To model complex systems, individual-based models (IBMs), sometimes called \"agent-based models\" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.

Physiological determinants of drug dosing (PDODD) are a promising approach for precision dosing. This study investigates the alterations of PDODD in diseases and evaluates a variational autoencoder (VAE) artificial intelligence model for PDODD. The PDODD panel contained 20 biomarkers, and 13 renal, hepatic, diabetes, and cardiac disease status variables. Demographic characteristics, anthropometric measurements (body weight, body surface area, waist circumference), blood (plasma volume, albumin), renal (creatinine, glomerular filtration rate, urine flow, and urine albumin to creatinine ratio), and hepatic (R-value, hepatic steatosis index, drug-induced liver injury index), blood cell (systemic inflammation index, red cell, lymphocyte, neutrophils, and platelet counts) biomarkers, and medical questionnaire responses from the National Health and Nutrition Examination Survey (NHANES) were included. The tabular VAE (TVAE) generative model was implemented with the Synthetic Data Vault Python library. The joint distributions of the generated data vs. test data were compared using graphical univariate, bivariate, and multidimensional projection methods and distribution proximity measures. The PDODD biomarkers related to disease progression were altered as expected in renal, hepatic, diabetes, and cardiac diseases. The continuous PDODD panel variables generated by the TVAE satisfactorily approximated the distribution in the test data. The TVAE-generated distributions of some discrete variables deviated from the test data distribution. The age distribution of TVAE-generated continuous variables was similar to the test data. The TVAE algorithm demonstrated potential as an AI model for continuous PDODD and could be useful for generating virtual populations for clinical trial simulations.

Cellular Potts models are broadly applied across developmental biology and cancer research. We overcome limitations of the traditional approach, which reinterprets a modified Metropolis sampling as ad hoc dynamics, by introducing a physical timescale through Poissonian kinetics and by applying principles of stochastic thermodynamics to separate thermal and relaxation effects from athermal noise and nonconservative forces. Our method accurately describes cell-sorting dynamics in mouse-embryo development and identifies the distinct contributions of nonequilibrium processes, e.g., cell growth and active fluctuations.

One of the key problems in active materials is the control of shape through actuation. A fascinating example of such control is the elephant trunk, a long, muscular, and extremely dexterous organ with multiple vital functions. The elephant trunk is an object of fascination for biologists, physicists, and children alike. Its versatility relies on the intricate interplay of multiple unique physical mechanisms and biological design principles. Here, we explore these principles using the theory of active filaments and build, theoretically, computationally, and experimentally, a minimal model that explains and accomplishes some of the spectacular features of the elephant trunk.

Plant-based meat analogs have increasingly attracted the attention of the food industry in recent years. However, the digestion behavior of this innovative solid food in human stomach is poorly understood. In this study, plant-based meat analogs with different internal structures were prepared with/without high-moisture extrusion technology and at different temperatures. A semi-dynamic gastric digestion system which involves the mimic processes of the secretion of gastric juice and the gastric emptying was applied. After extrusion treatment at high temperature (150 ℃), the EHT had the highest anisotropic index (H⊥/H∥=1.90) and an ideal meat-like structure. It was found that particle disintegration and swelling simultaneously occurred in the bolus of the EHT but not in the sample without extrusion treatment (the HLT) in the early stage of gastric digestion. This difference might be attributed to the compact and well-arranged anisotropic structure of the EHT resulting from the extrusion, and leads to difficult enzymatic hydrolyzation unless the particles swell and unfold the polymer chains. The difficulty in particle disintegration in the EHT during gastric digestion is the consequence of the relatively slow gastric emptying rate and the decrease of protein degradation. As a result, the EHT which underwent extrusion treatment at high temperature and possessed the best anisotropic fibrous structure exhibited the slowest gastric digestion. This novel solid food shows good potential as a desired nutritional food for people on diet.

Ageing leads to changes in the functionality of the digestive tract but the effect of age on digestion and absorption of nutrients remains unclear. The objective of this study was to investigate in vitro the digestion of two high-protein dairy products similar to cream cheese (24 % w/w proteins, 20 % w/w lipids) with opposite casein to whey protein ratios, 80:20 (WP-20), and 20:80 (WP-80). The new static digestion model adapted to the general older adult population (≥65 y.) proposed by INFOGEST was used, as well as the standard version of the protocol. Kinetics of proteolysis and lipolysis were compared between both models for each product, in the gastric and intestinal phases of digestion. In both cream cheeses, the degree of protein hydrolysis (DH-P) was significantly lower for older adults than for young adults at the end of the gastric phase (-19 % for WP-20, and -44 % for WP-80), and at the end of the intestinal phase (-16 % for WP-20, and -20 % for WP-80). The degree of lipid hydrolysis (DH-L) was also significantly lower for older adults than for young adults at the end of the digestion for WP-20 (-30 %), but interestingly it was not the case for WP-80 (similar DH-L were measured). Free fatty acids were also released faster from WP-80 than from WP-20 in both digestion conditions: after 5 min of intestinal digestion DH-L was already ≈32 % for WP-80 against 14 % for WP-20. This was attributed to the opposite casein to whey protein ratios, leading to the formation of different gel structures resulting in different patterns of deconstruction in the gastrointestinal tract. This study highlights the fact that it is essential to carefully consider the composition, structure, and digestibility of foods to develop products adapted to the specific needs of the older adult population.

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BACKGROUND: Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared.
RESULTS: We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods.
METHODS: Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.

Predictive neuromuscular simulations are a powerful tool for studying the biomechanics of human walking, and deriving design criteria for technical devices like prostheses or biorobots. Good agreement between simulation and human data is essential for transferability to the real world. The human foot is often modeled with a single rigid element, but knowledge of how the foot model affects gait prediction is limited. Standardized procedures for selecting appropriate foot models are lacking. We performed 2D predictive neuromuscular simulations with six different foot models of increasing complexity to answer two questions: What is the effect of a mobile arch, a toe joint, and the coupling of toe and arch motion through the plantar fascia on gait prediction? and How much of the foot\'s anatomy do we need to model to predict sagittal plane walking kinematics and kinetics in good agreement with human data? We found that the foot model had a significant impact on ankle kinematics during terminal stance, push-off, and toe and arch kinematics. When focusing only on hip and knee kinematics, rigid foot models are sufficient. We hope our findings will help guide the community in modeling the human foot according to specific research goals and improve neuromuscular simulation accuracy.

We used computer simulations of growth, mating and death of cephalopods and fishes to explore the effect of different life-history strategies on the relative prevalence of alternative male mating strategies. Specifically, we investigated the consequences of single or multiple matings per lifetime, mating strategy switching, cannibalism, resource stochasticity, and altruism towards relatives. We found that a combination of single (semelparous) matings, cannibalism and an absence of mating strategy changes in one lifetime led to a more strictly partitioned parameter space, with a reduced region where the two mating strategies co-exist in similar numbers. Explicitly including Hamilton\'s rule in simulations of the social system of a Cichlid led to an increase of dominant males, at the expense of both sneakers and dwarf males (\"super-sneakers\"). Our predictions provide general bounds on the viable ratios of alternative male mating strategies with different life-histories, and under possibly rapidly changing ecological situations.