What is Life?

In searching for life in extraterrestrial space, it is essential to act based on an unequivocal definition of life. In the twentieth century, life was defined as cells that self-replicate, metabolize, and are open for mutations, without which genetic information would remain unchangeable, and evolution would be impossible. Current definitions of life derive from statistical mechanics, physics, and chemistry of the twentieth century in which life is considered to function machine like, ignoring a central role of communication. Recent observations show that context-dependent meaningful communication and network formation (and control) are central to all life forms. Evolutionary relevant new nucleotide sequences now appear to have originated from social agents such as viruses, their parasitic relatives, and related RNA networks, not from errors. By applying the known features of natural languages and communication, a new twenty-first century definition of life can be reached in which communicative interactions are central to all processes of life. A new definition of life must integrate the current empirical knowledge about interactions between cells, viruses, and RNA networks to provide a better explanatory power than the twentieth century narrative.


INTRODUCTION
Scientifically, the first half of the twentieth century was the most successful period for empirically based sciences. Basically, explorations in physics and chemistry paved a path on which science could delimitate validity claims against all other concepts of thoughts such as the broad range of philosophical disciplines, theology, and poetry. Philosophers and physicists such as Wittgenstein, Carnap, Goedel, Russell, and Tarski suggested that exact sciences are strictly based on exact scientific sentences describing empirical facts coherent with observations and measurements in experimental setups (Wittgenstein, 1922;Carnap, 1931Carnap, , 1939Gödel, 1931).
The formal language to describe this was mathematical equations that would depict material reality. Information theory and cybernetic systems theory encouraged this progress (Bertalanffy, 1940;Wiener, 1948;Shannon and Weaver, 1949;Turing, 1950;Neumann, 1966). Milestone publication, "Principia Mathematica, " outlined by Bertrand Russel and Alfred North Whitehead was further developed by David Hilberts axiomatic system with error-free logical sentences (Whitehead and Russell, 1910/1912/1913Hilbert andBernays, 1934/1939). This exact scientific language was applied to nearly all disciplines of scientific investigations in natural sciences as well as social sciences.
Molecular biology, genetics, and biochemistry started their success stories, which have lasted until today. The role of physicalism in biology was so dominant that biology became a subdiscipline of physics and chemistry (Brenner, 2012). Because biological organisms, cells, tissues, and organs consist of molecules, constructed out of atoms, empirical and measurable features may be described by physics and chemistry. The genetic information storing molecules represent "aperiodic crystal structures" as assumed by Erwin Schroedinger.

THE PHYSICALISTIC PARADIGM IN THE BIOLOGY OF THE TWENTIETH CENTURY
"We shall assume the structure of a gene to be that of a huge molecule, capable only of discontinuous change, which consists of a rearrangement of the atoms and leads to an isomeric molecule. The rearrangement may affect only a small region of the gene, and a vast number of different rearrangements may be possible. The energy thresholds, separating the actual configuration from any possible isomeric ones, have to be high enough (compared with the average heat energy of an atom) to make the change-over a rare event. These rare events we shall identify with spontaneous mutations" (Schroedinger, 1944).
These spontaneous mutations occur strictly by chance and are statistically determined, according to natural laws. Evolutionarily relevant changes in molecular structures occur if, in replication processes. The replicated molecular structure is not an identical copy of the former sequence structures but a variation that is not identical with the master blueprint. This variation is a result of a replication error. Erwin Schroedinger introduced the "codescript" to denote that genetic information is a natural code. With the rise of molecular biology, and genetics, he adapted the code-metaphor to describe various features of the genetic code as the result of a molecular ensemble of nucleotides that underlies statistical fluctuations. These are the consequences out of thermodynamics of living systems. Schroedinger's book, "What is Life?" and his suggestion therein, "Life is physics and chemistry, " became one of the most influential works of the twentieth century.
A big step forward in this direction occurred, when Manfred Eigen introduced information theory to molecular biology and adapted information as a molecular property of matter, which may reproduce itself (Eigen, 1971). Eigen's quasispecies theory and its core assumption that mutation caused the diversity of RNA populations dominated paradigmatically nearly half a century (Biebricher and Eigen, 2006). It represents the "exploitation of the formal mathematical analogy of quasispecies dynamics and the statistical mechanics" of quantum theory (Domingo and Schuster, 2016). "Biological selection can be viewed as condensation or localization of sequence distribution in a limited area in sequence space" (Biebricher et al., 1985). That finally should lead to a theory of evolution based upon "biochemical kinetics" (Schuster, 2011).
Eigen follows the basics of information theory as a mathematical theory of communication that is quantifiable and underlies natural laws strictly. And he insisted that the genetic code is a natural language and not a metaphor (Eigen and Winkler, 1983).
All these assumptions are driven by the general agreements of natural scientists at that time: how to discuss matter, natural laws, and biological affairs. To summarize, the physicalistic paradigm in biology that dominated the language of observation as well as the language of theory in biology until today are as follows: • life is physics and chemistry (and information) • information is a characteristic of matter • difference between abiotic matter and biology is gradual • natural languages and codes are determined by their syntax structure • syntax structure of natural codes represent the logical structure of matter • mathematics is the only language that can exactly depict the logical structure of matter • evolution is variation (mutation) and selection • mutations are error replications that result out of elementary physical processes and indeterminate due to their quantummechanical nature.

