Spatiotemporal analysis of simultaneous single-unit activity in the dragonfly

Continuous neural spiking records were obtained from the mesothoracic ganglion of the dragonfly. For analysis the 12 s records of all 58 discriminated cells were “tracked” across three continuous behavioral states: pre-flight, flight and post-flight. The recorded spike amplitudes and angles (widths) for each cell were used to construct a simple map of individual cell positions relative to each other within the ganglion. Individual cell activity patterns were then characterized both with respect to neighboring cell locations and patterns of cell spiking observed across three behavioral states. The results indicated that this technique for constructing a “neighboring cell map” effectively reflects the known histological features of the ganglionic cell architecture. The gross firing histories of individual cells were found to correspond to the overall spike patterns of neighboring ganglionic cells as opposed to more distal cells. Such relationships suggest that the physical layout of this ganglionic network may help to determine or bias individual cell firing histories that occur during different behavioral states in the dragonfly.     

Spatiotemporal organization and mechanosensory function of podosomes

Podosomes are small, circular adhesions formed by cells such as osteoclasts, macrophages, dendritic cells, and endothelial cells. They comprise a protrusive actin core module and an adhesive ring module composed of integrins and cytoskeletal adaptor proteins such as vinculin and talin. Furthermore, podosomes are associated with an actin network and often organize into large clusters. Recent results from our laboratory and others have shed new light on podosome structure and dynamics, suggesting a revision of the classical “core-ring” model. Also, these studies demonstrate that the adhesive and protrusive module are functionally linked by the actin network likely facilitating mechanotransduction as well as providing feedback between these two modules. In this commentary, we briefly summarize these recent advances with respect to the knowledge on podosome structure and discuss force distribution mechanisms within podosomes and their emerging role in mechanotransduction.     

Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

We review here the development of Hodgkin–Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are “fast spiking” “regular spiking” “intrinsically bursting” and “low-threshold spike” cells. For each class, we fit “minimal” HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.     

A comparative study of electronic and neural networks involved in pattern recognition

The comparative study of electronic and neural networks involved in pattern recognition starts with the analogies of structure and function which exist between the electronic “basic integrative unit” and the neuron. Both elements represent the basic components in each system of networks and may be considered as functionally equivalent.According to the kind of response given to a standard input signal, four types of integrative units, either electronic or neural, may be distinguished: the fixed, the accommodative, the signal prolongating and the adaptive type.The integrative units perform many different functions. Those involved in pattern recognition, however, can all be grouped into three categories according to one of the following functions they perform: contrast detection, pattern detection and pattern discrimination. A “contrast detecting unit” gives responses in two senses, positive or negative, according to the position of the stimulus over its receptive field. A “pattern detecting unit” gives responses in one sense only, with a maximum for a pattern having the spatial distribution corresponding to the positive acting receptors of its receptive field. For performing the function of discrimination, which leads to reliable identification of any pattern, a network arrangement called a “maximum amplitude filter” is necessary. Examples of such units and arrangements existing in the nervous system are provided.It is concluded that a “logical analysis of neural networks” based on engineering principles is possible and that this could provide a new tool to the neurophysiologist in the study of the nervous system.     

Which aging in yeast is “true”?

Two model systems, “replicative aging” and “chronological aging” (CA), which are used for gerontological research on the yeast Saccharomyces cerevisiae, are compared. In the first case, the number of daughter cells generated by an individual mother cell before cell propagation irreversibly stops is analyzed. This makes the model very similar to the well-known Hayflick model. In the case of CA, the survival of yeast cell population in the stationary phase of growth is studied. It is noted that the second model is similar to the “stationary phase aging” model, which is used in the author’s laboratory for cytogerontological studies on animal and human cells. It is assumed that the concept of cell proliferation restriction as the main cause of age-related accumulation in the cells of multicellular organisms of macromolecular defects (primarily DNA damage) leading to deterioration of tissue and organ functioning and, as a result, to an increase in the death probability allows explaining how the aging process proceeds in almost any living organisms. Apparently, in all cases, this process is initiated by the appearance of slow propagating (or not propagating at all) cells, which leads to the termination of “dilution,” with the help of new cells, of macromolecular defects accumulating at the level of whole cell population. It is concluded that data on the geropromoter or geroprotector activity of various factors obtained in tests on the yeast CA model can be used with a high reliability to understand the mechanisms of human aging and longevity.     

