Cell Signaling networks (CSNs) are bio-chemical systems of interacting molecules in cells. Typically, these systems take as inputs chemical signals generated within the cell or communicated from outside. These trigger a cascade of chemical reactions that result in changes of the state of the cell and (or) generate some chemical output, such as prokaryotic chemotaxis or coordination of cellular division. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new ways to design computer systems for a variety of application areas. We are investigating the use of ACSNs to implement computation, signal processing and (or) control functionality. We review some of the the research issues which this raises. As a 'computational' device, a CSN is most naturally compared to a traditional analog computer. There may be applications where a molecular level analog computer, in the form of a CSN, may have distinct advantages. CSNs may offer capabilities of high speed and small size that cannot be realised with solid state electronic technology. More critically, where it is required to interface computation with chemical interaction, a CSN may bypass difficult stages of signal transduction that would otherwise be required. This could have direct application in so-called 'smart drugs' and other bio-medical interventions. Evolutionary Algorithms are non-deterministic search and ptimisation algorithms inspired by the principles of neo-Darwinism. Such techniques are relevant to the study of ACSNs because: the complex, and unpredictable, interactions between different components of CSNs make it very difficult to design them 'by hand' to meet specific performance objectives. However, natural evolution shows that in suitable circumstances, effective CSNs functionality can be achieved through evolutionary processes. 'Crosstalk' phenomena happen when signals from different pathways become mixed together. This arises very naturally in CSNs due to the fact that the molecules from all pathways may share the same physical reaction space. In traditional communications and signal processing engineering, crosstalk is regarded as a defect that therefore has the potential to cause system malfunction. This can also clearly be the case of crosstalk in CSNs. However, in the specific case of CSN's, crosstalk also has additional potential functionality, which may actually be constructive. It is also argued that key properties in biochemical networks are to be robust, this is so as to ensure their correct functioning. Such properties are highly desirable in dynamic engineered systems when subjected to internal and external uncertainty and perturbation. Acknowledgements: This work was supported by the European Community as part of the FP6 ESIGNET Project (12789).
Cellular Information Processing Networks (CIPNs) are chemical networks of interacting molecules occurring in living cells. Through complex molecular interactions, CIPNs are able to coordinate critical cellular activities in response to internal and external stimuli. We hypothesise that CIPNs may be abstractly regarded as subsets of collectively autocatalytic (i.e., organisationally closed) reaction networks. These closure properties would subsequently interact with the evolution and adaptation of CIPNs capable of distinct information processing abilities. This hypothesis is motivated by the fact that CIPNs may require a mechanism enabling the self-maintenance of core components of the network when subjected to internal and external perturbations and during cellular divisions. Indeed, partially replicated or defective CIPNs may lead to the malfunctioning and premature death of the cell.
In this thesis, we evaluate different existing computational approaches to model and evolve chemical reaction networks in silico. Following this literature review, we propose an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is a novel agent-based Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS.bl). The latter is derived from the Holland broadcast language formalism.
Our first series of experiments focuses on the emergence and evolution of selfmaintaining molecular organisations in the MCS.bl. Such experiments naturally relate to similar studies conducted in ACs such as Tierra, Alchemy and α-universes. Our results demonstrate some counter-intuitive outcomes, not indicated in previous literature. We examine each of these “unexpected” evolutionary dynamics (including an elongation catastrophe phenomenon) which presented various degenerate evolutionary trajectories. To address these robustness and evolvability issues, we evaluate several model variants of the MCS.bl. This investigation illuminates the key properties required to allow the self-maintenance and stable evolution of closed reaction networks in ACs. We demonstrate how the elongation catastrophe phenomenon can be prevented using a multi-level selectional model of the MCS.bl (which acts both at the molecular and cellular level). Using this multi-level selectional MCS.bl which was implemented as a parallel system, we successfully evolve an artificial CIPN to perform a simple pre-specified information processing task. We also demonstrate how signalling crosstalk may enable the cooperation of distinct closed CIPNs when mixed together in the same reaction space. We finally present the evolution of closed crosstalking and multitasking CIPNs exhibiting a higher level of complexity.
This paper is concerned with the modeling and evolving of cell signaling networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first provided, in which we describe the potential applications of modeling and evolving these biochemical networks in silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the ESIGNET project. Results obtained with these methods are summarized and discussed.
