The path to understanding causal relations Understanding the causal relationships between the different elements of a complex system is central to effective intervention. We spoke to Dr Ioannis Tsamardinos about the CAUSALPATH project’s work in developing new causal analysis algorithms, which could help researchers learn more about biological pathways and the human immune system The relationship between
cause and effect is central to human reasoning capacity, yet analysing and understanding it is highly challenging when it comes to complex systems with multiple interacting components. Researchers in the CAUSALPATH project aim to develop new algorithms for causal analysis and causal discovery, particularly with respect to molecular biological data, which could lead to new insights. “We want to develop new methods, new algorithms that are applicable to a family of problems and application domains, but we also want to apply them for the discovery of new knowledge in biology, specifically in the human immune system. We hope to discover new biological pathways and refine existing knowledge on biological pathways,” outlines Ioannis Tsamardinos, the project’s Principal Investigator. These methods are designed to integrate
information and data from various sources. “These methods are able to learn causality or refine what we know about causal relationships – among molecules for example, or other measured quantities – that come from various different data sets,” explains Tsamardinos. This could be not only different types of ‘omics’ data, such as proteomics or genomics, but also data of the same type generated under different experimental conditions. For example, a biologist may perform a study of the human immune system under certain conditions, then another biologist may make measurements on the same system from a different perspective. “These studies have different statistical distributions; however, the common factor is that they are looking at the same system,” points out Tsamardinos. Researchers aim to develop algorithms to piece together this information and
identify unifying causal mechanisms, looking particularly at single-cell data, measured mostly through mass cytometers. “Mass cytometers are a relatively new type of biotechnology. These machines can measure the concentrations of proteins in thousands of cells per second. So, they generate very detailed measurements that lend themselves to applying causal discovery and causal analysis methods,” says Tsamardinos.
Causal discovery methods The methods themselves build on relatively recent advances in the causal discovery field, introduced by Tsamardinos and colleagues, where researchers are now able to convert causal discovery problems to mathematical logic. This technique has enabled researchers to solve ever more
Causal models of the microworld: Differential Equation learning
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