Computational Systems Biology for Complex Diseases : Nonlinear Dynamics with Big-Data

 

Speaker:  L.uo-Nan Chen

                 university of chinese academy of sciences

Time: 4:00 p.m., Monday, Mar.20th, 2017

Venue: Conference Room 104, Science Building, Tsinghua University

 In this talk, I will present a few topics on computational systems biology, i.e., how to quantify disease states, predict disease transitions, and further infer causal relations in biological systems based on big-data with generic principles of nonlinear systems.

1.       We develop a model-free method to detect early-warning signals of critical transitions in complex diseases. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs.

2.       We propose a method to predict systems low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data.