Showing papers in "BioSystems in 2001"

The physics of symbols: bridging the epistemic cut.

Abstract: Evolution requires the genotype–phenotype distinction, a primeval epistemic cut that separates energy-degenerate, rate-independent genetic symbols from the rate-dependent dynamics of construction that they control. This symbol–matter or subject–object distinction occurs at all higher levels where symbols are related to a referent by an arbitrary code. The converse of control is measurement in which a rate-dependent dynamical state is coded into quiescent symbols. Non-integrable constraints are one necessary condition for bridging the epistemic cut by measurement, control, and coding. Additional properties of heteropolymer constraints are necessary for biological evolution.

Biocomplexity: adaptive behavior in complex stochastic dynamical systems.

Abstract: Existing methods of complexity research are capable of describing certain specifics of bio systems over a given narrow range of parameters but often they cannot account for the initial emergence of complex biological systems, their evolution, state changes and sometimes-abrupt state transitions. Chaos tools have the potential of reaching to the essential driving mechanisms that organize matter into living substances. Our basic thesis is that while established chaos tools are useful in describing complexity in physical systems, they lack the power of grasping the essence of the complexity of life. This thesis illustrates sensory perception of vertebrates and the operation of the vertebrate brain. The study of complexity, at the level of biological systems, cannot be completed by the analytical tools, which have been developed for non-living systems. We propose a new approach to chaos research that has the potential of characterizing biological complexity. Our study is biologically motivated and solidly based in the biodynamics of higher brain function. Our biocomplexity model has the following features, (1) it is high-dimensional, but the dimensionality is not rigid, rather it changes dynamically; (2) it is not autonomous and continuously interacts and communicates with individual environments that are selected by the model from the infinitely complex world; (3) as a result, it is adaptive and modifies its internal organization in response to environmental factors by changing them to meet its own goals; (4) it is a distributed object that evolves both in space and time towards goals that is continually re-shaping in the light of cumulative experience stored in memory; (5) it is driven and stabilized by noise of internal origin through self-organizing dynamics. The resulting theory of stochastic dynamical systems is a mathematical field at the interface of dynamical system theory and stochastic differential equations. This paper outlines several possible avenues to analyze these systems. Of special interest are input-induced and noise-generated, or spontaneous state-transitions and related stability issues.

Toward an integrated continuum model of cerebral dynamics: the cerebral rhythms, synchronous oscillation and cortical stability.

Abstract: Continuum models of cerebral cortex with parameters derived from physiological data, provide explanations of the cerebral rhythms, synchronous oscillation, and autonomous cortical activity in the gamma frequency range, and suggest possible mechanisms for dynamic self-organization in the brain. Dispersion relations and derivations of power spectral response for the models, show that a low frequency resonant mode and associated travelling wave solutions of the models' equations of state can account for the predominant 1/f spectral content of the electroencephalogram (EEG). Large scale activity in the alpha, beta, and gamma bands, is accounted for by thalamocortical interaction, under regulation by diffuse cortical excitation. System impulse responses can be used to model Event-Related Potentials. Further classes of local resonance may be generated by rapid negative feedbacks at active synapses. Activity in the gamma band around 40 Hz, associated with large amplitude oscillations of pulse density, appears at higher levels of cortical activation, and is unstable unless compensated by synaptic feedbacks. Control of cortical stability by synaptic feedbacks offers a partial account of the regulation of autonomous activity within the cortex. Synchronous oscillation occurs between concurrently excited cortical sites, and can be explained by analysis of wave motion radiating from each of the co-active sites. These models are suitable for the introduction of learning rules-most notably the coherent infomax rule.

From cells to computers: computing with membranes (P systems).

Abstract: The aim of this paper is to introduce to the reader the main ideas of computing with membranes, a recent branch of (theoretical) molecular computing. In short, in a cell-like system, multisets of objects evolve according to given rules in the compartments defined by a membrane structure and compute natural numbers as the result of halting sequences of transitions. The model is parallel, nondeterministic. Many variants have already been considered and many problems about them were investigated. We present here some of these variants, focusing on two central classes of results: (1) characterizations of the recursively enumerable sets of numbers and (2) possibilities to solve NP-complete problems in polynomial — even linear — time (of course, by making use of an exponential space). The results are given without proofs. An almost complete bibliography of the domain, at the middle of October 2000, is also provided.

