Recent technological breakthroughs in the life sciences, such as single-cell genomics and artificial intelligence, have led to impressive data collection efforts and created the promise that once we have obtained sufficient measurements, we will be able to predict how genes determine cellular phenotypes, how cells form tissues, and how organisms create ecosystems. In order to achieve this, we will need to understand the logic of emergence and self-organization, namely the rules by which multiple dynamic and often weak stochastic interactions between a set of individuals at a lower scale (genes, proteins, cells, or organisms), drive the emergence of robust and deterministic properties in form and function at a higher scale. This logic connects biological scales, which remains one of the biggest missing links in the life sciences as a whole. Without it, even a complete inventory of components of a living system will not explain how genes, cells, tissues, organisms, ecosystems, or societies function, which is one of the grand challenges of our era.
The Logic of Life initiative will apply and develop novel scale-crossing technologies to comprehensively measure interacting objects simultaneously across multiple spatial and temporal scales, and then digitalize and integrate them with multiscale visualization and simulation tools. This will reveal a wealth of connections between scales that our current knowledge of life does not yet incorporate. It will take a unique approach by integrating two disconnected domains, namely ecology and cell- and tissue biology and leverage their respective strengths to study self-organizing model systems of human development and tissue formation using approaches from statistical physics, control theory, multiscale mathematical modeling, single-molecule and material science, and artificial intelligence.
The ultimate goal is to use large scale-crossing datasets with single-molecule and single-cell resolution to achieve a deep understanding of the rules underlying emergence and self-organization, and to apply them and predict outcomes across all scales relevant for life, including embryogenesis, tissue formation, and disease progression. This requires learning from different experimental models to quantitatively compare the underlying interactions and the mechanisms by which they self-organize, and studying how these processes respond to changing conditions, are pushed across critical boundaries, and how they adapt to create robustness and resilience within their systems. It must also take into account the active mechanics and material properties of the interacting structures of life that are formed according to these rules.
Genomic sequences alone are often poor predictors of health outcomes, as the rules of the game by which cells read their genome within the complex multidimensional context of tissues and ecosystems are unclear. Logic of Life will drive a paradigm shift by uncovering these rules, allowing accurate predictions. This will not cure one specific disease but lay the foundation for curing many by providing game-changing insights into human disease progression and response to therapy. It will enable a deterministic control of emergent and self-organizing properties of in vitro tissue models with predictable outcomes, which will reduce the need for animal drug testing and enable in vitro creation of tissues. It will identify measures by which robustness in ecological communities can be re-established as a result of climate change and enable the design of next-generation artificial intelligent self-assembling soft materials. Finally, it will empower new generalizable theoretical frameworks that also apply to human societies and economies, but which are tractable to a much lesser extent.