Eric Werner

Cancer stem cells are controlled by developmental networks that are often topologically indistinguishable from normal, healthy stem cells. The question is why cancer stem cells can be both phenotypically distinct and have morphological effects so different from normal stem cells. The difference between cancer stem cells and normal stem cells lies not in differences their network architecture, but rather in the spatial-temporal locality of their activation in the genome and the resulting expression in the body. The metastatic potential cancer stem cells is not based primarily on their network divergence from normal stem cells, but on non-network based genetic changes that enable the evolution of gene-based phenotypic properties of the cell that permit its escape and travel to other parts of the body. Stem cell network theory allows the precise prediction of stem cell behavioral dynamics and a mathematical description of stem cell proliferation for both normal and cancer stem cells. I...

Previously, in Internet of Life, Chp 1 [137], it was shown that if the interaction protocol between parental genome networks is random then there is a loss of bilateral symmetry. Thus, a nonrandom meta-network, interaction protocol evolved as a precondition for the evolution of the first bilaterally symmetric organisms in the Precambrian more than 570 million years ago. In this chapter we investigate some of the consequence of nonran-dom interaction protocols for development and evolution. Computer simulations show that any nonrandom interaction protocol dynamically partitions the organism into two types of nonintersecting sections, one type controlled by the maternal and the other by the paternal haploid genome network. Thus, at any given time, all cells in a given section are exclusively controlled by only one of the two parental haploid genome networks. The partition is dynamic with sections changing identity, splitting or merging as the organism develops. The developmental effects of later partition states are superposed on earlier developmental states leading to complex mixtures of ancestrally inherited phenotypes. Each protocol has an identifying meta-network signature. Different protocol signatures partition the developing organism differently leading to different morphologies and capacities both mental and physical. As protocols and developmental networks diverge new species and phyla can emerge. Protocols are the bedrock of social and sexual intercourse between male and female genomes. For any diploid species their haploid protocols must cooperate to generate a coherent, complete and consistent embryo. If the two haploid protocols of potential sex partners diverge too much, network disfunction causes developmental pathologies, miscarriage or unviability. Evidence for this new theory of development and evolution comes from computational multicellular experiments, human and animal development , malformations, teratology, hybrids and gynandromorphs. Developmental networks and their meta-network protocols provide a fundamentally new explanatory framework for embryonic and post-embryonic development, developmental pathologies, animal and plant hybrids, heterosis, and evolutionary dynamics.

We present a general computational theory of stem cell networks and their developmental dynamics. Stem cell networks are special cases of developmental control networks. Our theory generates a natural classification of all possible stem cell networks based on their network architecture. Each stem cell network has a unique topology and semantics and developmental dynamics that result in distinct phenotypes. We show that the ideal growth dynamics of multicellular systems generated by stem cell networks have mathematical properties related to the coefficients of Pascal's Triangle. The relationship to cancer stem cells and their control networks is indicated. The theory lays the foundation for a new research paradigm for understanding and investigating stem cells. The theory of stem cell networks implies that new methods for generating and controlling stem cells will become possible.

A theory of how agents can come to understand a language is pre- sented. If understanding a sentenceis to associate an operator with � that transforms the representational state of the agent as intended by the sender, then coming to know a language involves coming to know the operators that correspond to the meaning of any sentence. This involves a higher order operator that operates on the possible transformations that operate on the representational capacity of the agent. We formalize these constructs using concepts and diagrams analogous to category theory.

Gynandromorphs are creatures where at least two di erent body sections are a di erent sex. Bilateral gynandromorphs are half male and half female. Here we develop a theory of gynandromorph ontogeny based on developmental control networks. The theory explains the embryogenesis of all known variations of gynandromorphs found in multicellular organisms. The theory also predicts a large variety of more subtle gynandromorphic morphologies yet to be discovered. The network theory of gynandromorph development has direct relevance to understanding sexual dimorphism (di erences in morphology between male and female organisms of the same species) and medical pathologies such as hemihyperplasia (asymmetric development of normally symmetric body parts in a unisexual individual). The network theory of gynandromorphs brings up fundamental open questions about developmental control in ontogeny. This in turn suggests a new theory of the origin and evolution of species that is based on cooperative i...