The cortico-basal ganglia-thalamocortical circuit with synaptic plasticity. II. Mechanism of synergistic modulation of thalamic activity via the direct and indirect pathways through the basal ganglia.

Abstract: A possible mechanism underlying the modulatory role of dopamine, adenosine and acetylcholine in the modification of corticostriatal synapses, subsequent changes in signal transduction through the ‘direct’ and ‘indirect’ pathways in the basal ganglia and variations in thalamic and neocortical cell activity is proposed. According to this mechanism, simultaneous activation of dopamine D1:D2 receptors as well as inactivation of adenosine A1:A2A receptors or muscarinic M4:M1 receptors on striatonigral:striatopallidal inhibitory cells can promote the induction of long-term potentiation:depression in the efficacy of excitatory cortical inputs to these cells. Subsequently augmented inhibition of the activity of inhibitory neurons of the output nuclei of the basal ganglia through the ‘direct’ pathway together with reduced disinhibition of these nuclei through the ‘indirect’ pathway synergistically increase thalamic and neocortical cell firing. The proposed mechanism can underlie such well known effects as ‘excitatory’ and ‘inhibitory’ influence of dopamine on striatonigral and striatopallidal cells, respectively; the opposite action of dopamine and adenosine on these cells; antiparkinsonic effects of dopamine receptor agonists and adenosine or acetylcholine muscarinic receptor antagonists. © 2001 Elsevier Science Ireland Ltd. All rights reserved.

Changes in power and coherence of brain activity in human sensorimotor cortex during performance of visuomotor tasks.

Abstract: Electrocorticograms (ECoG) were recorded using subdural grid electrodes in forearm sensorimotor cortex of six human subjects. The subjects performed three visuomotor tasks, tracking a moving visual target with a joystick-controlled cursor; threading pieces of tubing; and pinching the fingers sequentially against the thumb. Control conditions were resting and active wrist extension. ECoGs were recorded at 14 sites in hand- and arm-sensorimotor area, functionally identified with electrical stimulation. For each behavior we computed spectral power of ECoG in each site and coherence in all pair-wise sites. In three out of six subjects, gamma-oscillations were observed when the subjects started the tasks. All subjects showed widespread power decrease in the range of 11-20 Hz and power increase in the 31-60 Hz ranges during performance of the visuomotor tasks. The changes in gamma-range power were more vigorous during the tracking and threading tasks compared with the wrist extension. Coherence analysis also showed similar task-related changes in coherence estimates. In contrast to the power changes, coherence estimates increased not only in gamma-range but also at lower frequencies during the manipulative visuomotor tasks. Paired sites with significant increases in coherence estimates were located within and between sensory and motor areas. These results support the hypothesis that coherent cortical activity may play a role in sensorimotor integration or attention.

Rhythmic and non-rhythmic attractors in asynchronous random Boolean networks

Abstract: In multi-component, discrete systems, such as Boolean networks and cellular automata, the scheme of updating of the individual elements plays a crucial role in determining their dynamic properties and their suitability as models of complex phenomena. Many interesting properties of these systems rely heavily on the use of synchronous updating of the individual elements. Considerations of parsimony have motivated the claim that, if the natural systems being modelled lack any clear evidence of synchronously driven elements, then random asynchronous updating should be used by default. The introduction of a random element precludes the possibility of strictly cyclic behaviour. In principle, this poses the question of whether asynchronously driven Boolean networks, cellular automata, etc., are inherently bad choices at the time of modelling rhythmic phenomena. This paper focuses on this subsidiary issue for the case of Asynchronous Random Boolean Networks (ARBNs). It defines measures of pseudo-periodicity between states and sufficiently relaxed statistical constraints. These measures are used to guide a genetic algorithm to find appropriate examples. Success in this search for a number of cases, and the subsequent statistical analysis lead to the conclusion that ARBNs can indeed be used as models of co-ordinated rhythmic phenomena, which may be stronger precisely because of their in-built asynchrony. The same technique is used to find non-stationary attractors that show no rhythm. Evidence suggests that the latter are more abundant than rhythmic attractor. The methodology is flexible, and allows for more demanding statistical conditions for defining pseudo-periodicity, and constraining the evolutionary search.

