Avoiding transcription factor competition at promoter level increases the chances of obtaining oscillation

<p>Abstract</p> <p>Background</p> <p>The ultimate goal of synthetic biology is the conception and construction of genetic circuits that are reliable with respect to their designed function (e.g. oscillators, switches). This task remains still to be attained due to the inherent synergy of the biological building blocks and to an insufficient feedback between experiments and mathematical models. Nevertheless, the progress in these directions has been substantial.</p> <p>Results</p> <p>It has been emphasized in the literature that the architecture of a genetic oscillator must include positive (activating) and negative (inhibiting) genetic interactions in order to yield robust oscillations. Our results point out that the oscillatory capacity is not only affected by the interaction polarity but by how it is implemented at promoter level. For a chosen oscillator architecture, we show by means of numerical simulations that the existence or lack of competition between activator and inhibitor at promoter level affects the probability of producing oscillations and also leaves characteristic fingerprints on the associated period/amplitude features.</p> <p>Conclusions</p> <p>In comparison with non-competitive binding at promoters, competition drastically reduces the region of the parameters space characterized by oscillatory solutions. Moreover, while competition leads to pulse-like oscillations with long-tail distribution in period and amplitude for various parameters or noisy conditions, the non-competitive scenario shows a characteristic frequency and confined amplitude values. Our study also situates the competition mechanism in the context of existing genetic oscillators, with emphasis on the Atkinson oscillator.</p

Exploiting Nucleotide Composition to Engineer Promoters

The choice of promoter is a critical step in optimizing the efficiency and stability of recombinant protein production in mammalian cell lines. Artificial promoters that provide stable expression across cell lines and can be designed to the desired strength constitute an alternative to the use of viral promoters. Here, we show how the nucleotide characteristics of highly active human promoters can be modelled via the genome-wide frequency distribution of short motifs: by overlapping motifs that occur infrequently in the genome, we constructed contiguous sequence that is rich in GC and CpGs, both features of known promoters, but lacking homology to real promoters. We show that snippets from this sequence, at 100 base pairs or longer, drive gene expression in vitro in a number of mammalian cells, and are thus candidates for use in protein production. We further show that expression is driven by the general transcription factors TFIIB and TFIID, both being ubiquitously present across cell types, which results in less tissue- and species-specific regulation compared to the viral promoter SV40. We lastly found that the strength of a promoter can be tuned up and down by modulating the counts of GC and CpGs in localized regions. These results constitute a “proof-of-concept” for custom-designing promoters that are suitable for biotechnological and medical applications

A simple negative interaction in the positive transcriptional feedback of a single gene is sufficient to produce reliable oscillations

Negative and positive transcriptional feedback loops are present in natural and synthetic genetic oscillators. A single gene with negative transcriptional feedback needs a time delay and sufficiently strong nonlinearity in the transmission of the feedback signal in order to produce biochemical rhythms. A single gene with only positive transcriptional feedback does not produce oscillations. Here, we demonstrate that this single-gene network in conjunction with a simple negative interaction can also easily produce rhythms. We examine a model comprised of two well-differentiated parts. The first is a positive feedback created by a protein that binds to the promoter of its own gene and activates the transcription. The second is a negative interaction in which a repressor molecule prevents this protein from binding to its promoter. A stochastic study shows that the system is robust to noise. A deterministic study identifies that the dynamics of the oscillator are mainly driven by two types of biomolecules: the protein, and the complex formed by the repressor and this protein. The main conclusion of this paper is that a simple and usual negative interaction, such as degradation, sequestration or inhibition, acting on the positive transcriptional feedback of a single gene is a sufficient condition to produce reliable oscillations. One gene is enough and the positive transcriptional feedback signal does not need to activate a second repressor gene. This means that at the genetic level an explicit negative feedback loop is not necessary. The model needs neither cooperative binding reactions nor the formation of protein multimers. Therefore, our findings could help to clarify the design principles of cellular clocks and constitute a new efficient tool for engineering synthetic genetic oscillators.Comment: 25 pages, 12 figures, 3 table

