The Integrative Potential of Process in a Changing World: Introduction to a special issue on power, performativity and process

This editorial essay introduces a special issue that tackles the seemingly intractable challenge of re-conceptualizing power and performativity as continuously interweaving and co-emergent dynamics in the processes of organizing. It is in these processes, we argue, that new futures may be visibly made through the academic activism of our scholarly communities. We position our argument, and the six papers that comprise this special issue, in relation to Rosi Braidotti’s framing of Humanism, anti-humanism and the posthuman. We also suggest some future lines of inquiry to move studies of organizing forward into a posthuman world.

Emergent dynamics of a three-node regulatory network explain phenotypic switching and heterogeneity during helper T-cell differentiation

Naive helper (CD4+) T-cells can differentiate into distinct functional subsets including Th1, Th2, and Th17 phenotypes. Each of these phenotypes has a 'master regulator' - T-bet (Th1), GATA3 (Th2) and RORgT (Th17) - that inhibits the other two master regulators. Such mutual repression among them at a transcriptional level can enable multistability, thus enabling the co-existence of six experimentally observed phenotypes - Th1, Th2, Th17 and hybrid Th/Th2, Th2/Th17 and Th1/Th17. However, the dynamics of switching among these phenotypes, particularly in the case of epigenetic influence, remains unclear. Here, through mathematical modeling, we investigated the coupled transcription-epigenetic network dynamics to elucidate how epigenetic changes mediated by these 'master regulators' can influence the transition rates among these different phenotypes. Further, we show that the degree of plasticity exhibited by one phenotype depends on relative strength and duration of mutual epigenetic repression mediated among the master regulators. Our model simulations possibly explain the relatively higher plasticity of Th17 phenotype as noticed in vitro and in vivo. Together, our modeling framework characterizes phenotypic plasticity and heterogeneity in a naive helper (CD4+) T-cell population as an outcome of the emergent dynamics of a three node regulatory network, such as the one mediated by T-bet/GATA3/RORgT.

Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

Nonlinear emergent macroscale PDEs, with error bound, for nonlinear microscale systems

Many multiscale physical scenarios have a spatial domain which is large in some dimensions but relatively thin in other dimensions. These scenarios includes homogenization problems where microscale heterogeneity is effectively a ‘thin dimension’. In such scenarios, slowly varying, pattern forming, emergent structures typically dominate the large dimensions. Common modelling approximations of the emergent dynamics usually rely on self-consistency arguments or on a nonphysical mathematical limit of an infinite aspect ratio of the large and thin dimensions. Instead, here we extend to nonlinear dynamics a new modelling approach which analyses the dynamics at each cross-section of the domain via a multivariate Taylor series (Roberts and Bunder in IMA J Appl Math 82(5):971–1012, 2017. 10.1093/imamat/hxx021). Centre manifold theory extends the analysis at individual cross-sections to a rigorous global model of the system’s emergent dynamics in the large but finite domain. A new remainder term quantifies the error of the nonlinear modelling and is expressed in terms of the interaction between cross-sections and the fast and slow dynamics. We illustrate the rigorous approach by deriving the large-scale nonlinear dynamics of a thin liquid film on a rotating substrate. The approach developed here empowers new mathematical and physical insight and new computational simulations of previously intractable nonlinear multiscale problems.

Active motion of multiphase oil droplets: emergent dynamics of squirmers with evolving internal structure

Self-propelled motions of active droplets can be programmed by transforming their internal morphologies over time.

Persistence length regulates emergent dynamics in active roller ensembles

Active colloidal fluids, biological and synthetic, often demonstrate complex self-organization and the emergence of collective behavior. Spontaneous formation of multiple vortices has been recently observed in a variety of active...