WHAT WE KNOW TODAY ABOUT BIOLOGICAL PROCESSES
We may analyze the available parts of a machine to get a functional blueprint for its construction. Then, we may reproduce it trying to optimize the construction. This is an important motif in genetic engineering also. However, if we observe living organisms and their interactional patterns, beginning from single cells to tissues, organs, and complex organisms, we find a variety of non-mechanistic circumstances, features, and capabilities that cannot be part of a mechanistic explanation. These features include common coordination to adapt to new and unexpected environmental circumstances. Machines cannot create new programs out of functional blueprints (Witzany, 1995).
The study of living organisms as machines presupposes biological information as a result of coded content according to principles of biochemical kinetics. Biolinguistics, bioinformatics, information theory, systems theory of (context-free) languages, or similar mathematical theories of language and communication cannot explain the essential features of natural languages and the codes used in communication processes. They investigate quantifiable sets of signs by mathematical procedures as statistical mechanics and, therefore, completely forget the essential agents in real life world being necessary to use languages and codes (Witzany, 1995(Witzany, , 2000(Witzany, , 2010. The formalizable senderreceiver model that was used to describe natural communication processes cannot identify the context dependence of meaning and its deep grammar that may represent different, and even contradictory, meanings to the superficial grammar that is present in identical sign sequences (Austin, 1975;Searle, 1976;Habermas, 1987). The social character of real-life organisms is not within their expertise because social-interacting organisms do not behave like formalizable abiotic elements (Witzany and Baluska, 2012a). Social-interacting living organisms may generate new sign sequences, behavior, and interactional motifs, for which no algorithm is available in principle.
In biology, in contrast to physics, entanglement with language and communication is double-sided. Biological disciplines in the twentieth century were convinced that, if they use the language of physics, they would be accepted as part of the exact science community of natural sciences (Chomsky, 1965). On the other side, the explanatory models to coherently describe genetic information used physics and mathematics to explain the features of DNA sequences as physical properties (Stadler and Schuster, 1992).
Let us remember dominant terms in molecular biology and genetics in the second half of the last century; they include "genetic code, " "code without commas, " "genetic information, " "gene expression, " "transcription, " "translation, " "nucleotide sequences, " "messenger RNA, " "protein-coding sequences, " "cell-to-cell communication, " "open reading frame, " "immune response, " "recognition sites, " and so on. From a philosophy of science perspective, these terms could not be substantiated by the biological disciplines themselves, because they all were defined by physicochemical features, which clearly failed to coherently justify original linguistic terms (Witzany, guarantee the correct combination of signs and signified objects, and pragmatic rules are relevant for the correct combination of signs and the concrete context in which signs are used by a real sign-using agent. Rule-following agents may also fail to follow these rules. The meaning of sign sequences is dependent on shared rules of the population members: how to reach a common agreement upon what the signs designate and, most importantly, what the sign user tries to transport or trigger. Without common agreement, no coordination of common behavior can be reached (McCarthy, 1984). Even here, meaning (semantics) is a social function (Mead, 1934). Slight differences in experiences of real-world environments may lead to slightly different sign interpretation, as documented in the variety of dialects, for example, in bee languages as proved by Karl von Frisch (1971). That is important in optimizing energy costs because language/code-using agents do not need new signs for every circumstance but can use limited characters/signs and limited rules to generate unlimited sign sequences. To summarize that: -Communication depends on natural languages or codes, i.e., signs that can be combined to sign sequences -No natural language or code speaks itself or codes itself. There must be living agents that use such natural languages or codes -Inherently, communication is a social interaction -Correct use of natural languages or codes underlies syntactic (combinatorial), pragmatic (contextual), and semantic (content-specific) rules -The meaning of information is a social function.

