Conference abstracts

Session C2 - Computational Mathematical Biology with emphasis on the Genome

July 17, 16:00 ~ 16:30 - Room B2

An Approach to Control Partially Known Networks

Gemunu Gunaratne

University of Houston, USA   -   gemunu.gunaratne@gmail.com

Coupled networks are needed to represent a wide-range of problems including bio-molecular processes in cells, interactions within social groups, and ecosystems. One of the major goals of network analyses is to design methods to control such systems to a pre-specified target state. For example, in cellular processes, we may inquire if consequences of genetic mutations or chromosomal rearrangements can be mitigated through external intervention. The obvious approach is to start from a realistic model of the underlying network. Unfortunately, this is an extremely difficult task. Precise quantitative forms of interactions between biomolecules are currently unavailable, and are unlikely to be available in the near future. The model-independent approach proposed here relies on representing the “state" of a cell through its gene expression profile; i.e., the levels of mRNA within a cell. The data can be extracted using techniques such as RNASeq. Under the assumption, key ingredients needed for control are “response surfaces," each of which expresses how the gene expression profile responds to a specific external perturbation. The number of control nodes, i.e., nodes whose levels are to be externally controlled, can be systematically increased in order to reach the target state. Importantly, the most appropriate control node to be added at a given level is determined computationally from prior data. Analyses of synthetic models and (experiments on) nonlinear electrical circuits show that the target state can be typically reached with a few (3 or 4) control genes. This observation is consistent with seminal work on reprogramming mature cells to stem cells using the transcription factors Myc, Oct3/4, Sox2, and Klf4 [Takahashi and Yamanaka, Cell 126, 663, 2006] and on reprogramming fibroblasts to cardiomyocites using three transcription factors Gata4, Mef2c and Tbx5 [Ieda et al., Cell 142, 375, 2010]. The work outlined is a model-free approach to select, systematically, a sequence of genes whose levels need to be subjected to external control in order to approach the pre-specified final state. We propose experimental validations of the approach in the sleep-deprivation network and in an addiction network in Drosophila.

View abstract PDF



FoCM 2017, based on a nodethirtythree design.