Semi-Adaptive Evolution with Spontaneous Modularity of Half-Chaotic Randomly Growing Autonomous and Open Networks

Up until now, studies of Kauffman network stability have focused on the conditions resulting from the structure of the network. Negative feedbacks have been modeled as ice (nodes that do not change their state) in an ordered phase but this blocks the possibility of breaking out of the range of correct operation. This first, very simplified approximation leads to some incorrect conclusions, e.g., that life is on the edge of chaos. We develop a second approximation, which discovers half-chaos and shows its properties. In previous works, half-chaos has been confirmed in autonomous networks, but only using node function disturbance, which does not change the network structure. Now we examine half-chaos during network growth by adding and removing nodes as a disturbance in autonomous and open networks. In such evolutions controlled by a ‘small change’ of functioning after disturbance, the half-chaos is kept but spontaneous modularity emerges and blurs the picture. Half-chaos is a state to be expected in most of the real systems studied, therefore the determinants of the variability that maintains the half-chaos are particularly important in the application of complex network knowledge.

ESD Ideas: A Global Warming Scaling Law

In this study, we highlight a component of global warming variability, a scaling law that is based purely on fundamental physical properties of the climate system. We suggest that three similarity parameters define the system response to external forcing, and an argument of physical similarity with observed climate responses in the past can be made when all three parameters are identical for the current and historical climates. We determined that the scaling law of global warming is the (𝜆 + 1 + m) – power of time, where 𝜆 is prescribed by external forcing and m is defined by climate system internal dynamics. When the climate system develops in the direction of intensified positive feedbacks, the power m changes from m = −1 (negative feedbacks dominate) to m ≥ 1 (positive feedbacks dominate). We also establish that a “hothouse” climate with dominant positive feedbacks will be preceded by a climate having a property of incomplete similarity in feedbacks similarity parameters. It implies that the same future scenario may be produced by climate feedbacks of different magnitudes as long as their positive-to-negative ratio is the same.

Combination of successor representation-based appetitive learning and individual representation-based aversive learning is a good strategy and can be implemented in the cortico-basal ganglia circuits

While positive reward prediction errors (RPEs) and negative RPEs have equal impacts in the standard reinforcement learning, the brain appears to have distinct neural pathways for learning mainly from either positive or negative feedbacks, such as the direct and indirect pathways of the basal ganglia (BG). Given that distinct pathways may receive inputs unevenly from different neural populations and/or regions, how states or actions are represented can differ between the pathways. We explored whether combined use of different representations, coupled with different learning rates from positive and negative RPEs, has computational benefits. We considered an agent equipped with two learning systems, each of which adopted individual representation (IR) or successor representation (SR) of states. With varying the combination of IR or SR and also the learning rates from positive and negative RPEs in each system, we examined how the agent performed in a certain dynamic reward environment. We found that combination of SR-based system learning mainly from positive RPEs and IR-based system learning mainly from negative RPEs outperformed the other combinations, including IR-only or SR-only cases and the cases where the two systems had the same ratios of positive- and negative-RPE-based learning rates. In the best combination case, both systems show activities of comparable magnitudes with opposite signs, consistent with suggested profiles of BG pathways. These results suggest that particularly combining different representations with appetitive and aversive learning could be an effective learning strategy in a certain dynamic reward environment, and it might actually be implemented in the cortico-BG circuits.

