6,808 research outputs found
GUBS, a Behavior-based Language for Open System Dedicated to Synthetic Biology
In this article, we propose a domain specific language, GUBS (Genomic Unified
Behavior Specification), dedicated to the behavioral specification of synthetic
biological devices, viewed as discrete open dynamical systems. GUBS is a
rule-based declarative language. By contrast to a closed system, a program is
always a partial description of the behavior of the system. The semantics of
the language accounts the existence of some hidden non-specified actions
possibly altering the behavior of the programmed device. The compilation
framework follows a scheme similar to automatic theorem proving, aiming at
improving synthetic biological design safety.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347
Actors and factors - bridging social science findings and urban land use change modeling
Recent uneven land use dynamics in urban areas resulting from demographic change, economic pressure and the citiesâ mutual competition in a globalising world challenge both scientists and practitioners, among them social scientists, modellers and spatial planners. Processes of growth and decline specifically affect the urban environment, the requirements of the residents on social and natural resources. Social and environmental research is interested in a better understanding and ways of explaining the interactions between society and landscape in urban areas. And it is also needed for making life in cities attractive, secure and affordable within or despite of uneven dynamics.\ud
The position paper upon âActors and factors â bridging social science findings and urban land use change modelingâ presents approaches and ideas on how social science findings on the interaction of the social system (actors) and the land use (factors) are taken up and formalised using modelling and gaming techniques. It should be understood as a first sketch compiling major challenges and proposing exemplary solutions in the field of interest
The case for absolute ligand discrimination : modeling information processing and decision by immune T cells
Some cells have to take decision based on the quality of surroundings
ligands, almost irrespective of their quantity, a problem we name "absolute
discrimination". An example of absolute discrimination is recognition of
not-self by immune T Cells. We show how the problem of absolute discrimination
can be solved by a process called "adaptive sorting". We review several
implementations of adaptive sorting, as well as its generic properties such as
antagonism. We show how kinetic proofreading with negative feedback implements
an approximate version of adaptive sorting in the immune context. Finally, we
revisit the decision problem at the cell population level, showing how
phenotypic variability and feedbacks between population and single cells are
crucial for proper decision
Life-Death Ratio Approach by a Multiset-Based Type System
We introduce and study a multiset-based type system with ratio thresholds
motivated by an important regulatory mechanism inside a cell which try to maintain a
\life-death" ratio between some given lower and upper thresholds. We use such a type
system to control ratio thresholds in a bio-inspired and multisets-based formalism. For
this type system we prove a subject reduction theorem, together with soundness and
completeness theorems. A type inference for deducing the type of a system is presented
How many neurons are sufficient for perception of cortical activity?
Many theories of brain function assume that information is encoded and behaviour is controlled through sparse, distributed patterns of activity. It is therefore crucial to place a lower bound on the amount of neural activity that can drive behaviour and to understand how neuronal networks operate within these constraints. We use an all-optical approach to test this lower limit by driving behaviour with targeted two-photon optogenetic activation of small ensembles of L2/3 pyramidal neurons in mouse barrel cortex while using two-photon calcium imaging to record the impact on the local network. By precisely titrating the number of neurons in activated ensembles we demonstrate that the lower bound for detection of cortical activity is ~14 pyramidal neurons. We show that there is a very steep sigmoidal relationship between the number of activated neurons and behavioural output, saturating at only ~37 neurons, and that this relationship can shift with learning. By simultaneously measuring activity in the local network, we show that the activation of stimulated ensembles is balanced by the suppression of neighbouring neurons. This surprising behavioural sensitivity in the face of potent network suppression supports the sparse coding hypothesis and suggests that perception of cortical activity balances a trade-off between minimizing the impact of noise while efficiently detecting relevant signals
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 withinâand amongâbiological 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 frameworkâso far missingâfor 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 âattractor landscapeâ 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
Using simulations and artificial life algorithms to grow elements of construction
'In nature, shape is cheaper than material', that is a common truth for most of the plants and other living organisms, even though they may not recognize that. In all living forms, shape is more or less directly linked to the influence of force, that was acting upon the organism during its growth. Trees and bones concentrate their material where thy need strength and stiffness, locating the tissue in desired places through the process of self-organization.
We can study nature to find solutions to design problems. Thatâs where inspiration comes from, so we pick a solution already spotted somewhere in the organic world, that closely resembles our design problem, and use it in constructive way. First, examining it, disassembling, sorting out conclusions and ideas discovered, then performing an act of 'reverse engineering' and putting it all together again, in a way that suits our design needs. Very simple ideas copied from nature, produce complexity and exhibit self-organization capabilities, when applied in bigger scale and number. Computer algorithms of simulated artificial life help us to capture them, understand well and use where needed.
This investigation is going to follow the question : How can we use methods seen in nature to simulate growth of construction elements? Different ways of extracting ideas from world of biology will be presented, then several techniques of simulated emergence will be demonstrated.
Specific focus will be put on topics of computational modelling of natural phenomena, and differences in developmental and non-developmental techniques. Resulting 3D models will be
shown and explained
Bio-inspired Mechanisms for Artificial Self-organised Systems
Research on self-organization tries to describe and explain forms, complex patterns and behaviours that arise from a collection of entities without an external organizer. As researchers in artificial systems, our aim is not to mimic self-organizing phenomena arising in Nature, but to understand and to control underlying mechanisms allowing desired emergence of forms, complex patterns and behaviours. In this paper we analyze three forms of self-organization: stigmergy, reinforcement mechanisms and cooperation. For each forms of self-organisation, we present a case study to show how we transposed it to some artificial systems and then analyse the strengths and weaknesses of such an approach
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