Multigradient method for optimization of slow biotechnological processes

A new method (named a "jumping spider") is introduced for the optimization of slow biotechnological processes. The more traditional sequential experimentation (i.e., gradient search, simplex, etc.) is not well suited for slow dynamic processes, e.g., plant cell culture and differentiation. Therefore, a more simultaneous approach is proposed. A large number of initial experiments are performed, on the basis of which several of the initial experiments are selected as starting points. A search is then performed simultaneously from several gradient directions and the optimum is estimated by a quadratic approximation. In simulations, the spider generally climbs up the slopes quickly and the final estimator yields good maximum point estimates even on a complex topography. The spider may even approach more than one local maximum point simultaneously. As a model application, the average xylitol conversion rate of Candida guilliermondii was optimized in relation to cultivation volume (oxygen availability) and the concentration of nitrogen and phosphorus in the medium. A threefold increase in xylitol production was obtained with three experimental steps. (c) 1993 John Wiley & Sons, Inc.     

The slow process: A hypothetical cognitive adaptation for distributed cognitive networks

Human cognitive evolution is characterized by two special features that are truly novel in the primate line. The first is the emergence of "mindsharing" cultures that perform cooperative cognitive work, and serve as distributed cognitive networks. The second is the emergence of a brain that is specifically adapted for functioning within those distributed networks, and cannot realize its design potential without them. This paper proposes a hypothetical neural process at the core of this brain adaptation, called the "slow process". It enables the human brain to comprehend social events of much longer duration and complexity than those that characterize primate social life. It runs in the background of human cognitive life, with the faster moving sensorimotor interface running in the foreground. Most mammals can integrate events in the shorter time zone that corresponds to working memory. However, very few can comprehend complex events that extend over several hours (for example, a game or conversation) in what may be called the "intermediate" time zone. Adult humans typically live, plan, and imagine their lives in this time range, which seems to exceed the capabilities of our closest relatives, bonobos and chimpanzees. In summary, human cognition has both an individual and a collective dimension. Individual brains and minds function within cognitive-cultural networks, or CCNs, that store and transmit knowledge. The human brain relies on cultural input even to develop the basic cognitive capacities needed to gain access to that knowledge in the first place. The postulated slow process is a top-down executive capacity that evolved specifically to manage the cultural connection, and handle the cognitive demands imposed by increasingly complex distributed systems.     

Selecting neural networks for a committee decision

To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The proposed technique is tested in three aggregation schemes, namely majority vote, averaging, and aggregation by the median rule and compared with the ordinary neural networks fusion approach. The effectiveness of the approach is demonstrated on two artificial and three real data sets.     

Timing in reward and decision processes

Sensitivity to time, including the time of reward, guides the behaviour of all organisms. Recent research suggests that all major reward structures of the brain process the time of reward occurrence, including midbrain dopamine neurons, striatum, frontal cortex and amygdala. Neuronal reward responses in dopamine neurons, striatum and frontal cortex show temporal discounting of reward value. The prediction error signal of dopamine neurons includes the predicted time of rewards. Neurons in the striatum, frontal cortex and amygdala show responses to reward delivery and activities anticipating rewards that are sensitive to the predicted time of reward and the instantaneous reward probability. Together these data suggest that internal timing processes have several well characterized effects on neuronal reward processing.     

Probabilistic decision making by slow reverberation in cortical circuits

Recent physiological studies of alert primates have revealed cortical neural correlates of key steps in a perceptual decision-making process. To elucidate synaptic mechanisms of decision making, I investigated a biophysically realistic cortical network model for a visual discrimination experiment. In the model, slow recurrent excitation and feedback inhibition produce attractor dynamics that amplify the difference between conflicting inputs and generates a binary choice. The model is shown to account for salient characteristics of the observed decision-correlated neural activity, as well as the animal's psychometric function and reaction times. These results suggest that recurrent excitation mediated by NMDA receptors provides a candidate cellular mechanism for the slow time integration of sensory stimuli and the formation of categorical choices in a decision-making neocortical network.     

A decision tree model for the prediction of homodimer folding mechanism

The formation of protein homodimer complexes for molecular catalysis and regulation is fascinating. The homodimer formation through 2S (2 state), 3SMI (3 state with monomer intermediate) and 3SDI (3 state with dimer intermediate) folding mechanism is known for 47 homodimer structures. Our dataset of forty-seven homodimers consists of twenty-eight 2S, twelve 3SMI and seven 3SDI. The dataset is characterized using monomer length, interface area and interface/total (I/T) residue ratio. It is found that 2S are often small in size with large I/T ratio and 3SDI are frequently large in size with small I/T ratio. Nonetheless, 3SMI have a mixture of these features. Hence, we used these parameters to develop a decision tree model. The decision tree model produced positive predictive values (PPV) of 72% for 2S, 58% for 3SMI and 57% for 3SDI in cross validation. Thus, the method finds application in assigning homodimers with folding mechanism.     

Paradoxical evidence integration in rapid decision processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions.     

