12,312 research outputs found
Unexpected evolutionary dynamics in a string based artificial chemistry
This work investigates closure in Cell Signaling Networks,
which is one research area within the ESIGNET project.
We employ a string-based Artificial Chemistry based on
Hollandâs broadcast language (Molecular Classifier System,
Broadcast Language, or MCS.b). We present a series of
experiments focusing on the emergence and evolution of
self-maintaining molecular organizations. Such experiments
naturally relate to similar studies conducted in artificial
chemistries such as Tierra, Alchemy and Alpha-Universes.
However, our results demonstrate some counter-intuitive outcomes, not indicated in previous literature. Each of these âunexpectedâ evolutionary dynamics (including an elongation catastrophe phenomenon) are examined and explained both informally and formally. We also demonstrate how the elongation catastrophe can be prevented using a multi-level selectional model of the MCS.b (which acts both at the molecular and cellular level). This work provides complementary insights into the understanding of evolutionary dynamics in minimal artificial chemistries
MetaChem: An Algebraic Framework for Artificial Chemistries
We introduce MetaChem, a language for representing and implementing Artificial Chemistries. We motivate the need for modularisation and standardisation in representation of artificial chemistries. We describe a mathematical formalism for Static Graph MetaChem, a static graph based system. MetaChem supports different levels of description, and has a formal description; we illustrate these using StringCatChem, a toy artificial chemistry. We describe two existing Artificial Chemistries -- Jordan Algebra AChem and Swarm Chemistries -- in MetaChem, and demonstrate how they can be combined in several different configurations by using a MetaChem environmental link. MetaChem provides a route to standardisation, reuse, and composition of Artificial Chemistries and their tools
A stochastic model of catalytic reaction networks in protocells
Protocells are supposed to have played a key role in the self-organizing
processes leading to the emergence of life. Existing models either (i) describe
protocell architecture and dynamics, given the existence of sets of
collectively self-replicating molecules for granted, or (ii) describe the
emergence of the aforementioned sets from an ensemble of random molecules in a
simple experimental setting (e.g. a closed system or a steady-state flow
reactor) that does not properly describe a protocell. In this paper we present
a model that goes beyond these limitations by describing the dynamics of sets
of replicating molecules within a lipid vesicle. We adopt the simplest possible
protocell architecture, by considering a semi-permeable membrane that selects
the molecular types that are allowed to enter or exit the protocell and by
assuming that the reactions take place in the aqueous phase in the internal
compartment. As a first approximation, we ignore the protocell growth and
division dynamics. The behavior of catalytic reaction networks is then
simulated by means of a stochastic model that accounts for the creation and the
extinction of species and reactions. While this is not yet an exhaustive
protocell model, it already provides clues regarding some processes that are
relevant for understanding the conditions that can enable a population of
protocells to undergo evolution and selection.Comment: 20 pages, 5 figure
Studying complex adaptive systems using molecular classifier systems
Complex Adaptive Systems (CAS) are dynamical networks of interacting agents occurring in a variety of natural and artificial systems (e.g. cells, societies, stock markets). These complex systems have the ability to adapt, evolve and learn from experience. To study CAS, Holland proposed to employ agent-based systems in which Learning Classifier Systems (LCS) are used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations
are reflexive in CAS, e.g. in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based artificial chemistry based on Hollandâs Broadcast Language. In the MCS.b, no explicit fitness function is specified, moreover no distinction is made between messages and rules. In the
context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS : Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks
must replicate themselves prior to cell division. In this poster we present a series of experiments focusing on the self-replication ability of these CAS
A molecular approach to complex adaptive systems
Complex Adaptive Systems (CAS) are dynamical networks of interacting agents which as a whole determine the behavior, adaptivity and cognitive ability of the system. CAS are
ubiquitous and occur in a variety of natural and artificial systems (e.g., cells, societies, stock markets). To study CAS, Holland proposed to employ an agent-based system in which Learning Classifier Systems (LCS) were used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations are reflexive in CAS, e.g., in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based Artificial Chemistry based on Hollandâs broadcast language. In the MCS.b, no explicit fitness function or rule discovery mechanism is specified, moreover no distinction is made between messages and rules. In the context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS: Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks must replicate themselves prior to cell division. In this paper we present a series of experiments
focusing on the self-replication ability of these CAS. Results indicate counter intuitive outcomes as opposed to those inferred from the literature. This work highlights the current deficit of a theoretical framework for the study of Artificial Chemistries
The origin of large molecules in primordial autocatalytic reaction networks
Large molecules such as proteins and nucleic acids are crucial for life, yet
their primordial origin remains a major puzzle. The production of large
molecules, as we know it today, requires good catalysts, and the only good
catalysts we know that can accomplish this task consist of large molecules.
