Modeling and analysis of random and stochastic input flows in the chemostat model

In this paper we study a new way to model noisy input flows in the chemostat model, based on the Ornstein-Uhlenbeck process. We introduce a parameter β as drift in the Langevin equation, that allows to bridge a gap between a pure Wiener process, which is a common way to model random disturbances, and no noise at all. The value of the parameter β is related to the amplitude of the deviations observed on the realizations. We show that this modeling approach is well suited to represent noise on an input variable that has to take non-negative values for almost any time.European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Ministerio de Economía y Competitividad (MINECO). EspañaJunta de Andalucí

Stochastic analysis of a full system of two competing populations in a chemostat

This paper formulates two 3D stochastic differential equations (SDEs) of two microbial populations in a chemostat competing over a single substrate. The two models have two distinct noise sources. One is general noise whereas the other is dilution rate induced noise. Nonlinear Monod growth rates are assumed and the paper is mainly focused on the parameter values where coexistence is present deterministically. Nondimensionalising the equations around the point of intersection of the two growth rates leads to a large parameter which is the nondimensional substrate feed. This in turn is used to perform an asymptotic analysis leading to a reduced 2D system of equations describing the dynamics of the populations on and close to a line of steady states retrieved from the deterministic stability analysis. That reduced system allows the formulation of a spatially 2D Fokker-Planck equation which when solved numerically admits results similar to those from simulation of the SDEs. Contrary to previous suggestions, one particular population becomes dominant at large times. Finally, we brie y explore the case where death rates are added

Study of the chemostat model with non-monotonic growth under random disturbances on the removal rate

We revisit the chemostat model with Haldane growth function, here subject to bounded random disturbances on the input flow rate, as often met in biotechnological or waste-water industry. We prove existence and uniqueness of global positive solution of the random dynamics and existence of absorbing and attracting sets that are independent of the realizations of the noise. We study the longtime behavior of the random dynamics in terms of attracting sets, and provide first conditions under which biomass extinction cannot be avoided. We prove conditions for weak and strong persistence of the microbial species and provide lower bounds for the biomass concentration, as a relevant information for practitioners. The theoretical results are illustrated with numerical simulations

Bounded random fluctuations on the input flow in chemostat models with wall growth and non-monotonic kinetics

Dedicated to the memory of María José Garrido Atienza.This paper investigates a chemostat model with wall growth and Haldane consumption kinetics. In addition, bounded random fluctuations on the input flow, which are modeled by means of the well-known Ornstein-Uhlenbeck process, are considered to obtain a much more realistic model fitting in a better way the phenomena observed by practitioners in real devices. Once the existence and uniqueness of global positive solution has been proved, as well as the existence of deterministic absorbing and attracting sets, the random dynamics inside the attracting set is studied in detail to provide conditions under which persistence of species is ensured, the main goal pursued from the practical point of view. Finally, we support the theoretical results with several numerical simulations.Junta de Andalucía P12-FQM-1492Ministerio de Ciencia, Innovación y Universidades (MCIU). España PGC2018-096540-B-I00Junta de Andalucía (Consejería de Economía y Conocimiento) FEDER US-1254251Junta de Andalucía (Consejería de Economía y Conocimiento) P18-FR-450

Threshold Dynamics of a Stochastic Chemostat Model with Two Nutrients and One Microorganism

A new stochastic chemostat model with two substitutable nutrients and one microorganism is proposed and investigated. Firstly, for the corresponding deterministic model, the threshold for extinction and permanence of the microorganism is obtained by analyzing the stability of the equilibria. Then, for the stochastic model, the threshold of the stochastic chemostat for extinction and permanence of the microorganism is explored. Difference of the threshold of the deterministic model and the stochastic model shows that a large stochastic disturbance can affect the persistence of the microorganism and is harmful to the cultivation of the microorganism. To illustrate this phenomenon, we give some computer simulations with different intensity of stochastic noise disturbance

