Population–reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population–reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population–reaction model. We also show that population–reaction models can be applied to various ecological concepts, such as predator–prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms

Reaction dynamics analysis of an E. coli protein translation system by computational modeling

A single enzymatic reaction can often be described by Michaelis-Menten kinetics, but once reactions are connected to one other, it becomes difficult to understand and capture a complete description of the reaction dynamics due to its high dimensionality. To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro translation (IVT) system [1]. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady-state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand the complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale enzymatic reactions, thereby extracting the characteristics of the reaction network, including phase transitions. As the reconstituted IVT has been used for various applications inducing directed evolution of membrane proteins [2,3], the developed computational model might be useful in further enhancement of the yield of synthesized proteins using the reconstituted IVT. Please click Additional Files below to see the full abstract

Cooperative Adaptation to Establishment of a Synthetic Bacterial Mutualism

To understand how two organisms that have not previously been in contact can establish mutualism, it is first necessary to examine temporal changes in their phenotypes during the establishment of mutualism. Instead of tracing back the history of known, well-established, natural mutualisms, we experimentally simulated the development of mutualism using two genetically-engineered auxotrophic strains of Escherichia coli, which mimic two organisms that have never met before but later establish mutualism. In the development of this synthetic mutualism, one strain, approximately 10 hours after meeting the partner strain, started oversupplying a metabolite essential for the partner's growth, eventually leading to the successive growth of both strains. This cooperative phenotype adaptively appeared only after encountering the partner strain but before the growth of the strain itself. By transcriptome analysis, we found that the cooperative phenotype of the strain was not accompanied by the local activation of the biosynthesis and transport of the oversupplied metabolite but rather by the global activation of anabolic metabolism. This study demonstrates that an organism has the potential to adapt its phenotype after the first encounter with another organism to establish mutualism before its extinction. As diverse organisms inevitably encounter each other in nature, this potential would play an important role in the establishment of a nascent mutualism in nature

Robustness of a Reconstituted <i>Escherichia coli</i> Protein Translation System Analyzed by Computational Modeling

Robustness against environmental changes is one of the major features of biological systems, but its origin is not well understood. We recently constructed a large-scale computational model of an <i>Escherichia coli</i>-based reconstituted <i>in vitro</i> translation system that enumerates all protein synthesis processes in detail. Our model synthesizes a formyl-Met-Gly-Gly tripeptide (MGG peptide) from 27 initial molecular components through 968 biochemical reactions. Among the 968 kinetic parameters, 483 are nonzero parameters, and the simulator was used to determine how perturbations of 483 individual reactions affect the complex reaction network. We found that even when the kinetic parameter was changed from 100- to 0.01-fold, 94% of the changes hardly affected the two indicators of reaction dynamics in MGG peptide synthesis, which represent the yield of the MGG peptide and the initial lag-time of the peptide synthesis. Moreover, none of the indicators increased proportionally to these changes: e.g., a 100-fold increase in the kinetic parameter increased the yield by only 2.2-fold at most, indicating the insensitivity of the reaction network to perturbation. Robustness and insensitivity are likely to be a common feature of large-scale biological reaction networks

Robustness of a Reconstituted <i>Escherichia coli</i> Protein Translation System Analyzed by Computational Modeling

Robustness against environmental changes is one of the major features of biological systems, but its origin is not well understood. We recently constructed a large-scale computational model of an <i>Escherichia coli</i>-based reconstituted <i>in vitro</i> translation system that enumerates all protein synthesis processes in detail. Our model synthesizes a formyl-Met-Gly-Gly tripeptide (MGG peptide) from 27 initial molecular components through 968 biochemical reactions. Among the 968 kinetic parameters, 483 are nonzero parameters, and the simulator was used to determine how perturbations of 483 individual reactions affect the complex reaction network. We found that even when the kinetic parameter was changed from 100- to 0.01-fold, 94% of the changes hardly affected the two indicators of reaction dynamics in MGG peptide synthesis, which represent the yield of the MGG peptide and the initial lag-time of the peptide synthesis. Moreover, none of the indicators increased proportionally to these changes: e.g., a 100-fold increase in the kinetic parameter increased the yield by only 2.2-fold at most, indicating the insensitivity of the reaction network to perturbation. Robustness and insensitivity are likely to be a common feature of large-scale biological reaction networks

It's DONE: Direct ONE-shot learning with quantile weight imprinting

Learning a new concept from one example is a superior function of the human brain and it is drawing attention in the field of machine learning as a one-shot learning task. In this paper, we propose one of the simplest methods for this task with a nonparametric weight imprinting, named Direct ONE-shot learning (DONE). DONE adds new classes to a pretrained deep neural network (DNN) classifier with neither training optimization nor pretrained-DNN modification. DONE is inspired by Hebbian theory and directly uses the neural activity input of the final dense layer obtained from data that belongs to the new additional class as the synaptic weight with a newly-provided-output neuron for the new class, transforming all statistical properties of the neural activity into those of synaptic weight by quantile normalization. DONE requires just one inference for learning a new concept and its procedure is simple, deterministic, not requiring parameter tuning and hyperparameters. DONE overcomes a severe problem of existing weight imprinting methods that DNN-dependently interfere with the classification of original-class images. The performance of DONE depends entirely on the pretrained DNN model used as a backbone model, and we confirmed that DONE with current well-trained backbone models perform at a decent accuracy.Comment: 12 pages, 5 figure

A novel sequence-specific RNA quantification method using nicking endonuclease, dual-labeled fluorescent DNA probe, and conformation-interchangeable oligo-DNA

We have developed a novel, single-step, isothermal, signal-amplified, and sequence-specific RNA quantification method (L-assay). The L-assay consists of nicking endonuclease, a dual-labeled fluorescent DNA probe (DL-probe), and conformation-interchangeable oligo-DNA (L-DNA). This signal-amplified assay can quantify target RNA concentration in a sequence-specific manner with a coefficient of variation (Cv) of 5% and a lower limit of detection of 0.1 nM. Moreover, this assay allows quantification of target RNA even in the presence of a several thousandfold excess by weight of cellular RNA. In addition, this assay can be used to measure the changes in RNA concentration in real-time and to quantify short RNAs (<30 nucleotides). The L-assay requires only incubation under isothermal conditions, is inexpensive, and is expected to be useful for basic research requiring high-accuracy, easy-to-use RNA quantification, and real-time quantification