Network structures formed by cells inside multicellular organisms may contain leader (pacemaker) cells. Such leader cells occur in cultivated animal epithelial cells that grow at a higher rate than other network cells but are in contact with them (Samoilov and Vasiliev, 2009). Such behavior of cultivated cells is analogous to that during tissue regeneration after injury. Pacemaker cells that are the primary producers of chemical signals were also revealed in the populations of some unicellular eukaryotes that undergo morphogenetic changes. They are exemplified by Dictyostelium discoideum amoebas that collectively form a migrating “slug” and subsequently fruiting body under the influence of cyclic adenosinomonophosphate, a signal that is initially produced by a few leader cells (Mutzel, 1995). Colonial flagellates such as Eudorina provide another example: their 32-cell colonies contain leader cells that guide the movement of these colonies (Elenkin, 1936). This hierarchization tendency is also prominent in neuronal networks.

As mentioned above, cells belonging to the xylem, phloem, meristem, and other tissues form network structures within a plant organism. However, microbial network structures are also of direct relevance to the plant world because microbial cells ad their biofilms inhabit the surface and the interior of various plant organs, including leaves, stalks, and roots. A historical example to the point is that the biofilms of wild yeast Saccharomyces cerevisiae was collected by people in prehistoric times on the surface of the organs of various plants, including plum and grape leaves. The bacteria of the genus Rhizobium are known to form nodules that are responsible for nitrogen binding.

«Diverse species of angiosperms, gymnosperms, pteridophytes, and mosses provide the microbiota with a wide variety of ecological niches, including the surface of the plant organism and the interior of plant organs (colonized by endophytes) that are exemplifiedby root nodules inhabited, for example, by rhizobia. The relationships between microbiota representatives and the host plant vary between mutualism, commensalism, and parasitism; the same microbiota can play several different roles, depending on the host plant species, the location of the microorganisms involved on/in the plant organism, and other circumstances” (Oleskin and Shenderov, 2018, pp.271-272). “…The complex gamut of interactions in the microbiota–plant system is apparently based upon sophisticated chemical communication facilities. Importantly, these facilities are exemplified by quorum-sensing (QS) systems that enable prokaryotes to modify their metabolic, genetic, and behavioral activities depending on the density of their populations that is estimated from the concentrations of pheromones(autoinducers) produced by microbial cells… QS systems are involved in mcrobiota–host interactivity. For instance, the soil bacterium Rhodopseudomonas palustris incorporates plant host-produced p-coumarate in its QS pheromone” (Oleskin and Shenderov, 2018, p.272).

2. Rhizome Paradigm of Network Organization. This type of networks is characteristic of fungal mycelia, including those forming a part of mycorrhiza systems, and the rhizomes (rootstocks) of many plants. The rhizome concept was developed in philosophical terms by Gilles Deleuze and Felix Guattari in their prominent book A Thousand Plateaus (2004[1980]). They asserted that the rhizome in its metaphorical philosophical meaning has no beginning, no end, no center and no central principle. In contrast to the modular paradigm (see below), a rhizome-type network cannot be subdivided into nodes (vertices) and links (edges) that connect them, integrating the nodes into a whole colony. For instance, a fungal mycelium consists of links only, i.e. filaments (hyphae) as uniform components of the whole mycelium on which organs such as rhizoids or fruiting bodies can develop. «By branching and fusing with other hyphae>, the hyphae form a network-type structure known as the mycelium” (Kück et al., 2009, S.4). A relatively large number of fungal species undergo transitions between the mycelium and the yeast-like mode of growth, with hyphae being replaced by unconnected cells. If a fungus forms mycelium, the cells in it may retain some degree of individuality (separated by incomplete partitions, septa) or merge into continuous hyphae. Such rhizome-type networks overgrow plant roots or penetrate into their cells in mycorrhiza systems.

