Genetic regulatory networks (GRN) exist in any cell of any living organism. They are responsible for the morphogenesis and reactions of an organism to changes in the environment. Biologists-experimenters had obtained a great amount of data, which needs to be collected, analyzed, and classified. This is a costly process. Mathematicians, when observing some regular behavior, can try to express what they understood, as mathematical texts. They can make the hypotheses, and try to verify them using mathematical means. The conclusions can be reformulated in terms of biology, and then can be checked by performing experiments. So, the two-sided process emerges. On one side, mathematicians obtain realistic material for formulating problems. They are then stimulated by the correspondence of their propositions and confirming experimental data. In turn, biologists have indications, of how to manage and control processes governed by GRN. Within the framework of this Research Topic, articles that address relevant issues are welcome.
The main goal is to unite researchers working in the field of applied mathematics and biology around this topic. It will be useful for mathematicians to know the problems of research in the field of gene networks, their role in the functioning of living organisms. Biologists can be helped in their research by the mathematical apparatus and conclusions of specialists who create and study mathematical models in biology.
Examples of relevant topics:
- Mathematical modeling of GRN
- Biological oscillators
- Stochastic Gene Expression
- Cancer Gene Networks
- Predictive Modeling in Clinical Bioinformatics
- Differential equations models from experimental data
- Thermodynamics-based models
- Gene Regulatory Networks: Statistical Modeling
- Structural properties of gene network graphs
- Modeling Approaches to Study Plant GRN
- Gene network modeling identifying regulatory mechanisms in infectious diseases
- Multi-parameter exploration of dynamics of regulatory networks
- Boolean regulatory network reconstruction
- Stochastic simulations of net models
- Representing dynamic biological networks with multi-scale probabilistic models
- ODE-Based Modeling of Complex Regulatory Circuits
- Attractor calculation for large-scale Boolean gene regulatory networks
- Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data
- Qualitative Modeling
- Analysis and Control of Synthetic Regulatory Circuits
- Modeling of biological networks applied to systems pharmacology
- Stochastic modeling and numerical simulation of gene regulatory network,
- Control of Intracellular Molecular Networks Using Algebraic Methods
- Gene Regulatory Network Inference Based on Differential Equation Models
Genetic regulatory networks (GRN) exist in any cell of any living organism. They are responsible for the morphogenesis and reactions of an organism to changes in the environment. Biologists-experimenters had obtained a great amount of data, which needs to be collected, analyzed, and classified. This is a costly process. Mathematicians, when observing some regular behavior, can try to express what they understood, as mathematical texts. They can make the hypotheses, and try to verify them using mathematical means. The conclusions can be reformulated in terms of biology, and then can be checked by performing experiments. So, the two-sided process emerges. On one side, mathematicians obtain realistic material for formulating problems. They are then stimulated by the correspondence of their propositions and confirming experimental data. In turn, biologists have indications, of how to manage and control processes governed by GRN. Within the framework of this Research Topic, articles that address relevant issues are welcome.
The main goal is to unite researchers working in the field of applied mathematics and biology around this topic. It will be useful for mathematicians to know the problems of research in the field of gene networks, their role in the functioning of living organisms. Biologists can be helped in their research by the mathematical apparatus and conclusions of specialists who create and study mathematical models in biology.
Examples of relevant topics:
- Mathematical modeling of GRN
- Biological oscillators
- Stochastic Gene Expression
- Cancer Gene Networks
- Predictive Modeling in Clinical Bioinformatics
- Differential equations models from experimental data
- Thermodynamics-based models
- Gene Regulatory Networks: Statistical Modeling
- Structural properties of gene network graphs
- Modeling Approaches to Study Plant GRN
- Gene network modeling identifying regulatory mechanisms in infectious diseases
- Multi-parameter exploration of dynamics of regulatory networks
- Boolean regulatory network reconstruction
- Stochastic simulations of net models
- Representing dynamic biological networks with multi-scale probabilistic models
- ODE-Based Modeling of Complex Regulatory Circuits
- Attractor calculation for large-scale Boolean gene regulatory networks
- Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data
- Qualitative Modeling
- Analysis and Control of Synthetic Regulatory Circuits
- Modeling of biological networks applied to systems pharmacology
- Stochastic modeling and numerical simulation of gene regulatory network,
- Control of Intracellular Molecular Networks Using Algebraic Methods
- Gene Regulatory Network Inference Based on Differential Equation Models
