In recent years, new pandemic threats have become more and more frequent (SARS, bird flu, swine flu, Ebola, MERS, nCoV...) and analyses of data from the early spread more and more common and rapid. Particular interest is usually focused on the estimation of $ R_{0}$ and various methods, essentially based estimates of exponential growth rate and generation time distribution, have been proposed. Other parameters, such as fatality rate, are also of interest. In this talk, various sources of bias arising because observations are made in the early phase of spread will be discussed and also possible remedies proposed.[-]

In the second talk, I will present some of our work on this area. Our work on this area, where we have focused on transcriptomics and (phospho)proteomics to study signaling networks. Our tools range from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and transcription factor activities (PROGENy and DoRothEA, respectively) from gene expression and subsequently infer causal paths among them (CARNIVAL), to tools to infer logic models from phosphoproteomic and phenotypic data (CellNOpt and PHONEMeS). We have recently adapted these tools to single-cell data. I will illustrate their utility in cases of biomedical relevance, in particular to improve our understanding of cancer and to develop novel therapeutic opportunities. As main application I will discuss our work analysing, as a model for personalized medicine, large pharmaco-genomic screenings in cell lines. These screenings provide rich information about alterations in tumours that confer drug sensitivity or resistance. Integration of this data with prior knowledge provides biomarkers and offer hypotheses for novel combination therapies. Our own analysis as well as the results of a crowdsourcing effort (as part of a DREAM
challenge) reveals that prediction of drug efficacy from basal omics data is that discussed above is far from accurate, implying important limitations for personalised medicine. An important aspect that deserves detailed attention is the dynamics of signaling networks and how they response to perturbations such as drug treatment.
I will present how cell-specific logic models, trained with measurements upon perturbations, can provides new biomarkers and treatment opportunities not noticeable by static molecular characterisation.[-]

Modern technologies allow us to profile in high detail biomedical samples at fast decreasing costs. New technologies are opening new data modalities, in particular to measure at the single cell level. Prior knowledge, and biological networks in particular, are useful to integrate this data and distill mechanistic insight. This can help to interpret the result of machine learning or statistical analysis, as well as generate input features for these methods. In addition, they can be converted in dynamic mechanistic models to gain more specific insight. I will give an overview of these approaches showcasing some examples and approaches used in the field. [-]