Environment-sensing Mechanisms of Gene Expression and their Effects on the Dynamics of Genetic Circuits across Cell Generations

In genetic circuits, the constituent genes do not interact only between themselves, they are also affected by regulatory molecules of the host cells that support the circuits’ operation and by the environmental conditions. These factors, along with the intrinsic noise in gene expression, affect the functioning of the circuits. As such, to understand the structure of natural circuits and to engineer functional synthetic circuits, one needs to characterize thoroughly how external factors and perturbations from the environment may affect their behavior.This thesis focused on two cellular mechanisms through which the dynamics of gene expression becomes environment dependent: the intake of gene expression regulatory molecules from the media and the σ factor competition. The first mechanism determines the dynamics by which inducer molecules in the media enter the cell cytoplasm and trigger or repress the expression of the target gene. The second mechanism allows cells to change its gene expression profile to adapt to specific stress conditions.Following the characterization of the effects of these mechanisms on the expression dynamics of individual genes from live, single cell measurements, we then performed in silico assessments on how these effects at the single gene level propagate to the circuit level. Here, the dynamics of genetic circuits was observed in both non-dividing and dividing cell populations, where errors in the partitioning of molecules in cell division occur and introduce significant variance between sister cells.From these studies, with the knowledge on the factors of the host cells and their environment sensing mechanisms, more predictive models of the circuits’ dynamics are expected to emerge. The models would further help in identifying what circuit composition, properties of the host strains and environmental conditions are needed for the circuits to exhibit the desired behavior

Versatile and on-demand biologics co-production in yeast

Current limitations to on-demand drug manufacturing can be addressed by technologies that streamline manufacturing processes. Combining the production of two or more drugs into a single batch could not only be useful for research, clinical studies, and urgent therapies but also effective when combination therapies are needed or where resources are scarce. Here we propose strategies to concurrently produce multiple biologics from yeast in single batches by multiplexing strain development, cell culture, separation, and purification. We demonstrate proof-of-concept for three biologics co-production strategies: (i) inducible expression of multiple biologics and control over the ratio between biologic drugs produced together; (ii) consolidated bioprocessing; and (iii) co-expression and co-purification of a mixture of two monoclonal antibodies. We then use these basic strategies to produce drug mixtures as well as to separate drugs. These strategies offer a diverse array of options for on-demand, flexible, low-cost, and decentralized biomanufacturing applications without the need for specialized equipment

2007 IMSAloquium, Student Investigation Showcase

In conjunction with IMSA\u27s 20th Anniversary and to better represent and capture the sophistication and quality of the students\u27 exemplary investigations, IMSAIoquium: Student Investigation Showcase was developed as the new name for what has previously been termed Presentation Day.https://digitalcommons.imsa.edu/archives_sir/1015/thumbnail.jp

