Modelling intracellular signalling networks using

behaviour-based systems and the blackboard architecture

PEDRO PABLO GONZÁLEZ PÉREZCARLOS GERSHENSON

MAURA CÁRDENAS GARCÍAJAIME LAGUNEZ OTERO

Instituto de QuímicaUniversidad Nacional Autónoma de MéxicoCiudad Universitaria, 04510, MÉXICO, D.F.

Abstract: This paper proposes to model the intracellular signalling networks using a fusion of behaviour-based systemsand the blackboard architecture. In virtue of this fusion, the model developed by us, which has been named Cellulat,allows to take account two essential aspects of the intracellular signalling networks: (1) the cognitive capabilities ofcertain types of networks’ components and (2) the high level of spatial organization of these networks. A simpleexample of modelling of Ca2+ signalling pathways using Cellulat is presented here. An intracellular signalling virtuallaboratory is being developed from Cellulat.

Key-Words: behaviour-based systems, autonomous agents, blackboard architecture, intracellular signalling networks.

1 IntroductionEach cell in a multicellular organism receives specificcombinations of chemical signals generated by othercells or by themselves. The final effect of the signalsreceived by a cell can be translated in the regulation ofthe cell metabolism, in the alteration or maintenance ofits differentiation state, in the beginning of cellulardivision and in its death. Once the extracellular signalsbind to the receptors, different process of signalling areactivated, which generate complex networks ofintracellular signall ing.

The more experimental data about cellularfunctioning we have, the more useful the computationalmodels are to understand these signalling processes. Thecomputational models allow the visualization of thecomponents that integrate the network, in addition tobeing able to predict or to suggest the effect ofdisturbances caused on determined component orsections of the signall ing pathway. A great variety ofinformation processing computational models have beeninspired by the functioning of biological systems.Examples of these are the artificial neural networks,genetic algorithms and cellular automata. On the otherhand, several information processing systems haveconstituted different useful approaches to model thefunctioning of diverse biological systems [16].

Within computer sciences, the artificial intelligencearea has constituted one of the main scenarios to model

biological systems. This fact responds to the greatvariety of models, techniques and methods that supportthis research area, many of which are inherited ofdisciplines such as psychology, cognitive sciences andneuroscience. Between the main techniques of artificialintelligence and computer sciences commonly used tomodel cellular signalling networks are artificial neuralnetworks [1 and 18], boolean networks [4 ], petri nets[7], rule-based systems [3], cellular automata [4], andmulti-agent systems [5, 17 and 19].

The high complexity level of the intracellularcommunication networks causes them to be difficult tomodel when is considered some of these techniquesisolated. Nevertheless, when integrating the mostrelevant and necessary elements of these techniques ina single computational system, then it should be possibleto obtain a model much more robust of the intracellularsignalling pathway, and therefore, a better visualization,understanding and prediction of the processes andcomponents that integrate the networks.

The theory of behaviour-based systems mustconstitute a good approach to the intracellular signallingnetworks. However, a powerful intracellular signallingmodel should be achieved when the communicationbetween agents takes place through a shared datastructure, in which other cellular structures and elementsrelated with signalling pathways can be explicitlyrepresented. In this sense, the blackboard architecture

[15] results widely appropriate. In this paper, we postulate that an effective and

robust model of intracellular signalling can be obtainedwhen the main structural and functional characteristicsof behaviour-based systems and the blackboardarchitecture are joined. That is, a cell can be seen as asociety of autonomous agents, where each agentcommunicates with the others through the creation ormodification of signals on a shared data structure, named“blackboard”. The autonomous agents modeldeterminated functional components of the intracellularsignalling pathways, such as signalling proteins,enzymes and other mechanisms. The blackboard levelsrepresent different cellular structures committed with thesignalling pathway, whereas the different objects createdon the blackboard represent secondary messengers,activation or inhibition signals or others elementsbelonging to intracellular medium.

In this way, when the autonomous agents are used inan intracellular signalling model, the cognitivecapabilities of certain components of the signallingpathway can be taken in count; whereas the use ofblackboard architecture allows to capture the high levelof spatial organization exhibited by the intracellularsignalling networks.

This paper is structured as follows: the next sectionpresents an overview of the information processes ofsignalling intracellular networks. In section 3 somequestions about behaviour-based systems are discussed.Section 4 describes the basic components of blackboardarchitecture and their functioning. In section 5 theintracellular signalling model proposed by us ispresented and discussed. In section 6 an example ofmodelling the Ca2+ signalling pathway using theproposed model is shown. Some comments about currentand future work and conclusions are made in section 7.

