Metal ion homeostasis in Bacillus subtilis

Bacillus subtilis, a Gram-positive soil bacterium, provides a model system for the study of metal ion homeostasis. Metalloregulatory proteins serve as the arbiters of metal ion sufficiency and regulate the expression of metal homeostasis pathways. In B. subtilis, uptake systems are regulated by the highly selective metal-sensing repressors Fur (iron), Zur (zinc), and MntR (manganese). Metal efflux systems are regulated by MerR and ArsR family homologs which, by contrast, can be rather non-specific with regard to metal selectivity. A Fur homolog, PerR, functions as an Fe(II)-dependent peroxide stress sensor and regulates putative metal transport and storage functions.     

Authorizing Knowledge in Science and Anthropology

An analogy exists between today's "defenders" of science in the "science/culture wars" and 19th-century "defenders" of euclidean geometry. Current critics have appointed themselves as arbiters of truth in a manner analogous to that of 19th-century mathematicians and theologians who argued against noneuclidean geometry that challenge Euclid's mathematically, philosophically, and theologically entrenched fifth postulate. The science wars then and now are not about science versus antiscience, objectivity versus subjectivity, but about authority in science: what kind of science should be practiced, and who gets to define it?     

A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits

Boolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision. After calibrating the model on experimental data from two circuits, we demonstrate close agreement between model outputs and data on the other 111 circuits that have so far been experimentally constructed using the 2-input BLADE platform. Model simulations of the remaining 143 circuits that have yet to be tested experimentally predict excellent performance of the 2-input BLADE platform across the range of possible circuits. Circuits from both the tested and untested subsets that perform less well consist of a disproportionally high number of STOP sequences. Model predictions suggested that circuit performance declines with a decrease in recombinase expression and new experimental data was generated that confirms this relationship.     

Development of larval motor circuits in Drosophila

How are functional neural circuits formed during development? Despite recent advances in our understanding of the development of individual neurons, little is known about how complex circuits are assembled to generate specific behaviors. Here, we describe the ways in which Drosophila motor circuits serve as an excellent model system to tackle this problem. We first summarize what has been learned during the past decades on the connectivity and development of component neurons, in particular motor neurons and sensory feedback neurons. We then review recent progress in our understanding of the development of the circuits as well as studies that apply optogenetics and other innovative techniques to dissect the circuit diagram. New approaches using Drosophila as a model system are now making it possible to search for developmental rules that regulate the construction of neural circuits.     

From Spontaneous Motor Activity to Coordinated Behaviour: A Developmental Model

In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions.     

Neuronal Circuits with Whisker-Related Patterns

Neuronal circuits with whisker-related patterns, such as those observed in the rodent somatosensory barrel cortex, have been widely used as a model system for investigating the anatomical organization, development and physiological roles of functional neuronal circuits. Whisker-related patterns exist not only in the barrel cortex but also in subcortical structures along the trigeminal neuraxis from the brainstem to the cortex. Here, we briefly summarize the organization, formation, and function of each neuronal circuit with whisker-related patterns, including the novel axonal trajectories that we recently found with the aid of in utero electroporation. We also discuss their biological implications as model systems for the studies of functional neuronal circuits.     

Model‐guided combinatorial optimization of complex synthetic gene networks

Constructing gene circuits that satisfy quantitative performance criteria has been a long‐standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three‐gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high‐throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model‐guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.     

Perturbation analysis analyzed—mathematical modeling of intact and perturbed gene regulatory circuits for animal development

Gene regulatory networks for animal development are the underlying mechanisms controlling cell fate specification and differentiation. The architecture of gene regulatory circuits determines their information processing properties and their developmental function. It is a major task to derive realistic network models from exceedingly advanced high throughput experimental data. Here we use mathematical modeling to study the dynamics of gene regulatory circuits to advance the ability to infer regulatory connections and logic function from experimental data. This study is guided by experimental methodologies that are commonly used to study gene regulatory networks that control cell fate specification. We study the effect of a perturbation of an input on the level of its downstream genes and compare between the cis-regulatory execution of OR and AND logics. Circuits that initiate gene activation and circuits that lock on the expression of genes are analyzed. The model improves our ability to analyze experimental data and construct from it the network topology. The model also illuminates information processing properties of gene regulatory circuits for animal development.     

