Energy, Information and Evolution in Biology

Lectures

 Understanding and modeling of cellular metabolism (Matthias Heinemann, University of Groningen )

 

Lecture 1: Basics of carbon and energy metabolism, metabolic fluxes, respiration and aerobic fermentation, flux sensing

Lecture 2: Universal patterns of metabolic behavior across organisms, limitation in Gibbs energy dissipation rate governing metabolism

Lecture 3: Metabolic-intrinsic dynamics in control of the eukaryotic cell cycle

Thermodynamics of Chemical Reaction Networks (Francesco Avanzini, University of Padova)

 

Lecture 1: Dynamics and Thermodynamics

Dynamics; Local Detailed Balance; Basic Thermodynamics

Lecture 2: Topological properties of CRNs

Conservation laws and cycles of CRNs; Thermodynamic Potential

Lecture 3: Using Topology in Thermodynamics

Fundamental forces and thermodynamic potential; Illustrative Examples; a glance into circuit theory and energy transduction

The Statistical Physics Perspective on Immunology (Aleksandra Walczak, ENS Paris)

 

Lecture 1: Diversity in the immune system

Introduce measures of diversity; introduce Renyi entropies and clone size distributions. Introduce T and B cells, how they are made and basic estimates of diversity

Lecture 2: Optimal immune systems

Introduce Bayesian prediction and dynamic forecasting

Lecture 3. Phenotypic models of co-evolution

Introduce traveling waves in antigenic space; Introduce models of viral and immune evolution

Information Processing for Sensory systems and molecular signaling (Thierry Mora, ENS Paris)

 

Lecture 1: Basics of information theory and statistical inference

Shannon entropy, mutual information, channel capacity, Bayesian inference, likelihood and cross-entropy, posterior, prior. Gaussian example. Applications: Laughlin optimal information transmission, information in E. coli chemotaxis.

Lecture 2: Small numbers: fundamental limits on information processing.

Berg and Purcell bound, single-photon sensitivity in the retina, specificity in immune recognition.

Lecture 3: Collective sensing.

Collective coding in the retina, role of correlations. Positional coding in fly development.

The energetic cost of information processing from basic principles to biology. (Juan Parrondo, Universidad Complutense Madrid)
Thermodynamically consistent description of active matter (Gianmaria Falasco, University of Padova)

 

The origin of life as a planetary process (Eric Smith, The Santa Fe Institute)

 

Lecture 1: Living Systems in Planetary Contexts

Hierarchical organization of living systems, Architecture, function, and evolutionary divergences in metabolism, Energetics in metabolism and geochemistry, Individuation of the living state, The translation system and the central dogma for flow of information and control, Folding and folds

Lecture 2:  Functional and Evolutionary Comparative Analyses

Parsimony analysis of metabolic divergences and imputation of oldest metabolites and subnetworks, Primary organosynthesis and the prevalence of metabolites, Network-expansion analysis of synthetic dependencies, Two views of regularity in the genetic code, Generational structures in RNA through the emergence of translation, Comparative analysis and precedence among the protein folds

Lecture 3:  Making Causal Claims about the origin of biospheres

Biology’s theory of cause, The fragility of scenario arguments in domains with fragmentary knowledge, The need for combinatorial and probabilistic null models, Rule-based modeling for structured combinatorial spaces, the application of non-equilibrium phase-transition concepts to reaction spaces, Stress bottlenecks and information gateways, Binary folding models

Biological evolution: making sense of protein sequence data (Anne Bitbol, EPFL Lausanne)

 

Lecture 1: Models of evolution: fate of mutations in populations

Moran model; Wright-Fisher model; diffusion approximation; Mutant fixation probability and average mutant fixation time; Link to evolution experiments – Parameter regimes; Evolution beyond selection and genetic drift

 Lecture 2: Inference from protein sequences – traditional methods

Conservation: Shannon entropy; Statistical dependence between sites: mutual information; Pairwise maximum entropy models (Potts models); Detecting selection by comparing synonymous and nonsynonymous mutations

Lecture 3: Inference from protein sequences – protein language models

Transformers and protein language models; link to AlphaFold and structure prediction; Applications of protein language models: mutation effect prediction, sequence generation,interaction prediction; Models at the genome scale

Machine Learning and Protein Folding (Nazim Bouatta, Harvard Medical School)

 

Lecture 1: Algorithm space

A brief intro to protein biology. AlphaFold2 impacts on experimental structural biology. Co-evolutionary approaches. Space of ‘algorithms’ for protein structure prediction. Proteins as images (CNNs for protein structure prediction). End-to-end differentiable approaches. Attention and long-range dependencies. AlphaFold2 in a nutshell.

Lecture 2: AlphaFold2 architecture

Turning the co-evolutionary principle into an algorithm: EvoFormer. Structure module and symmetry principles (equivariance and invariance). OpenFold: retraining AlphaFold2 and insights into its learning mechanisms and capacity for generalization. Applications of variants of AlphaFold2 beyond protein structure prediction: AlphaFold Multimer for protein complexes, RNA structure prediction

Lecture 3:  AlphaFold2 limitations and insights learned from OpenFold

Limitations of AlphaFold2 and evolutionary ML pipelines. OpenFold: retraining AlphaFold2 yields new insights into its capacity for generalization

Implementing evolutionary properties in biochemical reaction networks (Philippe Nghe, ESPCI Paris)

 

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