MBI Emphasis Semester on Growth and Morphogenesis Spring 2017

Organizing Committee

Tomas Alarcon
Mathematical Biology, Centre de Recerca Matemàtica
Philip Maini
Centre for Mathematical Biology, University of Oxford
Frederik Nijhout
Biology, Duke University
Pablo Padilla
Institute for Applied Mathematics, National Autonomous University of Mexico (UNAM)

Morphogenesis, the origin of form during the development of an organism, constitutes the processes by which simple cellular arrays are transformed into highly structured and often complex tissues, organs and appendages. The mechanisms of morphogenesis are exceptionally complex and diverse, and are only partially understood. There is a large experimental literature on how various genetic, physiological and morphological perturbations alter morphogenesis, but the interpretation of those results is largely done through verbal, conceptual and diagrammatic models. Although such models have an internal logic they are not quantitatively rigorous and typically do not suggest specific mechanisms other than simple single-level biological processes like transcription or translation. Mathematical modeling has played an important role in developing a deeper understanding of the capacities and limitations of various mechanisms. Problems in morphogenesis have also led to the development of new mathematics such as Turing systems and the development of multiscale modeling approaches.

Traditionally, mathematical modeling has focused on one particular spatial scale. However, we know that biological function arises from the integration of processes acting across multiple scales. In many cases, these scales are intimately coupled so that a separation of scales is not possible. This leads to the problem of how to couple models of different forms (deterministic, stochastic, agent-based) across scales and also the challenge of how to analyze them, both mathematically and computationally.

To have impact in biology, mathematical models must be validated and then used to make biologically testable predictions, or to help explain biological phenomena. To date, biological data have been quite coarse and rather static (especially in development), so high-level modeling involving partial differential equations has, by and large, been appropriate. However, we are now at the dawn of a new era in which, for the first time, we have spatiotemporal data. Thus the new challenges facing us are:

  1. How to collect robust summary statistics from biological data, ranging from expression of biomarkers to the structural changes in the morphology of growing tissues?
  2. What is the appropriate level of model description consistent with the data available?
  3. How do we integrate multimodal, multiscale data to allow us to determine parameter values in our models and subsequently validate our models?

To achieve advances in these areas requires a broad range of expertise and we propose three workshops which will bring together experts from a number of different disciplines to present the state of the art in their fields and to work together to arrive at a consensus on what are specific, focused challenges that can be addressed over the next five years.

Events 2017

February 20, 2017 - February 24, 2017
March 06, 2017 - March 10, 2017