WHAT REMAINS TODAY FROM THESE TWENTIETH-CENTURY NARRATIVES?
We now examine the start of the third decade in the twentyfirst century: which assumptions of the former narratives are still valid, and which ones must be refuted or revised to better integrate empirical data than the previous ones? The debate in the philosophy of sciences on how to generate correct scientific sentences in observation and theory lasted from 1920 to the 1980's (Witzany, 2010). Some results of these debates were: 1. The concept of an axiomatic system with error-free logical sentences is impossible in principle. The project of exact science and exact scientific language that depicts reality in a 1:1 fashion is a pipe dream. Gödel proved that in every complex system, there is at least one formula or utterance, which can neither be proved nor refuted. If someone thinks about such an undecideable formula in a non-formalizable language, he has the opportunity to determine whether this formula is true or false. For a machine, this is impossible. 2. Universal Turing and von Neuman machines that could reproduce itself have been proposed for more than half a century. No single self-reproducing machine has been built or seen until today because the theoretical construction depends on wrong assumptions. 3. No natural language speaks itself, as no natural code codes itself. Natural languages or codes depend on usage by living agents that generate, arrange, and rearrange sign sequences that are syntactically, pragmatically, and semantically correct. The semiotic features of natural languages or codes exclude randomly derived sequences. No natural language or code emerges as a randomly derived mixture of an alphabet or characters without sign-using agents. 4. In real life, no context-free language exists. The meaning (semantics) of information in natural languages depends on the real-life context (pragmatics) within which linguistic signs are used and not on its syntax. 5. Every natural code or language must embody syntactic (combination), pragmatic (context), and semantic (content) rules, which cannot be reduced to one other. Such semiotic rules are rather conservative but-in contrast to natural laws-under circumstances may change and lead to rearrangements of sequences or their generation de novo. 6. The (1) central dogma of molecular biology (DNA-RNAprotein), (2) "one gene-one protein" thesis, and (3) "noncoding DNA is junk" has been falsified. There are several ways that environmental influences on proteins affect RNAs to react accordingly, which, itself, may be coded into DNA. RNA editing, splicing, and epigenetic imprintings demonstrate that one gene can be transcribed and translated into different proteins with different functions. Non-coding DNA, which is transcribed into non-coding RNAs, leads to an abundance of regulatory agents and networks essential for gene expression.
If we want to use the usual linguistic terms in biology also in the future and cannot use it according to the physicalism of the twentieth century, we should apply current knowledge about natural languages/codes and communication to biological processes (Mattick, 2009;Shapiro, 2009;Witzany, 2014aWitzany, , 2019.

LIFE: THE COMMUNICATIVE STRUCTURE
To investigate communication processes in the living world, we have to identify the various levels of communicative (signmediated) interactions. The different levels are intertwined but share level-specific interaction patterns (Witzany, 1993(Witzany, , 2000(Witzany, , 2015.
• Every cell or organism is constantly confronted with abiotic influences such as light, gravity, wind, water, dryness, heat, cold, etc., and has to interpret the relevance for itself and react accordingly to survive. In most cases, these abiotic influences result in experiences that have to be memorized. That facilitates a faster and more appropriate reaction if the same circumstances return. • Every organism is confronted with organisms that do not relate to the same or similar organisms of its population. These may be hunters and predators and may also be symbiotic partners essential for survival. Sign-mediated interactions between non-related organisms are of a certain quality and help us to understand the rich symbiotic life on this planet. • The most popular communication processes we can observe and investigate are interorganismic interactions, which mean coordination and organization of common behavior in populations of the same or related organisms. • Last but not the least, we may observe and investigate communication processes within an organism, i.e., between intercellular and intracellular parts.
All these levels of sign-mediated interactions, i.e., communication processes can be investigated throughout all domains of life as demonstrated within the last decade (Witzany, 2010(Witzany, , 2011b(Witzany, , 2012a(Witzany, , 2014b(Witzany, , 2017bWitzany and Baluska, 2012b;Witzany and Nowacki, 2016). I developed biocommunication theory to investigate communication processes within and among cells, tissues, organs, and organisms as sign-mediated interactions. Additionally, biocommunication theory investigates nucleotide sequences as a natural code, that is, language-like text, which follows in parallel three kinds of rules: combinatorial (syntactic), context sensitive (pragmatic), and content specific (semantic) (Witzany, 2015). Natural genome editing from a biocommunicative perspective means competent agent-driven generation and integration of meaningful nucleotide sequences into pre-existing genomic content arrangements and the ability to (re-) combine and re(regulate) them according to contextdependent (i.e., adaptational) purposes of the host organism (Witzany, 2000(Witzany, , 2016a.

Cellular Organisms
Cellular organisms of all domains of life actively compete for environmental resources. They assess their surroundings, estimate how much energy they need for particular goals, and then realize the optimum variant. They take measures to control certain environmental resources. They perceive themselves and can distinguish between self and non-self. These organisms process and evaluate information and then modify their behavior accordingly. All these coordinated activities are the result of communication processes in the four levels mentioned above in Figure 1 (Witzany, 2016a).

