Levels of Organization and Signal Transduction Codes

The topology of transcriptional networks, as we have seen, has a decisive role in the potential fates and functions a cell can adopt. Here we propose that the three stable configurations adopted by living systems - CELL, SELF, SENSE - increase their own complexity by changing TR topology in a precise manner: by interpolating new attractors and increasing the connectivity between signaling and response modes at each level (see Fig. 2 for a schematic representation). Let us illustrate this general idea by dissecting each level's structure.

The CELL level has two prototypical configurations: prokaryotes and eukaryo-tes. In both cases semiosis occurs to maintain the system's autonomy; it is by means of simple feedback loops that the CELL adjusts its own behavior. The main task to be performed by ST codes at the CELL level is to couple environmental changes with the subnetworks controlling cell growth. In fact, the way transcription is organized in prokaryotes assures that transcription is modulated by environmental signs; two-component regulatory systems work like adaptors coupling the presence of an specific energy source in the environment (e.g. glucose) to the transcription of

Fig. 2 Signal transduction modes and network organization properties displayed by living systems in their different stable configuration

enzymes involved in its metabolic processing. Transcriptional profiles are adjusted to keep the cell growing as conditioned by available resources, and the response modes are graded and reversible, including very simple signal-response curves of the linear and hyperbolic kinds and slightly more complex ones like sniffers and buzzers [23]. Irreversible and discontinuous modules, however, do not function in prokaryotic cell growth control. Nevertheless, in prokaryotes DNA replication and cell division are uncoupled from the transcription/metabolism subnetwork, an example of a discontinuous response mode. It has been proposed that in such cells when a reason (number of replication origins/cell mass) reaches a certain threshold, DNA replication and cell division are triggered, regardless of transcriptional patterns and metabolic needs of the cell [25]. DNA replication and cell division seem to be controlled by what qualifies as a one-way switch.

In eukaryotes - the more sophisticated configuration of CELL level - ST codes assure that environmental changes are coupled to transcriptional controls, but in addition, nucleated cells couple these changes to replication and division controls. With the advent of the nucleus, the increased genome size and its organization in chromosomes with independent origins of replication for various DNA segments, eukaryotic cells require coordination between cell growth, DNA replication, and cell division. In this context the organization of a cell cycle, with checkpoints that assure DNA integrity before, during and after its replication, assumes utmost importance. This particular kind of organization favors autonomy; eukaryotes depend only on the presence of growth factors in their environment during the initial hours of the GAP 1 (G1) cell cycle phase. At the same time, cell division is no longer a simple one-way switch transition dependent on one threshold transition (as in prokaryotes); in eukaryotes cell growth and cell division form a unified program, the cell cycle, with at least three critical points (the G1/S, G2/M, and M/G1 transitions). In fact, the molecular apparatus controlling these transitions are proteins called cyclins and the kinases they recruit and activate. The cyclins have an oscillatory expression pattern and a modular regulation by means of association with cyclin-dependent kinases (Cdks). It is the topology of the cyclin/Cdk network that enables environmental changes to be coupled to transcription, which thus integrates metabolic controls with DNA replication and cell division controls. The cell cycle control system has been described in the following terms by Tyson and collaborators [23]: "Progress through the cell cycle is viewed as a sequence of bifurcations. A small new born cell is attracted to the stable G1 steady-state. As it grows, it eventually passes the saddle-node bifurcation where the G1 steady state disappears, and the cell makes an irreversible transition into S/G2. It stays in S/G2 until it grows so large that the S/G2 steady state disappears, giving way to an infinite-period oscillation. Cyclin-B-dependent kinase soars, driving the cell into mitosis. The drop in Cdkl- cyclin B activity is the signal for the cell to divide, causing cell size to be halved and the control system is returned to its starting point, in the domain of attraction of G1 steady state." This is a strikingly detailed example of how ST codes can match categories very different from molecular devices in their logical typing as building blocks. The transcription topology associated with the eukaryotic cell cycle connects regulatory elements (cyclin/Cdk subnetwork interactions) with higher order motifs (by introducing positive and negative feedback loops) to create (or to select) stable response modes (toggle switches and oscillators associated with Gl/S and G2/M, and M/Gl, respectively). Response mode modules work together to coordinate otherwise separated cell physiology transitions; cell growth, DNA replication, and cell division.