Symbols and dynamics in the brain.

Abstract: The work of physicist and theoretical biologist Howard Pattee has focused on the roles that symbols and dynamics play in biological systems. Symbols, as discrete functional switching-states, are seen at the heart of all biological systems in the form of genetic codes, and at the core of all neural systems in the form of informational mechanisms that switch behavior. They also appear in one form or another in all epistemic systems, from informational processes embedded in primitive organisms to individual human beings to public scientific models. Over its course, Pattee's work has explored (1) the physical basis of informational functions (dynamical vs. rule-based descriptions, switching mechanisms, memory, symbols), (2) the functional organization of the observer (measurement, computation), (3) the means by which information can be embedded in biological organisms for purposes of self-construction and representation (as codes, modeling relations, memory, symbols), and (4) the processes by which new structures and functions can emerge over time. We discuss how these concepts can be applied to a high-level understanding of the brain. Biological organisms constantly reproduce themselves as well as their relations with their environs. The brain similarly can be seen as a self-producing, self-regenerating neural signaling system and as an adaptive informational system that interacts with its surrounds in order to steer behavior.

Optically programming DNA computing in microflow reactors.

Abstract: The programmability and the integration of biochemical processing protocols are addressed for DNA computing using photochemical and microsystem techniques. A magnetically switchable selective transfer module (STM) is presented which implements the basic sequence-specific DNA filtering operation under constant flow. Secondly, a single steady flow system of STMs is presented which solves an arbitrary instance of the maximal clique problem of given maximum size N. Values of N up to about 100 should be achievable with current lithographic techniques. The specific problem is encoded in an initial labeling pattern of each module with one of 2N DNA oligonucleotides, identical for all instances of maximal clique. Thirdly, a method for optically programming the DNA labeling process via photochemical lithography is proposed, allowing different problem instances to be specified. No hydrodynamic switching of flows is required during operation — the STMs are synchronously clocked by an external magnet. An experimental implementation of this architecture is under construction and will be reported elsewhere.

Bifurcation structure of a model of bursting pancreatic cells

Abstract: One- and two-dimensional bifurcation studies of a prototypic model of bursting oscillations in pancreatic -cells reveal a squid-formed area of chaotic dynamics in the parameter plane, with period-doubling bifurcations on one side of the arms and saddle-node bifurcations on the other. The transition from this structure to the so-called period-adding structure is found to involve a subcritical period-doubling bifurcation and the emergence of type-III intermittency. The period-adding transition itself is not smooth but consists of a saddle-node bifurcation in which (n+1)-spike bursting behavior is born, slightly overlapping with a subcritical period-doubling bifurcation in which n-spike bursting behavior loses its stability. © 2001 Elsevier Science Ireland Ltd. All rights reserved.

Evolution with material symbol systems

Abstract: Pattee's semantic closure principle is used to study the characteristics and requirements of evolving material symbols systems. By contrasting agents that reproduce via genetic variation with agents that reproduce via self-inspection, we reach the conclusion that symbols are necessary to attain open-ended evolution, but only if the phenotypes of agents are the result of a material, self-organization process. This way, a study of the inter-dependencies of symbol and matter is presented. This study is based first on a theoretical treatment of symbolic representations, and secondly on simulations of simple agents with matter-symbol inter-dependencies. The agent-based simulations use evolutionary algorithms with indirectly encoded phenotypes. The indirect encoding is based on Fuzzy Development programs, which are procedures for combining fuzzy sets in such a way as to model self-organizing development processes.

An improved surface-based method for DNA computation

Abstract: DNA computing is a novel method for solving a class of intractable computational problems, in which the computing time can grow exponentially with problem size. Up to now, many accomplishments have been achieved to improve its performance and increase its reliability, among which a surface-based method is an efficient candidate. In this paper, the surface-based approach proposed by Liu, Q., Wang, L., Frutos, A.G., Condon, A.E., Corn, R.M., and Smith, L.M., 2000, DNA computing on surfaces. Nature 403, 175-179 is analyzed and an improved surface-based method for DNA computation (i.e. the hybrid DNA/optical computing method) is proposed. Compared with Liu et al.'s approach, our method has some significant advantages such as low cost, short operating time, reusable surface and simple experimental steps. Moreover, the concept of combining easily patterned DNA computing steps with equally parallel, but generally uniform and not easily patterned optical computing steps is an important new direction.

