A realist evaluation of loneliness interventions for older people.

INTRODUCTION: the aim of this study was to develop a programme theory to inform the design of loneliness interventions and guide any future evaluations. METHODS: we undertook a mixed-method evaluation, informed by the realist approach, of different approaches to loneliness in one health and social care system in the East Midlands, UK. We used a combination of documentary analysis, interviews/focus groups with service providers and users and quantitative analysis to develop an initial programme theory. RESULTS: common aims of local interventions included enhancing social connectivity, providing emotional support and advice/information; recurring interventions included social activities, emotional support, advice and information, lunch clubs, learning new skills and practical support. None were robustly evaluated.Fifty-six service user or providers were involved in interviews or focus groups, which highlighted the causes of loneliness, preferred services, access to services, thoughts about intervention configuration and desired outcomes from services.The themes emerging from the interviews/focus groups from both service provider and service user perspectives were combined with all of the previous emerging data to create an overarching programme theory. Statements were constructed to allow service providers to think about which interventions might be useful to achieve specific outcomes in different contexts. CONCLUSION: the causes and consequences of loneliness vary widely between individuals, so a personalised approach is required to identify the causes and potential solutions. This study provides some high-level principles that can help commissioners and providers to tailor interventions to the individual needs of service users.

Question 7: the first units of life were not simple cells.

Five common assumptions about the first cells are challenged by the pre-biotic ecology model and are replaced by the following propositions: firstly, early cells were more complex, more varied and had a greater diversity of constituents than modern cells; secondly, the complexity of a cell is not related to the number of genes it contains, indeed, modern bacteria are as complex as eukaryotes; thirdly, the unit of early life was an 'ecosystem' rather than a 'cell'; fourthly, the early cell needed no genes at all; fifthly, early life depended on non-covalent associations and on catalysts that were not confined to specific reactions. We present here the outlines of a theory that connects findings about modern bacteria with speculations about their origins.

The correlation between architecture and mRNA abundance in the genetic regulatory network of Escherichia coli.

BACKGROUND: Two aspects of genetic regulatory networks are the static architecture that describes the overall connectivity between the genes and the dynamics that describes the sequence of genes active at any one time as deduced from mRNA abundances. The nature of the relationship between these two aspects of these networks is a fundamental question. To address it, we have used the static architecture of the connectivity of the regulatory proteins of Escherichia coli to analyse their relationship to the abundance of the mRNAs encoding these proteins. In this we build on previous work which uses Boolean network models, but impose biological constraints that cannot be deduced from the mRNA abundances alone. RESULTS: For a cell population of E. coli, we find that there is a strong and statistically significant linear dependence between the abundance of mRNA encoding a regulatory protein and the number of genes regulated by this protein. We use this result, together with the ratio of regulatory repressors to promoters, to simulate numerically a genetic regulatory network of a single cell. The resulting model exhibits similar correlations to that of E. coli. CONCLUSION: This analysis clarifies the relationship between the static architecture of a regulatory network and the consequences for the dynamics of its pattern of mRNA abundances. It also provides the constraints on the architecture required to construct a model network to simulate mRNA production.

Compositional complementarity and prebiotic ecology in the origin of life.

We hypothesize that life began not with the first self-reproducing molecule or metabolic network, but as a prebiotic ecology of co-evolving populations of macromolecular aggregates (composomes). Each composome species had a particular molecular composition resulting from molecular complementarity among environmentally available prebiotic compounds. Natural selection acted on composomal species that varied in properties and functions such as stability, catalysis, fission, fusion and selective accumulation of molecules from solution. Fission permitted molecular replication based on composition rather than linear structure, while fusion created composomal variability. Catalytic functions provided additional chemical novelty resulting eventually in autocatalytic and mutually catalytic networks within composomal species. Composomal autocatalysis and interdependence allowed the Darwinian co-evolution of content and control (metabolism). The existence of chemical interfaces within complex composomes created linear templates upon which self-reproducing molecules (such as RNA) could be synthesized, permitting the evolution of informational replication by molecular templating. Mathematical and experimental tests are proposed.

A thermodynamic view of networks.

