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Related papers: Mono- and Polyauxic Growth Kinetics: A Semi-Mechan…

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Boosting methods are widely used in statistical learning to deal with high-dimensional data due to their variable selection feature. However, those methods lack straightforward ways to construct estimators for the precision of the…

Methodology · Statistics 2021-06-10 Boyao Zhang , Colin Griesbach , Cora Kim , Nadia Müller-Voggel , Elisabeth Bergherr

Mathematical methods together with measurements of single-cell dynamics provide unprecedented means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions the…

Quantitative Methods · Quantitative Biology 2014-01-14 Christoph Zechner , Michael Unger , Serge Pelet , Matthias Peter , Heinz Koeppl

We present a coherent Bayesian framework for selection of the most likely model from the five genetic models (genotypic, additive, dominant, co-dominant, and recessive) commonly used in genetic association studies. The approach uses a…

Methodology · Statistics 2015-04-22 Harold Bae , Thomas Perls , Martin Steinberg , Paola Sebastiani

The lattice Boltzmann (LB) method is used to study the kinetics of domain growth of a binary fluid in a number of geometries modeling porous media. Unlike the traditional methods which solve the Cahn-Hilliard equation, the LB method…

comp-gas · Physics 2009-10-22 D. W. Grunau , T. Lookman , S. Y. Chen , A. S. Lapedes

In this paper, we derive an effective macroscale description suitable to describe the growth of biological tissue within a porous tissue-engineering scaffold. As in our recent work (Holden \textit{et al.} "A multiphase multiscale model for…

Tissues and Organs · Quantitative Biology 2019-10-01 E. C. Holden , B. S. Brook , S. J. Chapman , R. D. O'Dea

Motivation: Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy data sets. Over the years, a variety of heuristics have…

Quantitative Methods · Quantitative Biology 2011-11-07 Mariano Beguerisse-Diaz , Baojun Wang , Radhika Desikan , Mauricio Barahona

In this paper we address some modelling issues related to biological growth. Our treatment is based on a recently-proposed, general formulation for growth within the context of Mixture Theory (Journal of the Mechanics and Physics of Solids,…

Tissues and Organs · Quantitative Biology 2011-11-09 H. Narayanan , E. M. Arruda , K. Grosh , K. Garikipati

The growth-fragmentation equation models systems of particles that grow and reproduce as time passes. An important question concerns the asymptotic behaviour of its solutions. Bertoin and Watson ($2018$) developed a probabilistic approach…

Probability · Mathematics 2019-12-23 Benedetta Cavalli

This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking…

Neural and Evolutionary Computing · Computer Science 2009-07-03 James M Whitacre

The molecular motor protein kinesin plays a key role in fundamental cellular processes such as intracellular transport, mitotic spindle formation, and cytokinesis, with important implications for neurodegenerative and cancer disease…

Biomolecules · Quantitative Biology 2014-11-18 B. D. Jacobson , L. J. Herskowitz , S. J. Koch , S. R. Atlas

A chemostat enables long-term, continuous, exponential-phase growth in an environment limited as prescribed by the researcher. It is thus a potent tool for laboratory evolution - selecting for strains with desired phenotypes. However,…

Populations and Evolution · Quantitative Biology 2018-06-04 Aryeh Wides , Ron Milo

A. Bornh\"oft, R. Hanke-Rauschenbach, and K. Sundmacher, [Nonlinear Dyn., 73 (2013), pp. 535-549] introduced a qualitative simplification to the ADM1 model for anaerobic digestion. We obtain global results for this model by first analyzing…

Quantitative Methods · Quantitative Biology 2019-04-15 Tyler Meadows , Marion Weedermann , Gail S. K. Wolkowicz

An additive growth curve model with orthogonal design matrices is proposed in which observations may have different profile forms. The proposed model allows us to fit data and then estimate parameters in a more parsimonious way than the…

Statistics Theory · Mathematics 2012-01-06 Jianhua Hu , Guohua Yan , Jinhong You

High dimensional statistics deals with the challenge of extracting structured information from complex model settings. Compared with the growing number of frequentist methodologies, there are rather few theoretically optimal Bayes methods…

Statistics Theory · Mathematics 2018-08-21 Chao Gao , Aad W. van der Vaart , Harrison H. Zhou

The aim of this work is to learn models of population dynamics of physical systems that feature stochastic and mean-field effects and that depend on physics parameters. The learned models can act as surrogates of classical numerical models…

Machine Learning · Statistics 2024-10-28 Jules Berman , Tobias Blickhan , Benjamin Peherstorfer

Cooperative Co-evolution, through the decomposition of the problem space, is a primary approach for solving large-scale global optimization problems. Typically, when the subspaces are disjoint, the algorithms demonstrate significantly both…

Neural and Evolutionary Computing · Computer Science 2025-03-31 Wenjie Qiu , Hongshu Guo , Zeyuan Ma , Yue-Jiao Gong

We generalize the concept of the geometric phase in stochastic kinetics to a noncyclic evolution. Its application is demonstrated on kinetics of the Michaelis-Menten reaction. It is shown that the nonperiodic geometric phase is responsible…

Quantitative Methods · Quantitative Biology 2010-11-24 N. A. Sinitsyn , I. Nemenman

Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicines. Biomanufacturing processes require close monitoring and control, in the presence of complex bioprocess dynamics with many…

Artificial Intelligence · Computer Science 2022-07-26 Hua Zheng , Wei Xie , Ilya O. Ryzhov , Dongming Xie

Computational models are of increasing complexity and their behavior may in particular emerge from the interaction of different parts. Studying such models becomes then more and more difficult and there is a need for methods and tools…

Neural and Evolutionary Computing · Computer Science 2015-07-27 Stéphane Doncieux , Jean Liénard , Benoît Girard , Mohamed Hamdaoui , Joël Chaskalovic

During grain growth in multiphase system, a convincing understanding on how the evolution kinetics of an individual phase-grains relates to the growth rate of entire microstructure is yet to be achieved. Therefore, in this work, comfortably…

Materials Science · Physics 2021-08-06 P G Kubendran Amos , Ramanathan Perumal , Arnd Koeppe , Britta Nestler