Akaryotes
Akaryotes, such as bacteria and archaea, have been considered the most primitive organisms. They are also the oldest ones, with which cellular life started approximately 4 billion years ago. For a long time, they have been viewed as determined by a strict input-output reaction pattern. In the last three decades, this picture has changed dramatically. Their capability to organize, e.g., biofilm communities, using several messenger molecules, has been investigated in numerous studies (Witzany, 2011b(Witzany, , 2017b. They can coordinate common behavior like a multicellular organism (interorganismic) (Shapiro, 1998). Quorum sensing is a well-investigated process in akaryotic populations (Bassler, 1999). It is the start of common decision processes important for biofilm formation, bioluminescence, sporulation, or virulence (Kaiser and Losick, 1993;Ben Jacob et al., 2004). Their coordination and organization of common behavior by sign-mediated interactions made them the oldest biological agents that can colonize each ecological niche. Although they may colonize all kingdoms of life (protozoa, animals, fungi, plants) in a parasitic or even neutral way, additionally, they serve as essential symbionts to all higher eukaryotes, without which they could not survive (transorganismic) (Schauder and Bassler, 2001).
They are in a constant struggle with their predators and coevolutionary partners, that is, bacteriophages, which determine their gene word order (intraorganismic) and provide them with toxin/antitoxin or restriction/modification modules (Villarreal, 2005;Harms et al., 2018). Half of all bacteria in the oceans are killed every day by phages, but they survive because the other half reproduces so fast (Rohwer et al., 2014).

Eukaryotes
The emergence of single-celled eukaryotes was surely a key step in biological evolution. What was formerly assumed to be a result of small steps of mutations and their selection was later explained by Lynn Margulis and her serial endosymbiotic theory in a completely contrary way (Bermudes and Margulis, 1989;Margulis, 2004). She proved that the membrane-bound fixation, rather than mutation, of a community of former freeliving akaryotes was responsible. The coordination of such a community foundation depends on complex communication processes within the cellular membrane, that are fixed genetically. The crucial difference to akaryotes is the nucleus that assembles chromosomes. There are several indicators that the eukaryotic nucleus stems from a large double-stranded DNA virus that became an essential part of the group identity of the eukaryotic cell (Bell, 2006).
The natural signs in the communication processes of protozoa are hormones and secondary metabolites with which these organisms coordinate their behavioral motifs.
If we focus on protozoa, single-celled eukaryotes like ciliates, we can find various signaling molecules used to coordinate interactions, such as reproduction, mating, feeding, attack, and defense (Luporini et al., 1995(Luporini et al., , 2006Jacob et al., 2015). Several classes of hormones and other secretions, such as secondary metabolites, have been identified (Plattner, 2016). Ciliates synthesize and secrete cell-type-specific proteins into their extracellular medium. The proteins are then taken up by species-specific receptors and interpreted by population members to generate appropriate reactions (Witzany, 2016b).
Most probably, the kingdom of fungi emerged from singlecelled eukaryotes, as we can find single-celled as well as multicellular fungi, which are the evolutionary forerunners of animals and plants (Villarreal, 2005;Bonneville et al., 2020). The communication of fungi is rather complex and diverse, with a variety of substances that serve as semiochemicals (Regnier, 1971). Fungi feed on biotic surfaces, degrading them to soluble nutrients especially for plants. Fungi produce, release, and uptake a rich variety of semiochemicals for reproduction purposes, attack and defense activities, as well as developmental processes coordination and virulence (Witzany, 2012a). In single-celled fungi, we find quorum sensing, a similar communication pattern as in akaryotes. Fungi are communicating by semiochemicals such as mitogenactivated protein kinase, cAMPs, RAS, rapamycin, or calciumcalmodulin-calcineurin, to list the most prominent ones, each one used in different contextual needs (Hogan, 2006;Leeder et al., 2011;Potapova, 2012;Soll, 2012).
The evolution of animals started with the emergence of early metazoans and neuronal tissues in jelly fish (Yin et al., 2019). That was the start of electrical signaling in cell-tocell communication and was unarguably an evolutionary key innovation. The uptake and release of chemical signaling molecules within the whole body such as hormones and secondary metabolites were enriched by the much faster electrical signaling of neuronal networks. The central nervous system enabled electrical communication over far distances within the body (Witzany, 2014b).
Research on neurobiology demonstrated that neuronal communication in animals is the most complex and specialized form of intraorganismic biocommunication on earth (Kandel, 1976). In addition to hormonal and neuronal communication processes, animals communicate interorganismically and transorganismically via auditory, visual, and tactile signs, which lead to an abundance of expression patterns of animal communication. Animal communication is very diverse through genera, families, and species and reaches a complexity peak in human communication that enables human species to act with collective intentionality as a driving force of coordinated and organized division of labor (Tomasello, 2008). The variety of animal communication has been investigated extensively more recently in genera such as chimpanzees, elephants, wolfs, dogs, rodents, mice, rats, spiders, ants, termites, crows, parrots, birds, salamanders, chelonians, cetacean, fish, cephalopods, corals, and nematodes (Witzany, 2014b). Karl von Frisch received a Nobel Prize for deciphering bee languages and their dialects by investigating how moving patterns serve as signals to communicate nutrition sites.
Plants represent the evolutionary youngest organismic kingdom. Up until now, they are regarded as mechanistic growth automatons, maybe because of their sessile lifestyle and rather slow moving patterns in growth and developmental processes in comparism to animals. However, research within the last two decades has changed this perspective dramatically (Baluska et al., 2006;Baluska and Ninkovic, 2010;Perotto and Baluska, 2012;Blande and Glinwood, 2016). The complex communication profiles of plants with non-plants, other plant species, and within plant bodies on the intracellular and intercellular level indicate that plants can communicate at all levels in parallel, in contrast to animals with their centralized nervous system. The interwoven transorganismic communication with bacteria and fungi in the plant root zone and their intraorganismic communication within the plant body has been highly investigated (Bais et al., 2003). Intraorganismic communication in plants coordinates cellular growth, development, shape, and dynamics on the local level and in rather separated parts. Semiochemical communication happens by vesicular trafficking or via plasmodesmata.
Additionally, we can find physical communication by airborne, electric-like, hydraulic, and mechanical signs (Callaway, 2002;Braam, 2005). We may find nucleic acids, oligonucleotides, proteins and peptides, minerals, oxidative signals, gases, mechanical signals, electrical signals, fatty acids, oligosaccharides, growth factors, several amino acids, various secondary metabolite products, and simple sugars as signaling molecules. Today, we know 100,000 different metabolites used in plants (Dunn and Handelsman, 2002;Fleming, 2005).