2 An overview of signalling intracellularnetworks Each cell receives a great number of chemical signalsgenerated by other cells or by themselves. Theseregulate their metabolism, determine theirdifferentiation, and indicate when to divide or to start adeath process. In general the external signals aretransmitted to the interior of the cells through membranereceptors activating diverse transduction pathways,which can follow a same way and to generate a finalanswer or to branch themselves to give rise to others.For example activation of most membrane-associatedhormone receptors generates a diffusible intracellularsignal called a second messenger. Many different

receptors generate the same second messenger. Someintracellular messengers are: cAMP (cyclic AMP),cGMP (cyclic GMP), I3P (Inositol triphosphate), DG(diacylglycerol) and Ca2+ (calcium).

The second messenger activation triggers multiplesignal transduction pathways like: the adenilate cyclase,the hosphoinositide-calcium, the Mitogen activatedprotein kinase (MAPK), the JAK/STAT, thesphingomyelin-ceramide, and the ionic channel functionreceptors pathways.

In particular if we take the Ca2+ signalling pathway,the pathway looks like an interconnected network,because the answer is to different stimuli, but thedifferent pathways cross and generate al ternativetrajectories not easily visualized. The field of signaltransduction has centred on the discovery of increasingcrosstalk among signalling pathways. Indeed, ourunderstanding of the topology of signalling iscontinuously evolving. Early work in the signalling fieldfeatured the discovery of branched linear pathways,which, resembled trees with a cell surface receptor at theroot.

3 Behaviour-based systemsThe theory of behaviour-based systems [2 ] provides anew philosophy for the construction of autonomousagents, inspired by ethology. The goal of behaviour-based systems (BBS) is to provide control toautonomous agents. An autonomous agent interactsdirectly with its problem domain, it perceives itsenvironment through its sensors and acts on it throughits actuators. The autonomous agent environment iscommonly a dynamic, complex and unpredictableenvironment, in which the autonomous agent tries tosatisfy a set of goals or motivations, varying in time. Anautonomous agent decides by itself how to relate itsexternal and internal inputs with its motor actions, insuch way that its goals can be satisfied [13]. Adaptationis one of the desirable characteristics in autonomousagents. An autonomous agent is adaptive if it hasabilities that allow him to improve its performance intime.

A cell can be seen as an adaptive autonomous agentor as a society of adaptive autonomous agents, whereeach agent could exhibit a particular behaviourdepending on his cognitive capabilities. As anautonomous agent, the cell perceives its externalenvironment through its surface receptors and acts onthis one by means of the generation of new signals,which will be able to affect the behaviour of other cells.The cell's external environment is also dynamic and

complex, in which an extensive range of signals andcombinations of these can exist. Furthermore, like in anautonomous agent, the cell by itself decides how torelate the received external signals (signallingmolecules) to its internal signals (secondarymessengers), so that their goals (differentiation,proliferation, survival, among others) can be satisfied.Finally, the cell is able also to adapt to its environment.That is, the cell is constantly adapting its behaviour tothe changes that take place in the environment and to thesignals perceived.

4 Blackboard architectureThe blackboard architecture concept was conceived byartificial intelligence researchers in the 1970’s. The goalof this research was to handle the problem of sharedinformation between multiple expert agents in problemsolving. The architecture was developed for the firsttime in the system for language understanding, HearsayII [15]. Later i t has been used in a great variety ofproblem domains and abstracted in many environmentsfor the construction of systems [10 and 14].

Blackboard architecture is defined in terms of threebasic components: (1) a set of independent modules,named knowledge sources, which contain specificknowledge about a problem domain, (2) a shared datastructure, named blackboard, through which theknowledge sources communicate to each other, and (3)a control mechanism, which determines the order inwhich the knowledge sources will operate on theblackboard. Figure 1 shows the blackboard architecturecomponents and the relationships among them.

Fig. 1. Components of the Blackboard Architecture

The knowledge sources are modular softwaresubsystems that represent different points of view,different strategies and different knowledge types, about

how to solve a problem or part of a problem.The blackboard is used as a central storage for all

shared information. The information on a blackboardrepresents facts and deductions done by the knowledgesources during the problem solving. The knowledgesources produce changes on a blackboard, whichincrementally lead to the formation of a solution oracceptable set of solutions for the problem to be solved.

