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Crosstalk and the cooperation of collectively autocatalytic reaction networks

We examine a potential role of signalling crosstalk in Artificial Cell Signalling Networks (ACSNs). In this research, we regard these ACSNs as subsets of collectively autocatalytic (i.e., organizationally closed) reaction networks being able to both
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  Crosstalk and the Cooperation of Collectively AutocatalyticReaction Networks James Decraene, George G. Mitchell, Barry McMullin  Abstract —We examine a potential role of signalling crosstalkin Artificial Cell Signalling Networks (ACSNs). In this research,we regard these ACSNs or Artificial Biochemical Networks(ABNs) as collectively autocatalytic (i.e., closed) reaction net-works being able to both self-maintain and to carry out adistinct signal processing function. These signalling crosstalkphenomena occur naturally when different biochemical net-works become mixed together where a given molecular speciesmay contribute simultaneously to multiple ACSNs. It has beenreported in the biological literature, that crosstalk may haveeffects that are both constructive (e.g., coordinating cellu-lar activities, multi-tasking) and destructive (e.g., prematureprogrammed cell death). In this paper we demonstrate howcrosstalk may enable distinct closed ACSNs to cooperate withother. From a theoretical point of view, this work may give newinsights for the understanding of crosstalk in natural biochemi-cal networks. From a practical point view, this investigation mayprovide novel applications of crosstalk in engineered ABNs. I. I NTRODUCTION Cell Signalling Networks (CSNs) are biochemical net-works occurring in cells which are capable of signal pro-cessing or cognitive abilities. These abilities coordinate thecellular activities in response to internal and external stim-uli. CSNs are responsible for the intricate functioning andultimately survival of a cell in its dynamic environment. Bytaking the in silico counterparts of real CSNs - ArtificialCSNs (ACSNs) we use an evolutionary simulation platformto identify new computational paradigms which are directlyinspired by nature [1]. This evolutionary system is builtupon Artificial Chemistries (AC) which have been shown toprovide a suitable framework to model, simulate and analyseABNs [2].As CSNs are contained in cells and are randomly dis-tributed to offspring cells during cellular division, a mecha-nism is necessary to ensure the replication of CSNs prior tothe cellular division. This assertion applies to systems wherea genetic subsystem is present, as the latter still requiresa minimal CSNs to coordinate the translation of the ge-netic code (which may produce further CSN’s components).Closure is one candidate mechanism which would enablethe CSN’s self-replication or maintenance: A collectivelyautocatalytic reaction network (i.e., a closed system) is ableto produce all the catalysts and substrates for its reactions,thus achieving the self-maintenance of the system.Based on above assumption, we conjecture that ACSNs aresubsets of collectively autocatalytic reaction networks, see James Decraene is with the Research Institute for Networks and Com-munications Engineering, School of Electronic Engineering, Dublin CityUniversity, Dublin (phone: +353 1 700 7697; fax: +353 1 700 5508; email: Fig. I. Closure in ACSNs is also of interest from a practicalpoint of view, e.g., engineering ABNs which are autonomousself-maintaining/repairing cognitive systems.We may identify ABNs as networks which are made upof more than one specific ACSNs, each responsible for adistinct signal processing function (involving an input/outputrelationship) see Fig. I. Interactions between different AC-SNs may occur and this phenomenon is called signallingcrosstalk. This arises very naturally in real CSNs due to thefact that the molecules from all pathways may share the samephysical reaction space (the cell). Depending on the relativespecificities of the reactions there is then an automaticpotential for any given molecular species to contribute tosignal levels in multiple pathways. Fig. 1. Cell Signalling Network  X being a subset of the closed reactionnetwork  C  In traditional communication and signal processing engi-neering, crosstalk is regarded as a defect, an unintended  or undesigned  interaction between signals, that therefore has thepotential to cause system malfunction. This can also clearlybe the case of