Of biological and social interest are the following advantages of rhizome-type networks as exemplified by fungal mycelia (Dyakov, 2013):

• Maximization of the substrate area occupied by the offspring of a single fungal spore (the founder cell)

• Efficient long-range substance and energy transport

• Functional differentiation of the hyphae with the formation of specialized structures, including fruiting bodies such as mushrooms

By analogy with the rhizome, the currently developing global network society can be described as “a space of rapidly flowing streams without the center and the periphery in which multidimensional network communication creates a distributed node pattern” (Smorodinskaya, 2015, p.311).

3. Modular Paradigm of Network Organization is implemented in biological systems that consist of uniform structural units (modules); such systems are distinguished by the prevalence of a flat (leaderless) network organization pattern. A classical example in the animal kingdom is provided by cnidarians and bryozoans. Their organisms consist of repetitive units (zooids), e.g., polyps or medusae, that are connected by a single stalk (the coenosark). In this typically decentralized structure, each zooid performs actions (e.g., contracts and causes the liquid inside the stalk at its attachment site to move) that exert a weak effect on the whole network that includes numerous zooids. However, a single zooid’s impact is potentiated if its behavior happens to be in sync with that of a majority of other zooids in the modular network structure. For example, a sufficiently large group of zooids that contract synchronously to pump liquid overpower those with a different contraction rhythm; the latter slow down their contraction and change their own rhythm to match the network’s dominant rhythm (Marfenin, 2002, 2009)

Zooids can be functionally equivalent or differentiated in terms of their specialization which enables “labor division” (Dunn, 2005); in similar fashion, functionally differentiated partial creative leaders exist in decentralized network structures called hiramas in human society (see below). Apart from zooids (polyps) that take in food particles and move the liquid in the coenosarc, there are zooids involved in reproduction; other specialized zooid types are also possible. The greatest degree of functional specialization is attained by zooids in siphonophores such as Physalia. It contains such polyps as food-absorbing gastrozoids, dactylozoids perfoming protective functions, gonozoids that asexually produce medusa s wellas seval types of medusae. In systems of this kind, the role of the module is played not by a single zooid but by the cormidium, the minimum independently surviving ensemble of several functionally complementary zooids

In terms of this work, it is of much interest that, apart from the aforementioned invertebrate animals, the modular network paradigm is used by numerous plant species that are made up of autonomous repetitive units, as exemplified by strawberry runners or African violet leaves that can independently develop into new plant organisms.

4. Equipotential Paradigm of Network Organization. Network structures that conform with this paradigm are characterized by a completely flat, leaderless pattern (Radakov, 1972; Pavlov and Kasumyan, 2003). Examples are provided by schools of many fish or marine invertebrates (echinodermata and cephalopods) as well as by flocks of some bird and groups of cetaceans including whales. In the absence of a permanent leader, it is a chance individual that is the first to swim in a moving school, to be replaced by another individual within a fraction of a second. In schools of young pollacks, the time during which an individual “leads the way” varies from a fraction of a second (0.25-0.5 s) to several seconds… thereupon, “the fish is located in the middle or even in the rearmost part of the school” (Radakov, 1972, p.86). The operation of such a network largely depends on interactions between neighbors. There is relay transfer of information in the form of visual or mechanical signals, enabling the whole network (school) to carry out complicated coordinated movements, e.g., move in formation, disperse when attacked by a predator, or form a crescent-shaped structure to catch the prey between the crescent cusps. Similar artificial network structures in technical system can be designed on the basis of the relatively simple algorhithms that underlie the behavior of such equipotential leaderless structures. Large fish (e.g., salmon) schools tend to separate into relatively independent small fish groups (Croft et al., 2005), although there is also the opposite trend favoring the merging of small schools into larger ones. This is a manifestation of a general tendency that is characteristic of networks. Large networks tend to break down into compact clusters (subnetworks), which may merge or separate (“the fission-fusion pattern” that is also typical of some primates).

Such network structures that consist of quite uniform components are also characteristic of groups of plants of the same species. They are exemplified by palm trees growing on mountain slopes around Shenzhen and birch trees in Moscow parks. Such tree individuals within one local group often lack any significant functional differences, and the networks they form should, therefore, be called “equipotential networks”, similar to those established by fish.

