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Speaker: Jeremy Gunawardena (Harvard University, Cambridge)
Date: 28/11/2024
Time: 10:00 CEST
Host: Rosa Martinez-Corral (Barcelona Collaboratorium & Centre de Regulació Genòmica, Barcelona)

Cellular systems - enzymes, motors, allosteric proteins, genes, ion channels, receptors, etc - are often described by the functional dependence of some output property on the concentrations of input factors, with the system itself described as a Markov process. Such input-output functions are calculated by a variety of methods with apparently few common features. In fact, this typical biological heterogeneity conceals a remarkable underlying mathematical unity. All such input-output functions are rational functions of their inputs, whose coefficients are themselves rational functions of the Markov transition rates. There is a uniform procedure for calculating each function in terms of the linear-framework graph associated to the Markov process. Furthermore, input-output functions exhibit a Hopfield barrier: if the Markov process can reach thermodynamic equilibrium, then the degree of the rational function depends only on the numbers of input binding sites and is otherwise model independent. Hopfield barriers offer a powerful method for assessing energy expenditure in cellular systems, which does not require fitting models to data.

If you would like to attend the seminar, please register here.

 
 

Speaker: Natasa Przulj (BSC, Barcelona)
Date: 17/10/2024
Time: 10:00 CEST
Host: Nora Martin (CRG/Collaboratorium, Barcelona)

Large quantities of heterogeneous, interconnected, systems-level, molecular (multi-omic) data are increasingly becoming available. They provide complementary information about cells, tissues and diseases. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets, re-purpose known and discover new drugs to personalize medical treatment. This is nontrivial, because of computational intractability of many underlying problems on large interconnected data (networks, or graphs), necessitating the development of new algorithms for finding approximate solutions (heuristics).

We develop a versatile data fusion artificial intelligence (AI) framework, that also utilizes the state-of-the-art network science methods, to address key challenges in precision medicine from the multi-omics data: better stratification of patients, prediction of biomarkers and targets, and re-purposing of approved drugs to particular patient groups, applied to different types of cancer, Covid-19, Parkinson’s and other diseases. Our new methods stem from graph-regularized non-negative matrix tri-factorization (NMTF), a machine learning technique for dimensionality reduction, inference and co-clustering of heterogeneous datasets, coupled with novel network science algorithms. We utilize our new frameworks to develop methodologies for improving the understanding the molecular organization and diseases from the omics data embedding spaces.

If you would like to attend the seminar, please register here.

 
 

Speaker: Vikas Trivedi (EMBL Barcelona)
Date: 24/10/2024
Time: 10:00 CEST
Host: ‪Alejandro Torres-Sánchez (EMBL Barcelona)

How can tissue shapes and patterns emerge reproducibly and robustly in multicellular systems like animals?  Despite more than 100 years of embryology, it still remains unclear how gene networks, forces and mechanical properties and the metabolic state of the cells integrate together to self-organize complex structures. This is due to our inability to disentangle the combined action of these factors (biophysical properties, gene networks and metabolic activity) within populations of genetically equivalent cells. In our work we focus on understanding the interplay of these factors within the context of the establishment of body axes in metazoans. We take advantage of aggregates of embryonic stem cells (ESCs) that recapitulate hallmarks of early embryonic development in vitro and probe the first symmetry breaking event that establishes anteroposterior polarity in those aggregates. We aim to understand how gene expression controls tissue rheology which can then dictate spatial segregation of cell types, while the metabolic activity of the cells in the background influences signalling and cell fate decisions. In the long term we aspire to generate a theoretical framework that can capture these processes occurring at different time scales and the feedbacks that together generate a robust pattern in multicellular systems. 

If you would like to attend the seminar, please register here.

