On robustness in biology: from sensing to functioning

Living systems are subject to various types of spatial and temporal noise at all scales and stages. Nevertheless, evolving under the pressure of natural selection, biology has mastered the ability of dealing with stochasticity. This is particularly crucial because these systems encounter numerous situations which require taking robust and proper actions in the presence of noise. Due to the complexity and variability of these situations, it is impossible to have a prescribed plan for an organism that keeps it alive and fully functional. Therefore, they have to be active, rather than passive, by following three essential steps: I) gathering information about their fluctuating environment, II) processing the information and making decisions via circuits that are inevitably noisy, and finally, III) taking the appropriate action robustly with organizations crossing multiple scales. Although various aspects of this general scheme have been subject of many studies, there are still many questions that remain unanswered: How can a dynamic environmental signal be sensed collectively by cell populations? and how does the topology of interactions affect the quality of this sensing? When processing information via the regulatory network, what are the drawbacks of multifunctional circuits? and how does the reliability of the decisions decrease as the multifunctionality increases? Finally, when the right decision is made and a tissue is growing with feedbacks crossing different scales, what are the crucial features that remain preserved from one subject to another? How can one use these features to understand the mechanisms behind these processes? This thesis addresses the main challenges for answering these questions and many more using methods from dynamical systems, network science, and stochastic processes. Using stochastic models, we investigate the fundamental limits arising from temporal noise on collective signal sensing and context-dependent information processing. Furthermore, by combining stochastic models and cross-scale data analyses, we study pattern formation during complex tissue growth.Lebende Systeme sind in allen Größenordnungen und Stadien verschiedenen Arten von räumlichem und zeitlichem Rauschen ausgesetzt. Dennoch hat die Biologie, die sich unter dem Druck der natürlichen Selektion entwickelt hat, die Fähigkeit gemeistert, mit stochastischen Fluktuationen umzugehen. Dies ist besonders wichtig, da Organismen auf zahlreiche Situationen stoßen, die es erfordern, in Gegenwart von Rauschen robuste und angemessene Maßnahmen zu ergreifen. Aufgrund der Komplexität und Variabilität dieser Situationen ist es unmöglich, einen vorgeschriebenen Plan für einen Organismus zu haben, der ihn überlebens- und funktionsfähig hält. Daher können Organismen sich nicht passiv verhalten, sondern befolgen aktiv drei wesentliche Schritte: I) Das Sammeln von Informationen über ihre dynamische Umgebung, II) Das Verarbeiten von Informationen und das Treffen von Entscheidungen über Regelnetzwerke, die unvermeidlich mit Rauschen behaftet sind, und schließlich, III) das robuste Funktionieren durch organisierte Maßnahmen, welche mehrere Größenordnungen überbrücken. Obwohl verschiedene Aspekte dieses allgemeinen Schemas Gegenstand vieler Studien waren, bleiben noch viele Fragen unbeantwortet: Wie kann ein dynamisches externes Signal kollektiv von Zellpopulationen wahrgenommen werden? Wie beeinflusst die Topologie der Interaktionen die Qualität dieser Wahrnehmung? Was sind die Nachteile multifunktionaler Schaltkreise bei der Verarbeitung von Informationen über das Regelnetzwerk? Wie nimmt die Zuverlässigkeit der Entscheidungen mit zunehmender Multifunktionalität ab? Und abschließend, wenn die richtige Entscheidung getroffen wurde und ein Gewebe wächst und dabei Rückkopplungen auf verschiedenen Größenordnungen erfährt, was sind die entscheidenden Merkmale, die von einem Versuchsobjekt zum anderen erhalten bleiben? Wie kann man diese Merkmale nutzen, um die Prozesse zu verstehen? Diese Arbeit befasst sich mit den wichtigsten Herausforderungen zur Beantwortung dieser und vieler weiterer Fragen mit Methoden aus dynamischen Systemen, Netzwerkforschung und stochastischen Prozessen

Quantum gate algorithm for reference-guided DNA sequence alignment

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2 B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4 B bits or about 760 MB of storage (National Institute of General Medical Sciences). Today most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.Comment: 19 pages, 13 figure

A survey of molecular communication in cell biology : establishing a new hierarchy for interdisciplinary applications

Molecular communication (MC) engineering is inspired by the use of chemical signals as information carriers in cell biology. The biological nature of chemical signaling makes MC a promising methodology for interdisciplinary applications requiring communication between cells and other microscale devices. However, since the life sciences and communications engineering fields have distinct approaches to formulating and solving research problems, the mismatch between them can hinder the translation of research results and impede the development and implementation of interdisciplinary solutions. To bridge this gap, this survey proposes a novel communication hierarchy for MC signaling in cell biology and maps phenomena, contributions, and problems to the hierarchy. The hierarchy includes: 1) the physical propagation of cell signaling at the Physical Signal Propagation level; 2) the generation, reception, and biochemical pathways of molecular signals at the Physical and Chemical Signal Interaction level; 3) the quantification of physical signals, including macroscale observation and control methods, and conversion of signals to information at the Signal-Data Interface level; 4) the interpretation of information in cell signals and the realization of synthetic systems to store, process, and communicate molecular signals at the Local Data Abstraction level; and 5) applications relying on communication with MC signals at the Application level. To further demonstrate the proposed hierarchy, it is applied to case studies on quorum sensing, neuronal signaling, and communication via DNA. Finally, several open problems are identified for each level and the integration of multiple levels. The proposed hierarchy provides language for communication engineers to study and interface with biological systems, and also helps biologists to understand how communications engineering concepts can be exploited to interpret, control, and manipulate signaling in cell biology