5. Network Paradigm at the Level of Plant Communities and Ecosystems. In contrast to the population that often represents a homogeneous structure of uniform elements without functional differentiation, structures that form at higher ecological levels (including communities/cenoses and ecosystems) incorporate different components that are often functionally specialized. Let us consider plant communities (cenoses) that were subdivided, in classical works by L. G. Ramensky and Grime (see, Grime, 1977), into violents (competitors), explerents (ruderals), and patients (stress-tolerant species). As for a whole ecosystem, it is known to include organic substance producers, consumers, and reducers. Even though these functional types may overlap, each species in a community or an ecosystem typically fulfils a fixed function. In these terms, communities and ecosystems are comparable to caste social structures. These are exemplified by the decentralized network structures that are established by social insects (termites, ants, and bees) and naked mole-rats; such structures are denoted as eusocial structures in the literature (Zakharov, 1991, 2005). In an analogy to a plant community with competitors and ruderal and stress-tolerant species, eusocial systems formed by insects combine several different kinds of specialists, i.e., several castes, e.g, the reproductive and the worker caste (with several subcastes) in an ant colony.

An additional feature that is common to eusocial structures and community-level networks is that both kinds of structures combine the network (decentralized) and the hierarchical (centralized) principle. The eusocial structures of insects, e.g., ants, include “worker teams” (clans) with “team leaders” that are tasked with jobs like digging the ground or collecting aphid honeydew (Zakharov, 1995, 2005; Reznikova and Novgorodova, 1998; Hölldobler and Wilson, 2009). Hierarchical relationships are also established between different functional groups in an ant colony: foragers and scouts have a higher social rank and more prestige than those who nurture the larvae and take care of the queen. However, despite the “team leader” function and the rank differences, even the most prestigious individuals behave as only partial leaders within a higher-order network structure (a colony or a pleiad) that combines a large number of “worker teams”. In a similar fashion, different functional groups within a community or an ecosystem include species that differ in terms of their impotance. Some of them seem to dominate one of the groups or tiers (in an ecosystem). The oak tree plays the dominant role as the main competitor in some forests (in oak groves), but the whole forest represents only one of the components of an ecosystem that necessarily incorporates other tiers/functional groups. Therefore, the oak is only apartial leader in terms of the whole ecosystem-level decentralized network.

Chapter four. COORDINATION MECHANISMS IN NETWORK STRUCTURES IN THE PLANT WORLD

Coordination of the behavior of the nodes (cells, individuals) of any network structure from a bacterial biofilm to a fish school to a vervet troop to a networked organization in human society is a prerequisite for the network’s viability and efficient adaptation to the environment. In the classical work of Espinas (1898) Social Life of Animals, behavioral coordination was interpreted to signify that the same factor induces identical responses in analogous individuals. This is not a complete definition of the term “coordination”. Behavioral coordination does not necessarily imply that all nodes of a given network display identical responses to a stimulus/challenge. Actually, “coordinated behavior” often suggests differentiated behavioral responses of individuals, depending on their social roles and functions. For instance, the pheromones of the queen in an ant colony increase the social activities of all worker individuals, but each worker individual performs its own function; for instance, a forager provides the ant colony with food and a soldier protects the colony. Inside a network, mechanisms are at work that regulate the ratio between different kinds of specialists. Evidence was presented that removing all foragers from an ant colony causes a part of the remaining individuals to change their function so as to fill the vacant “positions” (Zakharov, 1991).

The main mechanisms of behavior coordination that function in network structures can be subdivided into the following types (see: Zakharov, 1991, 2005; Oleskin, 2012, 2014):

• Local contacts among individuals that influence one another’s behavior.

• Distant transfer of “messages” from individual to individual, often based on a relay system

• Impact of signals (chemical agents or physical fields) that spread throughout the hole network

Although this classification of coordination mechanisms was originally put forward without directly taking the plant world into consideration, all the three options are applicable to network structures in plants and their communities/ecosystems.