We present methods to predict and validate home and work places of anonymized users using their mobile network data. Knowledge of home and work place of a user is essential in order to find his (and overall population) mobility profiles. There are many methods that predict home and work places using GPS data. But unlike GPS data, mobile network data using GSM do not provide the exact location of a phone event. We use a novel criterion that combines an extracted feature from mobile data (i.e., Inactivity - no phone event for a given period of time) with open source data about location category % (i.e., Streetdirectory.com) to predict home location. Results show that the new criterion gives better prediction accuracy than inactivity alone. We predict work place using the idea that one goes to her work place on most of the weekdays but rarely on weekends. We validate our methods by comparing against the ground truth obtained from open source data. Validation results show that our proposed methods are about 25% more accurate than existing methods both for home and work place predictions.
Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of Cell Signaling Networks focuses on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs.
One major problem of using location data collected from mobile cellular networks for mobility modelling is the oscillation phenomenon. An oscillation occurs when a mobile phone intermittently switches between cell towers instead of connecting to the nearest cell tower. For the purpose of mobility modeling, the location data needs to be cleansed to approximate the mobile device's actual location. However, this constitutes a challenge because the mobile device's true location is not known. In this paper, we study the oscillation resolution problem. We propose an algorithm framework called DECRE (Detect, Expand, Check, Remove) to detect and remove oscillation logs. To make informed decisions DECRE includes four steps: Detect, to identify log sequences that may contain oscillation using a few heuristics based on the concepts of stable period and moving at impossible speed, Expand, to look before and after suspicious records to gain more information, Check, to check whether a cell tower is observed repeatedly (which is a strong indication of oscillation), and Remove, resolving oscillation by selecting a cell tower to approximate the mobile device's actual location. Our experimental results on travel diaries show that our oscillation resolution approach is able to remove records that are far from mobile device's ground-truth locations, improve the quality of the location data, and performs better than an existing method. Our performance study on large scale cell tower data shows that the MapReduce implementation of our approach is able to process 1 Terabyte of cell tower data in five hours using a small cluster.
We examine the role of self-maintenance (collective autocatalysis) in the evolution of computational biochemical networks. In primitive proto-cells (lacking separate genetic machinery) self-maintenance is a necessary condition for the direct reproduction and inheritance of what we here term Cellular Information Processing Networks (CIPNs). Indeed, partially reproduced or defective CIPNs may generally lead to malfunctioning or premature death of affected cells. We explore the interaction of this self-maintenance property with the evolution and adaptation of CIPNs capable of distinct information processing abilities. We present an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is an agent-based multi-level selectional Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS.bl). The latter is derived from the Holland broadcast language formalism. Using this system, we successfully evolve an artificial CIPN to improve performance on a simple pre-specified information processing task whilst subject to the constraint of continuous self-maintenance. We also describe the evolution of self-maintaining, cross-talking and multi-tasking, CIPNs exhibiting a higher level of topological and functional complexity. This proof of concept aims at contributing to the understanding of the open-ended evolutionary growth of complexity in artificial 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 Holland’s 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.
Multi-level selection has proven to be an affective mean to
provide resistance against parasites for catalytic networks
(Cronhjort and Blomberg, 1997). One way to implement these multi-level systems is to group molecules into several
distinct compartments (cells) which are capable of cellular
division (where an offspring cell replaces another cell). In
such systems parasitized cells decay and are ultimately displaced by neighboring healthy cells. However in relatively small cellular populations, it is also possible that infected cells may rapidly spread parasites throughout the entire cellular population. In which case, group selection may fail to provide resistance to parasites. In this paper, we propose a concurrent artificial chemistry (AC) which has been implemented on a cluster of computers where each cell is running on a single CPU. This multi-level selectional artificial chemistry called the Molecular Classifier Systems was based on the Holland broadcast language. An attribute inherent to such a concurrent
system is that the computational complexity of the
molecular species contained in a reactor may now affect the
fitness of the cell. This molecular computational cost may
be regarded as the chemical activation energy necessary for
a reaction to occur. Such a property is often not considered
in typical Artificial Life models. Our experimental results
obtained with this system suggest that this activation energy property may improve the resistance to parasites for catalytic networks. This work highlights some of the benefits that could be obtained using a concurrent architecture on top of computational efficiency. We first briefly present the Molecular Classifier Systems, this is then followed by a description of the multi-level concurrent model. Finally we discuss the benefits of using this multi-level concurrent model to enhance evolutionary stability for catalytic networks in our AC.
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