Phytoplankton assemblages and their periodicity in stratifying lake systems

A subjective analysis of the seasonal periodicity of phytoplankton populations in several natural lakes and experimental lake systems (Lund Tubes) has shown that the direction and patterns of change are both general and predictable. The paper distinguishes between autogenic successional changes, consistent with increasing community complexity and segregation, and allogenic changes resulting from turbulent disruption of the stratified system. The periodicity of the assemblages recognized is resolved through the interaction of two major variables – nutrient availability and column stability. A simple possibility matrix is proposed which can account for the observed changes in community structure. The principal pathways in eutrophic lakes (diatoms → Volcocales → Nostocales → dinoflagellates or Microcystis , with reversions through 'summer diatom'assemblages) and in mesotrophic lakes (diatoms → Chrysophyte/Sphaerocystis → dinoflagellates, with reversions through 'summer diatom-desmid'assemblages) are generally consistent with the growth and survival strategies of the principal algal species concerned.     

Dose-response aligned circuits in signaling systems

Cells use biological signal transduction pathways to respond to environmental stimuli and the behavior of many cell types depends on precise sensing and transmission of external information. A notable property of signal transduction that was characterized in the Saccharomyces cerevisiae yeast cell and many mammalian cells is the alignment of dose-response curves. It was found that the dose response of the receptor matches closely the dose responses of the downstream. This dose-response alignment (DoRA) renders equal sensitivities and concordant responses in different parts of signaling system and guarantees a faithful information transmission. The experimental observations raise interesting questions about the nature of the information transmission through DoRA signaling networks and design principles of signaling systems with this function. Here, we performed an exhaustive computational analysis on network architectures that underlie the DoRA function in simple regulatory networks composed of two and three enzymes. The minimal circuits capable of DoRA were examined with Michaelis-Menten kinetics. Several motifs that are essential for the dynamical function of DoRA were identified. Systematic analysis of the topology space of robust DoRA circuits revealed that, rather than fine-tuning the network's parameters, the function is primarily realized by enzymatic regulations on the controlled node that are constrained in limiting regions of saturation or linearity.     

Chaos in multi-looped negative feedback systems

Non-linear control systems with multiple negative feedback loops display periodicity, quasiperiodicity and period-doubling bifurcations leading to chaos. The possibility that normal fluctuations in physiological control may result from deterministic chaos in multi-looped negative feedback systems is discussed.     

Evolving digital circuits using hybrid particle swarm optimization and differential evolution

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher.     

Phase control of resonant systems: interference, chaos and high periodicity

Much progress has been made in understanding the effect of periodic forcing on epidemiological and ecological systems when that forcing acts on just one part of the system. Much less is known about situations in which several parts of the system are affected. In this case the interaction between the impacts of the different forcing components can lead to reinforcement of system responses or to their interference. This interference phenomenon is significant if some forcing components are anthropogenic for then management might be able to exercise sufficient control to bring about suppression of undesirable aspects of the forcing, for example resonant amplification and the problems this can cause. We set out the algebraic theory when forcing is weak and illustrate by example what can happen when forcing is strong enough to create subharmonics and chaotic states. Phase is the key control variable that can bring about interference, advantageously shift nonlinear response curves and create periodic states out of chaos. The phenomenon in which high period fluctuations appear to be generated by low period forcing is examined and different mechanisms compared in a two-strain epidemiological model. The effect of noise as a source of high period fluctuations is also considered.     

Roles of positive and negative feedback in biological systems

We discuss the influence of positive and negative feedback on the stability of a system, which is not clear-cut, and involves complex, mathematical problems. We show in particular that positive feedback can have a stabilising effect on some systems. We also point out the role that positive feedback plays in the digital treatment of signals required by cellular signalling, drawing on analogies from electronics, and the role that negative feedback plays in making a system robust against alteration of its parameters. Both positive and negative feedback can be seen as important enhancers of the properties of biological systems.     

Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology

Drugs and food exert their reinforcing effects in part by increasing dopamine (DA) in limbic regions, which has generated interest in understanding how drug abuse/addiction relates to obesity. Here, we integrate findings from positron emission tomography imaging studies on DA's role in drug abuse/addiction and in obesity and propose a common model for these two conditions. Both in abuse/addiction and in obesity, there is an enhanced value of one type of reinforcer (drugs and food, respectively) at the expense of other reinforcers, which is a consequence of conditioned learning and resetting of reward thresholds secondary to repeated stimulation by drugs (abuse/addiction) and by large quantities of palatable food (obesity) in vulnerable individuals (i.e. genetic factors). In this model, during exposure to the reinforcer or to conditioned cues, the expected reward (processed by memory circuits) overactivates the reward and motivation circuits while inhibiting the cognitive control circuit, resulting in an inability to inhibit the drive to consume the drug or food despite attempts to do so. These neuronal circuits, which are modulated by DA, interact with one another so that disruption in one circuit can be buffered by another, which highlights the need of multiprong approaches in the treatment of addiction and obesity.     

