The long-term goal of the Dynamical Systems Biology lab is to understand how the behavior of living systems (specifically cells and tissues) emerges from the interactions among their components (genes and proteins within cells, and cells within tissues). To pursue that goal, we use the inherently nonlinear and dynamical character of biological systems, both deterministic and stochastic, as a constraint to identify the operating principles through which living matter self-organizes in space and time. Our work involves a close interplay between experimental observations and theoretical/computational approaches coming from statistical and nonlinear physics. The phenomena that we study include the coordination of dynamical processes in single cells, the origins and effects of randomness and heterogeneity in cellular behavior, and the response of cells and organs (such as the brain) to time-varying environments. The organisms studied range from bacteria to humans

www.upf.edu/web/dsb 

We are  interested in understanding the problem of growth and size/shape homeostasis in biology. This topic includes the regulation of size at the cellular level. Questions of interest are, how do cells control their size to achieve homeostasis?, what is the interplay between mechanical cues and gene regulation to trigger division?, At the collective level we are trying to elucidate, and formalize, the mechanisms  shaping tissues. Questions of interest include, how do 3D cellular packing determines the physicochemical behavior of tissues?,

Finally, we are interested in combining approaches from big data and modeling to understand how viral zoonoses propagate and lead to outbreaks. We are currently applying these tools to quantitatively model the spread of filoviruses in bats in the African continent.

www.thesimbiosys.com 

We study the regulation of differentiation during vertebrate development. We combine data generated in vivo, in vitro and in silico to study how the properties of this complex network of interactions affect the role of proteins that regulate differentiation in the nervous system. To do this, we use in toto microscopy combined with theoretical tools and laboratory-developed algorithms that allow us to quantify the effect of key regulators on the balance between stem cell loss and differentiation. Furthermore, we consider the Non-linear regulation in signaling cascades and its impact on pharmacological treatment. We focus on the proven fact that many of these target proteins are within non-linear networks, which change their function and dynamics, and therefore, the response to a potential inhibition treatment. The presence of positive and negative feedback loops and other network motifs can produce abnormal dose-response curves, multi-stability, memory, loss of sensitivity, and modulate response to a drug.

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Our group delves into the captivating realm where mathematics and computational research intersect with biology and biomedicine. These systems, which are highly nonlinear, include subjects like cancer, RNA viruses, and the dynamics of ecosystems and biodiversity preservation. We find ourselves deeply intrigued by the dynamic aspects of these systems, particularly the asymptotic and transient dynamics, bifurcations, and noise-induced phenomena. Our research encompasses both modelling and the mathematical analyses of experimental systems within oncology and the study of viruses together with the investigation of ecosystems' dynamics by using the theories of dynamical systems and statistical physics. These approaches help us uncover essential dynamical phenomena such as warning signals, scaling laws, and universal principles that govern these intricate systems. Additionally, we possess an unquenchable curiosity for characterizing bifurcation phenomena that dictate crucial events, such as the clearance of cancer cells, the extinction of RNA viruses, or the persistence of biodiversity in endangered ecosystems. 

www.crm.cat/math-bio/ 

The group of Nonlinear Physics at the University of Santiago de Compostela (Spain) was founded some 30 years ago. Since then the main interest has been focused on understanding the complexity in Nature from very different points of view aiming to predict, control and manipulate the different processes analyzed. A summary of the research lines active at the group is below:

- Physics of fluids: fluid instabilities, chemical reaction triggered instabilities, numerical simulations, etc.

- Environmental studies: weather forecast, climate change, Lagrangian transport, modelling.

- Modelling of Fluids in biological conduits: cardiology, otoneurology, etc.

- Image processing involving artificial intelligence techniques.

- Turing and wave instabilities, pattern formation in complex systems.

- Complex networks

- Social modeling

www.usc.es/en/investigacion/grupos/gfnl 

Our Group performs research in several domains of nonlinear dynamics and non-equilibrium statistical mechanics, through theoretical and numerical methods.

We are currently interested in the dynamics of disordered networks of model neurons. Our aim is to fill the gap between the microscopic and the mesoscopic descriptions of these systems. We resort to tools from chaos theory, bifurcation theory, random matrix theory, or dimensionality reduction techniques, among others.

Our group members have also experience on other biophysical problems like the propagation of excitation waves in the cardiac tissue, or fungal growth dynamics.

ifca.unican.es/en-us/research/dynamics-and-fluctuations-in-nonlinear-systems 

Our research group is invested in understanding, modelling, and analysing evolutionary mechanisms across various scales, from molecular sequences to ecosystems. We integrate observational data with mathematical approaches to study the dynamics of viruses and RNA populations, with a particular emphasis on the relationship between genotype and phenotype.

