Becerra, A. GargantillaGutierrez, M.Lahoz-Beltra, R.
16页
查看更多>>摘要：In nature, bacteria exhibit a limited repertoire of behaviors in response to environmental changes. Synthetic biology has now opened up the possibility of programming cells or unicellular organisms in order to enable them to perform certain tasks, which would allow the programming of 'intelligent' bacteria. Many of the theoretical ideas that Liberman proposed last century, for example his seminal idea that a cell is a computer, are now being put into practice with bacterial colonies in both wet and in silico experiments.These bacteria may one day be used to solve a wide range of problems whose solution requires their adaptation to external changes either within a bioreactor, organ or tissue of a patient or through the design of microbial-synthetic consortia oriented to their use in bioprocesses to produce medicines, biofuels or biomaterials. In this work, we show the possibility of programming synthetic bacteria with a previously trained perceptron neural network. First, we illustrate how a colony of bacteria endowed with a perceptron is able to solve an optimization problem in silico. Secondly, we study by means of in silico simulations how a perceptron can be applied to program behaviors in bacteria leading to social interactions and to the formation of complex communities that in the future would be useful in biotechnology. Finally, we go a step further, and study how the above perceptron designed to program bacterial behavior is implemented in a genetic circuit designed for this purpose. Once the genetic circuit was obtained, it was engineered into a plasmid.
查看更多>>摘要：Quantum stochasticity carries two incompatible implications. One is for the statistical divergence upon the prior absence of complete controllability over boundary conditions applied to mechanistic causation. One more alternative is for the statistical convergence upon the posterior decidability of measurement despite the absence of the prior decidability. Decidable measurement lacking the prior decidability is retrocausal. The quantum physical likelihood for the life world may derive from the statistical convergence proceeding in a durable manner. This observation suggests that there must have been some type of observers even internal to the lifeless world, otherwise no likelihood of identifying the objects of interest could be available there. Measurement activity intrinsic to the internal observers is indexical, while the similar activity specific to the external observer like us can be symbolic. The difference is in the phenomenological qualification since both are the observers of different types. A most conspicuous case demonstrating the phenomenological difference is revealed in the different nature of time to be employed and experienced there. Time serves as a principal attribute qualifying the phenomenon to be experienced as such. Qualification of time is observer dependent.
查看更多>>摘要：The hippocampus is an essential brain region for spatial memory and learning. Recently, a theoretical model of the hippocampus based on temporal difference (TD) learning has been published. Inspired by the successor representation (SR) learning algorithms, which decompose value function of TD learning into reward and state transition, they argued that the rate of firing of CA1 place cells in the hippocampus represents the probability of state transition. This theory, called predictive map theory, claims that the hippocampus representing space learns the probability of transition from the current state to the future state. The neural correlates of expecting the future state are the firing rates of the CA1 place cells. This explanation is plausible for the results recorded in behavioral experiments, but it is lacking the neurobiological implications. Modifying the SR learning algorithm added biological implications to the predictive map theory. Similar with the simultaneous needs of information of the current and future state in the SR learning algorithm, the CA1 place cells receive two inputs from CA3 and entorhinal cortex. Mathematical transformation showed that the SR learning algorithm is equivalent to the heterosynaptic plasticity rule. The heterosynaptic plasticity phenomena in CA1 were discussed and compared with the modified SR update rule. This study attempted to interpret the TD algorithm as the neurobiological mechanism occurring in place learning, and to integrate the neuroscience and artificial intelligence approaches in the field.
查看更多>>摘要：Animals, humans, and organizations are known to adjust how (much) they explore complex environments that exceed their information processing capacity, rather than relentlessly search for the optimal action. The adjusted depth of exploration is supposed to depend on the aspiration level internal to the agent. This action selection tendency is known as satisficing. The Risk-sensitive Satisficing (RS) model implements satisficing in the reinforcement learning framework through conversion of action values into gains (or losses) relative to the aspiration level. The risk-sensitive evaluation of action values by RS has been shown to be effective in reinforcement learning. In this paper, first we analyze RS in comparison with UCB and Thompson sampling algorithms. We also show that RS shows differential risk-attitudes considering the risks. Then we propose the Softsatisficing policy that is a stochastic equivalent of RS and further analyze the exploratory behavior of risk-sensitive satisficing that RS and Softsatisficing implement. We emphasize that Softsatisficing has the potential of modeling risk-sensitive foraging and other decision-making behaviors by humans, animals, and organizations.
查看更多>>摘要：Traditionally ignored in comparison with the energy flow, the interest on the information flow and in general on the informational perspective is nowadays manifest in biological fields. Herein we will revisit classical ideas on biomolecular information processing, basically from Efim Liberman and Michael Conrad, weaving them together with new views on the information flow as captured by cellular signaling systems and with the development of biological complexity. A consistent new explanatory framework looms-potentially contributing to a new cell theory. It would incorporate fundamental conceptualizations on the mechanisms of molecular recognition and informational architectures, the life cycle and the characterization of meaning, and finally the emergence of biological complexity. Concerning the evolutionary process, this informational approach depicts an indefinite series of recursion processes performed in the open-ended environment of the real world, potentially affected by multiple contingencies modifying the informational architectures involved in recursion. Consequently, a large variety of molecular tools and systems would have been incorporated speeding up the variability of genomes and facilitating their displacement in sequence space. To the extent to which the comparison with human mobility in physical space may hold, a power law could be hypothesized interconnecting the variability outcomes of the different evolutionary 'vehicles' or variation modes. Contributing to a renewed discussion on the evolutionary process is another essential goal of the present work.
查看更多>>摘要：The analysis of evolutionary data allows uncovering information about the organisms and how they have adapted and evolved. This information could provide us with new insights about the specialisation of organisms (or part of them), how they adapt, how similar they are with other species, among others. Unfortunately, this evolutionary history can only be estimated, and for that, several computational methods exist. Among the methods, optimisation methods are one of the main approaches to deal with this problem, with multiobjective optimisation producing promising results. In this paper, we deal with multiobjective phylogenetic inference, using a multi-modal metaheuristic approach that exploits the decision space in the multiobjective formulation of the problem. In particular, we incorporate a new metric based on a topological tree distance. We compare the method with state of the art algorithms in terms of performance. Additionally, we perform a thorough analysis of a study case on a yeast Saccharomyces cerevisiae dataset. Results show that our proposal is able to improve the diversity of solutions while improving or keeping the quality of solutions in terms of hypervolume.
查看更多>>摘要：Analytical simulation of spectral features for evolution of biosystem based on the model of system with infinite number of conserved random links with environment is conducted. Assumption of conservation presumes that the links are executed by appropriate continuity equation. Besides, it is believed that any exchange process between system and environment has an energy dimension. The solution for a total energy exchange quantity E, average efficiency of energy exchange gamma and related entropy S is found. Solution E demonstrates the interlayer spectral structure when the continuous spectrum is interleaved with the quasi-discrete one. In turn, spectrum for entropy S comes in the bandpass quasi-continuous form. The pair nature of discrete points, relation between optimization and discreteness is also demonstrated. Taken altogether above features enable to think on existence of an obligatory energy infrastructure for evolving biosystem. This infrastructure is formed by combination of the spectral elements, such as nodes and internode segments.
NSTL
Elsevier