Computational models of collective foraging

Abstract: In this paper the behaviour of a bee colony is modelled as society of communicating agents acting in parallel and synchronising their behaviour. Two computational approaches for defining the agents behaviour are introduced and compared. Their common features as well as the complementary aspects making them suitable for merging together into a more complex model.

A single-fractal analysis of cellular analyte-receptor binding kinetics utilizing biosensors.

Abstract: A fractal analysis of a confirmative nature only is presented for cellular analyte–receptor binding kinetics utilizing biosensors. Data taken from the literature can be modeled by using a single-fractal analysis. Relationships are presented for the binding rate coefficient as a function of the fractal dimension and for the analyte concentration in solution. In general, the binding rate coefficient is rather sensitive to the degree of heterogeneity that exists on the biosensor surface. It is of interest to note that examples are presented where the binding coefficient, k exhibits an increase as the fractal dimension (Df) or the degree of heterogeneity increases on the surface. The predictive relationships presented provide further physical insights into the binding reactions occurring on the surface. These should assist in understanding the cellular binding reaction occurring on surfaces, even though the analysis presented is for the cases where the cellular ‘receptor’ is actually immobilized on a biosensor or other surface. The analysis suggests possible modulations of cell surfaces in desired directions to help manipulate the binding rate coefficient (or affinity). In general, the technique presented is applicable for the most part to other reactions occurring on different types of biosensor or other surfaces.

Noise-induced impulse pattern modifications at different dynamical period-one situations in a computer model of temperature encoding.

Abstract: We used a minimal Hodgkin–Huxley type model of cold receptor discharges to examine how noise interferes with the non-linear dynamics of the ionic mechanisms of neuronal stimulus encoding. The model is based on the assumption that spike-generation depends on subthreshold oscillations. With physiologically plausible temperature scaling, it passes through different impulse patterns which, with addition of noise, are in excellent agreement with real experimental data. The interval distributions of purely deterministic simulations, however, exhibit considerable differences compared to the noisy simulations especially at the bifurcations of deterministically period-one discharges. We, therefore, analyzed the effects of noise in different situations of deterministically regular period-one discharges: (1) at high-temperatures near the transition to subthreshold oscillations and to burst discharges, and (2) at low-temperatures close to and more far away from the bifurcations to chaotic dynamics. The data suggest that addition of noise can considerably extend the dynamical behavior of the system with coexistence of different dynamical situations at deterministically fixed parameter constellations. Apart from well-described coexistence of spike-generating and subthreshold oscillations also mixtures of tonic and bursting patterns can be seen and even transitions to unstable period-one orbits seem to appear. The data indicate that cooperative effects between low- and high-dimensional dynamics have to be considered as qualitatively important factors in neuronal encoding.

Knowledge representation and qualitative simulation of salmon redd functioning. Part I: qualitative modeling and simulation.

Abstract: This work aims at representing empirical knowledge of freshwater ecologists on the functioning of salmon redds (spawning areas of salmon) and its impact on mortality of early stages. For this, we use Qsim, a qualitative simulator. In this first part, we provide unfamiliar readers with the underlying qualitative differential equation (QDE) ontology of Qsim: representing quantities, qualitative variables, qualitative constraints, QDE structure. Based on a very simple example taken of the salmon redd application, we show how informal biological knowledge may be represented and simulated using an approach that was first intended to analyze qualitatively ordinary differential equations systems. A companion paper (Part II) gives the full description and simulation of the salmon redd qualitative model. This work was part of a project aimed at assessing the impact of the environment on salmon populations dynamics by the use of models of processes acting at different levels: catchment, river, and redds. Only the latter level is dealt with in this paper.

Origins of life: a route to nanotechnology.

