Control Theory: On the Way to New Application Fields

Control theory is an interdisciplinary field that is located at the crossroads of pure and applied mathematics with systems engineering and the sciences. Recently, deep interactions are emerging with new application areas, such as systems biology, quantum control and information technology. In order to address the new challenges posed by the new application disciplines, a special focus of this workshop has been on the interaction between control theory and mathematical systems biology. To complement these more biology oriented focus, a series of lectures in this workshop was devoted to the control of networks of systems, fundamentals of nonlinear control systems, model reduction and identification, algorithmic aspects in control, as well as open problems in control

Structural modelling and robustness analysis of complex metabolic networks and signal transduction cascades

The dissertation covers the topic of structural robustness of metabolic networks on the basis of the concept of elementary flux modes (EFMs). It is shown that the number of EFMs does not reflect the topology of a network sufficiently. Thus, new methods are developed to determine the structural robustness of metabolic networks. These methods are based on systematic in-silico knockouts and the subsequent calculation of dropped out EFMs. Thereby, together with single knockouts also double and multiple knockouts can be used. After evaluation of these methods they are applied to metabolic networks of human erythrocyte and hepatocyte as well as to a metabolic network of Escherichia coli (E. coli). It is found that the erythrocyte has the lowest structural robustness, followed by the hepatocyte and E. coli. These results coincide very well with the circumstance that human erythrocyte and hepatocyte and E. coli are able to adapt to conditions with increasing diversity. In a further part of the dissertation the concept of EFMs is expanded to signal transduction pathways consisting of kinase cascades. The concept of EFMs is based on the steady-state condition for metabolic pathways. It is shown that under certain circumstances this steady-state condition also holds for signalling cascades. Furthermore, it is shown that it is possible to deduce minimal conditions for signal transduction without knowledge about the kinetics involved. On the basis of these assumptions it is possible to calculate EFMs for signalling cascades. But due to the fact that these EFMs do no longer just have mass flux but also information flux, they are now called elementary signalling modes (ESMs).Die Dissertation behandelt die strukturelle Robustheit von metabolischen Netzwerken auf der Basis des Konzepts der elementaren Flussmoden (EFMen). Es wird gezeigt, dass die Anzahl der EFMen die Topologie eines metabolischen Netzes nicht ausreichend widerspiegelt. Darauf aufbauend werden neue Methoden entwickelt, um die strukturelle Robustheit metabolischer Netze zu bestimmen. Diese Methoden beruhen auf systematischen in-silico-Knockouts und der anschließenden Bestimmung des Anteils an weggefallenen EFMen. Dabei können neben Einfach-Knockouts auch Doppel- oder Mehrfach-Knockouts verwendet werden. Nach der Evaluierung werden diese Methoden auf metabolische Netzwerke des menschlichen Erythrozyten und Hepatozyten, sowie des Bakteriums Escherichia coli (E. coli) angewendet. Es zeigt sich, dass der Erythrozyt die im Vergleich geringste strukturelle Robustheit besitzt, gefolgt vom Hepatozyten und E. coli. Diese Ergebnisse stimmen sehr gut mit der Beobachtung überein, dass sich die menschlichen Erythrozyten und Hepatozyten, sowie E. coli an zunehmend verschiedene Bedingungen anpassen können. In einem weiteren Teil der Dissertation wird das Konzept der EFMen auf Signaltransduktionswege bestehend aus Kinase-Kaskaden erweitert. Das Konzept der EFMen beruht auf der Annahme eines quasi-stationären Zustands für metabolische Netzwerke. Es wird gezeigt, dass dieser quasi-stationäre Zustand unter bestimmten Bedingungen auch in Signal-Kaskaden angenommen werden kann. Weiterhin wird gezeigt, dass man ohne Kenntnis der beteiligten Kinetiken Minimalbedingungen für die Signalweiterleitung ableiten kann. Auf Basis dieser Annahmen lassen sich für Signal-Kaskaden EFMen berechnen. Aber aufgrund der Tatsache, dass sie nicht mehr nur Masse-, sondern auch Informationsfluss beschreiben, werden sie nun als elementare Signalmoden (ESMen) bezeichnet