Transient and stochastic dynamics in cellular processes

This Thesis studies different cellular and cell population processes driven by non-linear and stochastic dynamics. The problems addressed here gravitate around the concepts of transient dynamics and relaxation from a perturbed to a steady state. In this regard, in all processes studied, stochastic fluctuations, either intrinsically present in or externally applied to these systems play an important and constructive role, by either driving the systems out of equilibrium, interfering with the underlying deterministic laws, or establishing suitable levels of heterogeneity. The first part of the Thesis is committed the analysis of genetically regulated transient cellular processes. Here, we analyse, from a theoretical standpoint, three genetic circuits with pulsed excitable dynamics. We show that all circuits can work in two different excitable regimes, in contrast to what was previously speculated. We also study how, in the presence of molecular noise, these excitable circuits can generate periodic polymodal pulses due to the combination of two noise induced phenomena: stabilisation of an unstable spiral point and coherence resonance. We also studied an excitable genetic mechanism for the regulation of the transcriptional fluctuations observed in some pluripotency factors in Embryonic Stem cells. In the embryo, pluripotency is a transient cellular state and the exit of cells from it seems to be associated with transcriptional fluctuations. In regard to pluripotency control, we also propose a novel mechanism based on the post-translational regulation of a small set of four pluripotency factors. We have validated the theoretical model, based on the formation of binary complexes among these factors, with quantitative experimental data at the single-cell level. The model suggests that the pluripotency state does not depend on the cellular levels of a single factor, but rather on the equilibrium of correlations between the different proteins. In addition, the model is able to anticipate the phenotype of several mutant cell types and suggests that the regulatory function of the protein interactions is to buffer the transcriptional activity of Oc4, a key pluripotency factor. In the second part of the Thesis we studied the behaviour of a computational cell signalling network of the human fibroblast in the presence of external fluctuations and signals. The results obtained here indicate that the network responds in a nontrivial manner to background chatter, both intrinsically and in the presence of external periodic signals. We show that these responses are consequence of the rerouting of the signal to different network information-transmission paths that emerge as noise is modulated. Finally, we also study the cell population dynamics during the formation of microbial biofilms, wrinkled pellicles of bacteria glued by an extracellular matrix that are one of the simplest cases of self-organised multicellular structures. In this Thesis we develop a spatiotemporal model of cellular growth and death that accounts for the experimentally observed patterns of massive bacterial death that precede wrinkle formation in biofilms. These localised patterns focus mechanical forces during biofilm expansion and trigger the formation of the characteristic ridges. In this sense, the proposed model suggests that the death patterns emerge from the mobility changes in bacteria due to the production of extracellular matrix and the spatially inhomogeneous cellular growth. An important prediction of the model is that matrix productions is crucial for the appearance of the patterns and, therefore for winkle formation. We have also experimentally validated validated this prediction with matrix deficient bacterial strains, which show neither death patterns nor wrinkles.En aquesta Tesi s’estudien diferents processos intracel·lulars i de poblacions cel·lulars regits per dinàmica estocàstica i no lineal. El problemes biològics tractats graviten al voltant el concepte de dinàmica transitòria i de relaxació d’un estat dinàmic pertorbat a l’estat estacionari. En aquest sentit, en tots els processos estudiats, les fluctuacions estocàstiques, presents intrínsecament o aplicades de forma externa, hi tenen un paper constructiu, ja sigui empenyent els sistemes fora de l’equilibri, interferint amb les lleis deterministes subjacents, o establint els nivells d’heterogeneïtat necessaris. La primera part de la Tesi es dedica a l’estudi de processos cel·lulars transitoris regulats genèticament. En ella analitzem des d’un punt de vista teòric tres circuits genètics de control de polsos excitables i, contràriament al que s’havia especulat anteriorment, establim que tots ells poden treballar en dos tipus de règim excitable. Analitzem també com, en presència de soroll molecular, aquests circuits excitables poden generar polsos periòdics i multimodals degut a la combinació de dos fenòmens induïts per soroll: l’estabilització estocàstica d’estats inestables i la ressonància de coherència. D’altra banda, estudiem com un mecanisme genètic excitable pot ser el responsable de regular a nivell transcripcional les fluctuacions que s’observen experimentalment en alguns factors de pluripotència en cèl·lules mare embrionàries. En l’embrió, la pluripotència és un estat cel·lular transitori i la sortida de les cèl·lules d’aquest sembla que està associada a fluctuacions transcripcionals. En relació al control de la pluripotència, presentem també un nou mecanisme basat en la regulació post-traduccional d’un petit conjunt de 4 factors de pluripotència. El model teòric proposat, basat en la formació de complexos entre els diferents factors de pluripotència, l’hem validat mitjançant experiments quantitatius en cèl·lules individuals. El model postula que l’estat de pluripotència no depèn dels nivells cel·lulars d’un únic factor, sinó d’un equilibri de correlacions entre diverses proteïnes. A més, prediu el fenotip de cèl·lules mutants i suggereix que la funció reguladora de les interaccions entre les quatre proteïnes és la d’esmorteir l’activitat transcripcional d’Oct4, un dels principals factors de pluripotència. En el segon apartat de la Tesi estudiem el comportament d’una xarxa computacional de senyalització cel·lular de fibroblast humà en presència de senyals externs fluctuants i cíclics. Els resultats obtinguts mostren que la xarxa respon de forma no trivial a les fluctuacions ambientals, fins i tot en presència d’una senyal externa. Diferents nivells de soroll permeten modular la resposta de la xarxa, mitjançant la selecció de rutes alternatives de transmissió de la informació. Finalment, estudiem la dinàmica de poblacions cel·lulars durant la formació de biofilms, pel·lícules arrugades d’aglomerats de bacteris que conformen un dels exemples més simples d’estructures multicel·lulars autoorganitzades. En aquesta Tesi presentem un model espai-temporal de creixement i mort cel·lular motivat per l’evidència experimental sobre l’aparició de patrons de mort massiva de bacteris previs a la formació de les arrugues dels biofilms. Aquests patrons localitzats concentren les forces mecàniques durant l’expansió del biofilm i inicien la formació de les arrugues característiques. En aquest sentit, el model proposat explica com es formen els patrons de mort a partir dels canvis de mobilitat dels bacteris deguts a la producció de matriu extracel·lular combinats amb un creixement espacialment heterogeni. Una important predicció del model és que la producció de matriu és un procés clau per a l’aparició dels patrons i, per tant de les arrugues. En aquest aspecte, els nostres resultats experimentals en bacteris mutants que no produeixen components essencials de la matriu, confirmen les prediccions

