicellular organisms are the stable results of complex intertwined processes that occur in biological cells. Among the many others, chromatin dynamics significantly contributes\u2014by modulating access to genes\u2014to differential gene expression, and ultimately to determine cell types. Here, we propose a dynamical model of differentiation based on a simplified bio-inspired methylation mechanism in Boolean models of GRNs. Preliminary results show that, as the number of methylated nodes increases, there is a decrease in attractor number and networks tend to assume dynamical behaviours typical of ordered ensembles. At the same time, results show that this mechanism does not affect the possibility of generating path dependent differentiation: cell types determined by the specific sequence of methylated genes

The choice of μ-vinyliminium ligand substituents is key to optimize the antiproliferative activity of related diiron complexes

Diiron vinyliminium complexes constitute a large family of organometallics displaying a promising anticancer potential. The complexes [Fe2Cp2(CO)(mu-CO){mu-eta(1):eta(3)-C(R-3)C(R-4)CN(R-1)(R-2)}]CF3SO3 (2a-c, 4a-d) were synthesized, assessed for their behavior in aqueous solutions (D2O solubility, Log P-ow, stability in D2O/Me2SO-d(6) mixture at 37 degrees C over 48 h) and investigated for their antiproliferative activity against A2780 and A2780cisR ovarian cancer cell lines and the nontumoral one Balb/3T3 clone A31. Cytotoxicity data collected for 50 vinyliminium complexes were correlated with the structural properties (i.e. the different R-1-R-4 substituents) using the partial least squares methodology. A clear positive correlation emerged between the octanol-water partition coefficient and the relative antiproliferative activity on ovarian cancer cell lines, both of which appear as uncorrelated to the cancer cell selectivity. However, the different effects played by the R-1-R-4 substituents allow tracing guidelines for the development of novel, more effective compounds. Based on these results, three additional complexes (4p-r) were designed, synthesized and biologically investigated, revealing their ability to hamper thioredoxin reductase enzyme and to induce cancer cell production of reactive oxygen species

Noise reduction in gravitational wave interferometers using feedback

We show that the quantum locking scheme recently proposed by Courty {\it et al.} [Phys. Rev. Lett. {\bf 90}, 083601 (2003)] for the reduction of back action noise is able to significantly improve the sensitivity of the next generation of gravitational wave interferometers.Comment: 12 pages, 2 figures, in print in the Special Issue of J. Opt. B on Fluctuations and Noise in Photonics and Quantum Optic

Intelligenza artificiale: test di Turing e alcune questioni filosofiche

Presentazione e discussione critica del test di Turing e degli aspetti filosofici correlati all'intelligenza artificiale