Expanding Biological Engineering from Single Enzymes to Cellular Pathways

The emerging field of synthetic biology evolved from biological research much the same way synthetic chemistry evolved from chemical research; with accumulated knowledge of the structure of single genes and proteins and the methodologies to manipulate them, researchers turn to forward engineer complex biological systems to effectively manipulate living systems. Much like in the case of enzyme engineering, a rationally designed biological network is currently beyond our reach, and we turn to directed evolution to circumvent this gap in knowledge. Yet the unique nature of live biological networks uncovered new challenges previously unmet by single-gene molecular technologies, and extrapolation of current technologies to the manipulation of multi-component has proven laborious and inefficient. To establish engineering technologies for living cells, novel directed evolution techniques are sought for that are compatible with simultaneous manipulation of multiple biological components in vivo. In this work, we explore techniques for library DNA mutagenesis in the context of single and multiple genes. Chapter 1 provides an overview of the challenges in expanding current in vivo directed evolution methods from single enzymes, to the design pathways and cells. Chapter 2 describes the design and characterization of an assay for combinatorial directed evolution of a single metabolic enzyme. In Chapter 3 we present the utilization of our DNA assembly system, Reiterative Recombination, for attenuation of metabolic pathways. We use a library of promoters to combinatorially vary the expression of genes in the heterologous lycopene biosynthetic pathway in S. cerevisiae. Finally, Chapter 4 explores the calibration of the dynamic range of genetic selection, using metabolic enzyme activity as probe for cell survival

Information-transfer characteristics in network motifs

ネットワークの三角構造が担う情報伝達の役割を解明 --数理モデルから生物の情報処理メカニズムに迫る--. 京都大学プレスリリース. 2023-01-30.Information processing in biological systems is realized by the appropriate transmission of information flows over complex networks, such as gene regulatory, signal transduction, and neural networks. These information flows are affected by the input-signal characteristics and structural properties of network systems, such as the network topology, regulation rules, and intrinsic and environmental noise. Many biological networks frequently include several typical patterns called network motifs, which are considered to play important roles in biological functions. However, their information-theoretic properties, particularly the dependence of the information flows in each network on the input signal, remain poorly understood. In our previous study [Mori and Okada, Phys. Rev. Res. 2, 043432 (2020)], we developed a graphical expansion method to describe transfer entropy (TE), a measure of information flow, in Boolean networks in terms of multiple information pathways. There, the input signal was limited to a simple case, and the effect of the input-signal characteristics on TE was not clarified. In this paper, we improve our method to render it applicable to Boolean networks that receive input signals with arbitrary stochastic characteristics. Our formula expresses how TE is determined by the input-signal characteristics, the assignment of Boolean functions, and the noise magnitude. We find that, in both positive and negative feedback loops, TE hardly depends on the signal timescale. In contrast, coherent and incoherent feedforward loops show low- and high-pass filtering properties, respectively, for a time-varying signal, which is consistent with previous reports. The emergence of either low- or high-pass filtering is determined by the Fourier components of the Boolean functions on specific pathways transmitting information flows. Thus our formula reveals the mechanism of information transfer in network motifs and provides insights into the origin of information processing in biological networks

Deciphering living networks : Perturbation strategies for functional genomics

Thesis: Deciphering living networks: Perturbation strategies for functional genomics Alberto de la Fuente [email protected] Molecular Cell Physiology Free University Amsterdam Advisors: Prof.Dr. H.V. Westerhoff Prof.Dr. J.L. Snoep Supervisor: Dr. P.J. Mendes Using modern experimental techniques it is possible to measure the concentrations of a great many, and ultimately all, cellular constituents such as mRNAs, proteins and metabolites. Given these experimental technologies, astronomical amounts of new data will appear. To enable us to see the forest for the trees, we need to find ways in which best to analyze the data so as to obtain better understanding of biochemical systems and predictive power. When those new ways of analyzing the data are found, this may even lead to a preference for a certain type of data or certain experimental methodologies. This may then help direct experimentation towards the highest possible impact for understanding of biochemical systems. Ideally, the three levels of biochemical organization, i.e. mRNAs, proteins and metabolites, are studied all together in an integrated fashion. However, due to the number of components and complexity of such integrated systems it is reasonable to try to decompose the system and to study the subsystems or to use simplified descriptions of the whole system. It will be important to decompose the system into subsystems that behave in isolation in much the same way as they do when they are embedded in the whole system. This is exactly what I deal with in my dissertation; on the one hand I show how and when it is possible to study the systems properties of metabolism in vivo, ignoring the effects of gene and protein expression, and on the other hand I develop a quantitative concept in terms of Metabolic Control Analysis to describe the properties of the whole system in a simplified form, i.e. as a gene network a description of only the dynamics of gene expression without explicit accounting for metabolites and proteins. This concept enables the inference of the topology of such gene networks from experimental data. The analysis guides the experimenter towards the specific experiments that need to be done in order to be able to infer the interactions between genes on a genome scale. After introducing the relevant preliminaries in Chapter 1, in Chapter 2 I introduce the concept of hierarchical biochemical systems and show how to express their properties in terms of properties of the individual flux-disconnected modules of which it is composed. In particular, I focus on the study of metabolic systems. I propose several methods with the goal of distinguishing regulation that takes place at the metabolic level only from regulation that involves transcription or translation, thus quantifying the relative importance of each of these processes to the global systems behavior. I verify the experimental applicability of these methods by analyzing data obtained by simulation of a biochemical system. In Chapter 3 I introduce the concept of the gene network. Gene networks are network models in which the nodes represent gene activities (mRNA levels) and the edges correspond to regulatory interactions between them. Such models are highly phenomenological because they do not represent explicitly the proteins and metabolites that mediate those interactions. I show the use of Regulatory Strengths to quantify gene-gene interactions and show how to express these coefficients in terms of the biochemical system underlying these interactions. This approach establishes a clear and formal link between the phenomenological gene network modeling and more detailed approaches considering the hierarchical structuring of biochemical networks as introduced in Chapter 2.Snoep, J.L. [Promotor]Westerhoff, H.V. [Promotor]Mendes, P. [Copromotor

