Adaptive Leap Research Articles

Research Framework of Regional Economic-Ecological System Vulnerability Evolution and Adaptive Leap under the Background of Climate Change

This study focuses on the vulnerability evolution mechanism of regional economic-ecological systems and its adaptive leapfrogging path under the background of climate change. Based on the theory of complex adaptive systems and the framework of social-ecological system coupling, a regional economic-ecological system vulnerability assessment index system was constructed. Using the system dynamics model and scenario simulation method, the impact transmission mechanism of climate change on regional economic-ecological systems was analyzed, and the spatiotemporal evolution law of system vulnerability was revealed. The study found that climate change has aggravated the vulnerability of regional economic-ecological systems by changing resource endowments, disturbing industrial structure, and affecting ecological service functions. In response to this problem, this study proposed a leapfrogging path based on adaptive management: including building a climate-resilient industrial system, optimizing resource allocation efficiency, enhancing ecosystem service functions, and improving regional adaptive governance mechanisms. The research results have important theoretical value and practical significance for promoting regional sustainable development and ecological civilization construction.

Harnessing adaptive novelty for automated generation of cancer treatments

Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However, it also leads to a rich design space which is difficult to investigate and optimize. An additional problem emerges when they are applied to cancer treatment. A heterogeneous and highly adaptable tumour can quickly become resistant to primary therapy, making it inefficient. To automate the design of potential therapies for such complex cases, we propose a computational model for fast, novelty-based machine learning exploration of the nanoparticle design space. In this paper, we present an evolvable, open-ended agent-based model, where the exploration of an initially small portion of the given state space can be expanded by an ongoing generation of adaptive novelties, whenever the simulated tumour makes an adaptive leap. We demonstrate that the nano-agents can continuously reshape themselves and create a heterogeneous population of specialized groups of individuals optimized for tracking and killing different phenotypes of cancer cells. In the conclusion, we outline further development steps so this model could be used in real-world research and clinical practice.

Host-selected mutations converging on a global regulator drive an adaptive leap towards symbiosis in bacteria.

Host immune and physical barriers protect against pathogens but also impede the establishment of essential symbiotic partnerships. To reveal mechanisms by which beneficial organisms adapt to circumvent host defenses, we experimentally evolved ecologically distinct bioluminescent Vibrio fischeri by colonization and growth within the light organs of the squid Euprymna scolopes. Serial squid passaging of bacteria produced eight distinct mutations in the binK sensor kinase gene, which conferred an exceptional selective advantage that could be demonstrated through both empirical and theoretical analysis. Squid-adaptive binK alleles promoted colonization and immune evasion that were mediated by cell-associated matrices including symbiotic polysaccharide (Syp) and cellulose. binK variation also altered quorum sensing, raising the threshold for luminescence induction. Preexisting coordinated regulation of symbiosis traits by BinK presented an efficient solution where altered BinK function was the key to unlock multiple colonization barriers. These results identify a genetic basis for microbial adaptability and underscore the importance of hosts as selective agents that shape emergent symbiont populations.

Theory of Evolutionary Molecular Engineering through Simultaneous Accumulation of Advantageous Mutations

We examined the effectiveness of an “adaptive leap” strategy using the “mutation scrambling” method as an efficient optimization technique (Uchiyama, 2000;J. Biochem.128, 441–447) for cases where mutational (rough) additivity holds in fitness. The mutation scrambling method is composed of the following three processes: (1) preliminary selection of several advantageous single-point mutations introduced in a wild-type sequence; (2) preparation of various multiple-point mutants incorporating the advantageous mutant residue or wild-type residue at each of the selected sites, by scrambling the mutant residues and wild-type residues (this process is called mutation scrambling); and (3) selection of the fittest through screening of the mutant pool. The fitness distribution in the mutant pool is controlled by the mixing ratio of the mutant residues to the wild-type residues. We focused on the mutant fitness distribution and obtained the optimal mixing ratio which efficiently generates superior multiple-point mutants with high fitnesses. As a result, we found that the optimal ratio lies between 7/3 and 9/1 in realistic cases. Particularly, this strategy works well in cases where the number of component mutations is large and the size of the population to be screened is small. Analysis of the mutant fitness distributions with various mixing ratios is also useful to explore local fitness landscapes.