Abstract: The pleiotropic factor TGFbeta is a major tumor suppressor but also key regulator of epithelial to mesenchymal transition (EMT) and remodeling of the extracellular matrix (ECM) fostering tumor progression. The molecular mechanisms controlling these different functionalities remain poorly understood and it is widely believed that the cell context is decisive. In lung cancer, one of the deadliest cancer entities, increased TGFbeta levels have been associated with poor outcome and thus there is an urgent, unmet clinical need to resolve the molecular impact of TGFbeta. By a global proteomics approach, we demonstrate that the level of the TGFbeta type I and type II receptors is highly variable whereas the core components of the signaling pathway remain rather stable. Furthermore, dynamic pathway modeling of the TGFbeta induced canonical SMAD signaling cascade provides evidence that the TGFbeta receptors are the most sensitive regulatory node. Accordingly, we developed a targeted mass spectrometry-based approach and show that the amounts of the TGFbeta receptors greatly differ in lung cancer cell lines and confirm this observation in lung adeno carcinoma patients. Our results point to a critical role of the abundance of TGFbeta receptors and to an association with lung cancer recurrence.

Abstract: The ocean is the largest ecosystem on Earth and yet we know very little about it. This is particularly true for the plankton that drift within, even though they form the base of marine food webs and are key players in Earth’s biogeochemical cycles. Ocean plankton are at least as important for the Earth system as the forests on land, but most of them are invisible to the naked eye and thus are largely uncharacterized. To increase our understanding of this underexplored world, a multidisciplinary consortium, Tara Oceans, was formed around the 36m research schooner Tara, which sampled plankton at more than 210 sites and multiple depth layers in all the major oceanic regions during expeditions from 2009-2013 (Karsenti et al. Plos Biol., 2011). This talk will summarize the foundational resources from the project, which collectively represent the largest DNA sequencing effort for the oceans (see Science special issue May 22, 2015 and Cell, Nov 14, 2019), and analyses that illustrate several aspects of the Tara Oceans’ eco-systems biology approach to address microbial contributions to ecological and evolutionary processes. The project provides unique resources for several scientific disciplines that are foundational for mapping ocean biodiversity of a wide range of organisms that are rarely studied together, exploring their interactions, and integrating biology into our physico-chemical understanding of the ocean, as well as for identifying new organisms and genes of biotechnological interest. These resources, and the scientific innovations emerging to understand them, are furthermore critical towards developing baseline ecological context and predictive power needed to track the impact of climate change on the ocean.

Abstract: As technologies in our industry advance in complexity, speed, sensitivity and ease of use, the opportunity to use multiple approaches to answer high-dimensional biological questions is changing and expanding our knowledge of immunology. To expand growth and future in our field, the combined use of high-parameter analytical and sorting flow cytometry instruments, imaging flow cytometry, advanced spectral cytometry, deep proteomic databases, and advanced single-cell multiomic tools (proteomic and genomic) will help define new functional biomarkers needed to facilitate a better understanding of the complexities of immune health and disease.

Abstract: Environmental sequencing, that is metagenomics, has become a major driver for uncovering microbial biodiversity and increasingly also for cataloging molecular functions on our planet. The exponentially increasing metagenomes need computational tools and resources to allow researchers to access and digest these valuable data. Based on computational methods and resources, often developed in our group, but also by utilizing public resources, here I (i) introduce into our work on the gut microbiome, arguable the best-studied microbial community with its internal and environmental (host) interactions, serving as a model for other habitats. Systems approaches include large-scale perturbations by drugs of both individual strains and synthetic communities, with the aims to increase our still limited understanding, the establishment of novel diagnostics and individualized medication guidance. I (ii) further show on how to apply the underlying concepts to other habitats, like ocean and soil, to arrive at basic understanding of the underexplored microbial diversity on earth. We study, for example, how molecular function is evolving and spreading and, in analogy to microbial diagnostics and treatment for human health, prepare the grounds for bioindicators and remediation strategies in various habitats towards improving planetary health.

Summary: Petabyte cancer big data ranging over single cells to temporal space is changing our way of systems understanding of cancer. Various AI technologies enhanced with supercomputers and big storages are its driving force. This session focuses on the new discoveries and topics which may not be investigated with such technologies.

Summary: In this session we will cover the great challenges in current imaging and imaging analysis. From deep learning to data analysis we will host talks that aim to unravel systems properties of complex biological systems including tissues. Multi-scale analysis of biological systems  demans both advanced imaging but also new algorithms and tools. Talks will include recent examples of large-scale imaging breakthroughs made possible with new algorithms. 