Visualizing circuits and systems using transgenic reporters of neural activity

Genetically encoded sensors of neural activity enable visualization of circuit-level function in the central nervous system. Although our understanding of the molecular events that regulate neuronal firing, synaptic function, and plasticity has expanded rapidly over the past 15 years, an appreciation for how cellular changes are functionally integrated at the circuit level has lagged. A new generation of tools that employ fluorescent sensors of neural activity promises unique opportunities to bridge the gap between cellular level and system level analysis. This review will focus on genetically encoded sensors. A primary advantage of these indicators is that they can be nonselectively introduced to large populations of cells using either transgenic-mediated or viral-mediated approaches. This ability removes the nontrivial obstacles of how to get chemical indicators into cells of interest, a problem that has dogged investigators who have been interested in mapping neural function in the intact CNS. Five different types of approaches and their relative utility will be reviewed here: first, reporters of immediate-early gene (IEG) activation using promoters such as c-fos and arc; second, voltage-based sensors, such as GFP-coupled Na+ and K+ channels; third, Cl*-based sensors; fourth, Ca2+-based sensors, such as Camgaroo and the troponin-based TN-L15; and fifth, pH-based sensors, which have been particularly useful for examining synaptic activity of highly convergent afferents in sensory systems in vivo. Particular attention will be paid to reporters of IEG expression, because these tools employ the built-in threshold function that occurs with activation of gene expression, provoking new experimental questions by expanding the timescale of analysis for circuit-level and system-level functional mapping.     

Stable oscillations in mathematical models of biological control systems

Summary Oscillations in a class of piecewise linear (PL) equations which have been proposed to model biological control systems are considered. The flows in phase space determined by the PL equations can be classified by a directed graph, called a state transition diagram, on anN-cube. Each vertex of theN-cube corresponds to an orthant in phase space and each edge corresponds to an open boundary between neighboring orthants. If the state transition diagram contains a certain configuration called a cyclic attractor, then we prove that for the associated PL equation, all trajectories in the regions of phase space corresponding to the cyclic attractor either (i) approach a unique stable limit cycle attractor, or (ii) approach the origin, in the limitt→∞. An algebraic criterion is given to distinguish the two cases. Equations which can be used to model feedback inhibition are introduced to illustrate the techniques.     

Adaptive properties of differential learning rates for positive and negative outcomes

The concept of the reward prediction error—the difference between reward obtained and reward predicted—continues to be a focal point for much theoretical and experimental work in psychology, cognitive science, and neuroscience. Models that rely on reward prediction errors typically assume a single learning rate for positive and negative prediction errors. However, behavioral data indicate that better-than-expected and worse-than-expected outcomes often do not have symmetric impacts on learning and decision-making. Furthermore, distinct circuits within cortico-striatal loops appear to support learning from positive and negative prediction errors, respectively. Such differential learning rates would be expected to lead to biased reward predictions and therefore suboptimal choice performance. Contrary to this intuition, we show that on static “bandit” choice tasks, differential learning rates can be adaptive. This occurs because asymmetric learning enables a better separation of learned reward probabilities. We show analytically how the optimal learning rate asymmetry depends on the reward distribution and implement a biologically plausible algorithm that adapts the balance of positive and negative learning rates from experience. These results suggest specific adaptive advantages for separate, differential learning rates in simple reinforcement learning settings and provide a novel, normative perspective on the interpretation of associated neural data.     

Coupled positive and negative feedback circuits form an essential building block of cellular signaling pathways

Cellular circuits have positive and negative feedback loops that allow them to respond properly to noisy external stimuli. It is intriguing that such feedback loops exist in many cases in a particular form of coupled positive and negative feedback loops with different time delays. As a result of our mathematical simulations and investigations into various experimental evidences, we found that such coupled feedback circuits can rapidly turn on a reaction to a proper stimulus, robustly maintain its status, and immediately turn off the reaction when the stimulus disappears. In other words, coupled feedback loops enable cellular systems to produce perfect responses to noisy stimuli with respect to signal duration and amplitude. This suggests that coupled positive and negative feedback loops form essential signal transduction motifs in cellular signaling systems.     

The triality principle as a possible cause of the periodicity of evolving systems

Evolution proceeds in phases, alternatingly convergent and divergent. During the divergent phases, many variants of an evolutionary system arise, and in the convergent phases, these are brought together in a new, higher unity, which in turn varies, and so on. Thus the mechanism of evolution is trialistic, proceeding according to the Hegelian principle (in the widest sense) of thesis, antithesis and synthesis. This mechanism is at the same time mirrored in the structure of the evolving systems, being most clearly expressed in the derivation of periodic systems of the individual levels of evolution. These relationships will be discussed using examples from symbiosis research, population dynamics and biogenesis.