SCALABLE MODELING APPROACHES IN SYSTEMS IMMUNOLOGY

Systems biology seeks to build quantitative predictive models of biological system behavior. Biological systems, such as the mammalian immune system, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus, mechanistic, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with the high-dimensional parameter space arising when building reasonably detailed models. Another challenge is to devise integrated frameworks incorporating behavioral characteristics manifested at various organizational levels seamlessly. In this dissertation, I present two research projects addressing problems in immunological, or biological systems in general, using quantitative mechanistic models and machine learning, touching on the aforementioned challenges in scalable modeling. First, I aimed to understand how cell-to-cell heterogeneities are regulated through gene expression variations and their propagation at the single-cell level. To better understand detailed gene regulatory circuit models with many parameters without analytical solutions, I developed a framework called MAchine learning of Parameter-Phenotype Analysis (MAPPA). MAPPA combines machine learning approaches and stochastic simulation methods to dissect the mapping between high- dimensional parameters and phenotypes. MAPPA elucidated regulatory features of stochastic gene-gene correlation phenotypes. Next, I sought to quantitatively dissect immune homeostasis conferring tolerance to self-antigens and responsiveness to foreign antigens. Towards this goal, I built a series of models spanning from intracellular to organismal levels to describe the recurrent reciprocal relationships between self-reactive T cells and regulatory T cells in collaboration with an experimentalist. This effort elucidated critical immune parameters regulating the circuitry enabling the robust suppression of self-reactive T cells, followed by experimental validation. Moreover, by bridging these models across organizational scales, I derived a framework describing immune homeostasis as a dynamical equilibrium between self-activated T cells and regulatory T cells, typically operating well below thresholds that could result in clonal expansion and subsequent autoimmune diseases. I start with an introduction with a perspective linking seemingly contradictory behaviors of the immune system at different scales: microscopic “noise” and macroscopic deterministic outcomes. By connecting these aspects in the adaptive immune system analogously with an ansatz from statistical physics, I introduced a view on how robust immune homeostasis ensues