About this Research Topic
Understanding cancer means understanding the biological systems that underlie its development. The dynamics of these systems reflects a complex interplay of genes, proteins and biological processes that drive the growth, spread and response of tumor cells to pharmacological treatments. Given the complexity of the cancer progression dynamics, an interdisciplinary effort bringing together clinicians and biologists with mathematical and computational modelers is crucial to disentangle it. Mathematical modelling and computational simulations provide tools for analysing experimental data as well as for systematic, quantitative, and multi-scale in silico experimentation. To achieve these objectives in computational cancer biology, mathematical models in cancer biology must not only reproduce the observed data and have predictive power, but also enjoy structural properties that guarantee their usefulness in the theory-experiment-theory research cycle.
Three fundamental concepts in Mathematical System Theory such as observability, controllability, and stability are currently of particular importance in the study of the structural properties of mathematical models of complex biological systems.
It is widely shared that a mathematical model is not only a descriptor of experimental data, but also a probe with which we explore the physical, biological and/or chemical processes that govern the dynamics of a system. When creating a mathematical model, and/or when testing and validating it for the purpose of its use precisely as a means of exploration and prediction, the analyses aimed at establishing observability, controllability and stability are of primary necessity.
Of no less importance is the observation that one of the proofs of our understanding of complex biological systems is measured by our ability to control them. Although control theory offers mathematical tools for steering engineered systems towards a desired state, a framework to control complex biological systems is missing.
Observability, controllability and stability are basic principles of mathematical modelling that can also be implemented in software codes, to automate the analysis of the model, and to advertise the user about possible bugs in its usability.
For this Research Topic, we invite the submission of original research manuscripts, reviews, and commentaries on computational methods and software tools to analyse observability, controllability and stability, as well as on computational methods on all the related subjects such as identifiability analyses, model calibration, and numerical simulation of cancer computational models.
Papers on computational methods, software tools, and advanced engineering concepts applied to cancer biology are invited. Topics of interest include, but are not restricted to:
● observability and controllability
● stability and stabilization
● optimization of controllability
● driver nodes and optimal driver nodes selection
● partial observability
● structural identifiability
● practical identifiability
● parameter inference
● network inference in biology
in the context of:
● cell-based computational models of cancer biology
● computational models of cancer initiation, growth, invasion of the surrounding stroma, tumor cell migration, intravascular transport, metastatic colonization
● pharmacokinetics and pharmacodynamics models of anti-cancer therapies
● gene and PPI network models of anticancer drug responses, efficacy, side effects.
We solicit the submission of well-balanced manuscripts presenting in detail the computational methodologies, their applicative domains and specific case studies. Unbalanced manuscripts relying heavily only on mathematical or algorithmic contents, or reporting a general description of the possible application field of the presented methods with no cases studies treated in detail, do not fall into the primary scope of this Research Topic.
Three fundamental concepts in Mathematical System Theory such as observability, controllability, and stability are currently of particular importance in the study of the structural properties of mathematical models of complex biological systems.
It is widely shared that a mathematical model is not only a descriptor of experimental data, but also a probe with which we explore the physical, biological and/or chemical processes that govern the dynamics of a system. When creating a mathematical model, and/or when testing and validating it for the purpose of its use precisely as a means of exploration and prediction, the analyses aimed at establishing observability, controllability and stability are of primary necessity.
Of no less importance is the observation that one of the proofs of our understanding of complex biological systems is measured by our ability to control them. Although control theory offers mathematical tools for steering engineered systems towards a desired state, a framework to control complex biological systems is missing.
Observability, controllability and stability are basic principles of mathematical modelling that can also be implemented in software codes, to automate the analysis of the model, and to advertise the user about possible bugs in its usability.
For this Research Topic, we invite the submission of original research manuscripts, reviews, and commentaries on computational methods and software tools to analyse observability, controllability and stability, as well as on computational methods on all the related subjects such as identifiability analyses, model calibration, and numerical simulation of cancer computational models.
Papers on computational methods, software tools, and advanced engineering concepts applied to cancer biology are invited. Topics of interest include, but are not restricted to:
● observability and controllability
● stability and stabilization
● optimization of controllability
● driver nodes and optimal driver nodes selection
● partial observability
● structural identifiability
● practical identifiability
● parameter inference
● network inference in biology
in the context of:
● cell-based computational models of cancer biology
● computational models of cancer initiation, growth, invasion of the surrounding stroma, tumor cell migration, intravascular transport, metastatic colonization
● pharmacokinetics and pharmacodynamics models of anti-cancer therapies
● gene and PPI network models of anticancer drug responses, efficacy, side effects.
We solicit the submission of well-balanced manuscripts presenting in detail the computational methodologies, their applicative domains and specific case studies. Unbalanced manuscripts relying heavily only on mathematical or algorithmic contents, or reporting a general description of the possible application field of the presented methods with no cases studies treated in detail, do not fall into the primary scope of this Research Topic.
Keywords: Systems Theory, Dynamical Networks, Control Theory, Inferential Statistics, Computational Biology
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.
