Computational approaches and theory for developmental biology

Our research integrates experimental methods with a range of computational and theoretical approaches to understand the complexities of developmental processes. The complexity of embryonic development, involving multiple interacting components and dynamic processes, necessitates the use of quantitative models to gain deeper insights. These models serve as powerful tools to synthesize experimental data, generate testable hypotheses, and reveal emergent properties that may not be apparent from experimental observations alone.

At the core of our approach is close collaboration between experimentalists and theoreticians. This ensures that our computational models are grounded in biological reality and that our experimental designs are informed by theoretical predictions. Such collaboration is crucial for developing models that accurately capture the essence of developmental processes and for designing experiments that can effectively test and refine these models.

We use tools from dynamical systems theory to construct and analyse mathematical models of biological processes. These models have been particularly insightful in understanding the complex transcriptional networks controlling pattern formation in developing tissues. For instance, our analysis of the network controlling gene expression patterns in the ventral neural tube revealed a multistate switch responding to a graded signal. Mathematical modelling of this network demonstrated that its topology allows for either switch-like or oscillatory behaviour, controlled by a simpler subcircuit we termed the AC-DC motif. This discovery illustrates how quantitative modelling can reveal underlying principles of gene regulatory networks that may not be immediately apparent from experimental data alone.

Inspired by Conrad Waddington’s landscape metaphor for development, we’ve developed, with our collaborators, a mathematical framework based on catastrophe theory and dynamical systems methods. This framework provides the foundation for quantitative landscape models of cellular differentiation. These models can be fitted to experimental data and used to make quantitative predictions about differentiation processes. Our approach suggests that cell fate decisions can be described by a small number of decision structures, providing a powerful tool for understanding the principles governing developmental decisions. The broad applicability of this method is allowing us to extend it to various datasets and experimental modalities.

We’ve also applied techniques from engineering, specifically optimal control theory, to develop a framework for analysing morphogen signalling strategies. This approach helps identify mechanisms that produce rapid, precise, and reproducible cell-fate decisions during embryonic tissue patterning. By combining this framework with Waddington-like landscape models, we can explore how feedback and interactions at various levels guide cells to the correct fate.

In addition to these specific modelling approaches, we utilize methods from deep learning and artificial intelligence to analyse large datasets generated by genomic and imaging experiments. These computational tools allow us to extract meaningful patterns and relationships from complex, high-dimensional data, further enhancing our understanding of developmental processes. By bridging theoretical and experimental biology, we aim to deepen our understanding of the mechanisms governing embryonic development and cellular decision-making.

SELECTED PUBLICATIONS

  • Pezzotta A, Briscoe J. (2023)
    Optimal control of gene regulatory networks for morphogen-driven tissue patterning.
    Cell Systems 14:940-952
  • Sáez M, Briscoe J, Rand DA. (2022)
    Dynamical landscapes of cell fate decisions.
    Interface Focus 12:20220002
  • Sáez M, Blassberg R, Camacho-Aguilar E, Siggia ED, Rand DA, Briscoe J. (2021)
    Statistically derived geometrical landscapes capture principles of decision-making dynamics during cell fate transitions.
    Cell Systems S2405-4712(21)00336-7
  • Perez-Carrasco R, Barnes CP, Schaerli Y, Isalan M, Briscoe J, Page KM. (2018)
    Combining a Toggle Switch and a Repressilator within the AC-DC Circuit Generates Distinct Dynamical Behaviors
    Cell Systems doi: 10.1016/j.cels.2018.02.008