Development of a synthetic biology approach to targeted directed evolution of proteins in vivo

This thesis describes the development of a novel platform for targeted directed evolution, designed to operate entirely in vivo. The system comprises a fusion of T7 RNA polymerase and activation-induced deaminase (AID); targeting is achieved by the placement of the sequence of interest under the control of a T7 promoter, whereby transcription by the polymerase exposes the DNA to mutation by the AID moiety of the fusion. The localisation effect serves to target mutation to the area downstream of the promoter, and increase the rate of that mutation compared to non-directed background activity. The system, and appropriate controls and targets, are constructed and tested in a plasmid-based experimental work-fow. The targets are, further, integrated into the genome of Escherichia coli to allow high-throughput analysis of the mutation rate of a single copy target. Nucleotide sequencing is used to confrm both enhanced mutagenesis of the system, and a high degree of targeting. The system is then applied to a test case, diversifcation of a transcription factor (LasR from Pseudomonas aeruginosa, encoded by the lasR gene), with an eye to producing an orthogonal signal/response pair with promoter PLas. A logic gate-based flter is designed and constructed to allow tight moderation of a feedback loop to control the mutator, allowing it to be 'shut off' once desired function is exhibited by the target protein.Open Acces

Information processing in biological complex systems: a view to bacterial and neural complexity

This thesis is a study of information processing of biological complex systems seen from the perspective of dynamical complexity (the degree of statistical independence of a system as a whole with respect to its components due to its causal structure). In particular, we investigate the influence of signaling functions in cell-to-cell communication in bacterial and neural systems. For each case, we determine the spatial and causal dependencies in the system dynamics from an information-theoretic point of view and we relate it with their physiological capabilities. The main research content is presented into three main chapters. First, we study a previous theoretical work on synchronization, multi-stability, and clustering of a population of coupled synthetic genetic oscillators via quorum sensing. We provide an extensive numerical analysis of the spatio-temporal interactions, and determine conditions in which the causal structure of the system leads to high dynamical complexity in terms of associated metrics. Our results indicate that this complexity is maximally receptive at transitions between dynamical regimes, and maximized for transient multi-cluster oscillations associated with chaotic behaviour. Next, we introduce a model of a neuron-astrocyte network with bidirectional coupling using glutamate-induced calcium signaling. This study is focused on the impact of the astrocyte-mediated potentiation on synaptic transmission. Our findings suggest that the information generated by the joint activity of the population of neurons is irreducible to its independent contribution due to the role of astrocytes. We relate these results with the shared information modulated by the spike synchronization imposed by the bidirectional feedback between neurons and astrocytes. It is shown that the dynamical complexity is maximized when there is a balance between the spike correlation and spontaneous spiking activity. Finally, the previous observations on neuron-glial signaling are extended to a large-scale system with community structure. Here we use a multi-scale approach to account for spatiotemporal features of astrocytic signaling coupled with clusters of neurons. We investigate the interplay of astrocytes and spiking-time-dependent-plasticity at local and global scales in the emergence of complexity and neuronal synchronization. We demonstrate the utility of astrocytes and learning in improving the encoding of external stimuli as well as its ability to favour the integration of information at synaptic timescales to exhibit a high intrinsic causal structure at the system level. Our proposed approach and observations point to potential effects of the astrocytes for sustaining more complex information processing in the neural circuitry

Development of novel orthogonal genetic circuits, based on extracytoplasmic function (ECF) σ factors

