Interface Focus最新文献

Combining engineering and biology surely must be a route to delivering solutions to the world's most pressing problems in depleting resources, energy and the environment. Engineers and biologists have long recognized the power in coupling their disciplines and have evolved a healthy variety of approaches to realizing technologies. Yet recently, there has been a movement to narrow the remit of engineering biology. Its definition as 'the application of engineering principles to the design of biological systems' ought to encompass a broad church. However, the emphasis is firmly on construction '…of novel biological devices and systems from standardized artificial parts' within cells. Thus, engineering biology has become synonymous with synthetic biology, despite the many longstanding technologies that use natural microbial communities. The focus on the nuts and bolts of synthetic organisms may be deflecting attention from the significant challenge of delivering solutions at scale, which cuts across all engineering biology, synthetic and natural. Understanding, let alone controlling, every component of an engineered system is an unrealistic goal. To realize workable solutions in a timely manner we must develop systematic ways of engineering biology in the face of the uncertainties that are inherent in biological systems and that arise through lack of knowledge.

High-throughput 16S rRNA gene amplicon sequencing technology is widely applied for environmental microbiota structure analysis to derive knowledge that informs microbiome-based surveillance and oriented bioengineering. However, it remains elusive how the selection of 16S rRNA gene hypervariable regions and reference databases affects microbiota diversity and structure profiling. This study systematically evaluated the fitness of different frequently used reference databases (i.e. SILVA 138 SSU, GTDB bact120_r207, Greengenes 13_5 and MiDAS 4.8) and primers of 16S rRNA gene in microbiota profiling of anaerobic digestion and activated sludge collected from a full-scale swine wastewater treatment plant (WWTP). The comparative results showed that MiDAS 4.8 achieved the highest levels of taxonomic diversity and species-level assignment rate. For whichever sample groups, microbiota richness captured by different primers decreased in the following order: V4 > V4-V5 > V3-V4 > V6-V8/V1-V3. Using primer-bias-free metagenomic data results as the judging standard, V4 region also best characterized microbiota structure and well represented typical functional guilds (e.g. methanogens, ammonium oxidizers and denitrifiers), while V6-V8 regions largely overestimated the archaeal methanogens (mainly Methanosarcina) by over 30 times. Therefore, MiDAS 4.8 database and V4 region are recommended for best simultaneous analysis of bacterial and archaeal community diversity and structure of the examined swine WWTP.

Engineered ecosystems span multiple volume scales, from a nano-scale to thousands of cubic metres. Even the largest industrial systems are tested in pilot scale facilities. But does scale affect outcomes? Here we look at comparing different size laboratory anaerobic fermentors to see if and how the volume of the community affects the outcome of community coalescence (combining multiple communities) on community composition and function. Our results show that there is an effect of scale on biogas production. Furthermore, we see a link between community evenness and volume, with smaller scale communities having higher evenness. Despite those differences, the overall patterns of community coalescence are very similar at all scales, with coalescence leading to levels of biogas production comparable with that of the best-performing component community. The increase in biogas with increasing volume plateaus, suggesting there is a volume where productivity stays stable over large volumes. Our findings are reassuring for ecologists studying large ecosystems and industries operating pilot scale facilities, as they support the validity of pilot scale studies in this field.

A heterotrophic-specialist model was proposed previously to divide wastewater treatment plant (WWTP) heterotrophs into sub-guilds of consumers of readily or slowly degradable substrates (RDS or SDS, respectively). The substrate degradation rate model coupled to metabolic considerations predicted that RNA and polyhydroxyalkanoate (PHA) levels would be positively correlated in the activated sludge communities with high RNA and PHA occurring in RDS-consumers, and low RNA with no PHA accumulation occurring in SDS-consumers because their external substrates are always present. This prediction was verified in previous studies and in the current one. Thus, RNA and PHA levels were used as biomarkers of the RDS- and SDS-consumer sub-guilds for cell sorting using flow cytometry of samples from three WWTPs. Subsequently, 16S rRNA gene amplicon sequencing revealed that the sorted groups were highly similar over time and among WWTPs, and demonstrated a clear segregation by RNA levels. Predicted ecophysiological traits based on 16S rRNA phylogeny suggested that the high-RNA population showed RDS-consumer traits such as higher rrn copy numbers per genome. Using a mass-flow immigration model, it appeared that the high-RNA populations exhibited high immigration rates more frequently than low-RNA populations, but the differences in frequencies were less with increasing solids residence times.