A model of gradient interpretation based on morphogen binding

We propose a model in which pattern formation is controlled by several concentration gradients of “morphogens” and by allosteric proteins which bind them. In this model, each protein can bind up to two molecules of each morphogen and has an “active state” when one molecule of each morphogen is bound. The concentration of the active state of such a “morphogen binding protein” varies with position in a way that depends on the values given the binding constants. In a contour map of the active state concentration, the contours can have a variety of simple shapes.Simply-shaped regions of cell differentiation can be defined directly by concentration contours of a morphogen binding protein using a threshold-sensing mechanism. More complex shapes may be generated using several proteins and a “winner-take-all” rule according to which each protein specifies some particular sort of cell differentiation and the differentiation of cells in any position is governed by the protein with the highest active state concentration.We present an application of our model to the vertebrate limb skeleton; we use the “winner-take-all” mechanism and thirteen morphogen binding proteins, eleven of which specify cartilage formation. In this model we use one morphogen binding protein to specify the shaft of a typical long bone and one for each epiphysis. Our model is reasonably successful in imitating the in vivo positions and orientations of developing bones and in generating simple, plausible-looking articular surfaces.In addition to the morphogen-binding model we propose a mechanism which could transform morphogen-binding patterns into high-amplitude patterns capable of controlling the activity of structural genes. This “amplifying mechanism” can account for two previously unexplained features of limb skeletal development: the early formation of the diffusely-bounded “scleroblastema” in the limb bud and the center-to-edge gradations in cartilage formation rate which are later seen within individual chondrification foci.A simple modification of the morphogen-binding model provides an explanation for the general anatomical phenomenon of metamerism: The model can account for the formation of inexactly repeating patterns (such as the pattern of the vertebral column) and suggests a mechanism by which such patterns could (1) evolve from exactly repeating patterns, and (2) acquire, in further evolution, a high degree of specialization of the individual repeating units.The most promising approach for testing the morphogen-binding model would appear to involve experiments in which cytoplasm is transferred between cells at various stages of pattern development. Support for the model could also come from the discovery of certain kinds of hereditary limb defects.     

Neuronal activity controls the development of interneurons in the somatosensory cortex

BACKGROUND

Neuronal activity in cortical areas regulates neurodevelopment by interacting with defined genetic programs to shape the mature central nervous system. Electrical activity is conveyed to sensory cortical areas via intracortical and thalamocortical neurons, and includes oscillatory patterns that have been measured across cortical regions.

OBJECTIVE

In this work, we review the most recent findings about how electrical activity shapes the developmental assembly of functional circuitry in the somatosensory cortex, with an emphasis on interneuron maturation and integration. We include studies on the effect of various neurotransmitters and on the influence of thalamocortical afferent activity on circuit development. We additionally reviewed studies describing network activity patterns.

METHODS

We conducted an extensive literature search using both the PubMed and Google Scholar search engines. The following keywords were used in various iterations: “interneuron”, “somatosensory”, “development”, “activity”, “network patterns”, “thalamocortical”, “NMDA receptor”, “plasticity”. We additionally selected papers known to us from past reading, and those recommended to us by reviewers and members of our lab.

RESULTS

We reviewed a total of 132 articles that focused on the role of activity in interneuronal migration, maturation, and circuit development, as well as the source of electrical inputs and patterns of cortical activity in the somatosensory cortex. 79 of these papers included in this timely review were written between 2007 and 2016.

CONCLUSION

Neuronal activity shapes the developmental assembly of functional circuitry in the somatosensory cortical interneurons. This activity impacts nearly every aspect of development and acquisition of mature neuronal characteristics, and may contribute to changing phenotypes, altered transmitter expression, and plasticity in the adult. Progressively changing oscillatory network patterns contribute to this activity in the early postnatal period, although a direct requirement for specific patterns and origins of activity remains to be demonstrated.

