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 k_BT/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.

One of the greatest mysteries concerning the origin of life is how it has emerged so quickly after the formation of the earth. In particular, it is not understood how the intricate structures of metabolic cycles, which power the non-equilibrium activity of cells and support their functions under homeostatic conditions, have come into existence in the first instances. These structures have emerged from a dilute primordial soup of chemicals that have turned out to be suitable partners in certain reactions in the roles of reactants and catalysts. While it is generally expected that non-equilibrium conditions would have been necessary for the formation of these primitive metabolic structures, the focus has so far been on externally imposed non-equilibrium conditions, such as temperature or proton gradients. I introduce an alternative paradigm in which naturally occurring non-reciprocal interactions between catalysts that can potentially partner together in a cyclic reaction lead to their rapid recruitment into self-organized functional structures [1]. Within this paradigm, we uncover different classes of self-organized cycles that form through exponentially rapid coarsening processes, depending on the parity of the cycle and the nature of the interaction motifs, which are all generic but have readily tuneable features. Our results also shed light on possibilities that may be explored in designing efficient synthetic cycles. Moreover, we identify programmable non-reciprocal interactions as a tool to achieve the ability to employ a common set of building blocks that can self-organize into a multitude of different structures [2]. The design rule is composed of reciprocal interactions that lead to the equilibrium assembly of the different structures, through a process denoted as multifarious self-assembly, and non-reciprocal interactions that give rise to non-equilibrium dynamical transitions between the structures. The design of such self-organized shape-shifting structures can be implemented at different scales, from nucleic acids and peptides to proteins and colloids.

[1] Vincent Ouazan-Reboul, Jaime Agudo-Canalejo, and Ramin Golestanian, Nature Communications 14, 4496 (2023)

[2] Saeed Osat and Ramin Golestanian, Nature Nanotechnology 18, 79–85 (2023)

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

Fate decisions in developing tissues involve cells transitioning between a set of discrete cell states. Geometric models, often referred to as Waddington landscapes, are an appealing way to describe differentiation dynamics and developmental decisions. We consider the differentiation of neural and mesodermal cells from pluripotent mouse embryonic stem cells exposed to different combinations and durations of signalling factors. We developed a principled statistical approach using flow cytometry data to quantify differentiating cell states. Then, using a framework based on Catastrophe Theory and approximate Bayesian computation, we constructed the corresponding dynamical landscape. The result was a quantitative model that accurately predicted the proportions of neural and mesodermal cells differentiating in response to specific signalling regimes. Taken together, the approach we describe is broadly applicable for the quantitative analysis of differentiation dynamics and for determining the logic of developmental cell fate decisions.

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.

The spontaneous generation of patterns and structures occurs in many living systems and is linked to biological form and function. Such processes often take place on domains which themselves evolve in time, and they can be guided by or coupled to geometrical features. The role of geometry in the self-organisation of functional structures however is not understood. I will present two biophysical examples that illustrate how geometry directs spatial organization at different scales. I will discuss how boundary geometry controls a topological defect transition that guides lumen nucleation in embryonic development [1], and how shape can act as a form of memory in cell-cell signaling [2].

[1] Guruciaga et al. arXiv:2403.08710 (2024)

[2] Dullweber et al. arXiv:2402.08664v2 (2024)

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.
 

Computational methods are an ideal complement for inferring non directly measurable quantities such as forces or growth distribution. I will present a continuum approach where growth tensor is deduced with different objectives: for computing optimal locomotion of soft bodies on non-isotropic substrates, and for inferring the growth distribution that best matches a set of experimental measurements. Importantly, the uncertainty on the boundary conditions is also included in the inverse analysis, jointly with an iterative regularisation process. The methodology is applied  to different worm locomotion strategies, and axolotl limb growth. In each case it is revealed that non-isotropic friction and growth pattern are necessary conditions for reproducing experimental motions and deformations.

Complex systems unfold nonlinear interactions and can suffer regime shifts which are of paramount importance since they can involve the emergence and persistence of a disease or the extinction of a species within an ecosystem. Such regime shifts can be achieved by different mechanisms and their understanding is crucial to potentially anticipate them and have information about the time scales of such shifts. Dynamical systems theory and computational research allows the study of such mechanisms in a general way or for specific complex systems such as ecosystems or viral infections. In this seminar, I will discuss different mechanisms causing regime shifts, paying special attention to critical transitions given by tipping points. I will discuss important dynamical properties arising close to critical transitions such as transients and the so-called warning signals. These signals provide a powerful way to detect that a given system is approaching a critical transition without the necessity to have a detailed knowledge of the underlying system dynamics. To foster discussion and possible new collaborations, I will provide examples of these topics identified in experimental and natural systems.

TBA