Modeling formalisms in systems biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.Research supported by grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the development of improved microbial cell factories" (MIT-Pt/BS-BB/0082/2008)

Dynamical Models of biological networks

In der Molekularbiologie sind mathematische Modelle von regulatorischen und metabolischen Netzwerken essentiell, um von einer Betrachtung isolierter Komponenten und Interaktionen zu einer systemischen Betrachtungsweise zu kommen. Genregulatorische Systeme eignen sich besonders gut zur Modellierung, da sie experimentell leicht zugänglich und manipulierbar sind. In dieser Arbeit werden verschiedene genregulatorische Netzwerke unter Zuhilfenahme von mathematischen Modellen analysiert. Weiteres wird ein Modell einer in silico Zelle vorgestellt und diskutiert. Zunächst werden zwei zyklische genregulatorische Netzwerke - der klassische Repressilator und ein Repressilator mit zusätzlicher Autoaktivierung – im Detail mit analytischen Methoden untersucht. Um den Einfluß zufällig schwankender Molekülzahlen auf die Dynamik der beiden Systeme zu untersuchen, werden stochastische Modelle erstellt und die beiden oszillierenden Systeme verglichen. Weiteres werden mögliche Auswirkungen von Genduplikationen auf ein einfaches genregulatorisches Netzwerk untersucht. Dazu wird zunächst ein kleines Netzwerk von GATA Transkriptionsfaktoren, das eine zentrale Rolle in der Regulation des Stickstoffmetabolismus in Hefe spielt, modelliert und das Modell mit experimentellen Daten verglichen, um Parameterregionen einschränken zu können. Außerdem werden potentielle Topologien genregulatorischer Netzwerke von GATA Transkriptionsfaktoren in verwandten Fungi mittels sequenzbasierender Methoden gesucht und verglichen. Im letzten Teil der Arbeit wird MiniCellSim vorgestellt, ein Modell einer selbständigen in silico Zelle. Es erlaubt ein dynamisches System, das eine Protozelle mit einem genregulatorischen Netzwerk, einem einfachen Metabolismus und einer Zellmembran beschreibt, aus einer Sequenz abzuleiten. Nachdem alle Parameter, die zur Berechnung des dynamischen Systems benötigt werden, ohne zusätzliche Eingabe nur aus der Sequenzinformation abgeleitet werden, kann das Modell für Studien zur Evolution von genregulatorischen Netzwerken verwendet werden.In this thesis different types of gene regulatory networks are analysed using mathematical models. Further a computational framework of a novel, self-contained in silico cell model is described and discussed. At first the behaviour of two cyclic gene regulatory systems - the classical repressilator and a repressilator with additional auto-activation - are inspected in detail using analytical bifurcation analysis. To examine the behaviour under random fluctuations, stochastic versions of the systems are created. Using the analytical results sustained oscillations in the stochastic versions are obtained, and the two oscillating systems compared. In the second part of the thesis possible implications of gene duplication on a simple gene regulatory system are inspected. A model of a small network formed by GATA-type transcription factors, central in nitrogen catabolite repression in yeast, is created and validated against experimental data to obtain approximate parameter values. Further, topologies of potential gene regulatory networks and modules consisting of GATA-type transcription factors in other fungi are derived using sequence-based approaches and compared. The last part describes MiniCellSim, a model of a self-contained in silico cell. In this framework a dynamical system describing a protocell with a gene regulatory network, a simple metabolism, and a cell membrane is derived from a string representing a genome. All the relevant parameters required to compute the time evolution of the dynamical system are calculated from within the model, allowing the system to be used in studies of evolution of gene regulatory and metabolic networks