     

Qualitative dynamics of a network model of regulation of the immune system: A rationale for the IgM to IgG switch

A mathematical model has been developed that simulates some of the main features of a network theory of regulation of the immune system. According to the network viewpoint, the V regions (idiotypes) on antibodies and lymphocytes are self-antigens, to which other lymphocytes of the system can respond specifically, just as they respond to foreign antigens. The resultant couplings between the lymphocytes are considered to be basic for the regulation of the system.The present mathematical model simulates the interactions between cells that recognize the antigen (“positive cells”), and “negative cells” that have receptors that specifically recognize the V regions of the positive cells. The mathematical model incorporates only the interactions that are postulated to be important in the four steady states of the theory, and includes neither the antigen nor any accessory (“A”) cells. The effects of both antigen-specific and anti-idiotypic T and B cells are included, as well as antigen-specific and anti-idiotypic T cell factors, and the two main classes of antibodies. The model is a first order autonomous ordinary differential equation in two variables. We describe a geometric technique that gives strong information on the model, without explicitly solving the ordinary differential equation. This technique proves to be powerful in permitting us to systematically scan the parameter space of the model. The detailed analysis leads to support for the idea that the model provides a rationale for the switch observed in the immune system from the production of one major class of antibody (IgM) to the other major class (IgG). The analysis also leads to a new, previously unsuspected possibility for the nature of the suppressed state within the context of the postulates of the symmetrical network theory.     

Bursting synchronization dynamics of pancreatic β-cells with electrical and chemical coupling

Based on bifurcation analysis, the synchronization behaviors of two identical pancreatic β-cells connected by electrical and chemical coupling are investigated, respectively. Various firing patterns are produced in coupled cells when a single cell exhibits tonic spiking or square-wave bursting individually, irrespectively of what the cells are connected by electrical or chemical coupling. On the one hand, cells can burst synchronously for both weak electrical and chemical coupling when an isolated cell exhibits tonic spiking itself. In particular, for electrically coupled cells, under the variation of the coupling strength there exist complex transition processes of synchronous firing patterns such as “fold/limit cycle” type of bursting, then anti-phase continuous spiking, followed by the “fold/torus” type of bursting, and finally in-phase tonic spiking. On the other hand, it is shown that when the individual cell exhibits square-wave bursting, suitable coupling strength can make the electrically coupled system generate “fold/Hopf” bursting via “fold/fold” hysteresis loop; whereas, the chemically coupled cells generate “fold/subHopf” bursting. Especially, chemically coupled bursters can exhibit inverse period-adding bursting sequence. Fast–slow dynamics analysis is applied to explore the generation mechanism of these bursting oscillations. The above analysis of bursting types and the transition may provide us with better insight into understanding the role of coupling in the dynamic behaviors of pancreatic β-cells.     

A model for repetitive firing in neurons

This paper describes a model for the generation of repetitive firing patterns in single neurons to be used as a module in large-scale network simulation studies. The model is based on the combination of extended versions of Hill's model for accomodation and of Kernell's model for adaptation. Both digital computer and electronic circuit realizations of the model are presented. The model is shown to produce strength-duration curves for accomodation which are compatible with available data from real neurons. Both “high ceiling” and “low ceiling” cell types can be matched by adjusting parameters in the model. An equation relating steady-state firing rate to amplitude of applied steady current is presented which includes the accumulation of potassium conductance changes with repetitive firing. The occurence of phasic and tonic responses to step stimulation is mapped in the parameter space of the model. Several representative response patterns to irregular inputs are presented.     