Abstract: The origins of life and nanotechnology are two seemingly disparate areas of scientific investigation. However, the fundamental questions of life's beginnings and the applied construction of a Drexlerian nanotechnology both share a similar problem; how did and how can self-reproducing molecular machines originate? Here we draw attention to the coincidence between nanotechnology and origins research with particular attention paid to the spontaneous adsorption and scanning tunneling microscopy investigation of purine and pyrimidine bases self-organized into monolayers, adsorbed to the surfaces of crystalline solids. These molecules which encode biological information in nucleic acids, can form supramolecular architectures exhibiting enantiomorphism with the complexity to store and encode putative protobiological information. We conclude that the application of nanotechnology to the investigation of life's origins, and vice versa, could provide a viable route to an evolution–driven synthetic life.

Life and reference

Abstract: The paper recommends a broadening of Howard Pattee's seminal distinction between a dynamic and a linguistic mode of living systems. It is observed that even the dynamic mode is always a semiotic mode although indexical and analogically coded rather than symbolic and digitally coded. The analogically coded messages corresponds to a kind of tacit knowledge hidden in macromolecular structure and shape (e.g. molecular complementarity) and in organismic architecture and communication, i.e. in the semiotic interactions of the body. It is claimed that the origin of referential processes is tied to the flow of historical singularities. The function of analog and digital codes in evolutionary systems is discussed.

Autocatalysis, information and coding.

Abstract: Autocatalytic self-construction in macromolecular systems requires the existence of a reflexive relationship between structural components and the functional operations they perform to synthesise themselves. The possibility of reflexivity depends on formal, semiotic features of the catalytic structure–function relationship, that is, the embedding of catalytic functions in the space of polymeric structures. Reflexivity is a semiotic property of some genetic sequences. Such sequences may serve as the basis for the evolution of coding as a result of autocatalytic self-organisation in a population of assignment catalysts. Autocatalytic selection is a mechanism whereby matter becomes differentiated in primitive biochemical systems. In the case of coding self-organisation, it corresponds to the creation of symbolic information. Prions are present-day entities whose replication through autocatalysis reflects aspects of biological semiotics less obvious than genetic coding.

Variability and coding efficiency of noisy neural spike encoders.

Abstract: Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders — an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na+ and K+ channels obeying Hodgkin–Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones.

Single-file diffusion and neurotransmitter transporters: Hodgkin and Keynes model revisited.

Abstract: Norepinephrine transporters (NETs) use the Na gradient to remove norepinephrine (NE) from the synaptic cleft of adrenergic neurons following NE release from the presynaptic terminal. By coupling NE to the inwardly directed Na gradient, it is possible to concentrate NE inside cells. This mechanism, which is referred to as co-transport or secondary transport (Lauger, 1991, Electrogenic Ion Pumps, Sinauer Associates) is apparently universal: Na coupled transport applies to serotonin transporters (SERTs), dopamine transporters (DATs), glutamate transporters, and many others, including transporters for osmolites, metabolites and substrates such as sugar. Recently we have shown that NETs and SERTs transport norepinephrine or serotonin as if Na and the transmitter permeated through an ion channel together ‘Galli et al., 1998, PNAS 95, 13260–13265; Petersen and DeFelice, 1999, Nature Neurosci. 2, 605–610’. These data are paradoxical because it has been difficult to envisage how NE, for example, would couple to Na if these ions move passively through an open pore. An ‘alternating access’ model is usually evoked to explain coupling: in such models NE and Na bind to NET, which then undergoes a conformational change to release NE and Na on the inside. The empty transporter then turns outward to complete the cycle. Alternating-access models never afford access to an open channel. Rather, substrates and co-transported ions are occluded in the transporter and carried across the membrane. The coupling mechanism we propose is fundamentally different than the coupling mechanism evoked in the alternating access model. To explain coupling in co-transporters, we use a mechanism first evoked by ‘Hodgkin and Keynes (1955) J. Physiol. 128, 61–88’ to explain ion interactions in K-selective channels. In the Hodgkin and Keynes model, K ions move single-file through a long narrow pore. Their model accounted for the inward/outward flux ratio if they assumed that two K ions queue within the pore. We evoke a similar model for the co-transport of transmitter and Na. In our case, however, coupling occurs not only between like ions but also between unlike ions (i.e. the transmitter and Na ). We made a replica of the Hodgkin and Keynes mechanical model to test our ideas, and we extended the model with computer simulations using Monte Carlo methods. We also developed an analytic formula for Na coupled co-transport that is analogous to the single-file Ussing equation for channels. The model shows that stochastic diffusion through a long narrow pore can explain coupled transport. The length of the pore amplifies the Na gradient that drives co-transport.

