Deterministic modeling is extremely useful in many fields in physics. However, in biology, this modeling strategy lacks such degree of generality. As a general rule, whenever one attempts to make a detailed description of a biological system, taking into account the system stochastic behavior is mandatory. The origins of stochasticity are manifold:
1. The environment surrounding the system changes randomly as time progresses, and this directly affects the system state.
2. The system dynamics are inherently stochastic, implying that two identical systems in a constant environment would undergo different fates.
3. A combination of the two previous causes. That is, the environment time evolution is stochastic, and the system response to adapt to the environmental changes are stochastic as well.
Interestingly, the above assertions are valid across many different scales: from intracellular processes to populations of macroscopic individuals. At the cellular level, the stochasticity-originating mechanisms are better understood. In general, these processes involve biochemical reaction networks that are basically driven by ligand-receptor interactions. As the count of molecules participating in these interactions is low in many instances, their stochastic nature becomes apparent, affecting the entire system behavior. Thus, in the middle of several innovative approaches, stochastic modeling has a prominent place in modern cell biology.
This Research Topic aims to be at the frontier of quantitative biology at the intracellular and cellular levels. We look for papers by researchers dealing with stochastic biological systems, and working at the interface between mathematical and computational modeling, on the one hand, and quantitative experimental biology, on the other. We are particularly interested in works that help to understand open questions where stochasticity plays a key role in an emergent biological function or property, at a single or multiple spatial and temporal scales.
The target audience are readers interested in novel advances in:
1. Stochastic modeling methods.
2. Experimental techniques for assessing and characterizing stochasticity at the intracellular and cellular levels.
3. Practical applications to fields like Synthetic Biology.
Contributed papers can be organized around two main general questions:
1. How individual cells process environmental information to perform a given response.
Examples are: gene expression at the single-gene and the gene-network levels, signaling pathways, cell motility, etc.
2. How cells interact among each other and with the environment to achieve a collective behavior.
Examples are: pattern formation, synchronization, collective cell motility, etc.
Deterministic modeling is extremely useful in many fields in physics. However, in biology, this modeling strategy lacks such degree of generality. As a general rule, whenever one attempts to make a detailed description of a biological system, taking into account the system stochastic behavior is mandatory. The origins of stochasticity are manifold:
1. The environment surrounding the system changes randomly as time progresses, and this directly affects the system state.
2. The system dynamics are inherently stochastic, implying that two identical systems in a constant environment would undergo different fates.
3. A combination of the two previous causes. That is, the environment time evolution is stochastic, and the system response to adapt to the environmental changes are stochastic as well.
Interestingly, the above assertions are valid across many different scales: from intracellular processes to populations of macroscopic individuals. At the cellular level, the stochasticity-originating mechanisms are better understood. In general, these processes involve biochemical reaction networks that are basically driven by ligand-receptor interactions. As the count of molecules participating in these interactions is low in many instances, their stochastic nature becomes apparent, affecting the entire system behavior. Thus, in the middle of several innovative approaches, stochastic modeling has a prominent place in modern cell biology.
This Research Topic aims to be at the frontier of quantitative biology at the intracellular and cellular levels. We look for papers by researchers dealing with stochastic biological systems, and working at the interface between mathematical and computational modeling, on the one hand, and quantitative experimental biology, on the other. We are particularly interested in works that help to understand open questions where stochasticity plays a key role in an emergent biological function or property, at a single or multiple spatial and temporal scales.
The target audience are readers interested in novel advances in:
1. Stochastic modeling methods.
2. Experimental techniques for assessing and characterizing stochasticity at the intracellular and cellular levels.
3. Practical applications to fields like Synthetic Biology.
Contributed papers can be organized around two main general questions:
1. How individual cells process environmental information to perform a given response.
Examples are: gene expression at the single-gene and the gene-network levels, signaling pathways, cell motility, etc.
2. How cells interact among each other and with the environment to achieve a collective behavior.
Examples are: pattern formation, synchronization, collective cell motility, etc.
