Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium

Microbial consortia form when multiple species colocalize and communally generate a function that none is capable of alone. Consortia abound in nature, and their cooperative metabolic activities influence everything from biodiversity in the global food chain to human weight gain. Here, we present an engineered consortium in which the microbial members communicate with each other and exhibit a “consensus” gene expression response. Two colocalized populations of Escherichia coli converse bidirectionally by exchanging acyl-homoserine lactone signals. The consortium generates the gene-expression response if and only if both populations are present at sufficient cell densities. Because neither population can respond without the other's signal, this consensus function can be considered a logical AND gate in which the inputs are cell populations. The microbial consensus consortium operates in diverse growth modes, including in a biofilm, where it sustains its response for several days

A systematic approach to cancer: evolution beyond selection.

Cancer is typically scrutinized as a pathological process characterized by chromosomal aberrations and clonal expansion subject to stochastic Darwinian selection within adaptive cellular ecosystems. Cognition based evolution is suggested as an alternative approach to cancer development and progression in which neoplastic cells of differing karyotypes and cellular lineages are assessed as self-referential agencies with purposive participation within tissue microenvironments. As distinct self-aware entities, neoplastic cells occupy unique participant/observer status within tissue ecologies. In consequence, neoplastic proliferation by clonal lineages is enhanced by the advantaged utilization of ecological resources through flexible re-connection with progenitor evolutionary stages

The ever unfolding story of cAMP signaling in trypanosomatids: vive la difference!

Kinetoplastids are unicellular, eukaryotic, flagellated protozoans containing the eponymous kinetoplast. Within this order, the family of trypanosomatids are responsible for some of the most serious human diseases, including Chagas disease (Trypanosoma cruzi), sleeping sickness (Trypanosoma brucei spp.), and leishmaniasis (Leishmania spp). Although cAMP is produced during the life cycle stages of these parasites, its signaling pathways are very different from those of mammals. The absence of G-protein-coupled receptors, the presence of structurally different adenylyl cyclases, the paucity of known cAMP effector proteins and the stringent need for regulation of cAMP in the small kinetoplastid cells all suggest a significantly different biochemical pathway and likely cell biology. However, each of the main kinetoplastid parasites express four class 1-type cyclic nucleotide-specific phosphodiesterases (PDEA-D), which have highly similar catalytic domains to that of human PDEs. To date, only TbrPDEB, expressed as two slightly different isoforms TbrPDEB1 and B2, has been found to be essential when ablated. Although the genomes contain reasonably well conserved genes for catalytic and regulatory domains of protein kinase A, these have been shown to have varied structural and functional roles in the different species. Recent discovery of a role of cAMP/AMP metabolism in a quorum-sensing signaling pathway in T. brucei, and the identification of downstream cAMP Response Proteins (CARPs) whose expression levels correlate with sensitivity to PDE inhibitors, suggests a complex signaling cascade. The interplay between the roles of these novel CARPs and the quorum-sensing signaling pathway on cell division and differentiation makes for intriguing cell biology and a new paradigm in cAMP signal transduction, as well as potential targets for trypanosomatid-specific cAMP pathway-based therapeutics

A Model of the Quorum Sensing System in Vibrio fischeri Using P Systems

Quorum sensing is a cell density dependent gene regulation system that allows an entire population of bacterial cells to communicate in order to regulate the expression of certain or specific genes in a coordinated way depending on the size of the population. In this paper we present a model of the Quorum Sensing System in Vibrio fischeri using a variant of membrane systems called P systems. In this framework each bacterium and the environment are represented by membranes and the rules are applied according to an extension of Gillespie’s Algorithm called Multicompartmental Gillespie’s Algorithm. This algorithm runs on more than one compartment and takes into account the disturbance produced when chemical substances diffuse from one compartment or region to another. Our approach allows us to examine the individual behaviour of each bacterium as an agent as well as the emergent behaviour of the colony as a whole and the processes of swarming and recruitment. Our simulations show that at low cell densities bacteria remain dark while at high cell densities some bacteria start to produce light and a recruitment process takes place that makes the whole colony of bacteria to emit light. Our computational modelling of quorum sensing could provide insights that may enable researchers to develop new applications where multiple agents need to robustly and efficiently coordinate their collective behaviour based only on a very limited information of the local environment

Towards a P Systems Pseudomonas Quorum Sensing Model

Pseudomonas aeruginosa is an opportunistic bacterium that exploits quorum sensing communication to synchronize individuals in a colony and this leads to an increase in the effectiveness of its virulence. In this paper we derived a mechanistic P systems model to describe the behavior of a single bacterium and we discuss a possible approach, based on an evolutionary algorithm, to tune its parameters that will allow a quantitative simulation of the system.Kingdom's Engineering and Physical Sciences Research Council EP/D021847/

Evolving quorum sensing in digital organisms

For centuries it was thought that bacteria live asocial lives. However, recent discoveries show many species of bacteria communicate in order to perform tasks previously thought to be limited to multicellular organisms. Central to this capability is quorum sensing, whereby organisms detect cell density and use this information to trigger group behaviors. Quorum sensing is used by bacteria in the formation of biofilms, secretion of digestive enzymes and, in the case of pathogenic bacteria, release of toxins or other virulence factors. Indeed, methods to disrupt quorum sensing are currently being investigated as possible treatments for numerous diseases, including cystic fibrosis, epidemic cholera, and methicillin-resistant Staphylococcus aureus. In this paper we demonstrate the evolution of a quorum sensing behavior in populations of digital organisms. Specifically, we show that digital organisms are capable of evolving a strategy to collectively suppress self-replication, when the population density reaches a specific, evolved threshold. We present the evolved genome of an organism exhibiting this behavior and analyze the collective operation of this “algorithm. ” Finally, through a set of experiments we demonstrate that the behavior scales to populations up to 400 times larger than those in which the behavior evolved