Because the knowledge sources respondopportunistically to changes on the blackboard, amechanism is necessary that controls these changes anddecides, at every moment, which actions should betaken. The control mechanism handles the interactionbetween the blackboard, the knowledge sources and theoutsourcing such as users and control or data acquisitionsubsystems.

5 Cellulat: an intracellular signallingnetwork modelOur proposal consists in modelling the cell as anautonomous agent, which in turn is composed by asociety of autonomous agents, which communicatesthrough blackboard with others. That is, in the proposedmodel the knowledge sources of the blackboardarchitecture are considered as autonomous agents too.The model proposed here constitutes a refinement andadaptation of an action selection mechanism structuredon a blackboard architecture previously developed by usnamed Internal Behaviours Network (IBeNet) [8, 9, 11and 12]. Although the IBeNet was initially built to actionselection in autonomous agents (physical robots,animats, or artificial creatures simulated on a computer),it constitutes a working environment for the bottom-upmodelling of information processing systemscharacterized by: (1) coordination and opportunisticintegration of several tasks in real time, (2) use ofseveral abstraction or context levels for the differenttypes of information that participate in the processingnetwork, (3) decision making, (4) action selection and(5) adaptation.

Although, the intracellular signalling modelproposed can be seen as the assignation of a newsemantic to the components of the IBeNet, we assumehere that this fact is equivalent to the assignation of thissame semantic to the components of the blackboardarchitecture, when the two following considerations aredone: (1) knowledge sources are seen as either internalautonomous agents or interface autonomous agents and(2) the blackboard architecture control is distributed nowbetween these two types of autonomous agents. The term“internal autonomous agents” has been used to identify

to the autonomous agents which tasks are related withthe creation or modification of signals on theblackboard. An internal autonomous agent gets a signalor combination or signals from a determinatedblackboard level and transduces these in other signal onthe same or other blackboard level. The way in which asignal is transduced depends of the cognitive capabilitiesof the internal autonomous agent On the other hand, thefunction of an interface autonomous agent is to establishthe communication between the blackboard and theexternal medium (they are similar to sensors or actuatorsin BBS). Not all external signals or combinations ofthese are recognized by an interface autonomous agent,this recognition depends both of the signalcharacteristics and the cognitive capabilities of theinterface autonomous agent.

In Figure 2 the architecture of intracellular signallingmodel can be appreciated. This is a translation ofblackboard architecture shown in Figure 1 takingaccount the considerations (1) and (2). In this way, it isnot necessary to explain the structural and functionaldetails of IBeNet here to understand the intracellularsignalling model proposed. It is only necessary to bear inmind the blackboard architecture functioning explainedin section 3 and the new considerations mentionedabove. The intracellular signalling model has beennamed Cellulat (a kind of animat which behaves as acell).

Fig. 2. Architecture of the Cellulat

Three main components define the Cellulat structure:the blackboard, the internal autonomous agents andinterface autonomous agents.

The blackboard represents the cell’s internalmedium. The blackboard levels correspond to differentcellular structures through which occur the signaltransduction. In this way, the cellular membrane, thecitosol and the nucleus could be represented as differentblackboard levels. The solution elements recorded on theblackboard represent two main types of intracellularsignals: secondary messenger molecules andactivation/inactivation signals. Both types of signals aresynthesized or created by internal autonomous agentsand these, either directly or indirectly, promote theactivation/inactivation of other internal autonomousagents. Other types of cellular elements or structures canbe represented on the blackboard too.

The internal autonomous agents model componentsof the intracellular signalling network such as proteins,enzymes and other mechanisms necessary to carry outthe signal transduction. On the other hand, the interfaceautonomous agents model the cell surface receptors andthe mechanisms for the secretion of signallingmolecules. Each agent, independently of his type, has acondition part and an action part; the way in which bothparts are linked depends on the complexity of theintracellular component modeled by agent. For thatreason, agents who model complex components coulduse more advanced techniques, such as neural networks,genetic algorithms, or any combination of othertechniques, to link both parts. Agents which model lesscomplex components could use more sophisticated butuseful techniques, such as production rules, booleannetworks or others. The work of both types of agents isevent-directed. This is, each intracellular signalregistered on the blackboard or each extracellular signalperceived constitutes an event, which could activate orinactivate one or more autonomous agents. When aninternal autonomous agent is activated then this oneexecutes his action, which consists in the synthesis ormodification of a signal on the blackboard.

