Multi-Silencing Algorithm (MSA) for analysis of ´Multi-Knockout´ and perturbation experiments

The Invention

The invention describes a new, reliable and efficient method for mapping and quantifying the contribution of elements to the overall performance of the system they compose.

Potential applications

Biology

·Analysis of RNA interference experiments in which there is simultaneous attenuation of the expression of a number of genes. The MSA algorithm enables a precise quantitative assessment of the true contribution of each network element (gene or protein traced in the data).

·Analysis of data acquired using other methods for multi-perturbation of genetic and metabolic networks, e.g. from analysis of gene knockout data obtained be conventional mutational methods.

Economics

·The method may be applied to fair and efficient cost allocation between the elements of a system, when only partial information exists on the performance of subgroups of the system.

Electronics

·The method may be applied to multi-perturbation analysis of digital circuits and electronic devices, deciphering the critical elements in the system, and possibly enabling its further minimization.

The need

With the genomic revolution a huge amount of data has become available concerning the expression of genes and proteins under a multitude of knockout conditions. There is a desperate need for the development of new and appropriate methods to analyze this information, identifying the underlying genetic and metabolic networks. This understanding is of course crucial to the development of successful therapeutics and diagnostics.

Advantages

·The method remains valid even when there's a large degree of redundancybetween the functional contribution of different elements.

·The method allows for identification of co-interacting elements.

·The method points to the next set of experiments that should best be performed, to maximize our knowledge of function localization in the system.

·The method is expected to yield insights into the principles that characterize network structure, its processing, hierarchical and modular organization, robustness and it's functioning under different conditions.

Stage of development

The MSA algorithm has been successfully tested in the analysis of evolved neurocontroller networks (up to 150 elements), in the analysis of reversible inactivation experiments studying spatial attention in cats, in the analysis of the RAD6 pathway of DNA repair, and large scale metabolic networks.

Patent

Pending

Project manager

Oren Calfon
VP Business Development, ICT

Project researchers

Alon Keinan
T.A.U Tel Aviv University, Exact Sciences
School of Computer Science

Isaac Meilijson
T.A.U Tel Aviv University, Exact Sciences
School of Mathematical Sciences

Eytan Ruppin
T.A.U Tel Aviv University, Exact Sciences
School of Computer Science