1. Local Contacts. At the cell level, local information transmission that enables behavior coordination largely depends on the aforementioned cytoplasmic bridges (plasmodesms) and cell envelope fusion that can involve direct contacts between outer cell membranes (in gram-negative bacteria) or between peptidoglucan layers (in gram-positive bacteria, Tetz et al., 1990). Plasmodesms are also characteristic of plant cells, and they are used to transfer both nutrients and regulatory/informational signals. Presumably, electromagnetic waves can spread from cell to cell via plasmodesms as “wave conductors” and communicate messages (Vysotsky et al., 1991).

“It has recently been demonstrated that bacterial cells are connected by nanotubes that transfer molecules between them. Such nanotubes form between the cells of the same species (Bacillus subtilis) and those belonging to different species, e.g., B. subtilis and E. coli... In a similar fashion, networks of intercellular membrane nanotubes connect mammalian cells” (Oleskin, 2014, p.84).

Apart from nanotubes, contact interactions among cells are facilitated by multifarious cell surface structures, including microfibrils, spike-like protrusions, cell wall evaginates, and gycocalyx layers (Oleskin et al., 2000). Cell contact-dependent coordination is also based on nondiffusible signal molecules that are attached to the cell generating them. Another cell can only respond to these signals via specific receptors if it directly contacts the signal-emitting cell. For example, a starving population of Myxococcus xanthus initiates cell aggregation with subsequent formation of spore-containing fruiting bodies. A prerequisite for this process is a sufficiently high cell density in the population, which enables direct contact between cells and, therefore, the perception of the cell surface-attached proteinaceous factor C (the product of the csgA gene).

As for the plant world, direct contact communication and behavior coordination has been also revealed at the level of whole plant individual. There are detailed data on the communication between the host plant and the parasite, e.g., the mistletoe (Viscum album).

2. Distant Chemical Interaction. Long-range signal transfer is essential for coordinating the behavior of both single cells and multicellular organisms and their parts (organs and tissues). Such long-range communication enables the regulation of spatial pattern formation in local cell groups, whole populations and associations of living organisms (e.g., bacterial biofilms) via diffusible chemical signals. For instance, complex patterns, such as concentric circles and hexagonal lattices, in Escherichia coli colonies result from the superpositon of the gradients of two kinds of signals: (i) the signals produced by the cells in the center of the colony and (ii) those released on the colony’s periphery (Budrene & Berg, 2002; Mittal et al., 2003). Growth and developmental processes coordinated at the level of a whole network structure are subject to regulation by long-range signals.

In the plant world, messages are communicated from individual to individual. Elm trees attached by insect pests release specific chemicals to “inform” their neighbors of the impending danger Both contact-based and distant communication is involved in interactions between plants and the microorganisms that overgrow them or grow in their cells. A typical example is the communication between nodular bacteria with the root cells of legumes.

Among the numerous chemical agents involved in distant chemical communication in plants, this work will give special attention to neurochemicals that, apart from their role in communication in plants, fungi, and microorganisms, are involved in transmitting impulses in the nervous system of the animal or human organism. Neurochemicals are discussed in Chapter 6 and in the experimental section of this work.

3. Signal Fields: the Role of the Matrix. As mentioned above, the configuration and the collective activities of the whole super-organismic structure, whether a microbial biofilm or a plant association, are subject to regulation by the superposition of the spatial gradients of many chemical signals that form a network structure-filling signal field. Apart from chemical signals, this information-containing environment includes physical fields. For instance, dividing cells emit UV waves (“the mitogenetic radiation”) which promotes the division of other cells. Physical factors (electromagnetic and ultrasound waves) are likely to make a contribution to bacterial cell-cell communication (Nikolaev, 2000).

The spreading of chemical signals within a network structure is promoted by a hydrophobic environment that is created by the extracellular matrix. In microbial systems, its hydrophilicity depends on acidic polysaccharides, glycsylphosphate-containing biopolymers such as teichoic acids, glycoproteins, and, in some bacteria (e.g., bacilli), polyglutamic acid and other peptides (Oleskin et al, 2000), as well as extracellular DNA strands to which aggregates of the protein pilin IV are attached (Jurcisek and Bakaletz, 2007). Similar to the matrix of animal tissues, the microbial matrix also contains fibrillar components.