 
 

Speaker: Saúl Ares (CNB-CSIC)
Date: 03/10/2024
Time: 10:00 CEST
Host: Jordi Garcia-Ojalvo (UPF, Barcelona)

This seminar explores the modeling of biological growth and patterning across diverse systems — the filamentous cyanobacterium Anabaena, Arabidopsis thaliana plants, Drosophila melanogaster flies, and an epidemic spreading across a population. We delve into how genetic and environmental factors influence the quasi-regular patterning of nitrogen-fixing specialized cells, called heterocysts, in Anabaena. The model highlights key genes and cellular processes that govern pattern appearance and maintenance, illustrating the impact of physical boundary conditions in biological systems. For Arabidopsis, we examine how light and temperature cues affect the growth of the hypocotyl through interactions between photoreceptors and thermal sensors, shedding light on plant morphogenesis. In Drosophila, we focus on how BMP2/4 signaling regulates cell proliferation and apoptosis to ensure precise organ size during development. Finally, epidemic spread has been a topic of grave concern in recent years, and it provides a perfect example to discuss both the limitations of mathematical prediction and the right level of complexity a model should have.

If you would like to attend the seminar, please register here

 
 

Speaker: Michael Stumpf (University of Melbourne, Australia)
Date: 19/09/2024
Time: 10:00

In 1967 Francis Crick and Sydney Brenner proposed that we need mathematical models of cells to understand their complexity and their behaviour; in 2001 mathematical modelling of cells was identified as a grand challenge of 21st Century science. In order to understand the complexity of life, in order to integrate and interpret experimental data, and in order to control cellular processes in biotechnology and synthetic biology we need a conceptual, analytical, and predictive framework – referred to as the CellMap by Sydney Brenner. I will discuss three facets, centred around cell differentiation and developmental process, of how we can start to distill “design principles” underlying cellular behaviour. Here design principles are understood as the essential properties that a system needs to possess to be able to fulfil certain functions. I will discuss the interplay of cell lineages, molecular networks, and phenotypic landscapes, their intricate interdependencies, and how they shape cell-fate decision making processes. These are, of course, only baby-steps towards Brenner’s dream of a CellMap, but taking them has already allowed us to map out and embark on a feasible path towards such models.

 
 

Speaker: Jan Brugues (TU Dresden, Germany)
Date: 11/07/2024
Time: 10:00

Early development across vertebrates and insects critically relies on robustly reorganizing the cytoplasm of fertilized eggs into individualized cells. This intricate process is orchestrated by large microtubule structures that traverse the embryo, partitioning the cytoplasm into physically distinct and stable compartments. Despite the robustness of embryonic development, here we uncover an intrinsic instability in cytoplasmic partitioning driven by the microtubule cytoskeleton. We reveal that embryos circumvent this instability through two distinct mechanisms: either by matching the cell cycle duration to the time needed for the instability to unfold or by limiting microtubule nucleation. These regulatory mechanisms give rise to two possible strategies to fill the cytoplasm, which we experimentally demonstrate in zebrafish and Drosophila embryos, respectively. In zebrafish embryos, unstable microtubule waves fill the geometry of the entire embryo from the first division. Conversely, in Drosophila embryos, stable microtubule asters resulting from reduced microtubule nucleation gradually fill the cytoplasm throughout multiple divisions. Our results indicate that the temporal control of microtubule dynamics could have driven the evolutionary emergence of species-specific mechanisms for effective cytoplasmic organization. Furthermore, our study unveils a fundamental synergy between physical instabilities and biological clocks, uncovering universal strategies for rapid, robust, and efficient spatial ordering in biological systems.

 
 

Speaker: Fèlix Ritort (Small Biosystems Lab, Departament de Física de la Matèria Condensada, Facultat de Física, Universitat de Barcelona
Date: 13/06/2024
Time: 10:00

Nonequilibrium pervades nature, from the expanding universe to climate dynamics, living cells and molecular machines. Key to nonequilibrium states is the entropy production rate σ  at which energy is dissipated to the environment. Despite its importance, σ  remains challenging to measure, especially in nanoscale systems with limited access to microscopic variables. Here I present a recently introduced variance sum rule for displacement and force variances that permits to measure σ  by constraining energetics through modelling [1,2]. We apply it to measure the first heat map of human red blood cells in experiments with laser optical tweezers and ultrafast life-imaging microscopy. We find a spatially heterogeneous σ  with finite-correlation length of half a micron ξ~0.5μm and global σ~ 106 kBT/s per single cell, in agreement with calorimetry estimates. The variance sum rule sets a new resource for measuring entropy production rates in active and living matter [3].

