4,853 research outputs found
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system
Biology has taken strong steps towards becoming a computer science aiming at
reprogramming nature after the realisation that nature herself has reprogrammed
organisms by harnessing the power of natural selection and the digital
prescriptive nature of replicating DNA. Here we further unpack ideas related to
computability, algorithmic information theory and software engineering, in the
context of the extent to which biology can be (re)programmed, and with how we
may go about doing so in a more systematic way with all the tools and concepts
offered by theoretical computer science in a translation exercise from
computing to molecular biology and back. These concepts provide a means to a
hierarchical organization thereby blurring previously clear-cut lines between
concepts like matter and life, or between tumour types that are otherwise taken
as different and may not have however a different cause. This does not diminish
the properties of life or make its components and functions less interesting.
On the contrary, this approach makes for a more encompassing and integrated
view of nature, one that subsumes observer and observed within the same system,
and can generate new perspectives and tools with which to view complex diseases
like cancer, approaching them afresh from a software-engineering viewpoint that
casts evolution in the role of programmer, cells as computing machines, DNA and
genes as instructions and computer programs, viruses as hacking devices, the
immune system as a software debugging tool, and diseases as an
information-theoretic battlefield where all these forces deploy. We show how
information theory and algorithmic programming may explain fundamental
mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and
Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George
Ellis (eds.), Cambridge University Pres
Modelling and simulating in systems biology: an approach based on multi-agent systems
Systems Biology is an innovative way of doing biology recently raised in bio-informatics contexts, characterised by the study of biological systems as complex systems with a strong focus on the system level and on the interaction dimension. In other words, the objective is to understand biological systems as a whole, putting on the foreground
not only the study of the individual parts as standalone parts, but also of their interaction and of the global properties that emerge at the system level by means of the interaction among the parts.
This thesis focuses on the adoption of multi-agent systems (MAS) as a suitable paradigm for Systems Biology, for developing models and simulation of complex biological systems. Multi-agent system have been recently introduced in informatics context as a suitabe paradigm for modelling and engineering complex systems. Roughly speaking, a MAS can be conceived as a set of autonomous and interacting entities, called agents, situated in some kind of nvironment, where they fruitfully interact and coordinate so as to obtain a coherent global system behaviour. The claim of this work is that the general properties of MAS make them an effective approach for modelling and building
simulations of complex biological systems, following the methodological principles identified by Systems Biology. In particular, the thesis focuses on cell populations as biological systems.
In order to support the claim, the thesis introduces and describes (i) a MAS-based model conceived for modelling the dynamics of systems of cells interacting inside cell environment called niches. (ii) a computational tool, developed for implementing the models and executing the simulations. The tool is meant to work as a kind of virtual laboratory, on top of which kinds of virtual experiments can be performed, characterised by the definition and execution of specific models implemented as MASs, so as to support the validation, falsification and improvement of the models through the observation and
analysis of the simulations. A hematopoietic stem cell system is taken as reference case study for formulating a specific model and executing virtual experiments
Recommended from our members
A review of modelling and verification approaches for computational biology
This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties
Stepping Beyond the Newtonian Paradigm in Biology. Towards an Integrable Model of Life: Accelerating Discovery in the Biological Foundations of Science
The INBIOSA project brings together a group of experts across many disciplines
who believe that science requires a revolutionary transformative
step in order to address many of the vexing challenges presented by the
world. It is INBIOSA’s purpose to enable the focused collaboration of an
interdisciplinary community of original thinkers.
This paper sets out the case for support for this effort. The focus of the
transformative research program proposal is biology-centric. We admit
that biology to date has been more fact-oriented and less theoretical than
physics. However, the key leverageable idea is that careful extension of the
science of living systems can be more effectively applied to some of our
most vexing modern problems than the prevailing scheme, derived from
abstractions in physics. While these have some universal application and
demonstrate computational advantages, they are not theoretically mandated
for the living. A new set of mathematical abstractions derived from biology
can now be similarly extended. This is made possible by leveraging
new formal tools to understand abstraction and enable computability. [The
latter has a much expanded meaning in our context from the one known
and used in computer science and biology today, that is "by rote algorithmic
means", since it is not known if a living system is computable in this
sense (Mossio et al., 2009).] Two major challenges constitute the effort.