A computational theory and model of the ontogeny and development of bilateral symmetry in multicellular organisms is presented. Understanding the origin and evolution of bilateral organisms requires an understanding of how bilateral symmetry develops, starting from a single cell. Bilateral symmetric growth of a multicellular organism from a single starter cell is explained as resulting from the opposite handedness and orientation along one axis in two daughter founder cells that are in equivalent developmental control network states. Several methods of establishing the initial orientation of the daughter cells (including oriented cell division and cell signaling) are discussed. The orientation states of the daughter cells are epigenetically inherited by their progeny. This results in mirror development with the two founding daughter cells generating complementary mirror image multicellular morphologies. The end product is a bilateral symmetric organism. The theory gives a unified ex...

We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled {\em in silico} cancers a...

A proof is presented that gene regulatory networks (GRNs) based solely on transcription factors cannot control the development of complex multicellular life. GRNs alone cannot explain the evolution of multicellular life in the Cambrian Explosion. Networks are based on addressing systems which are used to construct network links. The more complex the network the greater the number of links and the larger the required address space. It has been assumed that combinations of transcription factors generate a large enough address space to form GRNs that are complex enough to control the development of complex multicellular life. However, it is shown in this article that transcription factors do not have su cient combinatorial power to serve as the basis of an addressing system for regulatory control of genomes in the development of complex organisms. It is proven that given n transcription factor genes in a genome and address combinations of length k then there are at most n~k k-length tr...

Gene Regulatory Networks (GRNs) consisting of combinations of transcription factors (TFs) and their cis promoters are assumed to be sufficient to direct the development of organisms. Mutations in GRNs are assumed to be the primary drivers for the evolution of multicellular life. Here it is proven that neither of these assumptions is correct. They are inconsistent with fundamental principles of combinatorics of bounded encoded networks. It is shown there are inherent complexity and control capacity limits for any gene regulatory network that is based solely on protein coding genes such as transcription factors. This result has significant practical consequences for understanding development, evolution, the Cambrian Explosion, as well as multi-cellular diseases such as cancer. If the arguments are sound, then genes cannot explain the development of complex multicellular organisms and genes cannot explain the evolution of complex multicellular life.

Language learning is thought to be a highly complex process. One of the hurdles in learning a language is to learn the rules of syntax of the language. Rules of syntax are often ordered in that before one rule can applied one must apply another. It has been thought that to learn the order of n rules one must go through all n! permutations. Thus to learn the order of 27 rules would require 27! steps or 1.08889x10^{28} steps. This number is much greater than the number of seconds since the beginning of the universe! In an insightful analysis the linguist Block ([Block 86], pp. 62-63, p.238) showed that with the assumption of transitivity this vast number of learning steps reduces to a mere 377 steps. We present a mathematical analysis of the complexity of Block's algorithm. The algorithm has a complexity of order n^2 given n rules. In addition, we improve Block's results exponentially, by introducing an algorithm that has complexity of order less than n log n.

We outline the global control architecture of genomes. A theory of genomic control information is presented. The concept of a developmental control network called a cene (for control gene) is introduced. We distinguish parts-genes from control genes or cenes. Cenes are interpreted and executed by the cell and, thereby, direct cell actions including communication, growth, division, differentiation and multi-cellular development. The cenome is the global developmental control network in the genome. The cenome is also a cene that consists of interlinked sub-cenes that guide the ontogeny of the organism. The complexity of organisms is linked to the complexity of the cenome. The relevance to ontogeny and evolution is mentioned. We introduce the concept of a universal cell and a universal genome.

Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. And yet, the paradigms that underlie the field have not seen a similar expansiveness. Instead, most of these paradigms have been carried over from other fields like engineering, physics, and mathematics. As a result, a small set of concepts dominate the field. The traditional biologist is seen by many as outmoded and tolerated only as a source of data. In this view, the biologist’s ideas may even be considered conceptually and theoretically irrelevant. In this Perspective, we take a critical look at some of the paradigms of systems biology and question whether the biologist’s ideas, methods, and theories have really become outmoded. We see the future of systems biology as a tight coupling of in vivo and in vitro methods for bioengineering with in silico multicellular modeling and simulation.

Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. And yet, the paradigms that underlie the field have not seen a similar expansiveness. Instead, most of these ...

An intriguing unanswered question about the evolution of bilateral animals with internal skeletons is how an internal skeleton evolved in the first place. Computational modeling of the development of bilateral symmetric organisms suggests an answer to this question. Our hypothesis is that an internal skeleton may have evolved from a bilaterally symmetric ancestor with an external skeleton. By growing the organism inside-out an external skeleton becomes an internal skeleton. Our hypothesis is supported by a computational theory of bilateral symmetry that allows us to model and simulate this process. Inside-out development is achieved by an orientation switch. Given the development of two bilateral founder cells that generate a bilateral organism, a mutation that reverses the internal mirror orientation of those bilateral founder cells leads to inside-out development. The new orientation is epigenetically inherited by all progeny. A key insight is that each cell contained in the newly...

Cancer stem cells are controlled by developmental networks that are often topologically indistinguishable from normal, healthy stem cells. The question is why cancer stem cells can be both phenotypically distinct and have morphological effects so different from normal stem cells. The difference between cancer stem cells and normal stem cells lies not in differences their network architecture, but rather in the spatial-temporal locality of their activation in the genome and the resulting expression in the body. The metastatic potential cancer stem cells is not based primarily on their network divergence from normal stem cells, but on non-network based genetic changes that enable the evolution of gene-based phenotypic properties of the cell that permit its escape and travel to other parts of the body. Stem cell network theory allows the precise prediction of stem cell behavioral dynamics and a mathematical description of stem cell proliferation for both normal and cancer stem cells. I...

We aim at providing a general theoretical framework for designing agents with a communicative and social competence. Thereby, we develop the foundations for the design of systems of agents that behave as a social unit or group. A unified theory of communication, cooperation, and social structure is presented. First, a theory of the cognitive states, the information and intentional states, of an agent is given. A theory of communication is developed that gives a formal account of how messages affect the intentions or plans of an agent. The theory of intentions is used to define the concepts of social role and social structure. The unified theory of communication, intention, and social structures is used to develop a theory of social cooperation for multiagent systems. This fulfills a necessary condition for the design of complex agents that cooperate as a group. We apply the theories with an analysis of two examples: The contract net protocol and a Wittgensteinian language game.

... Page 24. AgentScape Infrastructure: challenges Large scale multi-agent systems – large number of agents ... large number of resources wide-area distributed systems (interconnection bandwidth and latency) ... Design Goals & Requirements Scalability: – large number of agents ...

Gene Regulatory Networks (GRNs) consisting of combinations of transcription factors (TFs) and their cis promoters are assumed to be sufficient to direct the development of organisms. Mutations in GRNs are assumed to be the primary drivers for the evolution of multicellular life. Here it is proven that neither of these assumptions is correct. They are inconsistent with fundamental principles of combinatorics of bounded encoded networks. It is shown there are inherent complexity and control capacity limits for any gene regulatory network that is based solely on protein coding genes such as transcription factors. This result has significant practical consequences for understanding develop- ment, evolution, the Cambrian Explosion, as well as multi-cellular diseases such as cancer. If the arguments are sound, then genes cannot explain the development of complex multicellular organisms and genes cannot explain the evolution of complex multicellular life.

We outline the global control architecture of genomes. A theory of genomic control information is presented. The concept of a developmental control network called a cene (for control gene) is introduced. We distinguish parts-genes from control genes or cenes. Cenes are interpreted and executed by the cell and, thereby, direct cell actions including communication, growth, division, differentiation and multi-cellular development. The cenome is the global developmental control network in the genome. The cenome is also a cene that consists of interlinked sub-cenes that guide the ontogeny of the organism. The complexity of organisms is linked to the complexity of the cenome. The relevance to ontogeny and evolution is mentioned. We introduce the concept of a universal cell and a universal genome.

We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled in silico cancers and natural in vivo cancers. The theory lays the foundation for a new research paradigm for understanding and investigating cancer. The theory of cancer networks implies that new diagnostic methods and new treatments to cure cancer will become possible.