Addiction: Decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm the brain's control circuit

Based on brain imaging findings, we present a model according to which addiction emerges as an imbalance in the information processing and integration among various brain circuits and functions. The dysfunctions reflect (a) decreased sensitivity of reward circuits, (b) enhanced sensitivity of memory circuits to conditioned expectations to drugs and drug cues, stress reactivity, and (c) negative mood, and a weakened control circuit. Although initial experimentation with a drug of abuse is largely a voluntary behavior, continued drug use can eventually impair neuronal circuits in the brain that are involved in free will, turning drug use into an automatic compulsive behavior. The ability of addictive drugs to co‐opt neurotransmitter signals between neurons (including dopamine, glutamate, and GABA) modifies the function of different neuronal circuits, which begin to falter at different stages of an addiction trajectory. Upon exposure to the drug, drug cues or stress this results in unrestrained hyperactivation of the motivation/drive circuit that results in the compulsive drug intake that characterizes addiction.     

A zonal model of cortical functions

A model of cortical functions is developed with the object of simulating the observed behavior of individual neurons organized in unit circuits and functional systems of the cerebellum, the cerebrum and the hippocampal formation. The neuronal model is capable of representing refractory and potentiated states, as well as the firing and lowest resting states. The unit circuits of each system consist of all common types of cells with known synaptic connections. In the cerebral system these unit circuits are interconnected to form columns as well as zones. A new discrete neural network equation, which takes account of interactions with the extracellular field, is proposed to simulate electrical activity in these circuits. A coherent theory of cortical activity and functions is derived that accounts for many of the observed phenomena, including those associated with the development of long-term potentiation and sequential memory. Three appendices are devoted to the theory of extracellular interactions, the derivation of non-linear network equations, and a computer program to simulate learning in the cortex.     

Early motor development from partially ordered neural-body dynamics: experiments with a cortico-spinal-musculo-skeletal model

Early human motor development has the nature of spontaneous exploration and boot-strap learning, leading to open-ended acquisition of versatile flexible motor skills. Since dexterous motor skills often exploit body-environment dynamics, we formulate the developmental principle as the spontaneous exploration of consistent dynamical patterns of the neural-body-environment system. We propose that partially ordered dynamical patterns emergent from chaotic oscillators coupled through embodiment serve as the core driving mechanism of such exploration. A model of neuro-musculo-skeletal system is constructed capturing essential features of biological systems. It consists of a skeleton, muscles, spindles, tendon organs, spinal circuits, medullar circuits (CPGs), and a basic cortical model. Through a series of experiments with a minimally simple body model, it is shown that the model has the capability of generating partially ordered behavior, a mixture of chaotic exploration and ordered entrained patterns. Models of self-organizing cortical areas for primary somatosensory and motor areas are introduced. They participate in the explorative learning by simultaneously learning and controlling the movement patterns. A scaled up version of the model, a human infant model, is constructed and put through preliminary experiments. Some meaningful motor behavior emerged including rolling over and crawling-like motion. The results show the possibility that a rich variety of meaningful behavior can be discovered and acquired by the neural-body dynamics without pre-defined coordinated control circuits.     

Sources of Variability in a Synthetic Gene Oscillator

Synthetic gene oscillators are small, engineered genetic circuits that produce periodic variations in target protein expression. Like other gene circuits, synthetic gene oscillators are noisy and exhibit fluctuations in amplitude and period. Understanding the origins of such variability is key to building predictive models that can guide the rational design of synthetic circuits. Here, we developed a method for determining the impact of different sources of noise in genetic oscillators by measuring the variability in oscillation amplitude and correlations between sister cells. We first used a combination of microfluidic devices and time-lapse fluorescence microscopy to track oscillations in cell lineages across many generations. We found that oscillation amplitude exhibited high cell-to-cell variability, while sister cells remained strongly correlated for many minutes after cell division. To understand how such variability arises, we constructed a computational model that identified the impact of various noise sources across the lineage of an initial cell. When each source of noise was appropriately tuned the model reproduced the experimentally observed amplitude variability and correlations, and accurately predicted outcomes under novel experimental conditions. Our combination of computational modeling and time-lapse data analysis provides a general way to examine the sources of variability in dynamic gene circuits.     

The neural circuits and synaptic mechanisms underlying motor initiation in C. elegans

C. elegans is widely used to dissect how neural circuits and genes generate behavior. During locomotion, worms initiate backward movement to change locomotion direction spontaneously or in response to sensory cues; however, the underlying neural circuits are not well defined. We applied a multidisciplinary approach to map neural circuits in freely behaving worms by integrating functional imaging, optogenetic interrogation, genetic manipulation, laser ablation, and electrophysiology. We found that a disinhibitory circuit and a stimulatory circuit together promote initiation of backward movement and that circuitry dynamics is differentially regulated by sensory cues. Both circuits require glutamatergic transmission but depend on distinct glutamate receptors. This dual mode of motor initiation control is found in mammals, suggesting that distantly related organisms with anatomically distinct nervous systems may adopt similar strategies for motor control. Additionally, our studies illustrate how a multidisciplinary approach facilitates dissection of circuit and synaptic mechanisms underlying behavior in a genetic model organism.     