Modeling of the metabolic energy dissipation for restricted tumor growth

Energy dissipation mostly represents unwanted outcome but in the biochemical processes it may alter the biochemical pathways. However, it is rarely considered in the literature although energy dissipation and its alteration due to the changes in cell microenvironment may improve methods for guiding chemical and biochemical processes in the desired directions. Deeper insight into the changes of metabolic activity of tumor cells exposed to osmotic stress or irradiation may offer the possibility of tumor growth reduction. In this work effects of the osmotic stress and irradiation on the thermodynamical affinity of tumor cells and their damping effects on metabolic energy dissipation were investigated and modeled. Although many various models were applied to consider the tumor restrictive growth they have not considered the metabolic energy dissipation. In this work a pseudo rheological model in the form of “the metabolic spring-pot element” is formulated to describe theoretically the metabolic susceptibility of tumor spheroid. This analog model relates the thermodynamical affinity of cell growth with the volume expansion of tumor spheroid under isotropic loading conditions. Spheroid relaxation induces anomalous nature of the metabolic energy dissipation which causes the damping effects on cell growth. The proposed model can be used for determining the metabolic energy “structure” in the context of restrictive cell growth as well as for predicting optimal doses for cancer curing in order to tailor the clinical treatment for each person and each type of cancer.     

Bifurcation analysis of the regulatory modules of the mammalian G1/S transition

MOTIVATION: Mathematical models of the cell cycle can contribute to an understanding of its basic mechanisms. Modern simulation tools make the analysis of key components and their interactions very effective. This paper focuses on the role of small modules and feedbacks in the gene-protein network governing the G1/S transition in mammalian cells. Mutations in this network may lead to uncontrolled cell proliferation. Bifurcation analysis helps to identify the key components of this extremely complex interaction network. RESULTS: We identify various positive and negative feedback loops in the network controlling the G1/S transition. It is shown that the positive feedback regulation of E2F1 and a double activator-inhibitor module can lead to bistability. Extensions of the core module preserve the essential features such as bistability. The complete model exhibits a transcritical bifurcation in addition to bistability. We relate these bifurcations to the cell cycle checkpoint and the G1/S phase transition point. Thus, core modules can explain major features of the complex G1/S network and have a robust decision taking function.     

Contact inhibition of what? An analytical review

Quite a number of phenomena having to do with cells' influences upon one another's movements have come to be regarded as expressions of “contact inhibition.” However, no single, central mechanism has been shown to underlie them all. Consequently, the term “contact inhibition” should not be used without operational modifiers. Inhibitions of individual cell movements imputed to be mediated by cell-cell contacts include inhibition of overlapping (which results in monolayering), of colony expansion, of cell speed (nuclear translocation), of ruffling, of orthogonal movement (proposed to explain spontaneous parallel alignment of cells), and of neighbor exchanges. The six inhibitions listed above are operationally distinct, and only two (overlapping and colony expansion) are known to result from a common mechanism. A seventh phenomenon, so-called “contact inhibition of cell division” (more operationally termed postconfluence inhibition of cell division) is in a separate category and is not considered here. Evidence eliminating action-at-a-distance is available only for the first three, and hence only these should at present be termed contact inhibitions. Inhibition of neighbor exchanges is yet hypothetical; at its extreme, it would immobilize cells in a confluent monolayer, but such immobilization has been found not to occur. Contact inhibition of overlapping, the most studied of the six, is not displayed by invasive cells with respect to normal cells; invasive tumor cells overlap freely upon normal cells, although not necessarily upon one another. Contact inhibition of overlapping, and its loss by invasive cells, can readily be interpreted, by means of the differential adhesion hypothesis, as consequences of cell-type-specific differences in cell-cell and cell-substratum “strengths of adhesion.” These strengths of adhesion are formulated as specific interfacial free energies, which are the only parameters of cellular adhesiveness that have been rigorously shown to determine equilibrium configurations of cell populations.     

Testing of geroprotectors in experiments on cell cultures: Choosing the correct model system

We believe that cytogerontological models, such as the Hayflick model, though very useful for experimental gerontology, are based only on certain correlations and do not directly apply to the gist of the aging process. Thus, the Hayflick limit concept cannot explain why we age, whereas our “stationary phase aging” model appears to be a “gist model,” since it is based on the hypothesis that the main cause of both various “age-related” changes in stationary cell cultures and similar changes in the cells of aging multicellular organism is the restriction of cell proliferation. The model is applicable to experiments on a wide variety of cultured cells, including normal and transformed animal and human cells, plant cells, bacteria, yeasts, mycoplasmas, etc. The results of relevant studies show that cells in this model die out in accordance with the Gompertz law, which describes exponential increase of the death probability with time. Therefore, the “stationary phase aging” model may prove effective in testing of various geroprotectors (anti-aging factors) and geropromoters (pro-aging factors) in cytogerontological experiments. It should be emphasized, however, that even the results of such experiments do not always agree with the data obtained in vivo and therefore cannot be regarded as final but should be verified in studies on laboratory animals and in clinical trials (provided this complies with ethical principles of human subject research).     