Both deterministic and stochastic forces shape biofilm communities, but the balance between those forces is variable. Quantifying the balance is both desirable and challenging. For example, drift-driven failure, a stochastic force, can be thought of as an organism experiencing 'bad luck' and manipulating 'luck' as a factor in real-world systems is difficult. We used an agent-based model to manipulate luck by controlling seed cevalues governing random number generation. We determined which organism among identical competitors experienced the greatest drift-driven failure, gave it a deterministic growth advantage and re-ran the simulation with the same seed. This enabled quantifying the growth advantage required to overcome drift, e.g. a 50% chance to thrive may require a 10-20% improved growth rate. Further, we found that crowding intensity affected that balance. At moderate spacings, there were wide ranges where neither drift nor selection dominated. Those ranges shrank at extreme spacings; close and loose crowding, respectively, favoured drift and selection. We explain how these results may partially illuminate two conundrums: the fact that a stably operating wastewater treatment plant's microbial community can vary greatly over time and the difference between equivalent and total community size in neutral community assembly models.

Life is a constant battle against equilibrium. From the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. We present a framework based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. Previous studies in human and non-human primates have shown that decreased consciousness states such as sleep and anaesthesia result in brain dynamics closer to the equilibrium. Furthermore, there is growing interest in the analysis of brain symmetry based on neuroimaging recordings and since it is a non-invasive technique, it can be extended to different brain imaging modalities and applied at different temporo-spatial scales. In the present study, we provide a detailed description of our methodological approach, paying special attention to the theories that motivated this work. We test, for the first time, the reversibility analysis in human functional magnetic resonance imaging data in patients suffering from disorder of consciousness. We verify that the tendency of a decrease in the asymmetry of the brain signal together with the decrease in non-stationarity are key characteristics of impaired consciousness states. We expect that this work will open the way for assessing biomarkers for patients' improvement and classification, as well as motivating further research on the mechanistic understanding underlying states of impaired consciousness.

Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.

The paper studies principles behind structured, especially symmetric, representations through enforced inter-agent conformity. For this, we consider agents in a simple environment who extract individual representations of this environment through an information maximization principle. The representations obtained by different agents differ in general to some extent from each other. This gives rise to ambiguities in how the environment is represented by the different agents. Using a variant of the information bottleneck principle, we extract a 'common conceptualization' of the world for this group of agents. It turns out that the common conceptualization appears to capture much higher regularities or symmetries of the environment than the individual representations. We further formalize the notion of identifying symmetries in the environment both with respect to 'extrinsic' (birds-eye) operations on the environment as well as with respect to 'intrinsic' operations, i.e. subjective operations corresponding to the reconfiguration of the agent's embodiment. Remarkably, using the latter formalism, one can re-wire an agent to conform to the highly symmetric common conceptualization to a much higher degree than an unrefined agent; and that, without having to re-optimize the agent from scratch. In other words, one can 're-educate' an agent to conform to the de-individualized 'concept' of the agent group with comparatively little effort.

Complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behaviour science, evolutionary developmental biology and the field of machine intelligence all seek to understand the scaling of biological cognition: what enables individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence with large-scale goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioural intelligence by scaling up homeostatic competencies of cells in metabolic space. In this article, we created a minimal in silico system (two-dimensional neural cellular automata) and tested the hypothesis that evolutionary dynamics are sufficient for low-level setpoints of metabolic homeostasis in individual cells to scale up to tissue-level emergent behaviour. Our system showed the evolution of the much more complex setpoints of cell collectives (tissues) that solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French flag problem in developmental biology). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve the target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover, we observed an unexpected behaviour of sudden remodelling long after the system stabilizes. We tested this prediction in a biological system-regenerating planaria-and observed a very similar phenomenon. We propose that this system is a first step towards a quantitative understanding of how evolution scales minimal goal-directed behaviour (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.