Chapter five. DECENTRALIZED NETWORK STRUCTURES IN HUMAN SOCIETY

This work is focused on comparing decentralized network structures formed by plants—-and those established in human society, with special attention to the organization of networked bodies that are responsible for plant resource protection in modern-day China. The following is a brief discussion of the typical properties ofnework structures in human society.

Decentralized network structures include a wide variety of diverse nonhierarchical (horizontal, flat) organizations. Their functioning is based on shared values, goals and behavioral norms that, in many networks, manifest themselves in specific rituals and distinctive features of network members, including, e.g., their dress style. By analogy with decentralized networks in living nature, we can denote these network-consolidating and –coordinating factors as the network matrix. Network organization principles are currently used by small and medium enterprises (SMEs) as well as giant transnational companies that have more than one controlling center (and no central headquarters); these principles are also implemented by various kinds of clubs, “think tanks”, creative research labs, educational institutions, alternative health care centers, charitable foundations, social movements, political organizations, and even some local administrative bodies exemplified by the Sivtsev Vrazhek Republic (a condominium in Moscow in the 1990s).

Network structures hold special value in terms of organizing interdisciplinary scientific research and development activities. An illustrative example is provided by a decentralized network structure that was spontaneously set up by Russian microbiologists in the late 20^th century. “These enthusiastic microbiologists belonged to different formal institutions, e.g., Moscow State University, the Institute of Microbiology of the Academy of Sciences of the Soviet Union, the Institute of Epidemiology and Microbiology of the Academy of Medical Sciences of the Soviet Union and others. They specialized in different fields of microbiology or other life sciences… However, all the scientists took an interest in the same subject, which can be denoted as “social microbiology”. They were fascinated by the social life of microorganisms, the collective behavior of their cells, the information exchange among them, and the microbial populations as coherent, organism-like systems. This common denominator of the enthusiasts’ theoretical approach to microbes can be termed “the population- and communication-centered paradigm in microbiology”. In essence, they developed a borderline field between microbiology and biopolitics.

Owing to their common views, the adherents of this paradigm established business relationships with one another. In the absence of a central bureaucratic leader, these relationships were decentralized and non-hierarchical. Despite their different backgrounds and institutions, the enthusiasts held meetings and conferences, conducted joint research initiatives, and published their results. The independently working scientists V. V. Vysotsky, P. L. Zaslavkaya, A. V. Mashkovtseva, O. I. Baulina, and others established an informal network in the mid-‘80s when they all participated in a microbiological conference at the town of Ivanovo. It was during this conference that they found out that they held similar views on the microbial population as a system composed of diverse individuals—each of them making its own contribution to the well-being of the whole system. The microbiologists decided to work together for the benefit of the whole network that was cemented by the new paradigm. For example, they produced the article “Polymorphism as a Developmental Trend in Populations of Prokaryotic Organisms” (Vysotsky et al., 1991). Several years elapsed after the fruitful meeting at Ivanovo, and the network group discovered the article “Bacteria as Multicellular Organisms” by the American scientist James Shapiro (1988) who held quite similar views. This network structre is schematically represented in Fig. 1 (reproduced from: Oleskin, 2014, p.190).

Fig. 1. A spontaneous network structure within the Russian scientific community in the late 20^th century (the 1960s–1990s). The network structure focused on the organization of microbial populations and on the communication between microbial cells. RAS, Russian Academy of Sciences; RAMS, Russian Academy of Medical Sciences. Note: 1. The period spent by a scientist at a particular institution is given in parentheses after the scientist’s name; 2. Some institutions changed their names during the period in question; 3. Some scientists changed their position and affiliation during the same period. From: Oleskin, 2014, p.190.

This spontaneous network structure in the Soviet (Russian) microbiological community, unlike a traditional hierarchical school of thought, was not founded by a single eminent scientist and supported by a group of his disciples. Instead, the network functioned as an invisible college: information was constantly exchanged between autonomous groups or individual scientists that were united by a common microbiological paradigm” (Oleskin, 2014, pp. 190-191).

In the present-day world, the experiences of such spontaneous networks could be fruitfully used by the developers of new efficient creative networks, including, notably, those dealing with environmental conservation.

