AIMC Topic: Models, Biological

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Deep manifold learning reveals hidden developmental dynamics of a human embryo model.

Science advances
In this study, postimplantation human epiblast and amnion development are modeled using a stem cell-based embryoid system. A dataset of 3697 fluorescent images, along with tissue, cavity, and cell masks, is generated from experimental data. A computa...

A computational framework for inferring species dynamics and interactions with applications in microbiota ecology.

NPJ systems biology and applications
We present MBPert, a generic computational framework for inferring species interactions and predicting dynamics in time-evolving ecosystems from perturbation and time-series data. In this work, we contextualize the framework in microbial ecosystem mo...

Limits on the computational expressivity of non-equilibrium biophysical processes.

Nature communications
Many biological decision-making tasks require classifying high-dimensional chemical states. The biophysical and computational mechanisms that enable classification remain enigmatic. In this work, using Markov jump processes as an abstraction of gener...

Conditional universal differential equations capture population dynamics and interindividual variation in c-peptide production.

NPJ systems biology and applications
Universal differential equations (UDEs) are an emerging approach in biomedical systems biology, integrating physiology-driven mathematical models with machine learning for data-driven model discovery in areas where knowledge of the underlying physiol...

Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.

Biomedical engineering online
Understanding Cytochrome P450 (CYP) enzyme-mediated metabolism is critical for accurate Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, which play a pivotal role in drug discovery. Traditional approaches, while foun...

Exploration of Predictive Potential of AI-enabled Portable System in Anticancer Drug Delivery: A Comparative Study with Modified Gompertz like Biphasic Response Model.

AAPS PharmSciTech
Mathematical models are conventionally used to understand the of tumor behaviors, but they generally lack in precisely correlating drug efficacy with tumor response. Artificial intelligence (AI) has forged a new avenue in cancer management, but requi...

Predicting the Site-Specific Toxicity of Metals to Fishes Using a New Machine Learning-Based Approach.

Environmental science & technology
Fishes of various trophic levels play an important role in the stability and balance of aquatic ecosystems. Metal contaminants can impair the survival and population fitness of fish at elevated concentrations. When universal water quality criteria (W...

Prediction of Internal Exposures after Virtual Oral Doses of Disparate Chemicals in Rats and Humans Using Simplified Physiologically Based Pharmacokinetic Models with Generated Input Parameters.

Chemical research in toxicology
Toxicological evaluation of industrial chemicals with a broad range of chemical structures, for example, bioactive food components, toxic food-derived compounds, and drugs, usually involves the estimation of human clearance by allometric extrapolatio...

Chemotactic navigation in robotic swimmers via reset-free hierarchical reinforcement learning.

Nature communications
Microorganisms have evolved diverse strategies to propel themselves in viscous fluids, navigate complex environments, and exhibit taxis in response to stimuli. This has inspired the development of miniature robots, where artificial intelligence (AI) ...

Tailoring neuromuscular dynamics: A modeling framework for realistic sEMG simulation.

PloS one
This study introduces an advanced computational model for simulating surface electromyography (sEMG) signals during muscle contractions. The model integrates five elements that simulate the chain of processes from motor intention to voltage variation...