Computing within bacteria: Programming of bacterial behavior by means of a plasmid encoding a perceptron neural network.

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.

Modeling a cancerous tumor development in a virtual patient suffering from a depressed state of mind: Simulation of somatic evolution with a customized genetic algorithm.

Cancer is a disease of evolutionary origin in which a group of cells in the body initiate evolutionary changes under certain circumstances that can lead to the formation of a tumor. It is currently believed that a hostile cell environment can lead to the cells of an organ or tissue initiating a whole series of physiological changes that will lead to the transformation of healthy cells into cancerous ones. During the process of transformation, cells evolve under a paradigm known as somatic evolution. In this work the first stages of the formation of a cancerous tumor have been simulated assuming that the cause of the formation is the genetic instability of the cells, being the cause of this instability the presence of chronic inflammation, phenomenon responsible for the appearance of a hostile cellular environment. The model simulates a virtual patient where an altered state of mind, whether depressive, stressed or similar, will lead to disturbed hormone levels that will eventually lead to a condition of chronic inflammation. A novelty of the work is the design of a genetic algorithm oriented to the simulation of somatic evolution, representing the cells by means of a vector that encodes the nodes of a stochastic network. These nodes represent the states of the genes, hallmarks of the cancer and genetic stability of a cell, simulating the formation of a tumor in the caverns of the colon. Another novelty of the model is the design of a virtual patient in which a chatbot for the simulation of the state of mind is hybridized with differential equations simulating both the hormones of the so-called hypothalamic-pituitary-adrenal axis and the cytokines involved in the mechanism of cellular inflammation. The work is a first step in the design of models that under a holistic vision allow the simulation and therefore a greater understanding of the different facets of a disease as complex as cancer.

Validation of laughter for diagnosis and evaluation of depression.

BACKGROUND: In the medical field, laughter has been studied for its beneficial effects on health and as a therapeutic method to prevent and treat major medical diseases. However, very few works, if any, have explored the predictive potential of laughter and its potential use as a diagnostic tool. METHOD: We registered laughs of depressed patients (n=30) and healthy controls (n=20), in total 934 laughs (517 from patients and 417 from controls). All patients were tested by the Hamilton Depression Rating Scale (HDRS). The processing was made in Matlab, with calculation of 8 variables per laugh plosive. General and discriminant analysis distinguished patients, controls, gender, and the association between laughter and HDRS test. RESULTS: Depressed patients and healthy controls differed significantly on the type of laughter, with 88% efficacy. According to the Hamilton scale, 85.47% of the samples were correctly classified in males, and 66.17% in women, suggesting a tight relationship between laughter and the depressed condition. LIMITATIONS: (i) The compilation of humorous videos created to evoke laughter implied quite variable chances of laughter production. (ii) Some laughing subjects might not feel comfortable when recording. (iii) Evaluation of laughter episodes depended on personal inspection of the records. (iv) Sample size was relatively small and may not be representative of the general population afflicted by depression. CONCLUSIONS: Laughter may be applied as a diagnostic tool in the onset and evolution of depression and, potentially, of neuropsychiatric pathologies. The sound structures of laughter reveal the underlying emotional and mood states in interpersonal relationships.

Modelling complex populations formed by proliferating, quiescent and quasi-quiescent cells: application to plant root meristems.

A proliferating population of cells may be considered complex when its proliferative or growth fraction P is lower than 1 and/or when it is formed by subpopulations with different mean cycle times. The present paper shows that in such complex populations exponential growth is consistent with a steady-state distribution of cells. Obviously, when P=1 then cell distribution is only a function of cell age. An analytical model has been developed to study complex populations including both quiescent fractions formed by cells with unreplicated genome (G(0) cells) and cells with fully duplicated chromosomes (Q(2) cells). The model also considers those quasi-quiescent cells in their last transit through G(1) and S (Q(1) and Q(s) cells) before becoming quiescent. In order to solve the difficulties of a direct analysis of the whole population, its kinetic parameters have been obtained by studying the negative exponential distribution of two subpopulations: one formed by the proliferating cells and another formed by the quasi-quiescent cells. Additionally, the model could be applied when quiescence is initiated at any other cycle phase different from G(1) and G(2), for instance, cells in the process of replicating their DNA or being at any other mitotic phases. The utility of the method was illustrated in populations which constitute the root meristems of both Allium cepa L. and Pisum sativum L. Three facts should be stressed: (1) the method seems to be rather powerful because it can be carried out from different sets of experimentally measured parameters; (2) the rate of division and, therefore, the population doubling time can be easily estimated by this method; and (3) it also allows the determination of the amount of cells that had become quiescent either before they had replicated their DNA (G(0)) or after having completed their replication (Q(2)), as well as those quasi-quiescent cells which are progressing throughout their last pre-replicative and replicative periods (thus Q(1) and Q(s), respectively).

