Tackling biological complexity
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Tackling biological complexity with BetaWB. Lorenzo Dematt é The Microsoft Research – University of Trento Centre for Computational and Systems Biology. Complexity in software.

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Research and objectives

Tackling biological complexity with BetaWB

Lorenzo DemattéThe Microsoft Research – University of Trento Centre for Computational and Systems Biology

Complexity in software

Computer scientists designed tools to tackle software complexity over the last 50 years. From high level languages, compilers and IDEs to automatic build, testing and model checking of modular components, Computer aided methods helped developers to tackle

Modern software is incredibly complex: for example, a modern OS has more than 10 millions lines of code, organized in 10s of layers! It is easy to wonder how software of this complexity can work at all, in a reliable way.

software complexity.

[1]

[2]

Complexity in biology

Biological systems share with software systems this great complexity.

Even more, lots of properties emerge from interaction of single component, resulting in an unbelievable dynamic complexity.

These biological networks interact at different levels: genetic control, protein interaction, metabolic networks.

Research and objectives

[3]

[4]

Can we use ideas and tools from computer science, which proved to be successful in tackling software complexity, to help scientists understand biology?

Can we made investigation of utterly complex interaction networks feasible for biologists?

The challenge is not only to find a way to “use computers to do biology”, but to do this in a way that scales with complexity and in a way that is easy to use for biologists.We designed a set of tools for a “biological programming language” (Beth, a language derived from process calculi)in which biological entities (genes, proteins, etc.) are described as executable programs.

The compiler / interpreter: BetaWB Stochastic simulator

Entities are compiled and executed by our stochastic simulator; the simulator mimics the interactions of molecules and proteins by running our biological program, in a in-silico experiment. The biological reality is reproduced on a computer, as it happens, for example, for flight simulators.

We are also adding simulation of diffusion, to track the movement of particles within the cell.

[5]

The Designer: visual model composition

The Plotter: interpreting results

A programming language may be difficult to use for a biologist: we addressed this issue by adding a graphical IDE to our tools. For now, this don’t mask completely the language, but it helps considerably in building a skeleton of complex systems with just some clicks.

Results of simulations must be interpreted by biologists to refine model and guide experiments. Our tool plots how the system evolved in time and how biological processes communicated, associated and modified during the simulation.

Future work

Our set of tools is only at the beginning: we want to integrate them better, to make them simpler to use and more powerful; for example we are working on visualization and navigation of big graphs for the plotter. Static analysis, like model checking and evaluation of the underlying Markov chain are also in our plans, as well as the development of parallel algorithms to speed up computations

[1] The Linux Kernel, Free Code Graphing Project.

[2] Microsoft Visual Studio 2005.

[3] GNOM project, Centro Nacional de Biotecnologia (CBN / CSIC) at the Universidad Autonoma de Madrid.

[4] Adai et al. LGL: creating a map of protein function with an algorithm for visualizing very large biological networks.

[5] Microsoft Flight Simulator X.