De novo Explorations of Sarcopenia via a Dynamic Model

Background: The cause of sarcopenia has been observed over decades by clinical trials, which, however, are still insufficient to systematically unravel the enigma of how resistance exercise mediates skeletal muscle mass.Materials and Methods: Here, we proposed a minimal regulatory network and developed a dynamic model to rigorously investigate the mechanism of sarcopenia. Our model is consisted of eight ordinary differential equations and incorporates linear and Hill-function terms to describe positive and negative feedbacks between protein species, respectively.Results: A total of 720 samples with 10 scaled intensities were included in simulations, which revealed the expression level of AKT (maximum around 3.9-fold) and mTOR (maximum around 5.5-fold) at 3, 6, and 24 h at high intensity, and non-monotonic relation (ranging from 1.2-fold to 1.7-fold) between the graded intensities and skeletal muscle mass. Furthermore, continuous dynamics (within 24 h) of AKT, mTOR, and other proteins were obtained accordingly, and we also predicted the delaying effect with the median of maximized muscle mass shifting from 1.8-fold to 4.6-fold during a 4-fold increase of delay coefficient.Conclusion: The de novo modeling framework sheds light on the interdisciplinary methodology integrating computational approaches with experimental results, which facilitates the deeper understandings of exercise training and sarcopenia.

Host–pathogen immune feedbacks can explain widely divergent outcomes from similar infections

A long-standing question in infection biology is why two very similar individuals, with very similar pathogen exposures, may have very different outcomes. Recent experiments have found that even isogenic Drosophila melanogaster hosts, given identical inoculations of some bacterial pathogens at suitable doses, can experience very similar initial bacteria proliferation but then diverge to either a lethal infection or a sustained chronic infection with much lower pathogen load. We hypothesized that divergent infection outcomes are a natural result of mutual negative feedbacks between pathogens and the host immune response. Here, we test this hypothesis in silico by constructing process-based dynamic models for bacterial population growth, host immune induction and the feedbacks between them, based on common mechanisms of immune system response. Mathematical analysis of a minimal conceptual model confirms our qualitative hypothesis that mutual negative feedbacks can magnify small differences among hosts into life-or-death differences in outcome. However, explaining observed features of chronic infections requires an extension of the model to include induced pathogen modifications that shield themselves from host immune responses at the cost of reduced proliferation rate. Our analysis thus generates new, testable predictions about the mechanisms underlying bimodal infection outcomes.

Integrated dominance mechanisms regulate reproductive architecture in Arabidopsis thaliana and Brassica napus

The production of seed in flowering plants is complicated by the need to first invest in reproductive shoots, inflorescences, flowers and fruit. Furthermore, in many species, it will be months between plants generating flowers and setting seed. How can plants therefore produce an optimal seed-set relative to environmental resources when the ‘reproductive architecture’ that supports seed-set needs to be elaborated so far in advance? Here, we address this question by investigating the spatio-temporal control of reproductive architecture in Arabidopsis (Arabidopsis thaliana) and Brassica napus. We show that resource- and resource-related signals such as substrate volume play a key role in determining the scale of reproductive effort, and that this is reflected in the earliest events in reproductive development, which broadly predicts the subsequent reproductive effort. We show that a series of negative feedbacks both within and between developmental stages prevent plants from over-committing to early stages of development. These feedbacks create a highly plastic, homeostatic system in which additional organs can be produced in the case of reproductive failure elsewhere in the system. We propose that these feedbacks represent an ‘integrated dominance’ mechanism that allows resource use to be correctly sequenced between developmental stages to optimise seed set.

Biogeochemical and plant trait mechanisms drive enhanced methane emissions in response to whole-ecosystem warming

Climate warming perturbs ecosystem carbon (C) cycling, causing both positive and negative feedbacks on greenhouse gas emissions. In 2016, we began a tidal marsh field experiment in two vegetation communities to investigate the mechanisms by which whole-ecosystem warming alters C gain, via plant-driven sequestration in soils, and C loss, primarily via methane (CH4) emissions. Here, we report the results from the first 4 years. As expected, warming of 5.1 ∘C more than doubled CH4 emissions in both plant communities. We propose this was caused by a combination of four mechanisms: (i) a decrease in the proportion of CH4 consumed by CH4 oxidation, (ii) more C substrates available for methanogenesis, (iii) reduced competition between methanogens and sulfate-reducing bacteria, and (iv) indirect effects of plant traits. Plots dominated by Spartina patens consistently emitted more CH4 than plots dominated by Schoenoplectus americanus, indicating key differences in the roles these common wetland plants play in affecting anaerobic soil biogeochemistry and suggesting that plant composition can modulate coastal wetland responses to climate change.