Biology subjects

Chandrasekhar, A.

Publications and source records attributed to Chandrasekhar, A..

6 recordsLinked to original sources

bioRxiv2026

Gravitropism Shapes the Pareto Front of Root System Architecture

The root systems of wild tomatoes (S. Pimpinellifolium) can be understood as biological networks in which the lateral roots branch from a single main root and together balance two competing objectives: minimizing the material cost of building the network (wiring cost) and minimizing the transport time from the root tips to the shoot (conduction delay). Our prior work showed that S. Pimpinellifolium root architectures cluster near the Pareto-optimal front defined by these two objectives, with morphological diversity resolving into four qualitative topologies ((Chandrasekhar and Julkowska, 2022)). That framework assumed lateral roots grow as straight lines - ignoring gradual onset of lateral root gravitropism, the tendency of roots to curve toward the gravity vector. Because curved trajectories are longer than straight lines, gravitropism directly increases both wiring cost and conduction delay, constraining which architectures are physically realizable and thereby reshaping the Pareto front itself. Here we extend the model to explicitly incorporate lateral root gravitropism, producing predicted architectures that align much more closely with observed S. Pimpinellifolium root systems. We present a computational method to infer gravitropic sensitivity directly from anatomical tracing data - without reorientation assays - and apply it to 2423 arbors across different root topologies, growth conditions, and hormone treatments. Incorporating gravitropism reveals variation invisible to the straight-line model:notably, lateral roots show reduced gravitropic sensitivity under salt stress, mirroring a phenomenon previously described only for main roots and overlooked in lateral roots until now.

plant biology↗

bioRxiv2025

Growth Cost and Transport Efficiency Tradeoffs Define Root System Optimization Across Varying Developmental Stages and Environments in Arabidopsis

Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana, we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.

plant biology↗

bioRxiv2025

Large Language Model Agent for Modular Task Execution in Drug Discovery

We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By combining LLM reasoning with domain-specific tools, the framework performs biomedical data retrieval, domain-specific question answering, molecular generation, property prediction, property-aware molecular refinement, and 3D protein-ligand structure generation. In a case study targeting BCL-2 in lymphocytic leukemia, the agent autonomously retrieved relevant biomolecular information--including FASTA sequences, SMILES representations, and literature--and answered mechanistic questions with improved contextual accuracy over standard LLMs. It then generated chemically diverse seed molecules and predicted 67 ADMET-related properties, which guided iterative molecular refinement. Across two refinement rounds, the number of molecules with QED > 0.6 increased from 34 to 55, and those passing at least four out of five empirical drug-likeness rules rose from 29 to 52, within a pool of 194 molecules. The framework also employed Boltz-2 to generate 3D protein-ligand complexes and provide rapid binding affinity estimates for candidate compounds. These results demonstrate that the approach effectively supports molecular screening, prioritization, and structure evaluation. Its modular design enables flexible integration of evolving tools and models, providing a scalable foundation for AI-assisted therapeutic discovery.

pharmacology and toxicology↗

bioRxiv2021

A mathematical framework for analyzing wild tomato root architecture

The root architecture of wild tomato, Solanum pimpinellifolium, can be viewed as a network connecting the main root to various lateral roots. Several constraints have been proposed on the structure of such biological networks, including minimizing the total amount of wire necessary for constructing the root architecture (wiring cost), and minimizing the distances (and by extension, resource transport time) between the base of the main root and the lateral roots (conduction delay). For a given set of lateral root tip locations, these two objectives compete with each other -- optimizing one results in poorer performance on the other -- raising the question how well S. pimpinellifolium root architectures balance this network design trade-off in a distributed manner. Here, we describe how well S. pimpinellifolium roots resolve this trade-off using the theory of Pareto optimality. We describe a mathematical model for characterizing the network structure and design trade-offs governing the structure of S. pimpinellifolium root architecture. We demonstrate that S. pimpinellifolium arbors construct architectures that are more optimal than would be expected by chance. Finally, we use this framework to quantify structural differences between arbors grown in the presence of salt stress, classify arbors into four distinct architectural ideotypes, and test for heritability of variation in root architecture structure.

plant biology↗

bioRxiv2019

Better tired than lost: turtle ant trail networks favor coherence over shortest paths

Creating a routing backbone is a fundamental problem in both biology and engineering. The routing backbone of the trail networks of arboreal turtle ants (Cephalotes goniodontus) connects many nests and food sources using trail pheromone deposited by ants as they walk. Unlike species that forage on the ground, the trail networks of arboreal ants are constrained by the vegetation. We examined what objectives the trail networks meet by comparing the observed ant trail networks with networks of random, hypothetical trail networks in the same surrounding vegetation and with trails optimized for four objectives: minimizing path length, minimizing average edge length, minimizing number of nodes, and minimizing opportunities to get lost. The ants trails minimized path length by minimizing the number of nodes traversed rather than choosing short edges. In addition, the ants trails reduced the opportunity for ants to get lost at each node, favoring nodes with 3D configurations most likely to be reinforced by pheromone. Thus, rather than finding the shortest edges, turtle ant trail networks take advantage of natural variation in the environment to favor coherence, keeping the ants together on the trails. Author SummaryWe investigated the trail networks of arboreal turtle ants in the canopy of the tropical forest, to ask what characterizes the colonys choice of foraging paths within the vegetation. We monitored day to day changes in the junctions and edges of trail networks of colonies in the dry forest of western Mexico. We compared the paths used by the ants to simulated random paths in the surrounding vegetation. We found that the paths of turtle ants prioritize coherence, keeping ants together on the trail, over minimizing the average edge length. The choice of paths reduces the number of junctions in the trail where ants could get lost, and favors junctions with a physical configuration that makes it likely that successive ants will reinforce the same path. Our work suggests that design principles that emphasize keeping information flow constrained to streamlined, coherent trails may be useful in human-designed distributed routing and transport networks or robot swarms.

ecology↗

bioRxiv2017

A distributed algorithm to maintain and repair the trail networks of arboreal ants

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithms success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

bioinformatics↗