Virus Communication
Traditionally, viruses have been seen as infective, disease-causing entities with often epidemic consequences that infect all kinds of organisms. Evolutionary, they have been viewed as parasites that escaped out of cells because they cannot reproduce by themselves but need cellular hosts (Villarreal and Witzany, 2010). More recently, this perspective has been corrected (Villarreal, 2005(Villarreal, , 2009aForterre and Prangishvili, 2013;Moelling and Broecker, 2019). The disease-causing agents are the minority. Most viruses are integrated into the cytoplasm or nucleoplasm of host cells, without harming the host.
The persistent lifestyle is the predominant one, and the symbiotic and coevolutionary lifestyle is omnipresent because, since the start of life, every cell, tissue, organ, and organism on this planet has been persistently infected by multiple viruses (Ryan, 2009;Broecker and Moelling, 2019a;Koonin et al., 2019). Their persistence, in most cases, does not occur as fully functional but as parts of infectious agents that remain as useful tools for cellular needs because such viral parts can be exapted and coopted by cellular organisms and which we can identify as noncoding RNAs with repetitive sequence syntax (Jurka et al., 2007;Witzany, 2009Witzany, , 2017c. Defective parts of infectious agents may serve as signals for immune functions against related genetic parasites (Ariza-Mateos and Gómez, 2017).
Viruses and their defective parts play essential roles in genetic content composition and arrangements that help organisms rearrange genetic content for adaptational purposes, such as in immunity systems or in the evolution of new organs, e.g., placenta and the role of syncytin genes (Villarreal, 2009a(Villarreal, , 2016aPerot et al., 2012;Koonin and Krupovic, 2017;Broecker and Moelling, 2019b). We know them as endogenous viruses and defectives, transposons, retrotransposons, long terminal repeats, non-long terminal repeats, long interspersed nuclear elements, short interspersed nuclear elements, alu's, group I introns, group II introns, phages, and plasmids. Because nearly all the remnants of former infectious genetic parasites share a repeat nucleotide syntax, in contrast to the protein-coding non-repeated nucleotide syntax, we now know that, in most cases, they remain as noncoding RNAs (see below) (Witzany, 2011d). In this respect, we may study cellular DNA as the preferred living habitat of an abundance of RNA inhabitants (Brookfield, 2005;Le Rouzic et al., 2007;Vennera et al., 2009;Villarreal and Witzany, 2013a).
We should be reminded that the human genetic content that codes for proteins is 1.5% only, whereas the non-coding but regulation-relevant genetic content is about 98.5% (Boland, 2017). Additionally, we must not forget that viruses represent the most abundant genetic elements on this planet, outnumbering cellular genetic content by 10 times. In this respect, cellular genomes seem like rare islands in an ocean of the global virosphere (Forterre and Prangishvili, 2009;Koonin, 2009;Rohwer et al., 2014).
Many infectious agents have been identified during the last decades as inhabitants of all prokaryotic as well as eukaryotic genomes (Koonin and Dolja, 2014;Koonin et al., 2015). They infect, insert and delete, cut and paste, or copy and paste. Many of them spread within the genome. They can change host genetic identity by insertion, recombination, or epigenetic regulation or re-regulation of genetic content, and co-evolve with the host (Witzany, 2006(Witzany, , 2014cCatania et al., 2020).
Most interestingly and unknown, the formerly termed RNA virus populations, quasispecies, are now recognized as highly interactive and cooperative agents (Villarreal and Witzany, 2013b). Virus communication demonstrates that quasispecies populations and subpopulations may cooperate and compete in parallel, dependent on the circumstantial context of host life (Villarreal and Witzany, 2019). Social interacting persistent viruses play important roles as host gene regulatory elements-in most cases represented by repetitive sequences-that may react to nearly every unexpected circumstance (Díaz-Muñoz et al., 2017;Sanjuán, 2018).
Viruses are the only biotic agents that can generate code sequences de novo, identify sequence-specific target sites, integrate into pre-existing genetic content, integrate without damage of previous coding regions, recombine according to adaptational purposes, and mark sequence sites to epigenetically fix identity content (Villarreal, 2005(Villarreal, , 2009b. The whole range of epigenetic marking, which is so essential for cellular-based organisms to coordinate the variety of developmental stages stems from these infectious agents and has been adapted to cellular needs (Witzany, 2009). Viruses may divide into multipartite genome segments, spread their parts non-randomly throughout host genomes, and reassemble into full functional viral genomes again (Sicard et al., 2016(Sicard et al., , 2019Lucía-Sanz and Manrubia, 2017).
Current research demonstrated that viruses communicate to coordinate their behavior: whether it should be lytic or remain in a lysogenic style. The semiochemicals used to communicate at interorganismic levels are peptides (AimP), which reduce the expression of the negative regulator of lysogeny (AimX) by binding to the transcription factor (AimR) promoting lysogeny (Erez et al., 2017;Stokar-Avihail et al., 2019). Interactional motifs in virus communication range from conflict to cooperation in various forms and mimicry, dependent on situational context (Mei and Zhang, 2019;Seligmann, 2019).
Viruses are the only living entities that may exchange genetic sequences as module-like tools between double-stranded DNA, single-stranded DNA, single-stranded RNA, double-stranded RNA, and retroviruses. Most interestingly, they may cooperate and compete as viral clouds in parallel (Koonin et al., 2015;Stedman, 2015Stedman, , 2018Berliner et al., 2018).
The most important behavioral motif from both an evolutionary and functional perspective is that viruses can integrate a persistent lifestyle into cellular host organisms by "addiction" modules (Villarreal, 2012(Villarreal, , 2016b. This means that former competing viral groups counterbalance each other, together with the host immune system (Koonin et al., 2019). One can find such counter-regulating paired genes of the addiction modules in the restriction/modulation (RM) systems, as well as the toxin/antitoxin (TA) systems (Mruk and Kobayashi, 2014). Insertion/deletion functions represent similar modules as do the RM systems. This infectious technique to colonize host genomes is the key process in generating a new sequence space without error replication (Villarreal, 2009b;Villarreal and Witzany, 2015).
To consider the genetic information in cellular genomes of all domains of life without the remnants of persistent viral infections would be as curious as to consider a language text consisting of various characters as molecular bricks without agents that can write coherent linguistic texts and are competent in generating it according to syntactic, pragmatic, and semantic rules (Witzany, 2012b(Witzany, , 2017c.