The first challenge is to design an original general system of abstractions
within the biological domain. The initial issue is descriptive leading to the
explanatory. There has not yet been a serious formal examination of the
abstractions of the biological domain. What is used today is an amalgam;
much is inherited from physics (via the bridging abstractions of chemistry)
and there are many new abstractions from advances in mathematics (incentivized
by the need for more capable computational analyses). Interspersed
are abstractions, concepts and underlying assumptions “native” to biology
and distinct from the mechanical language of physics and computation as
we know them. A pressing agenda should be to single out the most concrete
and at the same time the most fundamental process-units in biology
and to recruit them into the descriptive domain. Therefore, the first challenge
is to build a coherent formal system of abstractions and operations
that is truly native to living systems.
Nothing will be thrown away, but many common methods will be philosophically
recast, just as in physics relativity subsumed and reinterpreted
Newtonian mechanics.
This step is required because we need a comprehensible, formal system to
apply in many domains. Emphasis should be placed on the distinction between
multi-perspective analysis and synthesis and on what could be the
basic terms or tools needed.
The second challenge is relatively simple: the actual application of this set
of biology-centric ways and means to cross-disciplinary problems. In its
early stages, this will seem to be a “new science”.
This White Paper sets out the case of continuing support of Information
and Communication Technology (ICT) for transformative research in biology
and information processing centered on paradigm changes in the epistemological,
ontological, mathematical and computational bases of the science
of living systems. Today, curiously, living systems cannot be said to
be anything more than dissipative structures organized internally by genetic
information. There is not anything substantially different from abiotic
systems other than the empirical nature of their robustness. We believe that
there are other new and unique properties and patterns comprehensible at
this bio-logical level. The report lays out a fundamental set of approaches
to articulate these properties and patterns, and is composed as follows.
Sections 1 through 4 (preamble, introduction, motivation and major biomathematical
problems) are incipient. Section 5 describes the issues affecting
Integral Biomathics and Section 6 -- the aspects of the Grand Challenge
we face with this project. Section 7 contemplates the effort to
formalize a General Theory of Living Systems (GTLS) from what we have
today. The goal is to have a formal system, equivalent to that which exists
in the physics community. Here we define how to perceive the role of time
in biology. Section 8 describes the initial efforts to apply this general theory
of living systems in many domains, with special emphasis on crossdisciplinary
problems and multiple domains spanning both “hard” and
“soft” sciences. The expected result is a coherent collection of integrated
mathematical techniques. Section 9 discusses the first two test cases, project
proposals, of our approach. They are designed to demonstrate the ability
of our approach to address “wicked problems” which span across physics,
chemistry, biology, societies and societal dynamics. The solutions
require integrated measurable results at multiple levels known as “grand
challenges” to existing methods. Finally, Section 10 adheres to an appeal
for action, advocating the necessity for further long-term support of the
INBIOSA program.
The report is concluded with preliminary non-exclusive list of challenging
research themes to address, as well as required administrative actions. The
efforts described in the ten sections of this White Paper will proceed concurrently.
Collectively, they describe a program that can be managed and
measured as it progresses
Second Generation General System Theory: Perspectives in Philosophy and Approaches in Complex Systems
Following the classical work of Norbert Wiener, Ross Ashby, Ludwig von Bertalanffy and many others, the concept of System has been elaborated in different disciplinary fields, allowing interdisciplinary approaches in areas such as Physics, Biology, Chemistry, Cognitive Science, Economics, Engineering, Social Sciences, Mathematics, Medicine, Artificial Intelligence, and Philosophy. The new challenge of Complexity and Emergence has made the concept of System even more relevant to the study of problems with high contextuality. This Special Issue focuses on the nature of new problems arising from the study and modelling of complexity, their eventual common aspects, properties and approaches—already partially considered by different disciplines—as well as focusing on new, possibly unitary, theoretical frameworks. This Special Issue aims to introduce fresh impetus into systems research when the possible detection and correction of mistakes require the development of new knowledge. This book contains contributions presenting new approaches and results, problems and proposals. The context is an interdisciplinary framework dealing, in order, with electronic engineering problems; the problem of the observer; transdisciplinarity; problems of organised complexity; theoretical incompleteness; design of digital systems in a user-centred way; reaction networks as a framework for systems modelling; emergence of a stable system in reaction networks; emergence at the fundamental systems level; behavioural realization of memoryless functions
- …