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Full-Text Articles in Genetics and Genomics
Cancer Cell Population Growth Kinetics At Low Densities Deviate From The Exponential Growth Model And Suggest An Allee Effect., Kaitlyn E Johnson, Grant Howard, William Mo, Michael K Strasser, Ernesto A B F Lima, Sui Huang, Amy Brock
Cancer Cell Population Growth Kinetics At Low Densities Deviate From The Exponential Growth Model And Suggest An Allee Effect., Kaitlyn E Johnson, Grant Howard, William Mo, Michael K Strasser, Ernesto A B F Lima, Sui Huang, Amy Brock
Articles, Abstracts, and Reports
Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the …
Lineage Marker Synchrony In Hematopoietic Genealogies Refutes The Pu.1/Gata1 Toggle Switch Paradigm., Michael K Strasser, Philipp S Hoppe, Dirk Loeffler, Konstantinos D Kokkaliaris, Timm Schroeder, Fabian J Theis, Carsten Marr
Lineage Marker Synchrony In Hematopoietic Genealogies Refutes The Pu.1/Gata1 Toggle Switch Paradigm., Michael K Strasser, Philipp S Hoppe, Dirk Loeffler, Konstantinos D Kokkaliaris, Timm Schroeder, Fabian J Theis, Carsten Marr
Articles, Abstracts, and Reports
Molecular regulation of cell fate decisions underlies health and disease. To identify molecules that are active or regulated during a decision, and not before or after, the decision time point is crucial. However, cell fate markers are usually delayed and the time of decision therefore unknown. Fortunately, dividing cells induce temporal correlations in their progeny, which allow for retrospective inference of the decision time point. We present a computational method to infer decision time points from correlated marker signals in genealogies and apply it to differentiating hematopoietic stem cells. We find that myeloid lineage decisions happen generations before lineage marker …
Solving The Influence Maximization Problem Reveals Regulatory Organization Of The Yeast Cell Cycle., David L Gibbs, Ilya Shmulevich
Solving The Influence Maximization Problem Reveals Regulatory Organization Of The Yeast Cell Cycle., David L Gibbs, Ilya Shmulevich
Articles, Abstracts, and Reports
The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. …
Processes On The Emergent Landscapes Of Biochemical Reaction Networks And Heterogeneous Cell Population Dynamics: Differentiation In Living Matters., Sui Huang, Fangting Li, Joseph X Zhou, Hong Qian
Processes On The Emergent Landscapes Of Biochemical Reaction Networks And Heterogeneous Cell Population Dynamics: Differentiation In Living Matters., Sui Huang, Fangting Li, Joseph X Zhou, Hong Qian
Articles, Abstracts, and Reports
The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat. The theory is equally applicable not only to …
Combining Inferred Regulatory And Reconstructed Metabolic Networks Enhances Phenotype Prediction In Yeast., Zhuo Wang, Samuel A Danziger, Benjamin D Heavner, Shuyi Ma, Jennifer J Smith, Song Li, Thurston Herricks, Evangelos Simeonidis, Nitin Baliga, John D Aitchison, Nathan D Price
Combining Inferred Regulatory And Reconstructed Metabolic Networks Enhances Phenotype Prediction In Yeast., Zhuo Wang, Samuel A Danziger, Benjamin D Heavner, Shuyi Ma, Jennifer J Smith, Song Li, Thurston Herricks, Evangelos Simeonidis, Nitin Baliga, John D Aitchison, Nathan D Price
Articles, Abstracts, and Reports
Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many …