RNA Communication
The steps from viruses to pure RNA stem-loops can be much easily understood if we consider viroids, that is, short strands of circular, single-stranded RNA virus without a protein coat (Catalán et al., 2019). However, in evolutionary periods, interacting RNA networks most propably predated viruses and cellular life (Root-Bernstein and Dillon, 1997;Witzany, 2011b;Root-Bernstein and Root-Bernstein, 2015;Demongeot and Seligmann, 2019) Here, we focus on the RNA strand level, which clearly shows infective and host-manipulating properties (Diener, 1989;Flores et al., 2012Flores et al., , 2014. This is coherent with the RNA world of RNA stem-loop groups and RNA group identities as result of biotic behavior represented by biological selection processes (Petrov et al., 2014;Ariza-Mateos et al., 2019;Villarreal and Witzany, 2019;Demongeot and Seligmann, 2020).
To understand the social interactions between the proponents of the RNA world means to understand a fascinating sphere of what was formerly assumed to be impossible: that mere sequences of molecules do not behave like physical/chemical entities in an abiotic world but as competent agents on genetic code syntax that cooperate and organize, constitute, and generate sequence structures and groups that depend on group selection (Higgs and Lehman, 2015). As was shown, a single RNA stem-loop behaves like a random assembly of nucleotides without selective forces governed strictly by physical laws (Smit et al., 2006;Vaidya et al., 2012Vaidya et al., , 2013. Biological selection starts only if they are assembled to groups, consortia (Hayden and Lehman, 2006).
RNA world agents can serve as a template or be active as a catalyst (Gwiazda et al., 2012). This double function of genotype and phenotype is unique in biology, where we can find most organisms divided into genetic coding and protein-based bodies within their real-life interactions. In former times, it was thought that information transfer is a one-way road from genotype to phenotype, but with epigenetics and RNA biology, it became obvious that the reverse direction of information flow-indicating complex signmediated interactions (i.e., communication)-plays an important role in evolution, development, and adaptational flexibility (Shapiro, 2009(Shapiro, , 2014(Shapiro, , 2016Spadafora, 2016).
RNA stem-loop groups interact with other DNA, RNA, or proteins forming the most important ribo-nucleo-protein complexes (RNP), such as the subgroups of ribosome, spliceosome, and editosome (Mercer and Mattick, 2013). Their active site that leads to group behavior is the single-stranded loops or bulges being essential for self/non-self recognition and group identity.
Additionally, these single-stranded loops are actively prone to integration or rejection of foreign RNA stem-loops. Their highly interaction-prone nucleotide "surface" serves as signs (indices) for competing or cooperating RNA stem-loops, based on complementary base-pairing rules (Schudoma, 2011). This is relevant also in RNA mimicry as demonstrated recently (Ariza-Mateos and Gómez, 2017;Grüll and Massé, 2019).
We should consider such ensembles of RNA stem-loops as RNA populations, which are investigated as varieties of small non-coding RNAs such as snRNA, snoRNAs, piwi RNAs, tRNAs, rRNAs, mRNA, siRNAs, and microRNAs (Bartel, 2004;Carthew and Sontheimer, 2009;Malone and Hannon, 2009;Gebetsberger et al., 2017). Ensembles of RNA fragments that self-ligate into selfreplicating ribozymes may form cooperative networks unexpectedly (Briones et al., 2009;Cheng and Unrau, 2010). It has been shown that three-member networks represent cooperative growth dynamics. When such cooperative networks compete against selfish RNA stem-loop groups, they grow faster. This indicates that RNA populations can evolve higher complexity by cooperative interactions. This also demonstrated that cooperation outcompetes selfishness (Hayden and Lehman, 2006;Vaidya et al., 2012). The primacy of the selfish gene hypothesis, therefore, is outdated on the RNA level.
Non-coding RNAs interact with all relevant key players in the biological world: DNA, RNA, and proteins. They play important roles in nuclear organization, transcription, posttranscriptional, and epigenetic processes and are transcribed in the sense and/or antisense directions (Cech and Steitz, 2014;Long et al., 2017). They may be expressed in different cell types, subcellular compartments, and developmental stages, in every case in a context-dependent manner (Mattick and Gagen, 2001;Mattick, 2003;Clark et al., 2013). Non-coding RNAs can interact cooperatively in a module-like manner (Manrubia and Briones, 2007;Higgs and Lehman, 2015).
Basic tools of such RNA consortia are their complementary composition of base pairing stems and non-base-pairing (singlestranded) loops, which are results of an inherent property of RNA to fold back building stem-loop structures (Villarreal and Witzany, 2013b). The variety of regulations on proteincoding genes, as well as the processing of these regulatory RNAs by a number of defined steps in RNA splicing and RNA editing of RNA transcripts, makes an understanding nearly impossible because of the coordinated complexity (Witzany, 2016c). It is now very clear that non-coding RNAs build those agents that determine the regulation of all steps and substeps of gene regulation in cellular organisms (Mattick, 2009). The interactive connection between the RNA-world agents and cellular organisms is based on the abundance of infectious genetic parasites, viruses, and related agents that transfer and insert all relevant RNA features to the cellular world or, as Frantisek Baluska noted, "without infection, no evolution" (Baluska, 2009;Baluška and Witzany, 2014).
In contrast to DNA viruses, RNA viruses have much smaller genomes on RNA bases without proofreading and repair. "Errorprone" RNA viruses are a key narrative in physicalistic biology. However, it has been formerly assumed as deficit because the "error rate" is very high. The contrary perspective is more effective: to regard it as "innovation rate" because it assembles the property of invention of new sequence contents, de novo, that is, that have not been existent before. This is important for variation as well as infection, immunity, and identity, for both diversified viral and cellular populations and continued interaction between cellular immune systems and infectious genetic parasites throughout the whole history of life (Villarreal, 2015). "Error-threshold" was used to designate the critical state of too many error replications to stabilize newly derived variations (Shah et al., 2019). Replication must be faster than the breakdown rate of the newly derived strands. Because "errors" are not appropriate to describe RNA strand innovation we now may consider these events as "innovation overload."