Constraint and Contingency in Multifunctional Gene Regulatory Circuits

Gene regulatory circuits drive the development, physiology, and behavior of organisms from bacteria to humans. The phenotypes or functions of such circuits are embodied in the gene expression patterns they form. Regulatory circuits are typically multifunctional, forming distinct gene expression patterns in different embryonic stages, tissues, or physiological states. Any one circuit with a single function can be realized by many different regulatory genotypes. Multifunctionality presumably constrains this number, but we do not know to what extent. We here exhaustively characterize a genotype space harboring millions of model regulatory circuits and all their possible functions. As a circuit''s number of functions increases, the number of genotypes with a given number of functions decreases exponentially but can remain very large for a modest number of functions. However, the sets of circuits that can form any one set of functions becomes increasingly fragmented. As a result, historical contingency becomes widespread in circuits with many functions. Whether a circuit can acquire an additional function in the course of its evolution becomes increasingly dependent on the function it already has. Circuits with many functions also become increasingly brittle and sensitive to mutation. These observations are generic properties of a broad class of circuits and independent of any one circuit genotype or phenotype.     

From minimal signed circuits to the dynamics of Boolean regulatory networks

It is acknowledged that the presence of positive or negative circuits in regulatory networks such as genetic networks is linked to the emergence of significant dynamical properties such as multistability (involved in differentiation) and periodic oscillations (involved in homeostasis). Rules proposed by the biologist R. Thomas assert that these circuits are necessary for such dynamical properties. These rules have been studied by several authors. Their obvious interest is that they relate the rather simple information contained in the structure of the network (signed circuits) to its much more complex dynamical behaviour. We prove in this article a nontrivial converse of these rules, namely that certain positive or negative circuits in a regulatory graph are actually sufficient for the observation of a restricted form of the corresponding dynamical property, differentiation or homeostasis. More precisely, the crucial property that we require is that the circuit be globally minimal. We then apply these results to the vertebrate immune system, and show that the two minimal functional positive circuits of the model indeed behave as modules which combine to explain the presence of the three stable states corresponding to the Th0, Th1 and Th2 cells. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Self-Organization of Polarized Cell Signaling via Autocrine Circuits: Computational Model Analysis

Recent studies have suggested that autocrine signaling through epidermal growth factor receptor (EGFR) might be involved in generating or maintaining an intrinsic polarity in tissue cells, possibly via spatial localization of EGFR-mediated signaling. The difficulty of experimental investigation of autocrine signaling makes especially valuable an application of computational modeling for critical hypotheses about the dynamic operation of the underlying signaling circuits, both intracellular and extracellular. Toward this end, we develop and analyze here a spatially distributed dynamic computational model of autocrine EGFR signaling. Under certain conditions, the model spontaneously evolves into a state wherein sustained signaling is spatially localized on smaller than cell dimension, conferring a polarity to the otherwise nonpolar model cell. Conditions of a sufficiently large rate of autocrine EGFR ligand release and of a sufficiently small exogenous ligand concentration are qualitatively consistent with experimental observations of EGFR-mediated migration. Thus, computational analysis supports the concept that autocrine EGFR signaling circuits could play a role in helping generate and/or maintain an intrinsic cell spatial polarity, possibly related to migration as well as tissue organization. We additionally offer particular suggestions for critical nodes in the EGFR signaling circuits governing this self-organization capability.     

An electrogenetic toggle switch model

Synthetic biology uses molecular biology to implement genetic circuits that perform computations. These circuits can process inputs and deliver outputs according to predefined rules that are encoded, often entirely, into genetic parts. However, the field has recently begun to focus on using mechanisms beyond the realm of genetic parts for engineering biological circuits. We analyse the use of electrogenic processes for circuit design and present a model for a merged genetic and electrogenetic toggle switch operating in a biofilm attached to an electrode. Computational simulations explore conditions under which bistability emerges in order to identify the circuit design principles for best switch performance. The results provide a basis for the rational design and implementation of hybrid devices that can be measured and controlled both genetically and electronically.