About this Research Topic
After the success of the first Research Topic "Mathematical Modeling of Gene Networks", we are really proud to release this second special collection dedicated to exploring this theme.
Gene regulatory networks (GRNs) are fundamental to the functioning of any cell in living organisms, driving morphogenesis and responses to environmental changes. The data produced by experimentation in biology is voluminous, demanding extensive efforts in collection, analysis, and classification. Yet, this data presents a unique opportunity for mathematical interpretation, enabling mathematicians to formulate hypotheses and validate these using mathematical methods. The cycle of hypothesis and experimentation between mathematics and biology not only deepens our understanding but also enhances the practical application of mathematical models in interpreting biological processes.
This Research Topic aims to bring together experts from the fields of applied mathematics and biology to explore the multifaceted nature of GRNs. Through collaboration, mathematicians will gain insights into biological questions and the critical roles gene networks play in life processes, while biologists will benefit from the advanced modeling capabilities that mathematics offers. The synergy between these disciplines will accelerate the formulation of new theories and models, crucial for advancing our understanding and manipulation of gene networks.
To gather further insights into the mechanisms and dynamic behaviors of GRNs, we welcome articles addressing, but not limited to, the following themes:
- 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 networks
- Control of Intracellular Molecular Networks Using Algebraic Methods
- Gene Regulatory Network Inference Based on Differential Equation Models
Gene regulatory networks (GRNs) are fundamental to the functioning of any cell in living organisms, driving morphogenesis and responses to environmental changes. The data produced by experimentation in biology is voluminous, demanding extensive efforts in collection, analysis, and classification. Yet, this data presents a unique opportunity for mathematical interpretation, enabling mathematicians to formulate hypotheses and validate these using mathematical methods. The cycle of hypothesis and experimentation between mathematics and biology not only deepens our understanding but also enhances the practical application of mathematical models in interpreting biological processes.
This Research Topic aims to bring together experts from the fields of applied mathematics and biology to explore the multifaceted nature of GRNs. Through collaboration, mathematicians will gain insights into biological questions and the critical roles gene networks play in life processes, while biologists will benefit from the advanced modeling capabilities that mathematics offers. The synergy between these disciplines will accelerate the formulation of new theories and models, crucial for advancing our understanding and manipulation of gene networks.
To gather further insights into the mechanisms and dynamic behaviors of GRNs, we welcome articles addressing, but not limited to, the following themes:
- 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 networks
- Control of Intracellular Molecular Networks Using Algebraic Methods
- Gene Regulatory Network Inference Based on Differential Equation Models
Keywords: Mathematical Modeling, Gene Regulatory Networks, Interdisciplinary Research, Biological Dynamics, Computational Biology, Bio-mathematics, Dynamical Systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