SUMMARIZING CURRENT EMPIRICAL DATA
We have seen that the use of linguistic terms, such as genetic code, code without commas, transcription, translation, reading frame, immune response, etc., in biology, is still possible. However, the physicalistic paradigm that introduced these terms to biology is no longer valid. Physicalism, as well as other mathematical theories of language, cannot substantiate these terms because this is outside their expertise: Natural languages and codes are the result of social interacting agents, according to competent and commonly shared rules of sign use and allow living agents not only to coordinate and organize their behavioral features and capabilities but also to generate completely new sign sequences and behavioral adaptations that cannot be predicted or computed by algorithm-based procedures. Nucleotide sequences of the genetic code are not the result of the self-organization of matter. The genetic code is not the result of statistical mechanics (Table 1).
Today, we know that life is constituted of three levels of interactions: cells that are genetically regulated by RNA networks, which are remnants of former genome-invading agents such as viruses and their relatives. Quantitatively, cellular genes represent rare islands in an ocean of viruses and virusrelated infectious genetic parasites (virosphere). The invasion strategy of genomic parasites resulted in persistence within host genomes that now represent evolutionary novel genetic identities than before the invasion. Immune systems in cellular organisms represent communicating networks of persistent genetic parasites that serve as immune function against related parasites. Former competing genetic parasites, together with host immune function, generate a variety of regulatory tools that are counter regulated. The three interaction levels (a) RNA-groups, (b) viruses, and (c) cell-based organisms not only constitute and regulate but also inherently are basically open for generating new, unexpectable, and non-computable interaction profiles. This means genetic de novo sequences, new cooperation pathways, exaptation and new traits of former generated module like parts that evolved for different purposes, re-use of former degraded modules (viral defective minorities), and new behavioral motifs of cellular life forms.

