Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Win, Lose, or Tie: Mathematical Modeling of Ligand Competition at the Cell-Extracellular Matrix Interface.

ABSTRACT: Integrin transmembrane proteins conduct mechanotransduction at the cell-extracellular matrix (ECM) interface. This process is central to cellular homeostasis and therefore is particularly important when designing instructive biomaterials and organoid culture systems. Previous studies suggest that fine-tuning the ECM composition and mechanical properties can improve organoid development. Toward the bigger goal of fully functional organoid development, we hypothesize that resolving the dynamics of ECM-integrin interactions will be highly instructive. To this end, we developed a mathematical model that enabled us to simulate three main interactions, namely integrin activation, ligand binding, and integrin clustering. Different from previously published computational models, we account for the binding of more than one type of ligand to the integrin. This competition between ligands defines the fate of the system. We have demonstrated that an increase in the initial concentration of ligands does not ensure an increase in the steady state concentration of ligand-bound integrins. The ligand with higher binding rate occupies more integrins at the steady state than does the competing ligand. With cell type specific, quantitative input on integrin-ligand binding rates, this model can be used to develop instructive cell culture systems.

SUBMITTER: Karagoz Z

PROVIDER: S-EPMC8117103 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Mathematical Modeling of Ligand Competition at the Cell–Extracellular Matrix Interface

Project description:Modeling the competition of two ligands which have the ability to bind to the same receptor. Example is given from ECM proteins fibronectin and von Willebrand factor and their common receptor integrin alpha v beta 3.

2022-12-01 | MODEL2211300001 | BioModels

Different isoforms of Maxwell´s Demon, a conserved cell membrane protein, mediate lose/win decisions during cell competition

Project description:Cell competition promotes the elimination of weaker cells from a growing population. Here we investigate how cells of Drosophila wing imaginal discs distinguish “winners” from “losers” during cell competition. Using genomic and functional assays we have identified Maxwell`s Demon (Mwd), a cell membrane protein conserved in multicellular animals. Our results suggest that the membrane protein Mwd is a dedicated component of the cell competition response that is required and sufficient to label cells as “winners” or “losers”. In Drosophila, the mwd locus produces three isoforms, mwdubi, mwdLose-A and mwdLose-B. Basal levels of mwdubi are constantly produced. During competition the mwdLose isoforms are upregulated in prospective loser cells. Cell-cell comparison of relative mwdLose and mwdubi levels ultimately determine which cell undergoes apoptosis. This “extracellular code” may constitute an ancient mechanism to terminate competitive conflicts among cells. Two samples have been analysed: tub>dmyc>Gal4 transgene cells (competitor) and tub>cd2>Gal4 control cells (non competitor) at different time points (0, 12, 24 and 48 hours). Each experiment was replicated 6 times, three of them by dye swap.

2011-02-09 | E-GEOD-21230 | biostudies-arrayexpress

Different isoforms of Maxwell´s Demon, a conserved cell membrane protein, mediate lose/win decisions during cell competition

2011-02-09 | GSE21230 | GEO

Mathematical modeling of Trypanosoma brucei transcription

Project description:Trypanosome RNA polymerase II transcription is polycistronic, individual mRNAs being excised by trans splicing and polyadenylation. In this study, we refined the previously published mathematical model for bloodstream form parasites and extended it to the procyclic form. We used the model, together with known mRNA half-lives, to predict the abundances of individual mRNAs, assuming rapid, unregulated mRNA processing; then we compared the results with measured mRNA abundances. Remarkably, the abundances of most mRNAs in procyclic forms are predicted quite well by the model, being largely explained by variations in mRNA decay rates and length. In bloodstream forms substantially more mRNAs are less abundant than predicted. We list mRNAs that are likely to show particularly slow or inefficient processing, either in both forms or with developmental regulation. We also measured ribosome occupancies of all mRNAs in trypanosomes grown in the same conditions as were used to measure mRNA turnover. In procyclic forms there was a weak positive correlation between ribosome density and mRNA half-life, suggesting cross-talk between translation and mRNA decay; ribosome density was related to the proportion of the mRNA on polysomes, indicating control of translation initiation. Ribosomal protein mRNAs in procyclics appeared to be exceptionally rapidly processed but poorly translated. Through this study, we conclude that lLevels of mRNAs in procyclic form trypanosomes are determined mainly by length and mRNA decay, with some control of precursor processing. In bloodstream forms variations in nuclear events play a larger role in transcriptome regulation, suggesting acquisition of new control mechanisms during adaptation to mammalian parasitism. Ribosome profiling and mRNA libraries were constructed in triplicate from in vitro PCF and in duplicate from in vitro T. brucei Lister427, to understand global differntial gene transcription.

2016-03-16 | E-GEOD-79208 | biostudies-arrayexpress

Mathematical modeling of Trypanosoma brucei transcription

2016-03-16 | GSE79208 | GEO

Win-stay-lose-learn promotes cooperation in the prisoner's dilemma game with voluntary participation.