The synthetic biology field aims to apply the engineering 'design-build-test-learn' cycle for the implementation of synthetic genetic circuits modifying the behavior of biological systems. In order to reach this goal, synthetic biology projects use a set of fully characterized biological parts that subsequently are assembled into complex synthetic circuits following a rational, model-driven design. However, even though the bottom-up design approach represents an optimal starting point to assay the behavior of the synthetic circuits under defined conditions, the rational design of such circuits is often restricted by the limited number of available DNA building blocks. These usually consist only of a handful of transcriptional regulators that additionally are often borrowed from natural biological systems. This, in turn, can lead to cross-reactions between the synthetic circuit and the host cell and eventually to loss of the original circuit function. Thus, one of the challenges in synthetic biology is to design synthetic circuits that perform the designated functions with minor cross-reactions (orthogonality). To overcome the restrictions of the widely used transcriptional regulators, this project aims to apply extracytoplasmic function (ECF) σ factors in the design novel orthogonal synthetic circuits. ECFs are the smallest and simplest alternative σ factors that recognize highly specific promoters. ECFs represent one of the most important mechanisms of signal transduction in bacteria, indeed, their activity is often controlled by anti-σ factors. Even though it was shown that the overexpression of heterologous anti-σ factors can generate an adverse effect on cell growth, they represent an attractive solution to control ECF activity. Finally, to date, we know thousands of ECF σ factors, widespread among different bacterial phyla, that are identifiable together with the cognate promoters and anti-σ factors, using bioinformatic approaches. All the above-mentioned features make ECF σ factors optimal candidates as core orthogonal regulators for the design of novel synthetic circuits. In this project, in order to establish ECF σ factors as standard building blocks in the synthetic biology field, we first established a high throughput experimental setup. This relies on microplate reader experiments performed using a highly sensitive luminescent reporter system. Luminescent reporters have a superior signal-to-noise ratio when compared to fluorescent reporters since they do not suffer from the high auto-fluorescence background of the bacterial cell. However, they also have a drawback represented by the constant light emission that can generate undesired cross-talk between neighboring wells on a microplate. To overcome this limitation, we developed a computational algorithm that corrects for luminescence bleed-through and estimates the “true” luminescence activity for each well of a microplate. We show that the correcting algorithm preserves low-level signals close to the background and that it is universally applicable to different experimental conditions. In order to simplify the assembly of large ECF-based synthetic circuits, we designed an ECF toolbox in E. coli. The toolbox allows for the combinatorial assembly of circuits into expression vectors, using a library of reusable genetic parts. Moreover, it also offers the possibility of integrating the newly generated synthetic circuits into four different phage attachment (att) sites present in the genome of E. coli. This allows for a flawless transition between plasmid-encoded and chromosomally integrated genetic circuits, expanding the possible genetic configurations of a given synthetic construct. Moreover, our results demonstrate that the four att sites are orthogonal in terms of the gene expression levels of the synthetic circuits. With the purpose of rationally design ECF-based synthetic circuits and taking advantage of the ECF toolbox, we characterized the dynamic behavior of a set of 15 ECF σ factors, their cognate promoters, and relative anti-σs. Overall, we found that ECFs are non-toxic and functional and that they display different binding affinities for the cognate target promoters. Moreover, our results show that it is possible to optimize the output dynamic range of the ECF-based switches by changing the copy number of the ECFs and target promoters, thus, tuning the input/output signal ratio. Next, by combining up to three ECF-switches, we generated a set of “genetic-timer circuits”, the first synthetic circuits harboring more than one ECF. ECF-based timer circuits sequentially activate a series of target genes with increasing time delays, moreover, the behavior of the circuits can be predicted by a set of mathematical models. In order to improve the dynamic response of the ECF-based constructs, we introduced anti-σ factors in our synthetic circuits. By doing so we first confirmed that anti-σ factors can exert an adverse effect on the growth of E. coli, thus we explored possible solutions. Our results demonstrate that anti-σ factors toxicity can be partially alleviated by generating truncated, soluble variants of the anti-σ factors and, eventually, completely abolished via chromosomal integration of the anti-σ factor-based circuits. Finally, after demonstrating that anti-σ factors can be used to generate a tunable time delay among ECF expression and target promoter activation, we designed ECF/AS-suicide circuits. Such circuits allow for the time-delayed cell-death of E. coli and will serve as a prototype for the further development of ECF/AS-based lysis circuits

Error control in bacterial quorum communications

Quorum sensing (QS) is used to describe the communication between bacterial cells, whereby a coordinated population response is controlled through the synthesis, accumulation and subsequent sensing of specific diffusible chemical signals called autoinducers, enabling a cluster of bacteria to regulate gene expression and behavior collectively and synchronously, and assess their own population. As a promising method of molecular communication (MC), bacterial populations can be programmed as bio-transceivers to establish information transmission using molecules. In this work, to investigate the key features for MC, a bacterial QS system is introduced, which contains two clusters of bacteria, specifically Vibrio fischeri, as the transmitter node and receiver node, and the diffusive channel. The transmitted information is represented by the concentration of autoinducers with on-off keying (OOK) modulation. In addition, to achieve better reliability and energy efficiency, different error control techniques, including forward error correction (FEC) and Automatic Repeat reQuest (ARQ) are taken into consideration. For FEC, this work presents a comparison of the performance of traditional Hamming codes, Minimum Energy Codes (MEC) and Luby Transform (LT) codes over the channel. In addition, it applied several ARQ protocols, namely Stop-N-Wait (SW-ARQ), Go-Back-N (GBN-ARQ), and Selective-Repeat (SR-ARQ) combined with error detection codes to achieve better reliability. Results show that both the FEC and ARQ techniques can enhance the channel reliability, and that ARQ can resolve the issue of out-of-sequence and duplicate packet delivery. Moreover, this work further addresses the question of optimal frame size for data communication in this channel capacity and energy constrained bacterial quorum communication system. A novel energy model which is constructed using the experimental validated synthetic logic gates has been proposed to help with the optimization process. The optimal fixed frame length is determined for a set of channel parameters by maximizing the throughput and energy efficiency matrix