Network calisthenics

Stimulation of quiescent mammalian cells with mitogens induces an abrupt increase in E2F1–3 expression just prior to the onset of DNA synthesis, followed by a rapid decline as replication ceases. This temporal adaptation in E2F facilitates a transient pattern of gene expression that reflects the ordered nature of DNA replication. The challenge to understand how E2F dynamics coordinate molecular events required for high-fidelity DNA replication has great biological implications. Indeed, precocious, prolonged, elevated or reduced accumulation of E2F can generate replication stress that culminates in either arrest or death. Accordingly, temporal characteristics of E2F are regulated by several network modules that include feedforward and autoregulatory loops. In this review, we discuss how these network modules contribute to “shaping” E2F dynamics in the context of mammalian cell cycle entry.     

LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma

Many experimental and computational studies have identified key protein coding genes in initiation and progression of esophageal squamous cell carcinoma (ESCC). However, the number of researches that tried to reveal the role of long non-coding RNAs (lncRNAs) in ESCC has been limited. LncRNAs are one of the important regulators of cancers which are transcribed dominantly in the genome and in various conditions. The main goal of this study was to use a systems biology approach to predict novel lncRNAs as well as protein coding genes associated with ESCC and assess their prognostic values. By using microarray expression data for mRNAs and lncRNAs from a large number of ESCC patients, we utilized “Weighted Gene Co-expression Network Analysis” (WGCNA) method to make a big coding-non-coding gene co-expression network, and discovered important functional modules. Gene set enrichment and pathway analysis revealed major biological processes and pathways involved in these modules. After selecting some protein coding genes involved in biological processes and pathways related to cancer, we used “LncTar”, a computational tool to predict potential interactions between these genes and lncRNAs. By combining interaction results with Pearson correlations, we introduced some novel lncRNAs with putative key regulatory roles in the network. Survival analysis with Kaplan-Meier estimator and Log-rank test statistic confirmed that most of the introduced genes are associated with poor prognosis in ESCC. Overall, our study reveals novel protein coding genes and lncRNAs associated with ESCC, along with their predicted interactions. Based on the promising results of survival analysis, these genes can be used as good estimators of patients' survival, or even can be analyzed further as new potential signatures or targets for the therapy of ESCC disease.     

Evolution of the term “cellular senescence” and its impact on the current cytogerontological research

The term “cellular/cell senescence” was first introduced by Leonard Hayflick to describe the “age-related” changes in normal eukaryotic cells during aging in vitro, i.e., over the exhaustion of their mitotic potential. In the “classic” variant, it was assumed that cells “grow old” with the help of some internal mechanism, which leads to accumulation of various macromolecular defects (DNA damage in the first place). Currently, as a rule, “cellular senescence” means accumulation/appearance of particular “biomarkers of aging” in cells (they are most often transformed cells that do not demonstrate any replicative senescence) under the influence of various external factors (oxidative stress, H₂O₂, mitomycin C, ethanol, ionizing radiation, doxorubicin, etc.) that cause DNA damage. This phenomenon has been called DDR (DNA Damage Response). Among the said biomarkers, there are senescence-associated beta-galactosidase activity, expression of p53 and p21 proteins as well as of proteins involved in the regulation of inflammation, such as IL-6 or IL-8, activation of oncogenes, etc. Thus, “aging/senescence” of cells does not occur simply by itself—it takes place because of the influence of DNA-damaging agents. This approach, in my opinion, despite being very important to define a strategy to fight cancer, distracts us, yet again, from the study of the real mechanisms of aging. It should be emphasized that the “stationary phase aging” model developed in my laboratory also allows registering the occurrence of certain biomarkers of aging in cultured cells, but in this case they arise due to the restriction of their proliferation by contact inhibition, i.e., due to a rather physiological impact, which does not cause any damage to cells by itself (the situation is similar to what we observe in a whole multicellular organism).     