Howard Pattee's theoretical biology--a radical epistemological stance to approach life, evolution and complexity.

Abstract: This paper offers a short review of Pattee's main contributions to science and philosophy. With no intention of being exhaustive, an account of Pattee's work is presented which discusses some of his ideas and their reception. This is done through an analysis centered in what is thought to be his main contribution: the elaboration of an internal epistemic stance to better understand life, evolution and complexity. Having introduced this core idea as a sort of a posteriori cohesive element of a complex but highly coherent and complete system of thinking, further specific elements are also reviewed.

Knowledge representation and qualitative simulation of salmon redd functioning. Part II: qualitative model of redds.

Abstract: This paper describes a qualitative model of the functioning of salmon redds (spawning areas of salmon) and its impact on mortality rates of early stages. For this, we use Qsim, a qualitative simulator, which appeared adequate for representing available qualitative knowledge of freshwater ecology experts (see Part I of this paper). Since the number of relevant variables was relatively large, it appeared necessary to decompose the model into two parts, corresponding to processes occurring at separate time-scales. A qualitative clock allows us to submit the simulation of salmon developmental stages to the calculation of accumulated daily temperatures (degree-days), according to the clock ticks and a water temperature regime set by the user. Therefore, this introduces some way of real-time dating and duration in a purely qualitative model. Simulating both sub-models, either separately or by means of alternate transitions, allows us to generate the evolutions of variables of interest, such as the mortality rates according to two factors (flow of oxygenated water and plugging of gravel interstices near the bed surface), under various scenarios.

Self-description and the origin of the genetic code

Abstract: The genetic code presents an important conceptual challenge within the broader context of the origin of life. Translation of genetic information captures a fundamental property of living systems, i.e. the ability of decoding proteins (e.g. aminoacyl-tRNA synthetases) to reproduce themselves from self-contained RNA/DNA descriptors. Silvano Colombano and I, as graduate students with Howard Pattee in the 1970s, focused on achieving this closure of self-description and self-reproduction in the genetic code. Simulation and analysis of competitive models that allowed alternate code assignments, exploring initial conditions, arbitrary descriptor-catalyst relationships, and degree of non-linearity, indicated that these dynamical systems undergo bifurcations, transforming initial ambiguous stable states to unstable states. New, stable, steady states, progressively closer to a code, became available as the descriptor parameters were varied. The efficiency of utilization of raw materials for the production of a coding family of catalysts is proposed as a selection criterion that drives such systems towards a coded state.

Modeling insulin kinetics: responses to a single oral glucose administration or ambulatory-fed conditions.

Abstract: This paper presents a nonlinear mathematical model of the glucose-insulin feedback system, which has been extended to incorporate the beta-cells' function on maintaining and regulating plasma insulin level in man. Initially, a gastrointestinal absorption term for glucose is utilized to effect the glucose absorption by the intestine and the subsequent release of glucose into the bloodstream, taking place at a given initial rate and falling off exponentially with time. An analysis of the model is carried out by the singular perturbation technique in order to derive boundary conditions on the system parameters which identify, in particular, the existence of limit cycles in our model system consistent with the oscillatory patterns often observed in clinical data. We then utilize a sinusoidal term to incorporate the temporal absorption of glucose in order to study the responses in the patients under ambulatory-fed conditions. A numerical investigation is carried out in this case to construct a bifurcation diagram to identify the ranges of parametric values for which chaotic behavior can be expected, leading to interesting biological interpretations.

Multiple-vector self-adaptation in evolutionary algorithms.

Abstract: Self-adaptation is a common method for learning online control parameters in an evolutionary algorithm. In one common implementation, each individual in the population is represented as a pair of vectors ( x , σ ), where x is the candidate solution to an optimization problem scored in terms of f ( x ), and σ is the so-called strategy parameter vector that influences how offspring will be created from the individual. Experimental evidence suggests that the elements of σ can sometimes become too small to explore the given response surface adequately. The evolutionary search then stagnates, until the elements of σ grow sufficiently large as a result of random variation. A potential solution to this deficiency associates multiple strategy parameter vectors with a single individual. A single strategy vector is active at any time and dictates how offspring will be generated. Experiments are conducted on four 10-dimensional benchmark functions where the number of strategy parameter vectors is varied over 1, 2, 3, 4, 5, 10, and 20. The results indicate advantages for using multiple strategy parameter vectors. Furthermore, the relationship between the mean best result after a fixed number of generations and the number of strategy parameter vectors can be determined reliably in each case.