To put it simply, in prokaryotes cell growth is metabolism-driven and cell division is cell growth-driven, whereas in eukaryotes the cell cycle (which includes cell growth and cell division) is metabolism driven. This is only possible because prokaryotes and eukaryotes have different ST codes.

When cells have their dynamic behavior stabilized at the level of SELF configurations, new properties are developed due to increasing complexity in system organization. Besides the simple feedback loops employed by the semiotic structures at the previous level, SELF-configurations are able to autonomously adjust the optimal equilibrium values of their internal states by adaptative feedback loops. At such a level eukaryotic cells living as single-cell or multicell organisms will develop a differentiation skill, such that other attractors are interpolated onto the basic cell cycle structure. We suppose that this bifurcation assures, very much like the cell cycle checkpoints, that a specific cell state is to be coupled to a specific phenotypic transition. In single-lived eukaryotes this kind of organization results in life cycles with alternative forms, in multicell organisms this will reflect in embryogenesis with alternative cell fates.

Protozoans, the single-cell eukaryotes, may alternate between haploid and diploid life cycles, such as happens with budding yeast for example. Alternatively, as does the amoeba Dictyostelium discoideum, can switch from unicellular to a multicellular stage during their life cycle [26]. In any case, as ST codes are concerned, it seems that new attractors are interpolated into the previous cell cycle organization. In yeast, the G2/M module is replaced by a "G2/2M" module, opening the way for a choice between mitosis and meiosis after G2 steady-state disruption during cell cycle. In Dictyostelium, new attractors are interpolated to the cell cycle signal transduction system after the M/G1 module, so that newborn cells will be presented with the choice between continuous division or division arrest plus aggregation. If cells enter the aggregation mode they will be further presented with the choice between two alternative cell fates to generate fruiting bodies [27]. By integration of new attractors and increasing the connectivity between regulatory motifs, evolution transforms cell cycle transduction codes into cell fate transduction codes; starvation becomes a sign for aggregation, and oscillatory cAMP patterns become a sign for differentiation.

Typically, in order to differentiate, metazoan eukaryotic cells have to commit, migrate, and adhere to their target organ and perform specific functions [27]. An interesting idea that will require experimental corroboration is that each of these transitions (commitment, migration, contact) function as higher dimensional attractors to select cell populations during embryogenesis. In fact, the work of Huang and collaborators suggests that, at least in the case of neutrophil differentiation, final differentiation functions as an attractor by the stabilization of transcrip-tional profiles. Of course the level of detail available for cell differentiation networks is not comparable to that of cell cycle data, but solving cell differentiation codes as they emerge, constrained by cell cycle codes, would improve the overall quality of available explanation.

We turn now to consider the SENSE level, wherein canonical frugal and fancy configurations lie: the central nervous system; and the adaptative cell immune system of vertebrates. SENSE configurations have a basic semiotic structure with feedback loops, adaptative feedback loops, and learning feedback loops incorporated as regulatory modes. Besides the properties shared by the previous levels, they can autonomously change the mechanism by which the system adapts to changes. Here, the logic of interpolating new transitions to previously existing programs remains the same; in this case, environmental changes previously coupled to transcription, replication, and differentiation controls are additionally coupled to functional differentiation controls. The similarities between nervous and immune system have been emphasized over time, as both systems use specific molecular recognition events between single cells, cell-cell adhesion patterns, positional stability, and directed secretion to fulfill their respective functions. They have also both evolved highly sophisticated forms of information storage, providing the systems with memory capabilities. Their prototypical cells, neurons, and lymphocytes, are excitable cells which can be somatically induced to perform specific functions in a threshold dependent manner, which can be considered to be a form of somatic differentiation.