 

References

[1]  Di Terlizzi, I., Gironella, M., Herráez-Aguilar, D., Betz, T., Monroy, F., Baiesi, M., & Ritort, F. (2024). Variance sum rule for entropy production. Science, 383(6686), 971-976.

[2] Di Terlizzi, I., Baiesi, M., & Ritort, F. (2024). Variance sum rule: proofs and solvable models. New Journal of Physics, DOI: 10.1088/1367-2630/ad4fb9.

[3] Roldán, É. (2024). Thermodynamic probes of life. Science, 383(6686), 952-953.

 
 

Speaker: Mirko Francesconi (ENS Lyon, France)
Date: 23/05/2024
Time: 10:00

Transcriptomic data provide a systematic multivariate snapshot measurement of the state of a biological system. In my team, we develop transcriptomics data integration and modelling frameworks to reconstruct systems dynamics, identify and precisely quantify hidden phenotypic variation at multiple scales, from the molecular level to organismal physiology, and to make quantitative predictions about genetic and non-genetic cellular and molecular and mechanisms of phenotypic variation including at single-cell and single-individual level. In contrast to others, our strategies privilege interpretability and robustness. I will present examples of how I applied these strategies to predict complex phenotypes and mechanisms at the single-cell and single-organism level.  In particular, I will present RAPToR, a simple but precise computational method to infer absolute physiological age of a system from its transcriptome exploiting existing time series data as reference1. Importantly, ages estimated on the same reference are comparable across conditions, genetic backgrounds even across species. RAPToR, works in multiple model organisms or humans for both development and aging, for bulk, dissected tissues, single-individual and single-cell data. Moreover, provided with tissue/process -specific annotation RAPToR can provide tissue specific age estimates from whole-animal data.  Estimated physiological ages, (in combination with chronological age) can be used not only to precisely quantify the effect (and time of action) of genetic, environmental and inter-individual variation on development or aging speed but also to quantify their specific effects of on gene expression, suggesting their specific molecular determinants. In summary, large scale transcriptomic data can be exploited to both quantify complex phenotypes and learn molecular and cellular determinants of these phenotypes. These quantities inferred from gene expression can then be used to parametrize mathematical models to predict phenotypes.

 

References

1.Bulteau, R. & Francesconi, M. Real age prediction from the transcriptome with RAPToR. Nat Methods 1–7 (2022) doi:10.1038/s41592-022-01540-0

 
 

Speaker: Jané Kondev (Brandeis University, US)
Date: 23/04/2024
Time: 10:00

In this tutorial, we will examine the contents of a bacterial cell as well as the timing of the processes of the central dogma through the lens of simple, order magnitude estimates. The goal of these estimates is to develop a quantitative intuition about cells. For example, we might try to gain intuition about why bacterial cells cannot divide faster than every few minutes. Participants should bring pen and paper.
 

 
 

Speaker: Javier Buceta (I2SysBio, CSIC-UV)
Date: 09/05/2024
Time: 10:00

Bacteria employ various strategies to survive environmental stress. One of these strategies is filamentation wherein bacterial cells elongate instead of dividing. This adaptive response, essential for survival, is also associated with virulence. In this talk I will review our research on the topic where we have focused on understanding the mechanobiology cues driving this process and their implications. Thus, using quantitative fluorescent time-lapse microscopy, micropatterning, modeling, microfluidics, and FRAP we reveal how Min dynamics is coupled to the mechanical stress originating from bacterial filamentation and ultimately influences the post-stress bacterial division process.

 
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