Project description:Voluntary participation, demonstrated to be a simple yet effective mechanism to promote persistent cooperative behavior, has been extensively studied. It has also been verified that the aspiration-based win-stay-lose-learn strategy updating rule promotes the evolution of cooperation. Inspired by this well-known fact, we combine the Win-Stay-Lose-Learn updating rule with voluntary participation: Players maintain their strategies when they are satisfied, or players attempt to imitate the strategy of one randomly chosen neighbor. We find that this mechanism maintains persistent cooperative behavior, even further promotes the evolution of cooperation under certain conditions.

| S-EPMC5300200 | biostudies-literature

It's not just how the game is played, it's whether you win or lose.

Project description:Growing disparities of income and wealth have prompted extensive survey research to measure the effects on public beliefs about the causes and fairness of economic inequality. However, observational data confound responses to unequal outcomes with highly correlated inequality of opportunity. This study uses a novel experiment to disentangle the effects of unequal outcomes and unequal opportunities on cognitive, normative, and affective responses. Participants were randomly assigned to positions with unequal opportunities for success. Results showed that both winners and losers were less likely to view the outcomes as fair or attributable to skill as the level of redistribution increased, but this effect of redistribution was stronger for winners. Moreover, winners were generally more likely to believe that the game was fair, even when the playing field was most heavily tilted in their favor. In short, it's not just how the game is played, it's also whether you win or lose.

| S-EPMC6636996 | biostudies-literature

Dynamic aspiration based on Win-Stay-Lose-Learn rule in spatial prisoner's dilemma game.

Project description:Prisoner's dilemma game is the most commonly used model of spatial evolutionary game which is considered as a paradigm to portray competition among selfish individuals. In recent years, Win-Stay-Lose-Learn, a strategy updating rule base on aspiration, has been proved to be an effective model to promote cooperation in spatial prisoner's dilemma game, which leads aspiration to receive lots of attention. In this paper, according to Expected Value Theory and Achievement Motivation Theory, we propose a dynamic aspiration model based on Win-Stay-Lose-Learn rule in which individual's aspiration is inspired by its payoff. It is found that dynamic aspiration has a significant impact on the evolution process, and different initial aspirations lead to different results, which are called Stable Coexistence under Low Aspiration, Dependent Coexistence under Moderate aspiration and Defection Explosion under High Aspiration respectively. Furthermore, a deep analysis is performed on the local structures which cause defectors' re-expansion, the concept of END- and EXP-periods are used to justify the mechanism of network reciprocity in view of time-evolution, typical feature nodes for defectors' re-expansion called Infectors, Infected nodes and High-risk cooperators respectively are found. Compared to fixed aspiration model, dynamic aspiration introduces a more satisfactory explanation on population evolution laws and can promote deeper comprehension for the principle of prisoner's dilemma.

| S-EPMC7781394 | biostudies-literature

Win-Stay-Lose-Shift as a self-confirming equilibrium in the iterated Prisoner's Dilemma.

Project description:Evolutionary game theory assumes that players replicate a highly scored player's strategy through genetic inheritance. However, when learning occurs culturally, it is often difficult to recognize someone's strategy just by observing the behaviour. In this work, we consider players with memory-one stochastic strategies in the iterated Prisoner's Dilemma, with an assumption that they cannot directly access each other's strategy but only observe the actual moves for a certain number of rounds. Based on the observation, the observer has to infer the resident strategy in a Bayesian way and chooses his or her own strategy accordingly. By examining the best-response relations, we argue that players can escape from full defection into a cooperative equilibrium supported by Win-Stay-Lose-Shift in a self-confirming manner, provided that the cost of cooperation is low and the observational learning supplies sufficiently large uncertainty.

| S-EPMC8242928 | biostudies-literature

May the Best Molecule Win: Competition ESI Mass Spectrometry.

Project description:Electrospray ionization mass spectrometry has become invaluable in the characterization of macromolecular biological systems such as nucleic acids and proteins. Recent advances in the field of mass spectrometry and the soft conditions characteristic of electrospray ionization allow for the investigation of non-covalent interactions among large biomolecules and ligands. Modulation of genetic processes through the use of small molecule inhibitors with the DNA minor groove is gaining attention as a potential therapeutic approach. In this review, we discuss the development of a competition method using electrospray ionization mass spectrometry to probe the interactions of multiple DNA sequences with libraries of minor groove binding molecules. Such an approach acts as a high-throughput screening method to determine important information including the stoichiometry, binding mode, cooperativity, and relative binding affinity. In addition to small molecule-DNA complexes, we highlight other applications in which competition mass spectrometry has been used. A competitive approach to simultaneously investigate complex interactions promises to be a powerful tool in the discovery of small molecule inhibitors with high specificity and for specific, important DNA sequences.

| S-EPMC4632762 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data