Mini-Workshop: Dynamics of Stochastic Systems and their Approximation

The aim of this workshop was to bring together specialists in the area of stochastic dynamical systems and stochastic numerical analysis to exchange their ideas about the state of the art of approximations of stochastic dynamics. Here approximations are considered in the analytical sense in terms of deriving reduced dynamical systems, which are less complex, as well as in the numerical sense via appropriate simulation methods. The main theme is concerned with the efficient treatment of stochastic dynamical systems via both approaches assuming that ideas and methods from one ansatz may prove beneficial for the other. A particular goal was to systematically identify open problems and challenges in this area

Model reduction, mechanistic modelling and transience in models of stochastic chemical kinetics

Now, it is long known that gene expression and chemical kinetics are subject to random fluctuations. These lead to deviations from deterministic models that do not account for the random nature of biochemical kinetics. Successfully incorporating these stochastic dynamics is of great interest so that one can better model, and more closely understand, the intricate phenomena inherent in biological mechanisms. Many previous studies have been conducted in modelling such processes stochastically, for instance processes such as genetic autoregulation, Michaelis-Menten enzyme action and ant recruitment models. However, the majority of these studies explore only the steady state solutions of such processes while assuming mass-action kinetics, without considering: (1) extrinsic noise, (2) transience from an initial condition, or even (3) the finite, non-continuous nature of molecule or agent numbers. This thesis focuses on the aforementioned complex systems, with an emphasis on how to use toy models in responsible and informed ways. Responsible refers to a knowledge of how good our approximations of microscopic dynamics are and their limitations: Do we understand the assumptions that commonly employed approximations rely on? Informed refers to whether a model we design is sufficiently minimal or complex to represent the underlying biochemical (or economical) kinetics: Can we use alternative models of similar simplicity (possibly mechanistically informed) to more properly capture the dynamics of the system we are attempting to model? Further issues pursued in this thesis are whether common approximative methods can be extended to effectively include details of more complex underlying dynamics, or whether we can move beyond typical steady state solutions and explore transience from an initial condition. There are several main findings from our studies. We find that for non mass-action Hill-type propensities, often used in biochemical kinetics, that typically only assume time scale separation as the basis of approximation, that finite molecule number effects can greatly perturb their accuracy. Then, we show that the addition of non-Gaussian colored noise to biochemical rate parameters can capture intricate characteristics of gene expression that are not explicitly modelled. For common two-state gene models, we explore why they seem to be so effective at approximating gene expression, where it is known that several key rate limiting steps are ignored. Finally, we develop transient solutions to master equations describing Michaelis-Menten enzyme kinetics and ant recruitment, and we show how to extend the solutions therein to more general forms

Statistical mechanics of non equilibrium matter: from minimal models to morphogen gradients

Living systems are by definition far from thermodynamic equilibrium, a condition that can be maintained only at the cost of a continuous injection of energy at the microscale, e.g. via cellular metabolic processes, and dissipation into the surrounding environment. The absence of thermodynamic equilibrium, formalised in the breaking of the global detailed balance condition, allows for a wealth of exotic and often counterintuitive phenomena. Our understanding of the capabilities and limitations of living matter has been greatly informed by thermodynamic approaches, which have to be generalised with respect to their traditional counterparts in order to deal with systems subject to strong random fluctuations. The resulting toolkit of stochastic thermodynamics, in particular the concept of entropy production, gives us a quantitative handle on the degree of "non-equilibriumness" of such stochastic processes. Recently, stochastic thermodynamics has benefitted from cross-contamination with the field-theoretic literature and the techniques developed in the latter for the study of collective behaviour have opened the doors to the thermodynamic characterisation of increasingly complex systems. Starting from minimal mathematical models of single active particles and moving up across scales to the level of morphogenetic processes in real organisms (in particular, the formation of morphogen gradients), this thesis contributes to laying the foundations for a bridge between physical understanding and biological insight. While the focus is here on generic mechanisms and on the development of theoretical tools, the applicability to specific experimental scenarios will be pointed out where relevant.Open Acces

Collective analog bioelectronic computation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 677-710).In this thesis, I present two examples of fast-and-highly-parallel analog computation inspired by architectures in biology. The first example, an RF cochlea, maps the partial differential equations that describe fluid-membrane-hair-cell wave propagation in the biological cochlea to an equivalent inductor-capacitor-transistor integrated circuit. It allows ultra-broadband spectrum analysis of RF signals to be performed in a rapid low-power fashion, thus enabling applications for universal or software radio. The second example exploits detailed similarities between the equations that describe chemical-reaction dynamics and the equations that describe subthreshold current flow in transistors to create fast-and-highly-parallel integrated-circuit models of protein-protein and gene-protein networks inside a cell. Due to a natural mapping between the Poisson statistics of molecular flows in a chemical reaction and Poisson statistics of electronic current flow in a transistor, stochastic effects are automatically incorporated into the circuit architecture, allowing highly computationally intensive stochastic simulations of large-scale biochemical reaction networks to be performed rapidly. I show that the exponentially tapered transmission-line architecture of the mammalian cochlea performs constant-fractional-bandwidth spectrum analysis with O(N) expenditure of both analysis time and hardware, where N is the number of analyzed frequency bins. This is the best known performance of any spectrum-analysis architecture, including the constant-resolution Fast Fourier Transform (FFT), which scales as O(N logN), or a constant-fractional-bandwidth filterbank, which scales as O (N2).(cont.) The RF cochlea uses this bio-inspired architecture to perform real-time, on-chip spectrum analysis at radio frequencies. I demonstrate two cochlea chips, implemented in standard 0.13m CMOS technology, that decompose the RF spectrum from 600MHz to 8GHz into 50 log-spaced channels, consume < 300mW of power, and possess 70dB of dynamic range. The real-time spectrum analysis capabilities of my chips make them uniquely suitable for ultra-broadband universal or software radio receivers of the future. I show that the protein-protein and gene-protein chips that I have built are particularly suitable for simulation, parameter discovery and sensitivity analysis of interaction networks in cell biology, such as signaling, metabolic, and gene regulation pathways. Importantly, the chips carry out massively parallel computations, resulting in simulation times that are independent of model complexity, i.e., O(1). They also automatically model stochastic effects, which are of importance in many biological systems, but are numerically stiff and simulate slowly on digital computers. Currently, non-fundamental data-acquisition limitations show that my proof-of-concept chips simulate small-scale biochemical reaction networks at least 100 times faster than modern desktop machines. It should be possible to get 103 to 106 simulation speedups of genome-scale and organ-scale intracellular and extracellular biochemical reaction networks with improved versions of my chips. Such chips could be important both as analysis tools in systems biology and design tools in synthetic biology.by Soumyajit Mandal.Ph.D

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University

Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIs’ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the “Critical Infrastructure/Modelling, Simulation and Analysis – Data”. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance. In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: “Critical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuity”. The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above