Summary: The last two decades has seen a dramatic rise in experimental approaches to characterize behaviors at the single-cell level and this has been accompanied by the development of a wide range of computational methods for both the analysis of single-cell measurements and the modeling of single-cell behavior. The single-cell modeling session broadly covers all areas of computational biology associated with the biological behaviors at the single-cell level including stochastic dynamics, gene regulation, spatiotemporal dynamics, and questions about how cells self-organize, receive, process and respond to information, and communicate with other cells. 

Summary: This session will cover biological discoveries made in a variety of biotopes - from Oceans to the Human Gut. We will be discussing the major challenges involved in systematic sampling of biotopes and also what we can learn from doing so. We also aim to put this in the context of a changing climate and how potentially systems biology can help deal with some of these challenges.

Summary: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviors expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations.

Summary: The behavior of cells is impacted by many factors, such as  gene regulation, signaling, metabolism, transport, or mechanical forces. While studying these components in isolation can be informative, they all interact with each other and are ultimately part of the same system. The session will discuss models that capture cellular dynamics and regulation with an emphasis on the role played by the spatial organization of its components.

Abstract: AI and statistical learning can generate actionable predictions at the level of molecules, cells and humans.(1) EVcouplings: protein structure from experimental evolution; design of proteins for sustainability.(2) Perturbation Biology: designing targeted intervention from large-scale perturbation-response experiments.(3) CancerRiskNet: Identifying high risk of pancreatic cancer from real-world clinical records.(4) EVescape: Forecasting viral antibody escape.

ODD NUMBERED POSTERS (1, 3, 5, ...;)

Abstract: Major advances in phosphoproteomic technologies have led to the discovery of over 100,000 sites of protein phosphorylation in the human proteome. Connecting these phosphorylation sites to the protein kinases and signaling networks responsible for their creation and regulation has been the major bottleneck in deciphering how phosphorylation at these sites contributes to specific cellular phenotypes in health and disease. I will present ongoing work from the Cantley, Turk, Linding, and Yaffe laboratories aimed at conquering this ‘dark phosphoproteome’ by determining the motifs for the complete human serine/threonine kinome using solution phase oriented peptide library screening. Computational analysis of large public phosphoproteomic datasets using these motifs identifies distinct signaling pathways and networks that are up- or down-regulated in response to particular stimuli, and provides insights into complex biological responses that are mediated through cross-network signaling. Secondly, I will present ongoing work examining the control of cell senescence in cancer cells following DNA damage, revealing unexpected roles for MAP kinases in controlling the decision between senescence and proliferation in response to genotoxic stress.

Summary: This session covers pressing questions about the biology and epidemiology of the COVID-19 disease such as understanding disease systems and mechanisms, mapping infection, and distinguishing susceptibity and resilience. Recent systems biology and epidemiological data will be highlighted to discuss actionable paths towards SARS-CoV2 infection prevention and resilience. 

Summary: Artificial intelligence (AI), with its ability to propose decisions for world-wide threats, generate realistic videos and imitate human thought processes, is rapidly making its way into many domains of science and everyday life. At the same time, certain spectacular failings of this technology require an intervention by well contextualised human knowledge. In systems biology, AI is now complementing classical modeling approaches (ODE and Boolean models, for example) and is part of new hybrid modeling approaches. Three panel members, representing three modeling approaches, will discuss their benefits and drawbacks in the context of systems biology, a field with its own specific issues in data acquisition and knowledge representation. We open the discussion to topics of current limitations and possible future goals in the field, as well as the role of systems biology in the broader social context. We invite the audience to challenge the panel members with questions they would like to see addressed by the community.

Summary: Responsiveness to signals influencing cell phenotypic behaviors is a key characteristic of living organisms. Processing of encoded information through cellular signal transduction networks triggers the expression of genes and modulation of protein activities in metabolism and cytoskeleton, culminating in either "all-or-nothing" or “graded" cellular decisions. These decisions are affected by the heterogeneity in protein expression and cell size and in multicellular organisms link cellular processes to tissue effects. This session covers advances in technologies such as proteomics and life cell imaging that allow to monitor dynamic behavior at high temporal resolution and quantitative accuracy. Likewise advances in mathematical modelling approaches potentially linking the cell population scale to the tissue or single cell level will be covered. Synergies of AI based modeling and mechanistic modelling provide novel avenues to unravel principle mechanism that determine dynamic behavior at multiple scales. 