Thus the origin of large molecules is a chicken and egg problem in chemistry.
Here we present a mechanism, based on autocatalytic sets (ACSs), that is a
possible solution to this problem. We discuss a mathematical model describing
the population dynamics of molecules in a stylized but prebiotically plausible
chemistry. Large molecules can be produced in this chemistry by the coalescing
of smaller ones, with the smallest molecules, the `food set', being buffered.
Some of the reactions can be catalyzed by molecules within the chemistry with
varying catalytic strengths. Normally the concentrations of large molecules in
such a scenario are very small, diminishing exponentially with their size.
ACSs, if present in the catalytic network, can focus the resources of the
system into a sparse set of molecules. ACSs can produce a bistability in the
population dynamics and, in particular, steady states wherein the ACS molecules
dominate the population. However to reach these steady states from initial
conditions that contain only the food set typically requires very large
catalytic strengths, growing exponentially with the size of the catalyst
molecule. We present a solution to this problem by studying `nested ACSs', a
structure in which a small ACS is connected to a larger one and reinforces it.
We show that when the network contains a cascade of nested ACSs with the
catalytic strengths of molecules increasing gradually with their size (e.g., as
a power law), a sparse subset of molecules including some very large molecules
can come to dominate the system.Comment: 49 pages, 17 figures including supporting informatio
On RAF Sets and Autocatalytic Cycles in Random Reaction Networks
The emergence of autocatalytic sets of molecules seems to have played an
important role in the origin of life context. Although the possibility to
reproduce this emergence in laboratory has received considerable attention,
this is still far from being achieved. In order to unravel some key properties
enabling the emergence of structures potentially able to sustain their own
existence and growth, in this work we investigate the probability to observe
them in ensembles of random catalytic reaction networks characterized by
different structural properties. From the point of view of network topology, an
autocatalytic set have been defined either in term of strongly connected
components (SCCs) or as reflexively autocatalytic and food-generated sets
(RAFs). We observe that the average level of catalysis differently affects the
probability to observe a SCC or a RAF, highlighting the existence of a region
where the former can be observed, whereas the latter cannot. This parameter
also affects the composition of the RAF, which can be further characterized
into linear structures, autocatalysis or SCCs. Interestingly, we show that the
different network topology (uniform as opposed to power-law catalysis systems)
does not have a significantly divergent impact on SCCs and RAFs appearance,
whereas the proportion between cleavages and condensations seems instead to
play a role. A major factor that limits the probability of RAF appearance and
that may explain some of the difficulties encountered in laboratory seems to be
the presence of molecules which can accumulate without being substrate or
catalyst of any reaction.Comment: pp 113-12
Artificial life meets computational creativity?
I review the history of work in Artificial Life on the problem of the open-ended evolutionary growth of complexity in computational worlds. This is then put into the context of evolutionary epistemology and human creativity
Ab Initio Modeling of Ecosystems with Artificial Life
Artificial Life provides the opportunity to study the emergence and evolution
of simple ecosystems in real time. We give an overview of the advantages and
limitations of such an approach, as well as its relation to individual-based
modeling techniques. The Digital Life system Avida is introduced and prospects
for experiments with ab initio evolution (evolution "from scratch"),
maintenance, as well as stability of ecosystems are discussed.Comment: 13 pages, 2 figure
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