Adaptation and Stochasticity of Natural Complex Systems

The methods that fueled the microscale revolution (top-down design/fabrication, combined with application of forces large enough to overpower stochasticity) constitute an approach that will not scale down to nanoscale systems. In contrast, in nanotechnology, we strive to embrace nature’s quite different paradigms to create functional systems, such as self-assembly to create structures, exploiting stochasticity, rather than overwhelming it, in order to create deterministic, yet highly adaptable, behavior. Nature’s approach, through billions of years of evolutionary development, has achieved self-assembling, self-duplicating, self-healing, adaptive systems. Compared to microprocessors, nature’s approach has achieved eight orders of magnitude higher memory density and three orders of magnitude higher computing capacity while utilizing eight orders of magnitude less power. Perhaps the most complex of functions, homeostatis by a biological cell – i.e., the regulation of its internal environment to maintain stability and function – in a fluctuating and unpredictable environment, emerges from the interactions between perhaps 50M molecules of a few thousand different types. Many of these molecules (e.g. proteins, RNA) are produced in the stochastic processes of gene expression, and the resulting populations of these molecules are distributed across a range of values. So although homeostasis is maintained at the system (i.e. cell) level, there are considerable and unavoidable fluctuations at the component (protein, RNA) level. While on at least some level, we understand the variability in individual components, we have no understanding of how to integrate these fluctuating components together to achieve complex function at the system level. This thesis will explore the regulation and control of stochasticity in cells. In particular, the focus will be on (1) how genetic circuits use noise to generate more function in less space; (2) how stochastic and deterministic responses are co-regulated to enhance function at a system level; and (3) the development of high-throughput analytical techniques that enable a comprehensive view of the structure and distribution of noise on a whole organism level

Probabilistic control of HIV latency and transactivation by the Tat gene circuit

Copyright © 2020 National Academy of Sciences.The reservoir of HIV latently infected cells is the major obstacle for eradication of HIV infection. The “shock-and-kill” strategy proposed earlier aims to reduce the reservoir by activating cells out of latency. While the intracellular HIV Tat gene circuit is known to play important roles in controlling latency and its transactivation in HIV-infected cells, the detailed control mechanisms are not well understood. Here we study the mechanism of probabilistic control of the latent and the transactivated cell phenotypes of HIV-infected cells. We reconstructed the probability landscape, which is the probability distribution of the Tat gene circuit states, by directly computing the exact solution of the underlying chemical master equation. Results show that the Tat circuit exhibits a clear bimodal probability landscape (i.e., there are two distinct probability peaks, one associated with the latent cell phenotype and the other with the transactivated cell phenotype). We explore potential modifications to reactions in the Tat gene circuit for more effective transactivation of latent cells (i.e., the shock-and-kill strategy). Our results suggest that enhancing Tat acetylation can dramatically increase Tat and viral production, while increasing the Tat–transactivation response binding affinity can transactivate latent cells more rapidly than other manipulations. Our results further explored the “block and lock” strategy toward a functional cure for HIV. Overall, our study demonstrates a general approach toward discovery of effective therapeutic strategies and druggable targets by examining control mechanisms of cell phenotype switching via exactly computed probability landscapes of reaction networks.info:eu-repo/semantics/publishedVersio

Adjusting Phenotypes by Noise Control

Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks

Steps toward cell-like systems: spatio-temporal control of shared molecular resources for cell-free gene expression

The biosphere offers many promising economically and environmentally sustainable solutions to humanity’s increasing energy demand such as biomass conversion, chemical production, and pharmaceutical fermentation. These solutions could close the carbon loop and improve manufacturing efficiencies, but they all depend on accurate control of protein expression. To avoid limitations to protein control present in vivo such as membranes, homeostasis, and growth, artificial cell-like systems are being researched. These simplified systems are currently useful to study individual aspects of life such as regulating energy flux across membranes, responding to the environment, replication, and growth. These systems could be made more complex in the future to provide a simplified, engineered, cell-like platform for bioprocessing. Even at the single gene level, control of protein expression is hindered by resource sharing and bursting. To make proteins, genes require many reusable resources such as polymerase, ribosomes, and tRNAs which are shared among different genes. Resource sharing causes correlations in protein populations and limits steady state concentrations of competing genes. Bursty gene expression, periods of high expression, and thus high resource use, separated by periods of no expression, and thus no resource use, is a ubiquitous biological phenomenon that intimately links expression bursting and resource sharing. This dissertation investigates how gene expression bursting and variation is affected by expression resources being shared among genes and how the location of expression resources, either encapsulated or outside permeable lipid membranes, controls the level and the dynamics of cell-free protein expression. Cell-free protein synthesis systems, both crude and PURE, areused in combination with both physical PDMS barriers and defined lipid membranes to study the effects of shared and divided resource pools on gene expression bursting and protein production. Experimental results are supplemented with Gillespie simulations to add further insights. This work provides fundamental knowledge of protein expression and applied knowledge of the effects of resource sharing on cell-free gene expression bursting and variation in protein expression confined to cell-relevant volumes which are important steps toward artificial cell-like systems

Buffering and Amplifying Transcriptional Noise During Cell Fate Specification

The molecular processes that drive gene transcription are inherently noisy. This noise often manifests in the form of transcriptional bursts, producing fluctuations in gene activity over time. During cell fate specification, this noise is often buffered to ensure reproducible developmental outcomes. However, sometimes noise is utilized as a “bet-hedging” mechanism to diversify functional roles across a population of cells. Studies of bacteria, yeast, and cultured cells have provided insights into the nature and roles of noise in transcription, yet we are only beginning to understand the mechanisms by which noise influences the development of multicellular organisms. Here we discuss the sources of transcriptional noise and the mechanisms that either buffer noise to drive reproducible fate choices or amplify noise to randomly specify fates