Towards the implementation of distributed systems in synthetic biology

The design and construction of engineered biological systems has made great strides over the last few decades and a growing part of this is the application of mathematical and computational techniques to problems in synthetic biology. The use of distributed systems, in which an overall function is divided across multiple populations of cells, has the potential to increase the complexity of the systems we can build and overcome metabolic limitations. However, constructing biological distributed systems comes with its own set of challenges. In this thesis I present new tools for the design and control of distributed systems in synthetic biology. The first part of this thesis focuses on biological computers. I develop novel design algorithms for distributed digital and analogue computers composed of spatial patterns of communicating bacterial colonies. I prove mathematically that we can program arbitrary digital functions and develop an algorithm for the automated design of optimal spatial circuits. Furthermore, I show that bacterial neural networks can be built using our system and develop efficient design tools to do so. I verify these results using computational simulations. This work shows that we can build distributed biological computers using communicating bacterial colonies and different design tools can be used to program digital and analogue functions. The second part of this thesis utilises a technique from artificial intelligence, reinforcement learning, in first the control and then the understanding of biological systems. First, I show the potential utility of reinforcement learning to control and optimise interacting communities of microbes that produce a biomolecule. Second, I apply reinforcement learning to the design of optimal characterisation experiments within synthetic biology. This work shows that methods utilising reinforcement learning show promise for complex distributed bioprocessing in industry and the design of optimal experiments throughout biology

Dynamical Models of Biology and Medicine

Mathematical and computational modeling approaches in biological and medical research are experiencing rapid growth globally. This Special Issue Book intends to scratch the surface of this exciting phenomenon. The subject areas covered involve general mathematical methods and their applications in biology and medicine, with an emphasis on work related to mathematical and computational modeling of the complex dynamics observed in biological and medical research. Fourteen rigorously reviewed papers were included in this Special Issue. These papers cover several timely topics relating to classical population biology, fundamental biology, and modern medicine. While the authors of these papers dealt with very different modeling questions, they were all motivated by specific applications in biology and medicine and employed innovative mathematical and computational methods to study the complex dynamics of their models. We hope that these papers detail case studies that will inspire many additional mathematical modeling efforts in biology and medicin

Diversity and Stability in Food Webs : Impacts of Non-Equilibrium Dynamics, Topology and Variation

With progressive climate change, the preservation of biodiversity is becoming increasingly important. Only if the gene pool is large enough and requirements of species are diverse, there will be species that can adapt to the changing circumstances. To maintain biodiversity, we must understand the consequences of the various strategies. Mathematical models of population dynamics could provide prognoses. However, a model that would reproduce and explain the mechanisms behind the diversity of species that we observe experimentally and in nature is still needed. A combination of theoretical models with detailed experiments is needed to test biological processes in models and compare predictions with outcomes in reality. In this thesis, several food webs are modeled and analyzed. Among others, models are formulated of laboratory experiments performed in the Zoological Institute of the University of Cologne. Numerical data of the simulations is in good agreement with the real experimental results. Via numerical simulations it can be demonstrated that few assumptions are necessary to reproduce in a model the sustained oscillations of the population size that experiments show. However, analysis indicates that species "thrown together by chance" are not very likely to survive together over long periods. Even larger food nets do not show significantly different outcomes and prove how extraordinary and complicated natural diversity is. In order to produce such a coexistence of randomly selected species—as the experiment does—models require additional information about biological processes or restrictions on the assumptions. Another explanation for the observed coexistence is a slow extinction that takes longer than the observation time. Simulated species survive a comparable period of time before they die out eventually. Interestingly, it can be stated that the same models allow the survival of several species in equilibrium and thus do not follow the so-called competitive exclusion principle. This state of equilibrium is more fragile, however, to changes in nutrient supply than the oscillating coexistence. Overall, the studies show, that having a diverse system means that population numbers are probably oscillating, and on the other hand oscillating population numbers stabilize a food web both against demographic noise as well as against changes of the habitat. Model predictions can certainly not be converted at their face value into policies for real ecosystems. But the stabilizing character of fluctuations should be considered in the regulations of animal populations