Characterisation of a newly identified family of lipid transfer proteins at membrane contact sites

Non-vesicular intracellular lipid traffic is mediated by lipid transfer proteins (LTPs), which contain domains with an internal cavity that can solubilise and transfer lipids. One of the most widespread LTP folds is the Steroidogenic Acute Regulatory Transfer (StART) domain, which forms a hydrophobic pocket, and appears in proteins with different localisations and lipid specificities. The aim of this study was to characterise a new StART-like domain family, which we identified by a bioinformatics approach. I studied aspects of the localisations, functions and structural properties of six StART-like proteins in S. cerevisiae. The yeast StART-like proteins were endoplasmic reticulum (ER)-integral membrane proteins with transmembrane domains, and they localised at membrane contact sites: Lam1p/Lam3p, and Lam2p/Lam4p at junctions between ER and plasma membrane (PM); Lam5p/Lam6p at junctions between the ER and the vacuolar membrane, at nucleus-vacuole junction (NVJ) and at ER-mitochondria contacts. To study their functions, I purified the second StART-like domain of Lam4p, and I identified sterol as its lipid ligand from in vitro binding assays and in a spectroscopy approach with fluorescent ergosterol. We named the whole family LAM for Lipid transfer proteins Anchored at Membrane contact sites. The sterol binding property of the domains was related to a phenotype shared by LAM1, LAM2 and LAM3 delete strains, which showed an increased sensitivity to the sterol-sequestering polyene antifungal drug Amphotericin B (AmB). The two most sensitive strains (lam1∆ and lam3∆), displayed low sphingolipid levels, which is as yet unexplained. All AmB phenotypes were rescued by StART-like domains from the human LAMa, Lam2/4p and Lam5/6p, suggesting that these domains bind sterol. Simultaneous deletion of LAM1, LAM2, and LAM3 significantly reduced the extent of cortical ER-PM contacts, implying that they create the structure of the particularly punctate contact site they target. Finally, I started structural analysis of Lam4S2 to study the mechanism of sterol binding and to confirm our structural model

Machine learning methods for detecting structure in metabolic flow networks

Metabolic flow networks are large scale, mechanistic biological models with good predictive power. However, even when they provide good predictions, interpreting the meaning of their structure can be very difficult, especially for large networks which model entire organisms. This is an underaddressed problem in general, and the analytic techniques that exist currently are difficult to combine with experimental data. The central hypothesis of this thesis is that statistical analysis of large datasets of simulated metabolic fluxes is an effective way to gain insight into the structure of metabolic networks. These datasets can be either simulated or experimental, allowing insight on real world data while retaining the large sample sizes only easily possible via simulation. This work demonstrates that this approach can yield results in detecting structure in both a population of solutions and in the network itself. This work begins with a taxonomy of sampling methods over metabolic networks, before introducing three case studies, of different sampling strategies. Two of these case studies represent, to my knowledge, the largest datasets of their kind, at around half a million points each. This required the creation of custom software to achieve this in a reasonable time frame, and is necessary due to the high dimensionality of the sample space. Next, a number of techniques are described which operate on smaller datasets. These techniques, focused on pairwise comparison, show what can be achieved with these smaller datasets, and how in these cases, visualisation techniques are applicable which do not have simple analogues with larger datasets. In the next chapter, Similarity Network Fusion is used for the first time to cluster organisms across several levels of biological organisation, resulting in the detection of discrete, quantised biological states in the underlying datasets. This quantisation effect was maintained across both real biological data and Monte-Carlo simulated data, with related underlying biological correlates, implying that this behaviour stems from the network structure itself, rather than from the genetic or regulatory mechanisms that would normally be assumed. Finally, Hierarchical Block Matrices are used as a model of multi-level network structure, by clustering reactions using a variety of distance metrics: first standard network distance measures, then by Local Network Learning, a novel approach of measuring connection strength via the gain in predictive power of each node on its neighbourhood. The clusters uncovered using this approach are validated against pre-existing subsystem labels and found to outperform alternative techniques. Overall this thesis represents a significant new approach to metabolic network structure detection, as both a theoretical framework and as technological tools, which can readily be expanded to cover other classes of multilayer network, an under explored datatype across a wide variety of contexts. In addition to the new techniques for metabolic network structure detection introduced, this research has proved fruitful both in its use in applied biological research and in terms of the software developed, which is experiencing substantial usage.EPSR