(GENOME + CELL) Reprogramming Lab

University of Michigan

Direct Cellular Reprogramming

Our lab focuses on understanding the dynamics of genome organization in human cells, and how these dynamics shape cell fate in differentiation, cellular reprogramming, and cancer. We have a dual approach of generating time series data in-house using human cells and by using mathematics to identify patterns in data and fill in major unknowns.

Immune System Inspired Learning

We will use immune system architecture as a basis for generating novel machine learning algorithms. The immune system quickly adapts to a new threat with multi-threaded exploration/testing, selectively rewarding better threads for a threat-specific solution and efficiently coded memory. Advantages of immune system architecture:

- Able to find solutions to novel problems, mounting a defense while it learns

- Uses a highly evolved memory management system to avoid adversarial inputs

- Provides timely solutions, carefully balancing randomized exploration with a survival of the fittest strategy

Towards a Definition of Emergence

Various examples in the real world illustrate the emergence of structure over time. As these examples are quite diverse, e.g. structural assembly of a virus and muscle formation, identifying unifying principles is difficult. The purpose of this work is to investigate the common features of such systems with the goal of developing a systematic classification of properties associated with emergence.

Department of Computational Medicine and Bioinformatics

Department of Mathematics

Biomedical Engineering