Evolving hardware as model of enzyme evolution.

Organism growth and survival is based on thousands of enzymes organized in networks. The motivation to understand how a large number of enzymes evolved so fast inside cells may be relevant to explaining the origin and maintenance of life on Earth. This paper presents electronic circuits called 'electronic enzymes' that model the catalytic function performed by biological enzymes. Electronic enzymes are the hardware realization of enzymes defined as molecular automata with a finite number of internal conformational states and a set of Boolean operators modelling the active groups of the active site. One of the main features of electronic enzymes is the possibility of evolution finding the proper active site by means of a genetic algorithm yielding a metabolic ring or k-cycle that bears a resemblance to Krebs (k=7) or Calvin (k=4) cycles present in organisms. The simulations are consistent with those results obtained in vitro evolving enzymes based on polymerase chain reaction (PCR) as well as with the general view that suggests the main role of recombination during enzyme evolution. The proposed methodology shows how molecular automata with evolvable features that model enzymes or other processing molecules provide an experimental framework for simulation of the principles governing metabolic pathways evolution and self-organization.

Molecular automata assembly: principles and simulation of bacterial membrane construction.

The motivation to understand the basic rules and principles governing molecular self-assembly may be relevant to explain in the context of molecular biology the self-organization and biological functions exhibited within cells. This paper presents a molecular automata model to simulate molecular self-assembly introducing the concept of molecular programming to simulate the biological function or operation performed by an assembled molecular state machine. The method is illustrated modelling Escherichia coli membrane construction including the assembly and operation of ATP synthase as well as the assembly of the bacterial flagellar motor. Flagellar motor operation was simulated using a different approach based on state machine definition used in virtual reality systems. The proposed methodology provides a modelling framework for simulation of biological functions performed by cellular components and other biological systems suitable to be modelled as molecular state machines.

Cytoskeletal involvement in neuronal learning: a review.

This paper introduces the ideas of neural networks in the context of currently recognized cellular structures within neurons. Neural network models and paradigms require adaptation of synapses for learning to occur in the network. Some models of learning paradigms require information to move from axon to dendrite. This motivated us to examine the possibility of intracellular signaling to mediate such signals. The cytoskeleton forms a substrate for intracellular signaling via material transport and other putative mechanisms. Furthermore, many experimental results suggest a link between the cytoskeleton and cognitive processing. In this paper we review research on intracellular signaling in the context of neural network learning.

Cytoskeletal logic: a model for molecular computation via Boolean operations in microtubules and microtubule-associated proteins.

Adaptive behaviors and dynamic activities within living cells are organized by the cytoskeleton: intracellular networks of interconnected protein polymers which include microtubules (MTs), actin, intermediate filaments, microtubule associated proteins (MAPs) and other protein structures. Cooperative interactions among cytoskeletal protein subunit conformational states have been used to model signal transmission and information processing. In the present work we present a theoretical model for molecular computing in which Boolean logic is implemented in parallel networks of individual MTs interconnected by MAPs. Conformational signals propagate on MTs as in data buses and in the model MAPs are considered as Boolean operators, either as bit-lines (like MTs) where a signal can be transported unchanged between MTs ('BUS-MAP'), or as bit-lines where a Boolean operation is performed in one of the two MAP-MT attachments ('LOGIC-MAP'). Three logic MAPs have been defined ('NOT-MAP, 'AND-MAP', 'XOR-MAP') and used to demonstrate addition, subtraction and other arithmetic operations. Although our choice of Boolean logic is arbitrary, the simulations demonstrate symbolic manipulation in a connectionist system and suggest that MT-MAP networks can perform computation in living cells and are candidates for future molecular computing devices.

The kinetics of the autolytic phase of growth in cultures of Aspergillus niger.

The kinetics of the autolytic phase of growth in cultures of Aspergillus niger has been studied. Two different autolytic periods could be distinguished. One, consisting of a rapid (exponential) loss (62%) of mycelial weight, occurred between 36 and 117 hours of incubation. A second, consisting of a slow autolysis, occurred between the 117th and the 190th hour of incubation; the mycelial loss here being 5%. Based on the degree of autolysis (alpha = 67.0%), 92.5% and 7.5% are lost during the first and the second autolytic periods, respectively.