CONCLUSION
Primarily, life is a process. The main characteristic of this process is the coordinated organization of complex interactions that we see as protein-based organisms of three domains of life, their reproduction, and metabolism all mediated by complex interwoven gene regulation as a result of communication.
Living nature is structured and organized by language and communication within and among organisms, viruses, and RNA networks. If communication is damaged or disturbed, coordination and organization may be incomplete, and normal function becomes disregulated, leading to the broad variety of diseases. Without RNA world agents, no cellular gene regulation could take place. Without viruses and related infectious agents, these capabilities of RNA stem-loop group behavior as gene inventors and regulators would not have been integrated into cellular host genomes. Therefore, we must ask whether mutation ("error replication") is the correct term to desígnate genetic variation in the future. Error replications, which in most cases means DNA damage without successful repair, is an empirical fact but does not play important roles in genetic innovation. Evolutionary relevant genetic variations are the result of natural genome editing by competent agents such as viruses and RNA networks with their inherent competence to generate and modify nucleotide sequences. This is competent nucleotide sequence editing. In contrast to former convictions, this is an agent-based interaction process, which is far from statistical mechanics and biochemical kinetics. Instead of error replication, we should use now "genetic innovation, " which much better fits to the empirically documented events. Darwinian evolution then could be revised to "innovation and selection." If we want a new definition of life for astrobiological research, we must integrate the complementarity of cells, viruses, and RNA networks into a communicative life world. Life as a process depends on these interactional agents. Yes, all living agents are constituted by elements that underlie physics and chemistry. However, in contrast to abiotic planets, life on this planet depends on communication processes involved in all sign-mediated interactions of cells, viruses, and subviral RNA networks. Therefore, we can formulate a new definition of life: life is communicative interaction, which means life is primarily a social event.
Life is a social event. Social events are realized by communicative interactions on three complementary levels in parallel: cell communication, RNA communication, and virus communication.

DATA AVAILABILITY STATEMENT
All datasets generated for this study are included in the article/supplementary material.

AUTHOR CONTRIBUTIONS
The author confirms being the sole contributor of this work and has approved it for publication.