This volume contains thoroughly refereed versions of the best papers presented at the 4th European Workshop on Modelling Automomous Agents in a Multi-Agent World, held July 29-31, 1992 in S. Martino al Cimino, Italy. The book opens with an introductory survey by the volume editors not only on the collection of papers but also on the history and present situation of Distributed Artificial Intelligence (DAI) and its interdisciplinary relations to social sciences, artificial life, and economics. The 19 technical papers are organized into ...

;;; /info/www/ext/brs/A/E.Werner/E.Werner90A/descrip.lsp (putobj '|E.Werner90A| '|confpapers| '( (META ( (SYMBOL |E.Werner90A|) (TYPE |confpapers|) ) ALIST) (AUTHOR |E.Werner|) (TITLE "What Can Agents Do Together? ...

We outline a unified account of intention, information, possibility and ability. We present fundamental principles relating information to ability and possibility. A formal description of information is given that relates information to possibility reduction. We distinguish a theory of possiblity from a theory of ability or can. We define the semantics and investigate the logic of the modal auxiliary can. Next we show how information creates abilities. We then develope a formal theory of intentional states and then relate intention to ability. Finally, a unified view of the interrelationships among intention, information, possiblity and ability is presented. Introduction Perhaps it is no accident that the field of distributed artificial intelligence must confront some of the most fundamental conceptual difficulties underlying the sciences. In the quest to design and build an artificial socially intelligent agent, that acts autonomously yet cooperatively, it might be expected that foundational issues of the most basic sort should arise. The concepts of information, possibility, ability and intention lie in the bedrock of science. These concepts are central to distributed artificial intelligence [Werner 88a, 89b, 90a, b], to robotics, to artificial intelligence and to the sciences in general. And yet, the meaning of these concepts and their interrelationships is still shrouded in mystery. In this paper we will attempt to clarify some of the basic relationships between information, capability and intentions. The emphasis is on the interrelationships abstracting away from the details. cite: Decentralized AI, vol. 2, Proceedings of the Second European Workshop on Modeling Autonomous Agents in Multiagent Worlds, Demazeau, Y. & Muelller, J-P., (eds.), Saint-Quentin en Yvelines, France. Elsevier Science Publishers, 1991.

Logic in this century has been primarily concerned with the foundations of mathematics, the formalization of the mathematical concepts. Mathematics was the child of physics focusing on the properties and relations of inanimate objects. In fact, the century started with the ...

Eduard Muntaner-Perich, Josep Lluís Rosa Esteva, Using Dynamic Electronic Institutions to Enable Digital Business Ecosystems, Coordination, Organizations, Institutions, and Norms in Agent Systems II: AAMAS 2006 and ECAI 2006 International Workshops, COIN 2006 ...

Previously, in Internet of Life, Chp 1 [137], it was shown that if the interaction protocol between parental genome networks is random then there is a loss of bilateral symmetry. Thus, a nonrandom meta-network, interaction protocol evolved as a precondition for the evolution of the first bilaterally symmetric organisms in the Precambrian more than 570 million years ago. In this chapter we investigate some of the consequence of nonran-dom interaction protocols for development and evolution. Computer simulations show that any nonrandom interaction protocol dynamically partitions the organism into two types of nonintersecting sections, one type controlled by the maternal and the other by the paternal haploid genome network. Thus, at any given time, all cells in a given section are exclusively controlled by only one of the two parental haploid genome networks. The partition is dynamic with sections changing identity, splitting or merging as the organism develops. The developmental effects of later partition states are superposed on earlier developmental states leading to complex mixtures of ancestrally inherited phenotypes. Each protocol has an identifying meta-network signature. Different protocol signatures partition the developing organism differently leading to different morphologies and capacities both mental and physical. As protocols and developmental networks diverge new species and phyla can emerge. Protocols are the bedrock of social and sexual intercourse between male and female genomes. For any diploid species their haploid protocols must cooperate to generate a coherent, complete and consistent embryo. If the two haploid protocols of potential sex partners diverge too much, network disfunction causes developmental pathologies, miscarriage or unviability. Evidence for this new theory of development and evolution comes from computational multicellular experiments, human and animal development , malformations, teratology, hybrids and gynandromorphs. Developmental networks and their meta-network protocols provide a fundamentally new explanatory framework for embryonic and post-embryonic development, developmental pathologies, animal and plant hybrids, heterosis, and evolutionary dynamics.