Biology subjects

Kinjo, A. R.

Publications and source records attributed to Kinjo, A. R..

5 recordsLinked to original sources

bioRxiv2026

BacteReason: A Reasoning Model for Antimicrobial Resistance Prediction

The rapid global spread of antimicrobial resistance (AMR) has placed unprecedented pressure on clinical decision-making. Machine learning predictors of antibiotic susceptibility exist, but their lack of mechanistic grounding limits credibility. We present BO_SCPLOWACTEC_SCPLOWRO_SCPLOWEASONC_SCPLOW, a reasoning large language model (LLM) that predicts bacterial susceptibility to a target antibiotic, together with a mechanistic rationale. BacteReason is obtained by fine-tuning an open-weight LLM on clinical susceptibility data augmented with rationales that explain the molecular mechanisms. These rationales are produced by a proprietary teacher LLM prompted to explain known susceptibility outcomes. The teacher is interfaced via TogoMCP with a collection of biomedical knowledge-graph databases, grounding each reasoning step in retrieved evidence. On an extrapolation benchmark, BO_SCPLOWACTEC_SCPLOWRO_SCPLOWEASONC_SCPLOW achieves a relative improvement of 43% over the untuned baseline and 38% over the same base LLM fine-tuned without rationales, demonstrating that reasoning supervision improves prediction accuracy.

bioinformatics↗

bioRxiv2026

TogoMCP: Natural Language Querying of Life-Science Knowledge Graphs via Schema-Guided LLMs and the Model Context Protocol

Querying the RDF Portal knowledge graph maintained by DBCLS--which aggregates approximately 60 life-science databases--requires proficiency in both SPARQL and database-specific RDF schemas, placing this resource beyond the reach of most researchers. Large Language Models (LLMs) can, in principle, translate natural-language questions into executable SPARQL, but without schema-level context, they frequently fabricate non-existent predicates or fail to resolve entity names to database-specific identifiers. We present TogoMCP, a system that recasts the LLM as a protocol-driven inference engine orchestrating specialized tools via the Model Context Protocol (MCP). Two mechanisms are essential to its design: (i) the MIE (Metadata-Interoperability-Exchange) file, a concise YAML document that dynamically supplies the LLM with each target databases structural and semantic context at query time; and (ii) a two-stage workflow separating entity resolution via external REST APIs from schema-guided SPARQL generation. On a benchmark of 50 biologically grounded questions spanning five types and 23 databases, TogoMCP achieved a large improvement over an unaided baseline (Cohens d = 1.82, Wilcoxon p < 0.001), with win rates exceeding 80% for question types with precise, verifiable answers. An ablation study shows that all component configurations deliver significant improvements, with MIE schema files providing the largest marginal contribution on mean per-question score ({Delta} = +0.50 relative to a no-MIE condition, two-sided Wilcoxon p = 0.067; 90% bootstrap CI [+0.04, +0.94] excludes zero); a one-line instruction to load the relevant MIE file recovers the same mean improvement as a full procedural protocol, while the protocol additionally reduces downside risk (loss rate 1.6% vs. 4.8%, Fisher p = 0.036). These results suggest a general design principle: concise, dynamically delivered schema context is more valuable than complex orchestration logic for mean-score performance, while procedural guidance plays a complementary role in narrowing variance. Database URL: https://togomcp.rdfportal.org/

bioinformatics↗

bioRxiv2023

Plasticity-led and mutation-led evolutions are discrete modes of the same developmental gene regulatory network

The standard theory of evolution proposes that mutations cause heritable variations, which are naturally selected, leading to evolution. However, this mutation-led evolution (MLE) is being questioned by an alternative theory called plasticity-led evolution (PLE). PLE suggests that an environmental change induces adaptive phenotypes, which are later genetically accommodated. According to PLE, developmental systems should be able to respond to environmental changes adaptively. However, developmental systems are known to be robust against environmental and mutational perturbations. Thus, we expect a transition from a robust state to a plastic one. To test this hypothesis, we constructed a gene regulatory network (GRN) model that integrates developmental processes, hierarchical regulation, and environmental cues. We then simulated its evolution over different magnitudes of environmental changes. Our findings indicate that this GRN model exhibits PLE under large environmental changes and MLE under small environmental changes. Furthermore, we observed that the GRN model is susceptible to environmental or genetic fluctuations under large environmental changes but is robust under small environmental changes. This indicates a breakdown of robustness due to large environmental changes. Before the breakdown of robustness, the distribution of phenotypes is biased and aligned to the environmental changes, which would facilitate rapid adaptation should a large environmental change occur. These observations suggest that the evolutionary transition from mutation-led to plasticity-led evolution is due to a developmental transition from robust to susceptible regimes over increasing magnitudes of environmental change. Thus, the GRN model can reconcile these conflicting theories of evolution.

evolutionary biology↗

bioRxiv2023

Plasticity-led evolution as an intrinsic property of developmental gene regulatory networks

The Modern Evolutionary Synthesis seemingly fails to explain how a population can survive a large environmental change: the pre-existence of heritable variants adapted to the novel environment is too opportunistic, whereas the search for new adaptive mutations after the environmental change is so slow that the population may go extinct. Plasticity-led evolution, the initial environmental induction of a novel adaptive phenotype followed by genetic accommodation, has been proposed to solve this problem. However, the mechanism enabling plasticity-led evolution remains unclear. Here, we present computational models that exhibit behaviors compatible with plasticity-led evolution by extending the Wagner model of gene regulatory networks. The models show adaptive plastic response and the uncovering of cryptic mutations under large environmental changes, followed by genetic accommodation. Moreover, these behaviors are consistently observed over distinct novel environments. We further show that environmental cues, developmental processes, and hierarchical regulation cooperatively amplify the above behaviors and accelerate evolution. These observations suggest plasticity-led evolution is a universal property of complex developmental systems independent of particular mutations.

evolutionary biology↗

bioRxiv2018

Mechanism of evolution by genetic assimilation: Equivalence and independence of genetic mutation and epigenetic modulation in phenotypic expression

Conrad H. Waddington discovered the phenomenon of genetic assimilation through a series of experiments on fruit flies. In those experiments, artificially exerted environmental stress induced plastic phe-notypic changes in the fruit flies, but after some generations the same phenotypic variant started to appear without the environmental stress. Both the initial state (where the phenotypic changes were environmentally induced and plastic) and the final state (where the phenotypic changes were genetically fixed and constitutive) are experimental facts. However, it remains unclear how the environmentally induced phenotypic change in the first generation becomes genetically fixed in the central process of genetic assimilation itself. We have argued that the key to understanding the mechanism of genetic assimilation lies in epigenetics, and proposed the \"cooperative model\" in which the evolutionary process depends on both genetic and epigenetic factors. Evolutionary simulations based on the cooperative model reproduced the process of genetic assimilation. Detailed analysis of the trajectories has revealed genetic assimilation is a process in which epigenetically induced phe-notypic changes are incrementally and statistically replaced with multiple minor genetic mutations through natural selection. In this scenario, epigenetic and genetic changes may be considered as mutually independent but equivalent in terms of their effects on phenotypic changes. This finding rejects the common (and confused) hypothesis that epigenetically induced phe-notypic changes depend on genetic mutations. Furthermore, we argue that transgenerational epigenetic inheritance is not required for evolution by genetic assimilation.

evolutionary biology↗