IMforFUTURE is an innovative multidisciplinary and intersectoral research training programme which addresses current shortcomings in omics research. We aim to open the new research horizon in integration of genetics, glycomics, and epigenomics datasets into systems biology by developing innovative methods for high throughput omics and for integrative analysis of omics data. We focus on ageing, which is the biggest single risk factor for many diseases. By application of our novel methods to emerging datasets representing inflammation and immunology, IMforFUTURE will contribute to understanding of the underlying biological processes involved in diseases and ageing. To be successful in future multidisciplinary environments in Academia or Industry, ESRs need to be able to act as bridge between several diverse disciplines. Our ESRs need to overview all steps from data production via data analysis to data interpretation. In our consortium 7 academic and 4 industrial partners - experimental and theoretical - participate in research and training via teaching, offering secondments and hosting ESRs. We offer courses in high throughput methodology, genomics and statistics. Secondments will be to partners with complementary disciplines and intersectoral. Emphasis will be data management, data stewardship, entrepreneurship, and complementary skills. Interdisciplinary collaborations among ESRs will be stimulated by working on the same studies, in which new data will be generated, integrated with other datasets, analyzed with novel methods and interpreted. At the end of the project the ESRs will present and discuss their research in an integrated workshop. Our ESRs will be ready and equipped for new-generation multidisciplinary researchers. They will significantly contribute to omics research in relationship to human disease and health and knowledge translation. A conference to disseminate our work to researchers in Academia and Industry, and to stakeholders will be organized.

During cell division, the genetic material is divided into two equal parts by the mitotic spindle. This complex dynamic micro-machine is made of microtubules, chromosomes and a variety of accessory proteins. Microtubules extending from one centrosome bind to those from the other centrosome by motors and other cross-linking proteins, forming bundles that connect the centrosomes and the associated microtubules into the spindle. A central question is how microtubules extending from the centrosomes find each other and interact to assemble the spindle. Based on our previous study on how microtubules find chromosomes (Kalinina et al., 2012), we hypothesize that the angular movement of microtubules is crucial for them to find each other. Further, we hypothesize that the microtubules encounter each other at an angle, and that specific cross-linking proteins are required to align the microtubules into anti-parallel bundles, thereby assembling the spindle. The aim of the project is to develop a theoretical model of spindle assembly, which will include the angular movement of microtubules around the spindle pole. Our model, in combination with experiments, will elucidate whether the process of spindle assembly can be explained by the observed angular movement of the microtubules.

Chromosome segregation errors cause aneuploidy, a state of karyotype imbalance that accelerates tumor formation and impairs embryonic development. Even though mitotic errors have been studied extensively in cell cultures, the mechanisms generating various errors, their propagation and effects on genome integrity are not well understood. Moreover, very little is known about mitotic errors in complex tissues. The main goal of this project is to uncover the molecular origins of mitotic errors and their contribution to karyotype aberrations in healthy and diseased tissues. To achieve our goal, we have assembled an interdisciplinary team of experts in molecular and cell biology, cell biophysics, chromosomal instability in cancer, and theoretical physics. Our team will introduce novel approaches to study aneuploidy (superresolution microscopy, optogenetics, laser ablation, single cell karyotype sequencing) and apply them to state-of-the-art tissue cultures (mammalian organoids and tumoroids). In close collaboration, Tolić will establish assays to detect and quantify error types in cells, and Kops and Amon will use the assays on various healthy and cancer tissues. Tolić and Kops will uncover the molecular origins of errors, their propagation and impact on genome integrity, while Amon will lead the investigation of the mechanisms that ensure high chromosome segregation fidelity in healthy tissues. Interwoven in these collaborations are the efforts of Pavin, who will develop a theoretical model to describe the origin of errors and to quantitatively link chromosome segregation fidelity in single cells and tissues. Model and experiment will continuously inspire each other, to achieve deep understanding of how mitotic errors arise, how they propagate and how they impact on cell populations. Thus, the extensive sets of expertise present in our team will be combined and expanded with novel technologies to tackle the big challenge of the origins of aneuploidy in humans.

Low rank approximation techniques are the backbone of many modern data driven applications. Such applications have utilization ranging from the analysis of the safety and robustness of engineering problems, over stress testing in financial applications to general big data questions in applications of societal relevance. We will study both the theoretical as well as practical aspects of this problem. As prototypes we will consider dynamical systems in the presence of uncertainty. The quantities of interest are the mean, the variance and the exceedance probability for the solution field. The main challenge in the study of these problems is to beat the so-called curse of the dimensionalty (exponential increase in computing effort with the increase of the number of uncertain parameters). We will concentrate on the family of parameter dependent partial differential equations and we will develop and analyze robust and efficient numerical methods to tackle them. We will also study the modal problem and the frequency response problem, which are used to analyze dynamical systems. Both problems have a core computational task which is the computational analysis of the resolvent function. These problems fall into the category of modern data driven applications, even though we sample the system by simulation and not measurement. Main tools which we employ are randomized sampling in the image space of a matrix loosely based on maxvol algorithm and its variants and model order reduction by low rank compression. We also aim to use this project to lay foundation for a research group in Croatia which will study mathematical foundations of data driven applications (including those that fall into the area of machine learning). In the first instance, we will leverage our experience in numerical linear algebra and computational operator theory in the context of the study of multi parameter eigenvalue problems in thermoacustics and photonics.