Topics/Sessions

SINGLE CELL MODELING

Chairs: Steven Altschuler & Erik 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.

MACHINE LEARNING / ARTIFICIAL INTELLIGENCE

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.

CANCER SYSTEMS BIOLOGY

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.

COMPUTATIONAL PATHOLOGY

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.

TUMOR ECOSYSTEMS

Chairs: Christine Seers & Satoru Miyano

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.

SIGNAL TRANSDUCTION

Chairs: Doug Lauffenburger & Ursula Klingmüller

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.

NETWORK BIOLOGY & PRECISION MEDICINE

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.

DEEP HIDDEN PHYSICS

Chairs: Maziar Raissi & 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.

CLIMATE & BIOTOPES

Chairs: Peer Bork & Hiroaki Kitano

Summary: TBA

Image from https://www.nature.com/articles/d41586-020-00194-2

MICROBIOMES

Chairs: Luis Serrano & TBA

Summary: TBA

IMAGING

Chairs: Chris Bakal & Stephan Preibisch

Summary: TBA

MULTISCALE MODELS

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.

MODELS IN SPACE AND TIME

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.

PLANT SYSTEMS BIOLOGY

Chairs: TBA

Summary: TBA

WHOLE CELL MODELING

Chairs: Alex Hoffmann & Eytan Ruppin

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.

PREVENTING FUTURE PANDEMICS

Chairs: Christian Neri & Phillip Rosensteil

Summary: TBA

INTEGRATION OF COMPUTATIONAL APPROACHES

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.

CLINICAL IMPACT AND CHALLENGES OF SYSTEMS BIOLOGY

Chairs: Clemens Schmitt & Erich Wanker

Summary: TBA

YOUNG SCIENTIST FORUM/SESSION

Chairs: Students & Postdocs

Summary: TBA

CAREER TRACK

Chairs: Thomas Lemberger & Olaf Wolkenhauer

Summary: TBA

WILDCARD *.TALKS

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.