crosstalk in CSNs.However, in the specific case of CSNs, crosstalk also hasadditional potential functionalities, which may actually be  constructive: • Even where an interfering signal is, in effect, addinguncorrelated noise to a functional signal, this maysometimes improve overall system behaviour. This iswell known in conventional control systems engineeringin the form of so-called dither. Compare also, [3], [4]on constructive biological roles of noise. • The crosstalk mechanism provides a very generic wayof creating a large space of possible modifications or in-teractions between signalling pathways. Thus, althoughmany cases of crosstalk may be immediately negativein their impact, crosstalk may still be a key mechanismin enabling incremental evolutionary search for moreelaborate or complex cell signalling networks. Fig. 2. Crosstalk between Cell Signalling Networks X and Y  In this paper we present another potential constructive roleof crosstalk in ABNs: Signalling crosstalk is a key featureallowing distinct collectively autocatalytic reaction networksto cooperate when occurring in the same reaction space.Our seminal inspirations to this work srcinate from spe-cific experiments carried out by Fontana with the Alchemysystem [5]: When mixing two collectively autocatalytic re-action networks (which were obtained from previous in-dependent experiments), two outcomes could be observedaccording to the level of interaction between the two reactionnetworks:1) If no molecular interactions (i.e., no crosstalk) existbetween the two networks then one would displace theother network.2) If, to the contrary, some molecular interactions occurbetween the two crosstalking networks then a “meta”closed reaction network emerges which contains andmaintains both seed closed reaction networks.We extend this seminal investigation on crosstalk in ABNsusing an Artificial Chemistry (AC) called basedon the Molecular Classifier Systems (MCS) and Holland’sbroadcast language (BL) [6]. A number of key differencesexist between Alchemy and the • Alchemy is based on the λ − calculus formalism,whereas the employs the broadcast language(a term-rewriting system which was the precursor toHolland’s Learning Classifier Systems). • Similarly to Alchemy, molecules may interact and com-pete with each other. In addition to this first level of selection we introduced a higher level of selection:Molecules are contained in multiple reactors (i.e., cells)which are capable of competing with each other througha cellular division process. • We defined mutation operators at both the molecular andcellular level. No evolutionary operators were specifiedin Alchemy. • We evolved the seed closed reaction networks to carry-out pre-specified tasks. The meta reaction network hav-ing to therefore functionally carry out both pre-specifiedtasks. In Alchemy, the reaction networks were self-organized without any target functions.This paper is organized as follows: We first introducethe, we then present a first series of experimentsinvolving non-crosstalking reaction networks. Following this,we examine a second series of experiments using crosstalkingreaction networks where only cell level mutation applies.Finally, a third series of experiments is described where weemploy crosstalking reaction networks where both cellular and  molecular mutations occur. We finally outline potentialfuture work and conclude this paper.II. T HE A RTIFICIAL C HEMISTRY We first present the MCS metaphor and outline the Hol-land broadcast language which is employed to specify themolecular reactions. We then describe the reactor algorithmwhich was implemented on a concurrent system (using acluster of computers).  A. The Molecular Classifier Systems Molecular Classifier Systems are a class of string-rewritingbased AC inspired by Learning Classifier Systems (LCS). Asopposed to traditional string-rewriting systems, operationsare stochastic and reflexive (no distinction made betweenoperands and operators). The behaviour of the condition(binding) properties and action events (enzymatic functions)is defined by a language specified within the MCS. This“chemical” language defines and constrains the complexity of the chemical reactions that may be modelled and simulated.In this AC, all reactants are catalytic in the sense that they arenot consumed during reactions. These reactions result fromsuccessful molecular interactions which occur at random.When a reaction occurs, a product molecule is inserted intothe reactor.We proposed a