By supplementing the previous signaling transduction codes with new oscillatory modules with very specific spatiotemporal patterns, the system increases its own flexibility to unprecedented levels. During immune and nervous system function, entire cells acquire the status of regulatory elements able to create, propagate, or extinguish a sign, or sign construct, and the cell network topologies (hard wired in the nervous system and dynamically assembled during immune response) acquire the status of signaling and response modes. At the same time, neurons and lymphocytes are still organized according to lower level programs, i.e. cell growth, cell cycle, and cell fate controls. In neurons, particular patterns of chemical triggers are coupled to the propagation of electric waves through cell membrane, which are converted again into chemical triggers. In the immune system, different cell types will react to particular chemical triggers coupling them to a recognition process that will include active remodeling of genetic material, membrane protein repertoire, and cell fate. In both cases, between the stable states predicted by the previous ST codes, another potential transition appears that allows these to switch between active and resting states. Synapses, despite clear structural differences between their neural and immunological forms [28], are functionally organized cell to cell communication structures. They provide otherwise nonspecific soluble agents (neuromediators and cytokines) with specificity through their confinement to precise spatiotemporal patterns. From this perspective, it is interesting to think about neural and immune function in terms of their constitutive cell-signaling network topology, trying to identify subnetworks important for somatic function.

We have seen that signs are selected by ST codes operating at the different cell stabilization levels. We can envisage different mechanisms implied by the emergence of ST codes. Firstly, different aspects of a changing environment can become relevant and instructive to the system as new codes emerge. In this framework, properties of increasingly complex logical types, as quantities, interactions in time, and arrangements in space, would progressively acquire a signaling potential. They would become signs only by the emergence of suitable codes. If we examine the ST codes matching the CELL/SELF/SENSE categories for these sign/code relationships, the description goes as follows: quantities are signs to be integrated at CELL level through cell growth, and cell division codes; interactions in time are signs to be integrated at CELL level by cell cycle codes and at SELF level by cell fate codes; arrangements in space are signs to be integrated at the SELF level by cell fate codes and at the SENSE level by cell function codes.

Another mechanism can be superimposed upon the first, rather than being an alternative to it. Through organizational levels, by increasing the subnetworks' connectivity, and the number of its stable configurations (attractors), the system selects oscillatory behaviors of various timescales as potential operative modes. In Fig. 3 we show that a same-sign modality - intracellular oscillations - can control diverse cell responses: cell growth codes provide glycolitic oscillations with a metabolic meaning, a dynamic measure of energetic resources in microorganisms [29]; cell fate codes integrate cAMP oscillations as a differentiation trigger; cyclin/Cdk oscillation are given various meanings by cell cycle codes, controlling cell growth, DNA replication, and cell division; calcium oscillations are converted into differentiation triggers by




Regulated Transition



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Cell cycle


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Cell fate Cell function

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Fig. 3 Biochemical oscillations with different time scales regulate different cell behaviour

Fig. 3 Biochemical oscillations with different time scales regulate different cell behaviour cell fate codes and hormone secretion triggers by cell function codes in hepatic cells [29]; and neuronal function codes provide oscillating membrane potentials with a very precise biological meaning, switching cells from resting to active states and triggering neuronal firing.

Biochemical oscillations in biological systems are conserved through various hierarchical levels and have been the object of experimental studies [30,31] and theoretical analyses [32,33]. To fully understand the role of biochemical oscillations in cellular rhythms one must go back to nonequilibrium thermodynamics and some of the ideas coined by chemist Ilya Prigogine. In his Nobel lecture [34], he states that nonequilibrium may be a source of order by generating a precise kind of organization (self-organization) illustrated by dissipative structures. But for this to occur, a precise relation is to be established between the system's function (as expressed by chemical equations), its space-time structure (necessarily unstable), and fluctuations triggering such instabilities. It is the interplay between these three aspects that leads to most unexpected phenomena, including "order through fluctuations." The subject is so vast and full of implications that we could devote a whole section to this discussion. Nevertheless, for our present purposes it is sufficient to consider that every ST code we have described requires an oscillator and that this oscillator will conduct the cell among the different attractors implied by the system's organization at the code operating level.

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