source:

CCSS Meeting #36: Scaling in Regulatory Networks: Basic Theory and Implications for Systemic Evolution – Current affairs – Universiteit Utrecht

# CCSS Meeting #36: Scaling in Regulatory Networks: Basic Theory and Implications for Systemic Evolution

This lecture is an online discussion organised under our new **Scaling in Complex Systems** lecture series.

For the foreseeable future, lectures will remain predominantly online.

This Series: **Scaling in Complex Systems**

This academic year, we are introducing two series of lectures at the CCSS. Under our ‘Scaling in Complex Systems’ series, we shall hear from researchers investigating mechanisms of scaling, such as self-organized criticality, preferential processes, multiplicative processes and sample space reducing processes.

**Speaker Overview**

Rudolf Hanel (Medical University of Vienna & Complexity Science Hub Vienna) has been working with the Complex Systems Research Group of CSH since 2007. Rudolf finished his PhD in 1999 in theoretical physics, which was followed by work in medical physics, alongside publishing extensively on a range of topics including statistical physics, robotics, medical imaging , complex systems and evolution. Rudolf (AKA Rudi) is currently interested in understanding non-equilibrium processes, their thermodynamic properties and associated phase transitions.

**Abstract**

Emergent features of steadily driven non-equilibrium processes (e.g. cells, ecosystems, etc.) are not mutually independent. At coarse grained levels of description they can often be understood as regulatory networks that represent systems of typically non-linear dependencies. Ignorance on details of complex regulatory systems typically prevents us to fully specify such networks and reduces the direct predictive value of such models, particularly if some system components are themselves `anticipating subprocess’. However, even simple models can still generically inform us about dynamical properties we may expect from sufficiently large heterogenous regulatory networks. We use a most primitive `almost linear’ network model suffices to gain insight into how considered state variables, such as the abundance or activity of system features, all non-negative quantities, implement non-linear constraints on the system, causing it to exhibit large numbers of attractors corresponding to limit cycles or fixed points, and with some noise added, multistabie dynamics (i.e. punctuated equilibria) can be observed. More interestingly, there exits an extended range in the parameter-space of such systems where the system is very likely to operate `sustainably’ in a stable, or meta-stable way. Outside this range the system almost certainly becomes unstable (fully chaotic or exponential runaway dynamics). If we postulate that the overall stability of regulatory systems plays a role in systemic selection, or the evolution of system parameters, such models may explain emergent modularity and maybe also predominance of suppression mechanisms in regulatory systems as they increase in their diversity of features.Emergent features of steadily driven non-equilibrium processes (e.g. cells, ecosystems, etc.) are not mutually independent. At coarse grained levels of description they can often be understood as regulatory networks that represent systems of typically non-linear dependencies. Ignorance on details of complex regulatory systems typically prevents us to fully specify such networks and reduces the direct predictive value of such models, particularly if some system components are themselves `anticipating subprocess’. However, even simple models can still generically inform us about dynamical properties we may expect from sufficiently large heterogenous regulatory networks. We use a most primitive `almost linear’ network model suffices to gain insight into how considered state variables, such as the abundance or activity of system features, all non-negative quantities, implement non-linear constraints on the system, causing it to exhibit large numbers of attractors corresponding to limit cycles or fixed points, and with some noise added, multistabie dynamics (i.e. punctuated equilibria) can be observed. More interestingly, there exits an extended range in the parameter-space of such systems where the system is very likely to operate `sustainably’ in a stable, or meta-stable way. Outside this range the system almost certainly becomes unstable (fully chaotic or exponential runaway dynamics). If we postulate that the overall stability of regulatory systems plays a role in systemic selection, or the evolution of system parameters, such models may explain emergent modularity and maybe also predominance of suppression mechanisms in regulatory systems as they increase in their diversity of features.

**Meeting Details**

There will be 45-min lecture from the speaker, followed by a 45-min Question & Answer session.

**To attend the lecture, please click this link (link to be provided) at 15:00 on Thursday 12th November 2020****.**

**You are free to join the event without a Microsoft Teams account**, the link above will direct you to open Teams on the web or download the program, and you can easily join the event as a guest in Teams.

Need more instructions? Check this page external link or this short video external link.

12 November 2020 15:00 – 16:30

More informationCCSS Environment on Microsoft Teams

source:

CCSS Meeting #36: Scaling in Regulatory Networks: Basic Theory and Implications for Systemic Evolution – Current affairs – Universiteit Utrecht