simplification of the Holland broadcastlanguage [1] which is used as the MCS chemical languageresulting in the system. The differs fromthe srcinal MCS [7] by introducing more complex chemicalreactions (only replications may occur in the MCS). Amolecule may contain several condition/action rules whichdefine the binding and enzymatic properties. A reaction  between molecules occurs if at least one conditional partfrom any rules in a molecule A matches a target molecule B . A is regarded as an enzyme whereas B is regarded asa substrate molecule. When a reaction occurs, the actionpart from the satisfied rule in A is utilized to perform theenzymatic operations upon the bound substrate molecule B .This operation results in the production of another offspring(product). If several rules in A are satisfied by B , then oneof these rules is picked at random and employed to carry outthe enzymatic function.A number of differences exist between our simplifiedbroadcast language (BL) and the LCS, e.g., the LCS’salphabet is λ = { 1 , 0 , # } whereas the BL includesadditional symbols Λ = { 1 , 0 , ∗ , : , ♦ , △ , ′ , ▽ } . Thebasic elements of the BL are strings made from Λ called broadcast devices . A broadcast device is parsed into zero, oneor more broadcast units , where each unit represents a singlecondition/action rule. The symbol ∗ separates broadcast unitswithin a broadcast device. The symbol : separates a conditionfrom an action within a single broadcast unit. 0 s and 1 s arebasic informational symbols. { ♦ , ▽ , △} are single/multiplecharacter(s) wildcards that may also transpose matchedstrings into output strings. Quoted symbols (preceded by ′ )are prevented from interpretation. Fig. 3 depicts an examplebroadcast device which may bind and react with a copy of itself, this reaction is presented in Fig. 4 . Fig. 3. An example broadcast device Enzyme substrate product operation ∗ ▽ 1 : ▽ 0 1 : 0 ∅ no reaction ∗ ▽ 1 : ′ ∗ ▽ 0 : 1 ∗ 0 : 1 activation ∗ ′ ∗ 0 ▽ : 0 ▽ ∗ 0 : 1 0 : 1 inhibition ∗ ▽ : ▽ ∗ 00 : 11 ∗ 00 : 11 universal replication ∗ ▽ 0 : ▽ 0 ∗ ▽ 0 : ▽ 0 ∗ ▽ 0 : ▽ 0 self-replication ∗ ▽ 1 : ▽ 10 ∗ 0 : 1 ∗ 0 : 10 concatenation ∗ ▽ 1 : ▽ ∗ 0 : 1 ∗ 0 : cleavage TABLE IE XAMPLE OPERATIONS REALIZED WITH THE MCS. BL A detailed description is omitted in this paper, see [8] forfull specification of our BL implementation. Table I presentsa number of example operations that can be realized withthe  B. Multi-level selectional and concurrent model We implemented the as a multi-level selectionalmodel, we introduced multiple reactors where each of them Fig. 4. Example reaction contains a population of molecules. These reactors or cells may be subjected to cellular division, which results in thereplacement of the parent cell and creation of two offspringcells. However, the number of cells in the universe is fixed.As a result such a cellular division also triggers the removalof another cell selected at random. In a similar manner tomolecules, cells are competing with each other which isregarded as the second level of selection.Successful reactions do not lead to the removal or degra-dation of molecules in the reaction space. Thus the number m of molecules contained in a cell may increase until the cellis full (i.e., when m is equal to the cell maximum capacity c ). When a cell is full, a division occurs as follows: Half of the molecules contained in the cell are selected at random,then these molecules are removed from this cell and areinserted into the offspring cell. This newly created cell isthen inserted into the cellular population. Finally, a cell ispicked at random (other than the offspring and parent cell)and removed from the cell population, see Fig. 5. 1) Initialize molecular species, go to 3. 2) If simulation termination criterion issatisfied go to 8 else go to 3. 3) Collide two molecules selected at random, goto 4. 4) If the two selected molecules can react witheach other go to 5 else go to 2. 5) Create and insert product molecule into thecell, go to 6. 6) If the cell contains c molecules then go to 7else go to 2. 7) Divide and displace another cell selected atrandom, go to 2. 