Periodic Reporting for period 2 - SmartNets (Advanced simulation, analysis and interpretation of network structures in biological data)
Reporting period: 2022-09-01 to 2025-02-28
Think of the murmuration of starlings, the schooling of fish, or even hypes on social media or the spreading of power outs: the behaviour of a network is critically determined by its structure. This structure induces collective emergent behaviour that can only be understood by analysing the whole network in relation to its constituent parts. The relation between network structure and information processing capacity is essential at every scale: from molecules and genes to large neural networks to populations of behaving agents, on every level nodes form complex networks underlying the most essential functions of the brain, body and society. Only recently, with high-throughput techniques, have we begun to collect the vast amounts of data needed to study the structure and functioning of these networks. However, analysing these data is still a challenge and the nature of complex network processes are still poorly understood.
In order to compare networks, simulated or physical ones, or healthy versus diseased, network analysis and visualization tools are needed across three analysis dimensions: structure, activity and information processing. Many quantitative tools exist for analysing networks, but they are mostly restricted to one application domain or network type and often only address one of these dimensions. The novelty of our contributions lies in the combination of existing and new techniques, from different research domains, and across the three analysis dimensions. By transcending specific data sets or domains, we open up new insights into the properties, behaviour and dynamic evolution of biological networks.
In order to understand the relation between network connectivity, plasticity (how networks change) and activity, the researchers have obtained, through experiments and simulations, high quality and rich datasets, across levels and species: from molecular networks to whole brain neural activity of the fish Danionella. In order to analyse such rich datasets, the researchers developed new tools, based on amongst others information theory and topology, and expanded the use of existing tools into new domains, for instance from genetic to neural networks. Next, to assess the computations performed by biological networks, the researchers used computational models and experiments using virtual reality, acquiring datasets that show the relation between networks properties and their function. The analysis of these datasets required new tools, for instance to analyse body posture and movement form videos, but also task performance. All in all, these new datasets and analyses have resulted in new insights into the roles node non-linearity and heterogeneity, delays, the type of input a network receives and the dimensionality of the network’s activity play with respect to network functioning in different tasks and contexts. The obtained data and developed analyses methods form valuable resources for the community as a whole.
We trained a cohort of data scientists of the future, that will be able to analyse (biological) networks across levels, domains and types.
In order to compare networks, simulated or physical ones, or healthy versus diseased, network analysis and visualization tools are needed across three analysis dimensions: structure, activity and information processing. Many quantitative tools exist for analysing networks, but they are mostly restricted to one application domain or network type and often only address one of these dimensions. The novelty of our contributions lies in the combination of existing and new techniques, from different research domains, and across the three analysis dimensions. By transcending specific data sets or domains, we open up new insights into the properties, behaviour and dynamic evolution of biological networks.
In order to understand the relation between network connectivity, plasticity (how networks change) and activity, the researchers have obtained, through experiments and simulations, high quality and rich datasets, across levels and species: from molecular networks to whole brain neural activity of the fish Danionella. In order to analyse such rich datasets, the researchers developed new tools, based on amongst others information theory and topology, and expanded the use of existing tools into new domains, for instance from genetic to neural networks. Next, to assess the computations performed by biological networks, the researchers used computational models and experiments using virtual reality, acquiring datasets that show the relation between networks properties and their function. The analysis of these datasets required new tools, for instance to analyse body posture and movement form videos, but also task performance. All in all, these new datasets and analyses have resulted in new insights into the roles node non-linearity and heterogeneity, delays, the type of input a network receives and the dimensionality of the network’s activity play with respect to network functioning in different tasks and contexts. The obtained data and developed analyses methods form valuable resources for the community as a whole.
We trained a cohort of data scientists of the future, that will be able to analyse (biological) networks across levels, domains and types.
We have experienced setbacks in the early stages of the project: unforeseen pandemic (COVID-19) measures resulted in a delay in recruitment, resulting in delays in the research. Moreover, because of travel restrictions, lockdowns, and other pandemic measures workshops and secondments had to be cancelled, delayed or done online. Nevertheless, all researchers were recruited, have followed exciting training in network recording, modelling, computation and analysis. The early-career researchers have acquired data, both experimental and simulated, designed network models and developed analysis tools to measure network activity and network computation in relation to network structure. They have gotten together in network-wide workshops to build their professional network and have collaborated, resulting in joint publications and open datasets. Moreover, they have developed outreach activities, to engage the general public and inform them about the advances in network science. They have formed a tight cohort, in which they helped each other both professionally and personally.
The ESRs have gained experience in academia in both their in own institute and, through secondments, in partnering academic institutes and in industry. This way, they have obtained experience in many state-of-the-art techniques, including visualising high-dimensional neural, behavioural and simulated data, developing efficient simulation strategies for large-scale networks, developing analysis tools for feature extraction or dimensionality reduction and applying the derived solutions to technological implementations. Not every applied all these techniques, but they have all become acquainted with them through exposure during workshop activities and secondments.
We have continuously evaluated and updated the training program to enhance the scientific, complementary and soft skills of all ESRs, equipping them with the necessary expertise to pursue a career in the efficient simulation, analysis and interpretation of network structures in biological data. The ESRs themselves have also organized extra networking events. The training programme they have followed will prepare them for leadership roles in academia and industry. All fellows have benefitted from the expertise in both sectors through the active participation of each sector in secondments and network wide activities. Through these training events, but also shared seminars (together with ETN Zénith) and slack meetings, the ESRs were be able to meet, bond and build an inter‐sectoral, interdisciplinary, and international network of scientific and personal relationships, which will last far beyond the end date of the project. Together with the environment of excellence established by the participating organizations they now form a unique cohort of specialists in understanding network structures in biological data. Such professionals are in high demand and have outstanding career prospects.
We have continuously evaluated and updated the training program to enhance the scientific, complementary and soft skills of all ESRs, equipping them with the necessary expertise to pursue a career in the efficient simulation, analysis and interpretation of network structures in biological data. The ESRs themselves have also organized extra networking events. The training programme they have followed will prepare them for leadership roles in academia and industry. All fellows have benefitted from the expertise in both sectors through the active participation of each sector in secondments and network wide activities. Through these training events, but also shared seminars (together with ETN Zénith) and slack meetings, the ESRs were be able to meet, bond and build an inter‐sectoral, interdisciplinary, and international network of scientific and personal relationships, which will last far beyond the end date of the project. Together with the environment of excellence established by the participating organizations they now form a unique cohort of specialists in understanding network structures in biological data. Such professionals are in high demand and have outstanding career prospects.