Modeling Hermissenda: II. Effects of variations in type-B cell excitability, synaptic strength, and network architecture

Because the Hermissenda eye is relatively simple and its cells well characterized, it provides an attractive preparation for detailed computational analysis. To examine the neural mechanisms of learning in this system, we developed multicompartmental models of the type-A and type-B photoreceptors, simulated the eye, and asked three questions: First, how do conductance changes affect cells in a network as compared with those in isolation; second, what are the relative contributions of increases in B-cell excitability and synaptic strength to network output; and third, how do these contributions vary as a function of network architecture? We found that reductions in the type-B cells of two K⁺ currents, I _A and I _C, differentially affected the type-B cells themselves, with I _C reductions increasing firing rate (excitability) in response to light, and I _A reductions increasing quantal output (synaptic strength) onto postsynaptic targets. Increases in either type-B cell excitability or synaptic strength, induced directly or indirectly, each suppressed A-cell photoresponses, and the combined effect of both changes occurring together was greater than either alone. To examine the effects of network architecture, we compared the full network with a simple feedforward B-A pair and intermediate configurations. Compared with a feedforward pair, the complete network exhibited greater A-cell sensitivity to B-cell changes. This was due to many factors, including an increased number of B-cells (which increased B-cell impact on A-cells), A-B feedback inhibition (which slowed both cell types and altered spike timing relationships), and B-B lateral inhibition (which reduced B-cell sensitivity to intrinsic biophysical modifications). These results suggest that an emergent property of the network is an increase both in the rate of information acquisition (“learning”) and in the amount of information that can be stored (“memory”).     

The effect of inflammatory factors and their inhibitors on the hematopoietic stem cells fate

Inflammatory cytokines exert different effects on hematopoietic stem cells (HSCs), lead to the development of various cell lineages in bone marrow (BM) and are thus a differentiation axis for HSCs. The content used in this article has been obtained by searching PubMed database and Google Scholar search engine of English-language articles (1995–2020) using “Hematopoietic stem cell,” “Inflammatory cytokine,” “Homeostasis,” and “Myelopoiesis.” Inflammatory cytokines are involved in the differentiation and proliferation of hematopoietic progenitors to compensate for cellular death due to inflammation. Since each of these cytokines differentiates HSCs into a specific cell line, the difference in the effect of these cytokines on the fate of HSC progenitors can be predicted. Inhibitors of these cytokines can also control the inflammatory process as well as the cells involved in leukemic conditions. In general, inflammatory signaling can specify the dominant cell line in BM to counteract inflammation and leukemic condition via stimulating or inhibiting hematopoietic progenitors. Therefore, detection of the effects of inflammatory cytokines on the differentiation of HSCs can be an appropriate approach to check inflammatory and leukemic conditions and the suppression of these cytokines by their inhibitors allows for control of homeostasis in stressful conditions.     

Computer modeling of mechanisms of the information processing in the olfactory bulb. I. Model of the structure-functional organizations of neuronal elements in the olfactory bulb and receptor epithelium

A computer model of the olfactory bulb was constructed. The paper describes: 1) the general architecture of a model neuron network that reflects the neurophysiological experimental and theoretical data on the structural and functional organization of the peripheral part of the olfactory system, the olfactory bulb with inputs from olfactory receptor neurons; 2) the organization of each of three levels of the model: receptors, olfactory glomeruli, and basic neurons; and 3) a scenario of the computer model work. In some aspects, in particular, in the principle of information presentation, the treatment of the role of basic neurons (mitral and tufted cells), and their interrelations in modules, the model favorably differs from the available olfactory bulb models. The model is basic and provides further refinement of the architecture, an increase in the number of modules, and the modeling of the learning process.