8) End of simulation. Fig. 5. Multi-level reactor algorithm, each single cell/CPU runs thisalgorithm simultaneously. Furthermore this multi-level model was implemented asa concurrent system where each cell is run on a singleCPU. In this concurrent model, the fittest cells would notonly be the cells that exhibit a high rate of successfulreactions (when compared to the total number of molecularcollisions), but also cells that contain molecules that are fast  to compute. For example let us consider two cells containingcomplete reaction networks (i.e., all molecular collisions leadto the successful production of molecules). Those cells wouldmoreover contain molecules having different computationalcomplexities. In here the cell which possesses a smalleroverall molecular computational complexity will have the se-lective advantage. This computational complexity introducedin our model a notion of chemical kinetics and may alter thecellular growth rate (i.e., the cells fitness).III. E XPERIMENTS We present three series of experiments, in which weexplore the effects of signalling crosstalk in systems whereclosed reaction networks are employed. We first define thedifferent reaction networks X,Y  and Z  which are utilizedthroughout these series of experiments, see Table II. X Y Z A = ∗ ▽ 00 : ▽ 01 E  = ∗ ▽ 10 : ▽ 11 I  = ∗ ▽ 10 : ▽ 00 B = ∗ ▽ 00 : ▽ 00 F  = ∗ ▽ 10 : ▽ 10 J  = ▽ 1 ∗ ▽ 00 : ▽ 10 C  = ∗ ▽ 0 ⋄ : ▽ 00 G = ∗ ▽ 1 ⋄ : ▽ 10 K  = ∗ ▽ 10 : ▽ 10 D = ∗ ▽ 0 ⋄ : ▽ 01 H  = ∗ ▽ 1 ⋄ : ▽ 11 L = ▽ 1 ∗ ▽ 00 : ▽ 00 TABLE IIM OLECULAR SPECIES CONTAINED IN ACSN S X , Y  AND Z  No molecular species from X  may interact with anymolecular species from Y  and vice versa. X  and Y  arenon-crosstalking reaction networks. The species A,B,C  and D from X  may interact with species I,J,K  and L from Z  , whereas species J  and L may interact with B and C  from X  . X  and Z  are crosstalking reaction networks. X,Y  and Z  were obtained from previous experiments in whichthey were evolved to maximize the production of molecularspecies A,E  and I  respectively. Fig. 6 depicts the bipartitereaction network graphs of ACSNs X,Y  and Z  , note that X  and Y  possess the same network topology. A cell dominatedby a molecular species A is denoted as C  A . The numberof molecules of a given species A contained in a cell i isdenoted as n iA . All experiments are run for a pre-definedamount of time t max = 3600 (seconds). Finally, no self-replication reactions are allowed in these experiments (aswas the case in analogous Alchemy experiences).  A. Non-crosstalking networks In this first series of experiments, we investigate thedynamics of a system in which the non-crosstalking closedreaction networks X  and Y  are used. 30 concurrent cellsare employed and initialized with 10 molecules from eachspecies from both X  and Y  . A cell i divides if  n iA ≥ 200 ∧ n iE ≥ 200 . As previously presented in Section II-B,during cellular division half of the molecules in the parentcell are selected at random and transferred into the offspringcell. During this stochastic process some molecular speciesmay not be transmitted to the offspring cells, resulting in a“mutant” cell containing a different reaction network (whichmay not be closed). We refer to this error prone transmission X/Y B/FR1R2R4R7C/GR3 R5R6R9A/ER8D/H Z IR1R2R4R5KR3JLR6 Fig. 6. Bipartite reaction network graphs of ACSNs X/Y  and Z  . Thetopology of molecular interactions of  X and Y  are equivalent, e.g., thereaction R 4 would involve the molecular species B and C  in X , whereas R 4 would involve the molecular species F  and G in Y  . 1 ⋅ 10 1 1 ⋅ 10 2 1 ⋅ 10 3 1 ⋅ 10 4 10 20 30 40 50 60 70 800246810    N  u  m   b  e  r  o   f  m  o   l  e  c  u   l  e  s   N  u  m   b  e  r  o   f  c  e   l   l  u   l  a  r   d   i  s  p   l  a  c  e  m  e  n   t  s time (s)AECellular displacements Fig. 7. Growth of molecular species A and E  in a sample cell c 1 . Theimpulses represent the number of cellular displacements (i.e., the sum of allevents where c 1 displaced another cell and  was itself displaced by anothercell). as mutation at the cell level. No other mutations (e.g., at themolecular level) occur in the system at present.In Fig. 7, we observe an early phase where molecularspecies A and E  dominate each other in an alternatingfashion. In each of these alternated domination periods, n c 1 A or n c 1 E is increasing rapidly, typically 7 to 10 times higherthan the molecular amount of the other species. Moreoverthis phase is associated with recurrent displacement eventswhich punctuate each domination phase (showing a levelof activity at the cell population level). At t ≈ 32 adisplacement event occurs, following this we note that n c 1 E is now rapidly increasing, reaching up to 8 . 10 3 whereas n c 1 A stagnates at 2 . 