On Markov blankets and hierarchical self-organisation

Biological self-organisation can be regarded as a process of spontaneous pattern formation; namely, the emergence of structures that distinguish themselves from their environment. This process can occur at nested spatial scales: from the microscopic (e.g., the emergence of cells) to the macroscopic (e.g. the emergence of organisms). In this paper, we pursue the idea that Markov blankets – that separate the internal states of a structure from external states – can self-assemble at successively higher levels of organisation. Using simulations, based on the principle of variational free energy minimisation, we show that hierarchical self-organisation emerges when the microscopic elements of an ensemble have prior (e.g., genetic) beliefs that they participate in a macroscopic Markov blanket: i.e., they can only influence – or be influenced by – a subset of other elements. Furthermore, the emergent structures look very much like those found in nature (e.g., cells or organelles), when influences are mediated by short range signalling. These simulations are offered as a proof of concept that hierarchical self-organisation of Markov blankets (into Markov blankets) can explain the self-evidencing, autopoietic behaviour of biological systems.

 

On Markov blankets and hierarchical self-organisation
Ensor Rafael Palacios, Adeel Razi, Thomas Parr, Michael Kirchhoff, Karl Friston

Journal of Theoretical Biology

Source: www.sciencedirect.com

Generalizing RNA velocity to transient cell states through dynamical modeling

The introduction of RNA velocity in single cells has opened up new ways of studying cellular differentiation. The originally proposed framework obtains velocities as the deviation of the observed ratio of spliced and unspliced mRNA from an inferred steady state. Errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. With scVelo (https://scvelo.org), we address these restrictions by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to a wide variety of systems comprising transient cell states, which are common in development and in response to perturbations. We infer gene-specific rates of transcription, splicing and degradation, and recover the latent time of the underlying cellular processes. This latent time represents the cell’s internal clock and is based only on its transcriptional dynamics. Moreover, scVelo allows us to identify regimes of regulatory changes such as stages of cell fate commitment and, therein, systematically detects putative driver genes. We demonstrate that scVelo enables disentangling heterogeneous subpopulation kinetics with unprecedented resolution in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis. We anticipate that scVelo will greatly facilitate the study of lineage decisions, gene regulation, and pathway activity identification.

 

Generalizing RNA velocity to transient cell states through dynamical modeling
Volker Bergen, Marius Lange, Stefan Peidli, F. Alexander Wolf, Fabian J. Theis

Source: www.biorxiv.org

Nonlinearity + Networks: A 2020 Vision

I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which the entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include "higher-order" interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators.

 

Nonlinearity + Networks: A 2020 Vision
Mason A. Porter

Source: arxiv.org

Is the World Chaos, a Machine, or Evolving Complexity? How Well Can We Understand Life and World Affairs?

Chaos, machine, or evolving complexity? The butterfly effect suggests a world in chaos—with linkages so random or nuanced that just to measure or pre-state them is virtually impossible. To predict how they will interact is even less feasible. Thanks to “adjacent possibles” and the contradictory impulses of human behavior, much of our world appears to move in random spasms. Every new technology and policy outcome creates opportunities to push society in new and often unforeseen directions, driven by human agents who may introduce crucial but unpredictable goals, strategies, and actions. Against this view, complexity science seeks to identify patterns in interactive relationships. Many patterns can be plotted and, in some cases, foreseen. A comparison of political entities across the globe points to certain factors conducing to societal fitness. Analysis of states that have declined in fitness suggests why their strengths turned to weaknesses. A survey of societies that were relatively democratic points to several factors that contributed to their acquiring authoritarian regimes. Scientists and scholars can unveil some elements of order but should strive to do so without hubris. Wise policymakers will strive to channel both the “actuals” and “adjacent possibles” that then arise toward constructive futures.

 

NETSOL: New Trends in Social and Liberal Sciences

Year: 2019 / Volume : 2 / Area: Interdisciplinary Studies

Walter C. Clemens and Stuart A. Kauffman
Is the World Chaos, a Machine, or Evolving Complexity? How Well Can We Understand Life and World Affairs? pp.24-43.

Source: www.netsoljournal.net

Data-driven discovery of coordinates and governing equations

Governing equations are essential to the study of physical systems, providing models that can generalize to predict previously unseen behaviors. There are many systems of interest across disciplines where large quantities of data have been collected, but the underlying governing equations remain unknown. This work introduces an approach to discover governing models from data. The proposed method addresses a key limitation of prior approaches by simultaneously discovering coordinates that admit a parsimonious dynamical model. Developing parsimonious and interpretable governing models has the potential to transform our understanding of complex systems, including in neuroscience, biology, and climate science.

 

Data-driven discovery of coordinates and governing equations
Kathleen Champion, Bethany Lusch, J. Nathan Kutz, and Steven L. Brunton
PNAS November 5, 2019 116 (45) 22445-22451; first published October 21, 2019 https://doi.org/10.1073/pnas.1906995116

Source: www.pnas.org