Summary: The digitization of the diagnostics of tissue sections offers interesting application possibilities for patients, doctors and researchers. Not only the digitization process with the viewing software is one of the advantages, but also the possibility to apply decision support systems in the form of artificial intelligence (machine learning). Structured pathological diagnoses, digital histological multiplex images, molecular pathological data as well as known interactions between gene alterations and drugs are the basis for personalized medicine, where individual predictions can be made for each patient.

Summary: The two most important aspects for a career in science? Publications and Grants. Taking an interdisciplinary career path with systems biology approaches, should one focus on data science methodologies, experimental technologies, or biological/medical questions to reach a professorship? The session will combine short presentations with an interactive debate about career development. We invite experienced scientists to share their views and advice, and we welcome younger scientists who we will try to support with advice about a career centred around systems biology approaches. We also will be giving practical advice regarding grant writing, publications and personal development.

Summary: The inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings to synthesize a deeper understanding of cellular responses to genetic and chemical perturbations. In this session, we will explore novel methodological approaches to integrate and analyze this data to understand the general principles of the wiring of living cells and its context-dependent variations. We thus welcome submissions in the fields of genetic interactions, including synthetic lethal and genetic suppression, context-specific dependency mappings and drug-induced cell responses, as well as computational methods to interpret the corresponding large-scale data sets.

Summary: This session will entail talks across any hot topic or landmark work selected by all session chairs and organizers of ICSB 2022. They can be from any field of systems biology or associated fields. We will consider both contributed wildcard talks and approach researchers who has or is conducting exciting groundbreaking work.

Abstract: Current estimates of the global bacterial diversity exceed 1 billion species. By comparison, the number of bacterial species that are described and available for direct physiological and biochemical characterization in the laboratory amounts to less than 19,000. Microbes offer insights into novel biology, drive biogeochemical transformations and are key sources of biochemical innovation. Therefore their functions and interactions need to be better resolved. Insights from state-of-the-art, culture-independent studies of bacteria in their environment can guide the development of novel cultivation approaches that better mimic their natural niche, e.g., natural substrate conditions, growth in biofilms, or exploiting signal compounds and biotic interactions. A combination with high throughput, automated methods that allow multifactorial testing of a large variety of such cultivation conditions (“culturomics”) are particularly promising. These approaches have begun to return novel types of bacteria of interest for research and applications. A currently almost untapped source for novel insights is the wealth of molecular, environmental, and phenotypic data on microorganisms that are principally available but highly dispersed, heterogenous, and non-standardized. Recent integrative database projects address these challenges, render data accessible for semantic searches and artificial intelligence analyses, and thereby offer new, digital gateways to biodiscovery.

EVEN NUMBERED POSTERS (2, 4, 6, ...;)

Abstract:

Summary: In recent years, more and more studies are appearing where different computational approaches and methodologies are combined to address biological phenomena. On one hand, integration of different methodologies poses problems regarding the definition of the respective interface, for example when combining agent-based models with ordinary differential equations, or Boolean with genome-scale metabolic models. On the other hand, the successful combination offers the opportunity to answer biological questions not easily addressable otherwise. This session will present success cases where the combination of different computational methodologies and approaches has shed light on mechanisms underlying emergent properties of biological systems. 

Summary: Over the last decade, a large amount of data has been collected and made publicly available in cancer research. This has enabled development of new approaches in cancer research, ranging from predicting the functional nature of genetic alterations and assessing the effect of genetic and pharmacologic perturbations to predicting patient sensitivity to specific drugs and adaptive response in cancer cells. These methodologies represent critical contribution to the field of precision cancer medicine and support increasing clinical translational of computational and systems biology approaches to the clinic. This session will present some of the latest development in both basic and translational research using mathematical modeling, network- and deep learning-based, for the prediction of biological mechanisms, drug responses and personalized cancer medicine.

Summary: A rapidly changing environment challenges our understanding of plant metabolism, growth and development. It has become evident that application of systems biology approaches essentially supports the quantitative analysis of plant-environment interactions, plant resilience against diverse stressors and plant performance. This conference session will focus on methods, theories, and approaches in the field of plant systems biology comprising experimental omics analysis, flux analysis, network analysis, mathematical modelling, and bioinformatics.

Summary: The challenge of whole cell-modeling is indeed one of the most important ones in computational biology. In metabolism, there has been a series of genome scale modeling studies which we aim to involve in the session in addition to topics such as signaling and  regulation.

Summary: Systems-biology principles emerge across many orders of magnitude in length and time. This session will highlight leading research that tackles multiscale questions in biology through the integration of models and quantitative experiments. Topics will include the coupling of fast and slow processes, the extrapolation of molecular networks–modules to broader populations of cells and organisms, and the fusion of single-cell mechanisms with tissue-level phenomena.