10 2 . This cell is now saturated with molecularspecies E  and will not grow and divide any further. Inaddition we do not observe any further displacements thatmay be due to other cells, this suggests that the growth of   the other cells is also null (which could be due to a similarbehaviour where a molecular species saturates the cell). 0510152025300 20 40 60 80 100    N  u  m   b  e  r  o   f  c  e   l   l  s   time (s) C A C E Cellular displacementsSaturated cell Fig. 8. Domination of molecular species A and E  at the cell level. A cell i is dominated by A if  n iA >n iE and vice versa. The impulses represent thetotal number of cellular displacements occurring in the cellular population. Fig. 8 provides a cell population view of the simulation inwhich we may observe the domination of  A and E  at the celllevel. We first note that the early phase previously shown ina given cell can be generalized at the cell population level,i.e., the domination of cell C  A and C  E switches rapidly andis associated with a high overall cellular activity (i.e, thecellular growth rate which is best captured by the number of cellular displacements). We also distinguish that the numberof saturated cells increases rapidly when t ≈ 30 whichcorrelates with previous observations conducted in Fig. 7.However we note that the number of saturated cells doesnot exceed 28 throughout the simulation. A complementaryinvestigation (not documented here) revealed that the non-saturated cells contained reaction networks in which nosuccessful reactions could occur. These reaction networksresulted from cell level mutation.Additional experiments were conducted to explore anypotential differing dynamics. The above phenomenon wasfound to be exhibited in all of these experiments.Although based on a different AC, these experimentsshared a key property with experiments conducted inAlchemy: When two non-crosstalking closed reaction net-works are mixed together, one displaces the other one.  B. Crosstalking networks In the remaining sections, cellular species are discrimi-nated by the specific reaction network contained in a givencell (and not by the dominant molecular species as in previ-ous section). We now investigate the effects of crosstalkingclosed reaction networks upon the system’s dynamics. In thisexperiment, the cells are seeded with molecular species fromthe crosstalking reaction networks X  and Z  . A cell i dividesif  n iA ≥ 200 ∧ n iI  ≥ 200 . Any other experimental conditionsare identical to those described in the previous section.Our results showed that the interactions between molec-ular species from X  and Y  led to the production of newmolecular species M,N,O and P  (which may engage innovel reactions with existing molecular species). This newcellular species, denoted as C  1 , contains both networks X  and Y  , and presents an increased level of complexity (thereaction network now contains 12 molecular species and 55reactions, see Fig. 9). Moreover these C  1 cells were ableto self-maintain for a sustained period of time. This firstobservation also applied in analogous experiments conductedin Achemy, in which a meta-reaction network emerged andhad the ability to maintain both seed closed reaction networksthroughout the simulation.However, an additional phenomenon occurred which wasnot observed in Fontana’s AC. We distinguished a selectivedisplacement event between C  1 and a new cellular species. Inthis series of experiments, a level of diversity was maintaineddue to cell level mutation, as depicted in Fig. 10. At t ≈ 380 we note the emergence of a new cellular species, denoted as C  2 and shown in Fig. 9, which later displaced C  1 . Duringthis displacement phase, we note that the cell diversity alsoincreased suggesting that other cellular species may also havecontributed to the displacement of  C  1 cells. AR4R5 R13R20R6R7R27BR1R12R19R2R3R8R26R45R48R52CR14R21R9R15R16R17R18R28R46R49R53DR22R23R24R25R29JR54R55R37R44R41IKR36R47R10R30R34R35R50R40R42R43LR11 R32R51OR31R38R39MR33NP Fig. 9. Reaction network of cellular species C  1 which contains allmolecular species from ACSNs X and Z  in addition to new molecularspecies M,N,O and P  . In Fig. 11 we compare the fitness of reaction networks C  1 and C  2 . The fitness of a given cell i is defined as thenecessary (real) time t i to satisfy the condition n iA ≥ 200 ∧ n iI  ≥ 200 . With the present concurrent system, if a cell isfaster to satisfy the condition, then by definition it is a fittercell.We note in Fig. 11 that C  2 cells produce molecular species
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