Engineering stochasticity in gene expression

Stochastic fluctuations (noise) in gene expression can cause members of otherwise genetically identical populations to display drastically different phenotypes. An understanding of the sources of noise and the strategies cells employ to function reliably despite noise is proving to be increasingly important in describing the behavior of natural organisms and will be essential for the engineering of synthetic biological systems. Here we describe the design of synthetic constructs, termed ribosome competing RNAs (rcRNAs), as a means to rationally perturb noise in cellular gene expression. We find that noise in gene expression increases in a manner proportional to the ability of an rcRNA to compete for the cellular ribosome pool. We then demonstrate that operons significantly buffer noise between coexpressed genes in a natural cellular background and can even reduce the level of rcRNA enhanced noise. These results demonstrate that synthetic genetic constructs can significantly affect the noise profile of a living cell and, importantly, that operons are a facile genetic strategy for buffering against noise

HIV Promoter Integration Site Primarily Modulates Transcriptional Burst Size Rather Than Frequency

Mammalian gene expression patterns, and their variability across populations of cells, are regulated by factors specific to each gene in concert with its surrounding cellular and genomic environment. Lentiviruses such as HIV integrate their genomes into semi-random genomic locations in the cells they infect, and the resulting viral gene expression provides a natural system to dissect the contributions of genomic environment to transcriptional regulation. Previously, we showed that expression heterogeneity and its modulation by specific host factors at HIV integration sites are key determinants of infected-cell fate and a possible source of latent infections. Here, we assess the integration context dependence of expression heterogeneity from diverse single integrations of a HIV-promoter/GFP-reporter cassette in Jurkat T-cells. Systematically fitting a stochastic model of gene expression to our data reveals an underlying transcriptional dynamic, by which multiple transcripts are produced during short, infrequent bursts, that quantitatively accounts for the wide, highly skewed protein expression distributions observed in each of our clonal cell populations. Interestingly, we find that the size of transcriptional bursts is the primary systematic covariate over integration sites, varying from a few to tens of transcripts across integration sites, and correlating well with mean expression. In contrast, burst frequencies are scattered about a typical value of several per cell-division time and demonstrate little correlation with the clonal means. This pattern of modulation generates consistently noisy distributions over the sampled integration positions, with large expression variability relative to the mean maintained even for the most productive integrations, and could contribute to specifying heterogeneous, integration-site-dependent viral production patterns in HIV-infected cells. Genomic environment thus emerges as a significant control parameter for gene expression variation that may contribute to structuring mammalian genomes, as well as be exploited for survival by integrating viruses

Delay-Induced Transient Increase and Heterogeneity in Gene Expression in Negatively Auto-Regulated Gene Circuits

A generic feature in all intracellular biochemical processes is the time required to complete the whole sequence of reactions to yield any observable quantity-from gene expression to circadian rhythms. This widespread phenomenon points towards the importance of time delay in biological functions. Theoretically time delay is known to be the source of instability, and has been attributed to lead to oscillations or transient dynamics in several biological functions. Negative feedback loops, common in biochemical pathways, have been shown to provide stability and withstand considerable variations and random perturbations of biochemical parameters. The interaction of these two opposing factors-of instability and homeostasis-are features that are widespread in intracellular processes. To test the effect of these divergent forces in the dynamics of gene expression, we have designed and constructed simple negatively auto-regulated gene circuits consisting of a basic regulator and transcriptional repressor module, and compared it with one, which has delayed repression. We show, both theoretically and experimentally, that delayed repression induces transient increase and heterogeneity in gene expression before the gain of stability effected by the negative feedback. This design, therefore, seems to be suitable for conferring both stability and variability in cells required for adaptive response to a noisy environment

Forward and Reverse Engineering of Cellular Decision-Making

Cells reside in highly dynamic environments to which they must adapt. Throughout its lifetime, an individual cell receives numerous chemical and mechanical signals, communicated through dense molecular networks, and eliciting a diverse array of responses. A large number of these signals necessitate discrete, all-or-none responses. For instance, a cell receiving proliferation signals must respond by committing to the cell-cycle and dividing, or by not initiating the process at all; that is, the cell must not adopt an intermediate route. Analogously, a stem-cell receiving signals for different lineages must commit exclusively to one of these lineages. How individual cells integrate multiple, possibly conflicting, noisy inputs, and make discrete decisions is poorly understood. Detailed insight into cellular decision-making can enable cell-based therapies, shed light on diseases arising out of dysregulation of control, and suggest practical design strategies for implementing this behavior in synthetic systems for research and industrial use. In this thesis, we have employed both mathematical modeling and experiments to further elucidate the mechanistic underpinnings of decision-making in cells. First, we describe a computational study that assesses the entire space of minimal networks to identify topologies that can not only make decisions but can do so robustly in the dynamic and noisy cellular environment. Second, via model-driven, quantitative experiments in a megakaryocyte erythroid progenitor line, we demonstrate that a simple network with mutual antagonism and autoregulation captures the dynamics of the master transcription factors at the level of individual cells. Expansion of this model to account for extrinsic cues reconciles the competing stochastic and instructive theories of hematopoietic lineage commitment, and implicates cytokine receptors in broader regulatory roles. Third, to assess the impact of specific genetic perturbations on the distribution of the population, and on commitment trajectories of individual cells, we implemented the core mutual antagonism and autoregulation topology synthetically in yeast cells. Our approach of using orthogonal variants of a single core protein represents a general, modular design strategy for building synthetic circuits, and model-driven experiments elucidate how gene dosage, repression strength, and promoter architecture can modulate decision-making behavior