About Us

About Us

Welcome to the Tufts Center for Regenerative and Developmental Biology


The current popularity of stem cell approaches and the long-term almost exclusive focus on biochemical signaling and differentiation state of single cells have left an important gap in our knowledge and technology: development of powerful alternative approaches for understanding and controlling large-scale tissue growth via biophysical properties. It has long been known that a number of animal species can regenerate or remodel large portions of their bodies as adults (including complex neuromuscular appendages, internal organs, and craniofacial structures). it is imperative to learn how these processes occur in order to be able to induce regeneration of damaged tissues in man. More generally, control over large-scale patterning will address numerous areas of biomedicine, including reprogramming tumors, repair of birth defects, combatting degenerative diseases, and slowing aging.

TCRDB efforts focus on understanding how cell behavior is normally orchestrated throughout embryonic development and adulthood, and then learning to control it to achieve the regeneration of tissues and organs as well as normalizing neoplastic growth. A crucial component of the endogenous control system that recognizes and repairs damage is bioelectrical. Many cell types, not just nerve and muscle, communicate electrically to orchestrate individual cell activities toward the large-scale patterning needs of the body. Proof-of-principle studies have demonstrated that the regenerative potential of adult tissues can be unlocked by manipulating their biophysical properties. Our work includes development of new techniques for rational modulation of complex patterning and cell behavior by manipulation of endogenous ion flows and voltage gradients. Applications include induction of limb regeneration, spinal cord repair, induction of eye tissues, early non-invasive detection of cancer by aberrant physiological signatures, and the use of patterned bioelectric gradients to guide the self-assembly of complex multi-tissue constructs in synthetic bioengineering applications.

In addition to the biophysical approaches, we are leveraging novel artificial intelligence strategies to infer predictive models of complex biological regulation. This is the beginning of a bioinformatics of shape, where advances in computer science are helping human scientists close the gap between the ever-growing mountains of molecular data and insight into the control of growth and form. Our Machine Learning platforms have produced comprehensive models of regenerative and neoplastic pathways that are being analyzed to identify specific interventions leading to desired large-scale outcomes.