Currently, we are delving into the topological structure of genotype-to-phenotype maps in sequence spaces, aiming to uncover universal features that significantly influence evolution and adaptation. Our interests also extend to cultural evolution, neuroscience and dynamics of the brain. We investigate spatiotemporal phenomena in living systems, employing theoretical and computational methods derived from physics and mathematics. These methods are applied to the study genetic regulation and development in animals, plants, and bacteria. We are starting an experimental effort on sequential treatments to antibiotic resistance and genomic minimal cells. In response to the COVID-19 crisis, our research has broadened to encompass epidemic dynamics, where we examine the boundaries of model-based predictions and the impacts of pathogen evolution.

These diverse research areas are bound together by our overarching interest in the complex dynamics of life, and our utilization of tools from complex systems, statistical mechanics, and nonlinear dynamics.

www.cnb.csic.es/index.php/en/research/research-departments/systems-biology 

Interaction, cooperation and competition between different actors represent the main driving forces behind the evolution of the biological, sociological and technological systems around us. Similarly, many astrobiological systems are so complex that to represent them we must use complex networks formed by a multitude of nodes and their interactions. From this perspective, the aim of the Complexity and Astrobiology Group at the Centro de Astrobiología CSIC-INTA is to create a bridge between complexity theory and astrobiology to shed light on one of the most challenging scientific contexts of our century: the origin of life and its evolution from the creation of the most basic building blocks of life in the interstellar medium to the complexity of current biosphere.

complexityweb.com/aguirre/ 

This group focuses on two main research areas: Biological Evolution and Genotype-Phenotype Map, and Development and Cancer Modelling. We use computational and mathematical approaches to understand the evolutionary dynamics of biological systems and cancer development.

In the field of Biological Evolution and Genotype-Phenotype Map, the group explores how living organisms evolve over time based on genetic variations (genotype) that lead to observable traits (phenotype).  In the area of Development and Cancer Modelling, the group's focus is on understanding the behaviour and growth of diseases like cancer, especially in tumours composed of diverse and fast-growing cells. We study the spatial organization of tumour cells and the mechanisms that coordinate tissue growth and patterning, with tools based on dynamical systems, stochastic processes, and Bayesian inference. Additionally, the group highlights the remarkable achievement of embryonic tissue development, where intricate structures and organs form through a precisely coordinated process, that lead to spatiotemporal cellular configurations and physical characteristics crucial for organ formation during embryonic development.

mathuc3m.wixsite.com/studentprojects/gisc-bio 

The group covers a variety of aspects of living matter at cellular and multicellular scales:

- Mechanobiology of epithelial tissues, based on a continuum approach to collective cell migration. We model tissues as active matter and aim at identifying generic hydrodynamic instabilities of active fluids that may be relevant in the context of development, wound healing, and cancer invasion.

- Systems biology of cell decision making. Based on experimental data, we formulate deterministic and stochastic descriptions of the dynamics of molecular components within cells. Our focus is on how cells communicate to perform decisions, especially during development. Our research includes plants and animals. We are interested in how specificity is dynamically encoded and its effect on combinatorial responses.

- Collective phenomena in neuronal tissues, studying experimentally and numerically the behavior of neuronal assemblies cultured in vitro. We combine neuroengineering with tools from statistical physics to shape brain-like model systems to tackle open questions in complex systems and medicine, and to dive into biologically-driven artificial intelligence.

- Active bio-mimetic systems. Using experiments with micrometer size colloidal particles, we realize a variety of magnetic or chemical prototypes capable of navigating at low Reynolds number, and being controlled by external fields. These bio-mimetic systems may be relevant for application in microfluidics, drag-delivery, or noninvasive bio-medicine.

The Computational Biology and Complex systems group (BIOCOM-SC) involves researchers with a mathematical and/or physical background, from biophysics and nonlinear dynamics to nonequilibrium statistical mechanics or fluid mechanics. These different skills permit us to address increasingly complex problems in biology and other fields which, due to their inherent complexity, involve very different mechanisms. At this moment, BIOCOM-SC is the reference group in this field at the physics department at UPC. We study diverse biological processes, e.g. from molecular level (ion channel function) to cell (motion, contraction), organ (heart beating)  and population level (microbial proliferation, epidemiology). For that, we use different methods and perspectives with the goal of developing clinical applications for biomedicine and environmental applications for bioengineering.

https://biocomsc.upc.edu

Mathematics is the common language for describing physical, chemical and biological processes. Biological entities, from the molecular to the population level, are displaying a wide range of intricate dynamics: feedback loops and non-trivial temporal dynamics operate on different length scales, enable interaction with other living and non-living matter and are capable of Darwinian evolution. In this node, we study several aspects of these feedback loops, periodic (and chaotic) oscillations, antimicrobial resistance, structure of RNA viruses, and evolving replicator populations.