Morphometric modeling of olfactory circuits in the insect antennal lobe: I. Simulations of spiking local interneurons

Abstract: Inhibitory local interneurons (LNs) play a critical role in shaping the output of olfactory glomeruli in both the olfactory bulb of vertebrates and the antennal lobe of insects and other invertebrates. In order to examine how the complex geometry of LNs may affect signaling in the antennal lobe, we constructed detailed multi-compartmental models of single LNs from the sphinx moth, Manduca sexta, using morphometric data from confocal-microscopic images. Simulations clearly revealed a directionality in LNs that impeded the propagation of injected currents from the sub-micron-diameter glomerular dendrites toward the much larger-diameter integrating segment (IS) in the coarse neuropil. Furthermore, the addition of randomly-firing synapses distributed across the LN dendrites (simulating the noisy baseline activity of afferent input recorded from LNs in the odor-free state) led to a significant depolarization of the LN. Thus the background activity typically recorded from LNs in vivo could influence synaptic integration and spike transformation in LNs through voltage-dependent mechanisms. Other model manipulations showed that active currents inserted into the IS can help synchronize the activation of inhibitory synapses in glomeruli across the antennal lobe. These data, therefore, support experimental findings suggesting that spiking inhibitory LNs can operate as multifunctional units under different ambient odor conditions. At low odor intensities, (i.e. subthreshold for IS spiking), they participate in local, mostly intra-glomerular processing. When activated by elevated odor concentrations, however, the same neurons will fire overshooting action potentials, resulting in the spread of inhibition more globally across the antennal lobe. Modulation of the passive and active properties of LNs may, therefore, be a deciding factor in defining the multi-glomerular representations of odors in the brain.

The semiotics of control and modeling relations in complex systems.

Abstract: We provide a conceptual analysis of ideas and principles from the systems theory discourse which underlie Pattee's semantic or semiotic closure, which is itself foundational for a school of theoretical biology derived from systems theory and cybernetics, and is now being related to biological semiotics and explicated in the relational biological school of Rashevsky and Rosen. Atomic control systems and models are described as the canonical forms of semiotic organization, sharing measurement relations, but differing topologically in that control systems are circularly and models linearly related to their environments. Computation in control systems is introduced, motivating hierarchical decomposition, hybrid modeling and control systems, and anticipatory or model-based control. The semiotic relations in complex control systems are described in terms of relational constraints, and rules and laws are distinguished as contingent and necessary functional entailments, respectively. Finally, selection as a meta-level of constraint is introduced as the necessary condition for semantic relations in control systems and models.

The internalist enterprise on constructing cell motility in a bottom-up manner.

Abstract: Quantum coherence in the biological realm is constructed internally in a bottom-up manner. In particular, an actin filament sliding on myosin molecules in the presence of ATP to be hydrolyzed as a functional unit of muscle contraction exhibits magnetization as a marker of quantum coherence. The uniqueness of quantum coherence in biology is found in precipitating synchronous time in interaction from the interacting energy quanta, each of which has carried with itself synchronous time unique to the quantum in isolation. It exhibits a marked contrast to quantum coherence met in low temperature physics, in the latter of which no transformation of the nature of synchronous time is entertained.

From complexity to simplicity: nature and symbols.

Abstract: This paper reviews Pattee's ideas about the symbolic domain as a phenomenon related to the self-simplifying processes of certain hierarchical systems, such as the living. We distinguish the concepts of constraint, record, and symbol to explain how the Semantic Closure Principle, that is to say, the view that symbols are self-interpreted by the cell, emerges. Related to this, the notion of complementarity is discussed both as an epistemological and as an ontological principle. In the final discussion we consider whether autonomous systems can exist in which constraints are not symbolically preserved, and if biological symbols can be considered to have a descriptive nature.