Presynaptic mechanism for slow contrast adaptation in mammalian retinal ganglion cells

Visual neurons, from retina to cortex, adapt slowly to stimulus contrast. Following a switch from high to low contrast, a neuron rapidly decreases its responsiveness and recovers over 5-20 s. Cortical adaptation arises from an intrinsic cellular mechanism: a sodium-dependent potassium conductance that causes prolonged hyperpolarization. Spiking can drive this mechanism, raising the possibility that the same mechanism exists in retinal ganglion cells. We found that adaptation in ganglion cells corresponds to a slowly recovering afterhyperpolarization (AHP), but, unlike in cortical cells, this AHP is not primarily driven by an intrinsic cellular property: spiking was not sufficient to generate adaptation. Adaptation was strongest following spatial stimuli tuned to presynaptic bipolar cells rather than the ganglion cell; it was driven by a reduced excitatory conductance, and it persisted while blocking GABA and glycine receptors, K((Ca)) channels, or mGluRs. Thus, slow adaptation arises from reduced glutamate release from presynaptic (nonspiking) bipolar cells.     

A localized pattern photobleaching method for the concurrent analysis of rapid and slow diffusion processes.

A scanning pattern photobleaching method for the analysis of lateral transport is described and discussed. Fluorescence bleaching with a localized pattern allows for the concurrent analysis of motions over two very different characteristic distances: xi 0(-1), the repeat distance of the pattern, and W, the linear dimension of the illuminated region. The former motion is deduced from the decay of the modulation amplitude (of period xi 0(-1) of fluorescence scans with the attenuated pattern, the latter from the recovery of the average fluorescence intensity. Such analysis should prove useful for the study of samples with a wide range of diffusion coefficients, and for the separation of effects arising from lateral diffusion and association dynamics. Theoretical analyses are presented for three related problems: (a) the effect of pattern localization on the decay of the modulation amplitude, (b) the effect of the pattern modulation on the recovery of the average local fluorescence intensity, and (c) the effect of a limited diffusion space (with linear dimensions of only a few pattern periods) on the decay of the modulation amplitude.     

Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks

Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described.     

Manipulating decision processes in the human scalar timing system

Two experiments attempted to manipulate the decision processes used in a temporal generalisation task with humans. In Experiment 1, payoffs (points awarded or deducted) were used to try to alter behaviour when the standard duration was 400ms, and the comparison durations ranged from 100 to 700ms in 100ms steps. Two conditions which either encouraged or discouraged the subject to identify a comparison duration as the standard were compared with a neutral condition. Encouraging identifications of the standard increased the proportion of identifications of the standard, whereas the discouraging manipulation had more ambiguous effects. Using the "modified Church and Gibbon" model, it was shown that the effect of the encourage manipulation was an increase in the response threshold, consistent with the information-processing version of scalar timing theory. A second experiment compared encourage and discourage manipulations with a more difficult discrimination (comparison durations spaced in 50ms steps around the 400ms standard), and with more distinct payoff differences for the different conditions. Behavioural effects were much more marked in Experiment 2, with the encourage condition producing more identifications of a comparison duration as the standard for all comparison durations except the shortest, compared with the discourage condition. Modelling showed that the main theoretical difference between the two conditions was in a change in the response threshold, in a manner consistent with the scalar timing model.     

FERM – a decision support system for fermentation processes

For recombinant DNA product fermentations in the laboratory and pilot scale as well as for antibiotic fermentations at the industrial scale decision support program systems for personal computers were developed. It assists the operator to solve control and decision tasks. Applying this program the data and knowledge bases increase, which include measured and derived process data, parameters and comments of previous fermentation runs as well as algorithmic and declarative rules.     

Unsupervised logic-based mechanism inference for network-driven biological processes

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.     

Novel human N-acetyltransferase 2 alleles that differ in mechanism for slow acetylator phenotype

Three novel human NAT2 alleles (NAT2*5D, NAT2*6D, and NAT2*14G) were identified and characterized in a yeast expression system. The common rapid (NAT2*4) and slow (NAT2*5B) acetylator human NAT2 alleles were also characterized for comparison. The novel recombinant NAT2 allozymes catalyzed both N- and O-acetyltransferase activities at levels comparable with NAT2 5B and significantly below NAT2 4, suggesting that they confer slow acetylation phenotype. In order to investigate the molecular mechanism of slow acetylation in the novel NAT2 alleles, we assessed mRNA and protein expression levels and protein stability. No differences were observed in NAT2 mRNA expression among the novel alleles, NAT2*4 and NAT2*5B. However NAT2 5B and NAT2 5D, but not NAT2 6D and NAT2 14G protein expression were significantly lower than NAT2 4. In contrast, NAT2 6D was slightly (3.4-fold) and NAT2 14G was substantially (29-fold) less stable than NAT2 4. These results suggest that the 341T --> C (Ile(114) --> Thr) common to the NAT2*5 cluster is sufficient for reduction in NAT2 protein expression, but that mechanisms for slow acetylator phenotype differ for NAT2 alleles that do not contain 341T --> C, such as the NAT2*6 and NAT2*14 clusters. Different mechanisms for slow acetylator phenotype in humans are consistent with multiple slow acetylator phenotypes.     

Multi-state epidemic processes on complex networks

Infectious diseases are practically represented by models with multiple states and complex transition rules corresponding to, for example, birth, death, infection, recovery, disease progression, and quarantine. In addition, networks underlying infection events are often much more complex than described by meanfield equations or regular lattices. In models with simple transition rules such as the SIS and SIR models, heterogeneous contact rates are known to decrease epidemic thresholds. We analyse steady states of various multi-state disease propagation models with heterogeneous contact rates. In many models, heterogeneity simply decreases epidemic thresholds. However, in models with competing pathogens and mutation, coexistence of different pathogens for small infection rates requires network-independent conditions in addition to heterogeneity in contact rates. Furthermore, models without spontaneous neighbor-independent state transitions, such as cyclically competing species, do not show heterogeneity effects.