Cell-cycle

Cell cycle is the sequence of events that cells go through during their proliferation. Processes of the cell cycle have to be properly regulated to ensure that the new generation of cells will inherit all properties of parent cells. Thus, understanding of the details of cell cycle regulation will help to predict ways to control cell proliferation in various diseases. Current models of the molecular regulatory network that controls cell cycle are detailed only for the core module of budding yeast cells. With our modeling tools, we want to understand how this core machinery is regulated by internal and external signals in fission and budding yeasts and in mammalian cells. Furthermore, we will consider the spatial regulation of cell growth and thus we will derive models to catch the temporal and spatial control on polarized cell growth in fission yeast cells.

Computational aspects of cell cycle research will convey the requirements concerning the definition of languages and interfaces for an intuitive though formal specification of system dynamics to computer scientists, in a way that the technical details of the underlying processing are hidden from non-expert users. Parameter estimation, multi-run support for analysis and comparison of various mutant behaviors are just a few computational tools that are highly needed for cell cycle research. Furthermore, we draw requirements for the explicit inclusion of space representation in our models as well as define the needs of an effective visualization of results in time and space to provide users with a clear visual indication about system dynamics. Moreover, this research also drives the definition of the knowledge inference functionalities of the platform as data about multiple signaling networks have to be acquired and aggregated to define the overall system behavior.

Network biology

Under the heading of network biology we study biological entities interacting at different levels, going from genes to cells to species. Such network level consideration is essential in biology, as no biological system functions in isolation. While simplifications are required to analyze complex systems, eventually a true understanding of any biological process will require the analysis of the larger network of which it is part. With such an ambition, our initial focus is at the level of gene and protein interactions that result in gene regulatory, signaling, and metabolic networks.

Network biology elicits requirements for the modular modeling that is needed to build biological networks of increasing complexity via a compositional modeling framework that embeds single pathways into the larger networks of interacting agents. We experiment the scalability of the framework by considering networks of interacting species. Space is a critical dimension that conditions system dynamics: definition and solution of models enriched with spatial structures is a key point in algorithmic systems biology. Evolutionary studies of biological networks will lead to the definition of requirements for modeling languages in which biological entities can mutate their behavior over time and can be dynamically selected depending on user-defined fitness functions. Finally, network biology requires “clever” simulation platforms that allow users to experiment with different parameters and behaviors in the developed models.

Cell-level interactions and tissue biology

We consider cell-level interactions and tissue biology a fundamental biological problem because the complete understanding of biological systems can be imagined only in the context of their interactions with each other and with their environment. Cancer can be understood only at the tissue level where different cells control the proliferation of each other.

The multi-level interactions among biological entities and their environment inspire the definition of modeling, analysis and simulation strategies for dealing with complex systems. Here, we are targeting areas of investigation that go beyond the current capabilities of computational techniques and for which only ad-hoc solutions have been proposed thus far. Population studies, interaction with the environment in multi-level networks and in tissue arranged structures, coordination of signaling responses and cooperation of cells provide challenging examples of research items that are used to trigger the definition of hierarchical modeling languages and hierarchical solution approaches. The design of supporting abstraction and refinement mechanisms is essential to allow platform users to be able to handle the complexity of the systems at the modeling layer. The visualization of model evaluation that results in tissue-like structures is also a concern of computer science research. Interactive animation of system evolution will be designed as a way to better control the progress of the in-silico experiments and to provide intuitive verification and validation tools.