Networks can be described by the frequency distribution of the number of links associated with each node (the degree of the node). Of particular interest are the power law distributions, which give rise to the so-called scale-free networks, and the distributions of the form of the simplified canonical law (SCL) introduced by Mandelbrot, which give what we shall call the Mandelbrot networks. Many dynamical methods have been obtained for the construction of scale-free networks, but no dynamical construction of Mandelbrot networks has been demonstrated. Here we develop a systematic technique to obtain networks with any given distribution of the degrees of the nodes. This is done using a thermodynamic approach in which we maximise the entropy associated with degree distribution of the nodes of the network subject to certain constraints. These constraints can be chosen systematically to produce the desired network architecture. For large networks we therefore replace a dynamical approach to the stationary state by a thermodynamical viewpoint. We use the method to generate scale-free and Mandelbrot networks with arbitrarily chosen parameters. We emphasise that this approach opens the possibility of insights into a thermodynamics of networks by suggesting thermodynamic relations between macroscopic variables for networks.

Modelling autocatalytic networks with artificial microbiology.

Cells can usefully be equated to autocatalytic networks that increase in mass and then divide. To begin to model relationships between autocatalytic networks and cell division, we have written a program of artificial chemistry that simulates a cell fed by monomers. These monomers are symbols that can be assembled into linear (non-branched) polymers to give different lengths. A reaction is catalysed by a particular polymer or 'enzyme' that may itself be a reactant of that reaction (autocatalysis). These reactions are only studied within the confines of the 'cell' or 'reaction chamber'. There is a flux of material through the cell and eventually the mass of polymers reaches a threshold at which we analyse the cell. Our results indicate a similarity between the connectivity of the reaction network and that of real metabolic networks. Developing the model will entail attributing increased probabilities of reactions to polymers that are colocalised to evaluate the consequences of the dynamics of large assemblies of diverse molecules (hyperstructures) and of cell division.

Networks as constrained thermodynamic systems.

We show how a network of interconnections between nodes can be constructed to have a specified distribution of nodal degrees. This is achieved by treating the network as a thermodynamic system subject to constraints and then rewiring the system to maintain the constraints while increasing the entropy. The general construction is given and illustrated by the simple example of an exponential network. By considering the constraints as a cost function analogous to an internal energy, we obtain a characterisation of the correspondence between the intensive and extensive variables of the network. Applied to networks in living organisms, this approach may lead to macroscopic variables useful in characterising living systems.

Hypothesis: hyperstructures regulate initiation in Escherichia coli and other bacteria.

Hyperstructures or modules have been proposed to constitute a level of organisation intermediate between macromolecules and whole cells. In this model of intracellular organisation, hyperstructures compete and collaborate for existence within the membrane and cytoplasm. Those directly involved in the cell cycle include initiation, replication and division hyperstructures based on DnaA, SeqA and the 2-minute cluster, respectively. During the run-up to initiation, the mass to DNA ratio increases and, we contend, differential gene expression leads to some hyperstructures becoming more active and stable than others. This results in a drop in the diversity of hyperstructures, some of which release DnaA as they dissociate, and a DnaA-initiation hyperstructure forms. Subsequent DNA replication and cell division generate different daughter cells containing different hyperstructures. This has the advantage of increasing the phenotypic diversity of the population. In developing this model, we also invoke hyperstructures in the partitioning of origins of replication.

Hyperstructures, genome analysis and I-cells.

New concepts may prove necessary to profit from the avalanche of sequence data on the genome, transcriptome, proteome and interactome and to relate this information to cell physiology. Here, we focus on the concept of large activity-based structures, or hyperstructures, in which a variety of types of molecules are brought together to perform a function. We review the evidence for the existence of hyperstructures responsible for the initiation of DNA replication, the sequestration of newly replicated origins of replication, cell division and for metabolism. The processes responsible for hyperstructure formation include changes in enzyme affinities due to metabolite-induction, lipid-protein affinities, elevated local concentrations of proteins and their binding sites on DNA and RNA, and transertion. Experimental techniques exist that can be used to study hyperstructures and we review some of the ones less familiar to biologists. Finally, we speculate on how a variety of in silico approaches involving cellular automata and multi-agent systems could be combined to develop new concepts in the form of an Integrated cell (I-cell) which would undergo selection for growth and survival in a world of artificial microbiology.