1,949 research outputs found

    Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

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    BackgroundInteractions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.ResultsThrough simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G1 fixed point was found for deterministic updating schemes.ConclusionsIn general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component

    Cell fate reprogramming by control of intracellular network dynamics

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    Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.Comment: 61 pages (main text, 15 pages; supporting information, 46 pages) and 12 figures (main text, 6 figures; supporting information, 6 figures). In revie

    Building a more sustainable sensor network via protocol innovation

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    Traditionally, network protocols are designed based on the assumptions that network is powered by small batteries with scarce energy supply. However, emerging energy replenishment technologies such as ambient energy harvesting, wireless energy transferring, etc., provide alternatives to address the energy constraint problem but also introduce new challenges (e.g., energy heterogeneity). Been the core to achieve network sustainability, novel network protocols shall be designed to better exploit energy availabilities and tackle new challenges or issues exposed by emerging energy replenishment technologies. In this dissertation, we study how to build a more sustainable sensor network via network protocol innovation. Specifically, the study is conducted in four directions. First of all, we study how to improve energy utilization efficiency on individual sensor nodes as a foundation to improve the network sustainability. Secondly, we study how to prolong the network lifetime as a whole through dynamically and collaboratively tuning MAC layer operational parameters between neighboring nodes. Thirdly, we study the cross-layer design technique and propose a holistic routing and MAC protocol to further prolong the network lifetime. Fourthly, with given sensing coverage constraints, we jointly optimize the routing and sensing behaviors to further improve the network sustainability

    Transient and stochastic dynamics in cellular processes

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    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

    A Theory of Cortical Neural Processing.

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    This dissertation puts forth an original theory of cortical neural processing that is unique in its view of the interplay of chaotic and stable oscillatory neurodynamics and is meant to stimulate new ideas in artificial neural network modeling. Our theory is the first to suggest two new purposes for chaotic neurodynamics: (i) as a natural means of representing the uncertainty in the outcome of performed tasks, such as memory retrieval or classification, and (ii) as an automatic way of producing an economic representation of distributed information. We developed new models, to better understand how the cerebral cortex processes information, which led to our theory. Common to these models is a neuron interaction function that alternates between excitatory and inhibitory neighborhoods. Our theory allows characteristics of the input environment to influence the structural development of the cortex. We view low intensity chaotic activity as the a priori uncertain base condition of the cortex, resulting from the interaction of a multitude of stronger potential responses. Data, distinguishing one response from many others, drives bifurcations back toward the direction of less complex (stable) behavior. Stability appears as temporary bubble-like clusters within the boundaries of cortical columns and begins to propagate through frequency sensitive and non-specific neurons. But this is limited by destabilizing long-path connections. An original model of the post-natal development of ocular dominance columns in the striate cortex is presented and compared to autoradiographic images from the literature with good matching results. Finally, experiments are shown to favor computed update order over traditional approaches for better performance of the pattern completion process

    Tensor Network States: Optimizations and Applications in Quantum Many-Body Physics and Machine Learning

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    Tensor network states are ubiquitous in the investigation of quantum many-body (QMB) physics. Their advantage over other state representations is evident from their reduction in the computational complexity required to obtain various quantities of interest, namely observables. Additionally, they provide a natural platform for investigating entanglement properties within a system. In this dissertation, we develop various novel algorithms and optimizations to tensor networks for the investigation of QMB systems, including classical and quantum circuits. Specifically, we study optimizations for the two-dimensional Ising model in a transverse field, we create an algorithm for the kk-SAT problem, and we study the entanglement properties of random unitary circuits. In addition to these applications, we reinterpret renormalization group principles from QMB physics in the context of machine learning to develop a novel algorithm for the tasks of classification and regression, and then utilize machine learning architectures for the time evolution of operators in QMB systems

    On stability and controllability of conjunctive Boolean networks

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    A Boolean network (BN) is a finite state discrete time dynamical system. At each step, each variable takes a value from a binary set. The value update rule for each variable is a local function which depends only on a selected subset of variables. BNs have been used in modeling gene regulatory networks. We focus in this thesis on a special class of BNs, termed as conjunctive Boolean networks (CBNs). A BN is conjunctive if the associated value update rule is comprised of only AND operations. It is known that any trajectory of a finite dynamical system will enter a periodic orbit. Periodic orbits of a CBN are now completely understood. We first characterize in this thesis all periodic orbits of a CBN. In particular, we establish a bijection between the set of periodic orbits and the set of binary necklaces of a certain length. We further investigate the stability of a periodic orbit. Specifically, we perturb a state in the periodic orbit by changing the value of a single entry of the state. The trajectory, with the perturbed state being the initial condition, will enter another (possibly the same) periodic orbit in finite time steps. We then provide a complete characterization of all such transitions from one periodic orbit to another. In particular, we construct a digraph, with the vertices being the periodic orbits, and the (directed) edges representing the transitions among the orbits. We call such a digraph the stability structure of the CBN. We then investigate the orbit-controllability and state-controllability of a CBN. We ask the question of how one can steer a CBN to enter any periodic orbit or to reach any final state, from any initial state. Suppose that there is a selected subset of variables whose values can be controlled for some finite time steps, while other variables still follow the value update rule during all time. We establish in the thesis a necessary and sufficient condition for this subset such that the trajectory, with any initial condition, will enter any desired periodic orbit or reach any final state. We also provide algorithms specifying the methods of manipulating the values of these variables to realize these control goals
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