5.1 Cognitive capabilities of Cellulat’s agents It is known that certain types of proteins exhibit severalcognitive capabilities such as pattern recognition,memory and handling fuzzy data [5]. The evolution,learning and emergence of properties have also beensuggested in proteins networks [18]. In Cellulat, bothtypes of autonomous agents exhibit several cognit ivecapabilities including patter recognition, handlinguncertainty and fuzzy data, adaptive action selection,memory and learning; which allow to the autonomous

agents to exhibit an adaptive behaviour. These cognitivecapabilities are supported by several artificialintelligence paradigms including the followingapproaches: rule-based reasoning, probabilisticreasoning, artificial neural networks, genetic algorithms,boolean networks, and fuzzy logic systems.

5.2 Modelling spatial organization in CellulatAnother important aspect to consider in the modelling ofintracellular signalling networks is their spatialorganization. Experimental data recently obtainedsuggest clearly that many intracellular signallingnetworks exhibit a high level of spatial organization.Intracellular signalling models developed recentlyapproach to this question [6]. Cellulat allows to modelthe spatial organization taking account twoorganizational criteria of the blackboard architecture.One is the horizontal organization, given by the differentabstraction levels of the blackboard, which allow anintralevel signal processing. The other is the verticalorganization, given by columns that cross verticallydifferent blackboard levels. These columns arise asresult of the adjoining work of several internalautonomous agents that operate at a same section ofblackboard, which cover different blackboard levels. Wehave named these columns “agency columns”.Convergence and divergence of agency columns couldoccur, and these processes could be related withevolution and learning of the signalling network [11]. Inthis way, the model proposed allows to modelintracellular signalling pathways taking account theirspatial organization. That is, the two informationprocessing levels present in Cellulat (horizontal andvertical) allow to establish a “topology preserving map”.

6 Modelling the Ca2+ signalling pathwayusing Cellulat: A first approach Figure 3 illustrates a first approach of the intracellularsignalling pathway modelling using Cellulat. Ins thiscase, we have modelled the Ca2+ signalling pathway toa still heavy resolution level. This is, this first model notyet reveals structural details of the signalling networkcomponents.

As can be appreciated in Figure 3, the interfaceautonomous agents model the G-protein-linked surfacereceptors, whereas the internal autonomous agentsmodel components of the signalling network such as Gprotein, phospholipase C-beta, protein kinase C,mechanism to release calcium, and others proteins andenzymes. On the blackboard can be created different

types of signals by the proteins, enzymes and othersmechanisms that operate on it. Examples of these signalsare the followings: PI-biphosphate, inositol triphosphate,dyaclyglycerol, Ca2+, and others signals indicating theactivation state of the proteins and enzymes. In thisrepresentation, the blackboard levels have not beenidentified.

Fig. 3. Cellulat model of the Ca2+ signalling pathway.

In the model shown in Figure 3 it is necessary toconsider the following viewpoint: Since an internal orinterface autonomous agent can be structured by otherautonomous agents, then an internal autonomous agentrepresenting to a protein kinase C (or to others proteinsor enzymes) could encapsulate many proteins kinase C,where each one of these is an internal autonomous agenttoo. In this way, components of a same type couldjointly be encapsulated, which simplifiescomprehension.

7 ConclusionsIn this paper we have discussed why the fusion of thebehaviour-based systems theory and the blackboardarchitecture constitutes a very suitable approach for themodelling of cellular signalling networks, from a modeldeveloped by us which has been named Cellulat. As aresult of this fusion, Cellulat allows to model two basicaspects of the cellular signalling networks: (1) the

cognitive capabilities of certain types of proteins andenzymes, and (2) the high level of spatial organization fothe signalling networks. (1) was obtained whenconsidering the adaptive autonomous agents asfunctional components of the model, and using differenttechniques of artificial intelligence to support thecognitive capabilities mentioned in Section 5.1; whereas(2) was provided by the shared data structure, namedblackboard, which allows to model horizontal, verticaland spatial information processing.

A simple example of modelling of Ca2+ signallingpathways using Cellulat was shown. Although still muchwork to develop is left, as it is the case of the modellingof the signalling network components at a fine resolutionlevel, we believe the proposed model constitutes a newand useful approach for the modelling and understandingof intracellular signalling networks.

Presently, Cellulat is being used in the modelling ofthe signal transduction related with cellular senescence.Our future work is directed towards the creation of acellular signalling virtual laboratory, from the developedcomputational model. This virtual laboratory must allowto effect lesions on certain components or sections of thesignalling pathways, and to visualise the obtainedcellular behaviour as consequences of such lesions;helping to understand how the different cellularsignalling pathways interact.

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