Unlike centralized hierarchical bureaucracies, networks may include a large number of partial leaders. Networks in human society are typically characterized by the following features

• Broad specialization is preferred to narrow specialization (which is characteristic of hierarchical structures), i.e., network members attempt to address all the general goals to be attained by the whole network and to work as a single team;

• Informal interaction and personal relationships are stimulated among network members; people live and not just work in networks, in contrast to hierarchies where they just carry out their duties;

• Networks consider the fulfillment of the needs of their members not less important than the achievement of their business goals;

• Networks can only function if they are based on an efficient immaterial matrix that includes the network’s goals, strategies of achieving them, and shared ethical rules as well as behavioral norms

Network structures can be small in size (with just several members) or very large. Such networks clearly manifest their multi-level structure: a large network consists of many smaller ‘subnetworks”.

Networks can develop spontaneously (like the microbiologists’ network briefly discussed above). They can also be deliberately set up by a social engineer. Irrespective of their origin, they typically attain the following objectives:

1. Abolishing the hierarchy, equalizing the members’ social ranks, giving each member a chance to behave as a creative partial leader.

2. Helping the members overcome loneliness, providing them support, and promoting their sense of belonging.

A comparison of network structures and more traditional social hierarchies typified by bureaucracies reveals that a centralized hierarchy can outperform a network in terms of “efficiency criteria such as speed, message count, and frugal use of resources… The network lacked a central coordinating mechanism and spent more time negotiating procedures” (van Alstyne, 1997).

To be emphasized, however, is the creative potential of network structures, especially if they deal with challenging tasks that call for innovative ideas. Network members that suggest such novel ideas do not have to overcome the resistance of the “too busy” boss who is reluctant to change the usual routine. Network members are to a greater extent involved in the operation of the whole network than people who have to work in a hierarchical organization. To re-iterate, joining a network, to a much greater extent than joining a hierarchy, enables human individuals to feel like socially protected members of a coherent structure, e.g. a team of enthusiasts. “… Even virtual interactions among online social network users, e.g., on the Facebook or MySpace websites, promote mutual trust, self-diclosure, multi-aspected communication, and even intimacy, provided that the virtual network structures robustly function for a sufficiently long time” (Oleskin, 2014, p.187).

In sociological terms, networks tend to function as primary groups (Gemeinschaften, according to the classic German sociologist Ferdinand Tönnies, 1988). Within a network, communication among people tends to become informal, friendly, confidential, and multi-faceted; its members acquire a new network identity that is based on shared core values, common goals, and mutual trust (social capital). In contrast, hierarchies such as bureaucratic organizations are secondary groups from the sociological viewpoint (Gesellschaften in Tönnies’s terms) where individual communication is only focused on business interactons (“we are colleagues and not friends”).

Multi-faceted communication among network members promotes an integral, interdisciplinary approach to the issues and problems addressed by them. For instance, in networked scientific research teams, the generation of nontrivial, innovative ideas is facilitated and intuitive thinking is encouraged. Therefore, special hopes are pinned on networks with respect to interdisciplinary tasks that call for interaction among specialists in different fields of science. Such tasks include meeting such currently important challenges as global terrorism, ethnic conflicts, the environmental crisis threat, and various humanitarian missions, including helping orphans, the homeless, the aged, and the HIV positive. Importantly, “application of network principles to the activities of public associations reflects the general trend of practically using networks in various spheres of society, depends to a large extent on networks’ internal dynamism, their readiness to attempt to use new organizational and managerial paradigms to attain important goals, and provides for increased participation of public associations in developing and adopting decisions on socially important issues” (Zabbarov, 2011. Pp.4-5).

Multi-faceted communication among network members enables a network to perform several different social functions, either consecutively or in parallel. The same network structure may function as a creative research lab, charitable foundation, commercial enterprise, team of artists, political “pressure group”, alternative health care center, etc., a team of environmental activists, etc., depending on the aspect of the network’s multi-faceted mission that come to the forefront in a given situation. To re-emphasize, whatever the specific social role(s) at a given moment, a network structure always represents a close-knit group of people with much trust (social capital) and informal interaction among them.