Chairs: Steven Altschuler & Erik van Nimwegen

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.

Chairs: Trey Ideker & Hiroshi Mamitsuka

Summary: This session covers the exciting recent advances that are emerging at the intersection of machine learning and systems biology. An example is in formulation of predictive machine learning models, in which model structure and/or selection of parameters can be effectively guided by reference maps of biological structures and their functional state transitions. Such calibration is a critical component of formulating biological models, including models of molecular structures, pathways, cells, tissues, and human populations. Calibration requires not only in-depth understanding of the applied model and phenomena but also application of proper optimization algorithms, where the long-term goal is to find avenues for incorporating and applying methods of machine learning and artificial intelligence.

Chairs: Andrea Califano & Mariko Okada

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.

Chairs: Peter Wild & Nadine Flinner

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.

Chairs: Satoru Miyano & Kristen Naegle

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.

Chairs: Ursula Klingmüller & Julio Saez-Rodriguez

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.

Chairs: Michael Yaffe & Paola Picotti

Summary: Network biology is a fundamental branch of systems biology, which views, represents, and analyzes biological processes as networks of interacting components. Examples of these networks are protein-protein interaction networks, metabolic networks and gene regulatory networks. In this session, we will cover innovative large-scale network biology approaches involving single and multi’omics technologies that reveal novel interactions and regulatory mechanisms that control the phenotypes of normal and diseased cells. We will showcase how applications of this concept in the field of precision medicine can be used to guide personalized therapeutic approaches.

Chairs: Paris Perdikaris & Markus Heinonen

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.

Chairs: Peer Bork & Hiroaki Kitano

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.

Chairs: Charlie Boone & Patrick Aloy

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.

Chairs: Chris Bakal & Julia Sero

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.

Chairs: Kevin Janes & Mary Teruel

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.

Chairs: Pedro Mendes & Tobias Meyer

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.

Chairs: Nadine Töpfer & Thomas Naegele

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.

Chairs: Alex Hoffmann & Jonathan Karr (pending)

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.

Chairs: Christian Neri & Phillip Rosensteil

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 susceptibility and resilience. Recent systems biology and epidemiological data will be highlighted to discuss actionable paths towards SARS-CoV2 infection prevention and resilience.

Chairs: Ursula Kummer & Matteo Barberis

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.

Chairs: Clemens Schmitt & Erich Wanker

Summary: New omics technologies and the generation of massive medical and research data lead to a rapid change in clinical and translational research, with a wide future impact on health care to be expected. Systems medicine is an interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop strategies to generate and store new data with the aim to benefit patients. In this session, we assess the opportunities and challenges arising out of systems approaches in medicine. Also, current developments in medicine and healthcare and associated research needs will be discussed. We emphasize the role of systems biology and medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition, analysis and theoretical modeling to extract previously inaccessible knowledge for the benefit of patients.

Chairs: Ana Bulovic (HUB, DE), Jiexuan Jiang (MDC, DE), Catarina Marlene Stein (Charite, DE) & Diyal Dhamrait (TUB, DE)

Summary: This session is organised by students and postdocs.

Chairs: Thomas Lemberger & Olaf Wolkenhauer

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.

Chairs: Rune Linding & Edda Klipp

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.