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• W1463963537 abstract "At the single-cell level, cellular processes are commonly viewed as intricate biochemical networks the dynamics of which is reg- ulated by interactions between genes and proteins. Building a theoretical framework for studying cellular processes is a hard task, mainly because of the huge number of components involved and the high complexity of the associated regulatory mechanisms. From a qualitative point of view, Boolean networks are among the most popular approaches. In this formulation, the activity level of a network component is represented by a binary variable (taking the value 0 or 1) while interactions between the compo- nents are described by Boolean switching rules. A particular fea- ture of Boolean networks is their ability to self-organize into so- called basins of attraction that partition the state space of the net- work. Combining Boolean networks with Markov processes, we develop a simple stochastic model describing the time evolution of a protein-protein interaction network. To account for the inherent uncertainty of biological processes, we introduce the parameter p as a measure of the degree of intracellular disorder. This parame- ter is defined as the probability that one and only one component switches to its opposite activity level within a given time interval, therefore allowing transitions between basins to occur. We then propose a new generic property of the dynamic organization of these networks which concerns the probability distribution of the time spent in a basin of attraction. Using this property, we are led to a system of ordinary differential equations as a model of cell population dynamics. Furthermore, the general structure of the system is such that most macroscopic models encountered in cell population dynamics are retrieved. These findings are illustrated in an ovarian carcinoma by the simulation of a desynchronization curve in the S phase of the cell cycle that shows good agreement with BrdU labeling experimental data. Background c p x a a i f m n r o m s n o e m c m le ula r o p x a i g y e b n s r i b . e r t w t p r i o f m r . N T R | L 2 | S P E B R 0 | w . t e o / t e 2 * M O o s p i g a m l t n o a o o c a C w i . l i d M g i S a , -1 8 u e 0 ( w . . i s r e / b 2 0 Abstract At the single-cell level, cellular processes are commonly viewed as intricate biochemical networks the dynam- ics of which is regulated by interactions between genes and proteins. Building a theoretical framework for studying cellular processes is a hard task, mainly be- cause of the huge number of components involved and the high complexity of the associated regulatory mech- anisms. From a qualitative point of view, Boolean net- works are among the most popular approaches. In this formulation, the activity level of a network com- ponent is represented by a binary variable (taking the value 0 or 1) while interactions between the compo- nents are described by Boolean switching rules. A particular feature of Boolean networks is their abil- ity to self-organize into so-called basins of attraction that partition the state space of the network. Com- bining Boolean networks with Markov processes, we develop a simple stochastic model describing the time evolution of a protein-protein interaction network. To account for the inherent uncertainty of biological pro- cesses, we introduce the parameter p as a measure of the degree of intracellular disorder. This parameter is defined as the probability that one and only one com- ponent switches to its opposite activity level within a given time interval, therefore allowing transitions be- tween basins to occur. We then propose a new generic property of the dynamic organization of these networks which concerns the probability distribution of the time spent in a basin of attraction. Using this property, we are led to a system of ordinary differential equations as a model of cell population dynamics. Furthermore, the general structure of the system is such that most macroscopic models encountered in cell population dy- namics are retrieved. These findings are illustrated in an ovarian carcinoma by the simulation of a desyn- chronization curve in the S phase of the cell cycle that shows good agreement with BrdU labeling experimen- tal data. Abstract At the single-cell level, cellular processes are commonly viewed as intricate biochemical networks the dynam- ics of which is regulated by interactions between genes and proteins. Building a theoretical framework for studying cellular processes is a hard task, mainly be- cause of the huge number of components involved and the high complexity of the associated regulatory mech- anisms. From a qualitative point of view, Boolean net- works are among the most popular approaches. In this formulation, the activity level of a network com- ponent is represented by a binary variable (taking the value 0 or 1) while interactions between the compo- nents are described by Boolean switching rules. A particular feature of Boolean networks is their abil- ity to self-organize into so-called basins of attraction that partition the state space of the network. Com- bining Boolean networks with Markov processes, we develop a simple stochastic model describing the time evolution of a protein-protein interaction network. To account for the inherent uncertainty of biological pro- cesses, we introduce the parameter p as a measure of the degree of intracellular disorder. This parameter is defined as the probability that one and only one com- ponent switches to its opposite activity level within a given time interval, therefore allowing transitions be- tween basins to occur. We then propose a new generic property of the dynamic organization of these networks which concerns the probability distribution of the time spent in a basin of attraction. Using this property, we are led to a system of ordinary differential equations as a model of cell population dynamics. Furthermore, the general structure of the system is such that most macroscopic models encountered in cell population dy- namics are retrieved. These findings are illustrated in an ovarian carcinoma by the simulation of a desyn- chronization curve in the S phase of the cell cycle that shows good agreement with BrdU labeling experimen- tal data. Cellular processes: the modularity principle Abstract At the single-cell level, cellular processes are commonly viewed as intricate biochemical networks the dynam- ics of which is regulated by interactions between genes and proteins. Building a theoretical framework for studying cellular processes is a hard task, mainly be- cause of the huge number of components involved and the high complexity of the associated regulatory mech- anisms. From a qualitative point of view, Boolean net- works are among the most popular approaches. In this formulation, the activity level of a network com- ponent is represented by a binary variable (taking the value 0 or 1) while interactions between the compo- nents are described by Boolean switching rules. A particular feature of Boolean networks is their abil- ity to self-organize into so-called basins of attraction that partition the state space of the network. Com- bining Boolean networks with Markov processes, we develop a simple stochastic model describing the time evolution of a protein-protein interaction network. To account for the inherent uncertainty of biological pro- cesses, we introduce the parameter p as a measure of the degree of intracellular disorder. This parameter is defined as the probability that one and only one com- ponent switches to its opposite activity level within a given time interval, therefore allowing transitions be- tween basins to occur. We then propose a new generic property of the dynamic organization of these networks which concerns the probability distribution of the time spent in a basin of attraction. Using this property, we are led to a system of ordinary differential equations as a model of cell population dynamics. Furthermore, the general structure of the system is such that most macroscopic models encountered in cell population dy- namics are retrieved. These findings are illustrated in an ovarian carcinoma by the simulation of a desyn- chronization curve in the S phase of the cell cycle that shows good agreement with BrdU labeling experimen- tal data. Cellular processes: the modularity principle A cellular process (proliferation, differentiation, mi- gration): a collection of modules working together to execute a function and connected through gene ex- pression - a module: a protein-protein interaction network. Pictures here: 1. Proteins in action ! 2. Mitosis in liver, title: cell proliferation in rat liver. The figure shows an acinus with some mitosis occuring." @default.
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• W1463963537 date "2004-12-01" @default.
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• W1463963537 title "From Protein Interaction to Cell Population Dynamics A theoretical framework combining Boolean networks with Markov processes illustrated in an ovarian carcinoma" @default.
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