Effective interaction graphs arising from resource limitations in gene networks

Protein production in gene networks relies on the availability of resources necessary for transcription and translation, which are found in cells in limited amounts. As various genes in a network compete for a common pool of resources, a hidden layer of interactions among genes arises. Such interactions are neglected by standard Hill-function-based models. In this work, we develop a model with the same dimension as standard Hill-function-based models to account for the sharing of limited amounts of RNA polymerase and ribosomes in gene networks. We provide effective interaction graphs to capture the hidden interactions and find that the additional interactions can dramatically change network behavior. In particular, we demonstrate that, as a result of resource limitations, a cascade of activators can behave like an effective repressor or a biphasic system, and that a repression cascade can become bistable.United States. Air Force Office of Scientific Research (FA9550-12-1-0129)National Institute of General Medical Sciences (U.S.) (P50 GMO98792

A partially self-regenerating synthetic cell

Self-regeneration is a fundamental function of all living systems. Here we demonstrate partial molecular self-regeneration in a synthetic cell. By implementing a minimal transcription-translation system within microfluidic reactors, the system is able to regenerate essential protein components from DNA templates and sustain synthesis activity for over a day. By quantitating genotype-phenotype relationships combined with computational modeling we find that minimizing resource competition and optimizing resource allocation are both critically important for achieving robust system function. With this understanding, we achieve simultaneous regeneration of multiple proteins by determining the required DNA ratios necessary for sustained self-regeneration. This work introduces a conceptual and experimental framework for the development of a self-replicating synthetic cell. A fundamental function of living systems is regenerating essential components. Here the authors design an artificial cell using a minimal transcription-translation system in microfluidic reactors for sustained regeneration of multiple essential proteins

Phenotypic responses to interspecies competition and commensalism in a naturally derived microbial co-culture

The fundamental question of whether different microbial species will co-exist or compete in a given environment depends on context, composition and environmental constraints. Model microbial systems can yield some general principles related to this question. In this study we employed a naturally occurring co-culture composed of heterotrophic bacteria, Halomonas sp. HL-48 and Marinobacter sp. HL- 58, to ask two fundamental scientific questions: 1) how do the phenotypes of two naturally co-existing species respond to partnership as compared to axenic growth? and 2) how do growth and molecular phenotypes of these species change with respect to competitive and commensal interactions? We hypothesized – and confirmed – that co-cultivation under glucose as the sole carbon source would result in competitive interactions. Similarly, when glucose was swapped with xylose, the interactions became commensal because Marinobacter HL-58 was supported by metabolites derived from Halomonas HL- 48. Each species responded to partnership by changing both its growth and molecular phenotype as assayed via batch growth kinetics and global transcriptomics. These phenotypic responses depended on nutrient availability and so the environment ultimately controlled how they responded to each other. This simplified model community revealed that microbial interactions are context-specific and different environmental conditions dictate how interspecies partnerships will unfold

Molecular sequestration as a way to control variability and induce crosstalk: from theory to experiment

The mechanism of molecular sequestration is involved in many biological processes, ranging from growth factors signalling to transcriptional and post-transcriptional regulation. This kind of dynamics involves two types of molecular species, targets and sequesters, that bind to form a complex. In the framework of mass-action law, key features of this kind of systems appear to be threshold-like profiles of the amounts of free molecules as a function of the parameters determining their possible maximum abundance. In biology, stochastic fluctuations, i.e. noise, play an undisputed role at the molecular level. Such noise can be usually divided into an intrinsic component, due to the probabilistic nature of biochemical processes, and an extrinsic one, related to the coupling with the variability of the environment in which reactions take place. Several studies highlighted the relevance of the variability induced by extrinsic fluctuations in shaping cell decision making and differentiation in molecular networks. Bimodal distributions of gene expression levels are a common feature of this kind of processes. Indeed, the two modes of the distribution often indicate the presence of two different physiological states of the system. In this thesis, we investigate the consequences of the introduction of a source of extrinsic noise onto a system governed by molecular sequestration, focussing on the appearance of bimodal distributions. To do that, we first study a minimal stochastic model of molecular sequestration and introduce extrinsic noise through a fluctuating parameter. In this framework, we analytically show how bimodal distributions can appear and characterize them as a result of noise filtering mediated by the threshold response. We then investigate the behavior of the correlations between targets of the same sequester and show how extrinsic fluctuations can induce a positive correlation that counterbalances the negative one due to competition. Given these results, we move to investigate the appearance of bimodal phenotypes in the context of microRNA (miRNA)-mediated gene regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The interaction between miRNAs and targets is based on molecular sequestration and threshold-like responses are a known feature of this system. Recent studies suggested that miRNAs can be involved in the appearance of bimodal expression distributions of their target genes. To investigate this phenomenon, we characterize the system through an analytic and numerical approach and introduce extrinsic noise as a fluctuating miRNA transcription rate. We observe that bimodal distributions of target expression can appear for a wide range of parameters in presence of extrinsic noise. Furthermore, we show how the bimodal shape of the distribution can be tuned by the interplay between different target mRNAs competing for a common miRNA. In conclusion of this thesis we present some synthetic-biology experiments that are aimed at studying the role of extrinsic fluctuations in the appearance of bimodal distributions in the context of miRNA-mediated regulation

DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Overcoming Metabolic Burden in Synthetic Biology: a CRISPR interference approach

Synthetic Biology is gaining an increasingly important role in the scientific community and dedicated research centers are rising all over the world. This discipline introduced the engineering principles of abstraction, modularity and standardization in the biology world; the application of these principles is allowing the design of complex biological systems to program living cells, realizing all sorts of desired function in many fields. These systems consist of DNA sequences, rationally combined to program the genetic instructions for cell behavior customization. Each part should behave as a biological brick for the design of complex genetic programs through functional building blocks; each module undergoes an extensive characterization to provide documentation on its functioning, enabling the rational design of complex circuits. Mathematical modeling accompanies all the design procedure as a tool to describe the behavior of each single genetic module, in a bottom-up fashion that should allow the prediction of more complex systems obtained by the interconnection of pre-characterized parts. However, many unpredictability sources hamper the ideally rational design of those synthetic genetic devices, mainly due to the tangled context-dependency behavior of those parts once placed into an intrinsically complex biological living system. Among others, the finite amount of translational resources in prokaryotic cells leads to an effect called metabolic burden, as a result of which hidden interactions between protein synthesis rates arise, leading to unexpected counterintuitive behaviors. To face this issue, two actions have been proposed in this study: firstly, a recently proposed mathematical modeling solution that included a description of the metabolic load exerted by the expression of recombinant genes have been applied on a case study, highlighting its worth of use and working boundaries; second, a CRISPR interference-based architecture have been developed to be used as an alternative to high resource usage transcriptional protein regulators, studying the underlying mechanism in several circuital configurations and optimizing each forming part in order to achieve the desired specifications. In Chapter 1, an introduction on synthetic biology is presented; in the second part, a brief overview on CRISPR technology and the overall aim of the study are reported. In Chapter 2, a case study evaluating the use of mathematical modeling to properly include metabolic burden in rational design of a set of transcriptional regulator cascades is reported. Firstly, the circuits and expected behavior are introduced, along with the discussion about experimental data, dissenting from what initially predicted. Secondly, the comparison between the use of a classical Hill equation-based model and an improved version that explicitly consider the translational load exerted by the expression of recombinant genes is reported. In Chapter 3, the design and deep characterization of a BioBrick$^{TM}$-compatible CRISPR interference-based repression set of modules is shown; expression optimization of the molecular players is reported and its usability as a low-burden alternative is demonstrated with experimental data and mathematical modeling. Working boundaries, peculiar aspects and rooms for improvements are then highlighted. In Chapter 4, preliminary studies aimed to improve the CRISPR interference system are reported and some of its context-dependencies are highlighted. Effects on repression efficiency due to alteration in the sequence of the RNA molecules addressing the CRISPR machinery to the desired target are discussed; evaluation of problems and opportunities related to the expression of more of this RNA guides are then highlighted. Lastly, an example of behavior of the system in presence of a competitor transcriptional regulator is reported. In Chapter 5 the overall conclusions of this thesis work are drawn

Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology

Living organisms are differentiated by their genetic material-millions to billions of DNA bases encoding thousands of genes. These genes are translated into a vast array of proteins, many of which have functions that are still unknown. Previously, it was believed that simply knowing the genetic sequence of an organism would be the key to unlocking all understanding. However, as DNA sequencing technology has become affordable, it has become clear that living cells are governed by complex, multilayered networks of gene regulation that cannot be deduced from sequence alone. Synthetic biology as a field might best be characterized as a learn-by-building approach, in which scientists attempt to engineer molecular pathways that do not exist in nature. In doing so, they test the limits of both natural and engineered organisms

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes

Effect of transcription factor resource sharing on gene expression noise

Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it\u27s TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression