Subconductance gating and voltage sensitivity of sarcoplasmic reticulum K(+) channels: a modeling approach.

Sarcoplasmic reticulum (SR) K(+) channels are voltage-regulated channels that are thought to be actively gating when the membrane potential across the SR is close to zero as is expected physiologically. A characteristic of SR K(+) channels is that they gate to subconductance open states but the relevance of the subconductance events and their contribution to the overall current flowing through the channels at physiological membrane potentials is not known. We have investigated the relationship between subconductance and full conductance openings and developed kinetic models to describe the voltage sensitivity of channel gating. Because there may be two subtypes of SR K(+) channels (TRIC-A and TRIC-B) present in most tissues, to conduct our study on a homogeneous population of SR K(+) channels, we incorporated SR vesicles derived from Tric-a knockout mice into artificial membranes to examine the remaining SR K(+) channel (TRIC-B) function. The channels displayed very low open probability (Po) at negative potentials (≤0 mV) and opened predominantly to subconductance open states. Positive holding potentials primarily increased the frequency of subconductance state openings and thereby increased the number of subsequent transitions into the full open state, although a slowing of transitions back to the sublevels was also important. We investigated whether the subconductance gating could arise as an artifact of incomplete resolution of rapid transitions between full open and closed states; however, we were not able to produce a model that could fit the data as well as one that included multiple distinct current amplitudes. Our results suggest that the apparent subconductance openings will provide most of the K(+) flux when the SR membrane potential is close to zero. The relative contribution played by openings to the full open state would increase if negative charge developed within the SR thus increasing the capacity of the channel to compensate for ionic imbalances.

TRIC-B channels display labile gating: evidence from the TRIC-A knockout mouse model.

Sarcoplasmic/endoplasmic reticulum (SR) and nuclear membranes contain two related cation channels named TRIC-A and TRIC-B. In many tissues, both subtypes are co-expressed, making it impossible to distinguish the distinct single-channel properties of each subtype. We therefore incorporated skeletal muscle SR vesicles derived from Tric-a-knockout mice into bilayers in order to characterise the biophysical properties of native TRIC-B without possible misclassification of the channels as TRIC-A, and without potential distortion of functional properties by detergent purification protocols. The native TRIC-B channels were ideally selective for cations. In symmetrical 210 mM K(+), the maximum (full) open channel level (199 pS) was equivalent to that observed when wild-type SR vesicles were incorporated into bilayers. Analysis of TRIC-B gating revealed complex and variable behaviour. Four main sub-conductance levels were observed at approximately 80 % (161 pS), 60 % (123 pS), 46 % (93 pS), and 30 % (60 pS) of the full open state. Seventy-five percent of the channels were voltage sensitive with Po being markedly reduced at negative holding potentials. The frequent, rapid transitions between TRIC-B sub-conductance states prevented development of reliable gating models using conventional single-channel analysis. Instead, we used mean-variance plots to highlight key features of TRIC-B gating in a more accurate and visually useful manner. Our study provides the first biophysical characterisation of native TRIC-B channels and indicates that this channel would be suited to provide counter current in response to Ca(2+) release from the SR. Further experiments are required to distinguish the distinct functional properties of TRIC-A and TRIC-B and understand their individual but complementary physiological roles.

Micro-scale interactions between Arabidopsis root hairs and soil particles influence soil erosion.

Soil is essential for sustaining life on land. Plant roots play a crucial role in stabilising soil and minimising erosion, although these mechanisms are still not completely understood. Consequently, identifying and breeding for plant traits to enhance erosion resistance is challenging. Root hair mutants in Arabidopsis thaliana were studied using three different quantitative methods to isolate their effect on root-soil cohesion. We present compelling evidence that micro-scale interactions of root hairs with surrounding soil increase soil cohesion and reduce erosion. Arabidopsis seedlings with root hairs were more difficult to detach from soil, compost and sterile gel media than those with hairless roots, and it was 10-times harder to erode soil from roots with than without hairs. We also developed a model that can consistently predict the impact root hairs make to soil erosion resistance. Our study thus provides new insight into the mechanisms by which roots maintain soil stability.

Toward Engineering Biosystems With Emergent Collective Functions.

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

An Orthogonal Multi-input Integration System to Control Gene Expression in Escherichia coli.

In many biotechnological applications, it is useful for gene expression to be regulated by multiple signals, as this allows the programming of complex behavior. Here we implement, in Escherichia coli, a system that compares the concentration of two signal molecules, and tunes GFP expression proportionally to their relative abundance. The computation is performed via molecular titration between an orthogonal σ factor and its cognate anti-σ factor. We use mathematical modeling and experiments to show that the computation system is predictable and able to adapt GFP expression dynamically to a wide range of combinations of the two signals, and our model qualitatively captures most of these behaviors. We also demonstrate in silico the practical applicability of the system as a reference-comparator, which compares an intrinsic signal (reflecting the state of the system) with an extrinsic signal (reflecting the desired reference state) in a multicellular feedback control strategy.

BSim 2.0: An Advanced Agent-Based Cell Simulator.

Agent-based models (ABMs) provide a number of advantages relative to traditional continuum modeling approaches, permitting incorporation of great detail and realism into simulations, allowing in silico tracking of single-cell behaviors and correlation of these with emergent effects at the macroscopic level. In this study we present BSim 2.0, a radically new version of BSim, a computational ABM framework for modeling dynamics of bacteria in typical experimental environments including microfluidic chemostats. This is facilitated through the implementation of new methods including cells with capsular geometry that are able to physically and chemically interact with one another, a realistic model of cellular growth, a delay differential equation solver, and realistic environmental geometries.

In-Silico Analysis and Implementation of a Multicellular Feedback Control Strategy in a Synthetic Bacterial Consortium.

Living organisms employ endogenous negative feedback loops to maintain homeostasis despite environmental fluctuations. A pressing open challenge in Synthetic Biology is to design and implement synthetic circuits to control host cells' behavior, in order to regulate and maintain desired conditions. To cope with the high degree of circuit complexity required to accomplish this task and the intrinsic modularity of classical control schemes, we suggest the implementation of synthetic endogenous feedback loops across more than one cell population. The distribution of the sensing, computation, and actuation functions required to achieve regulation across different cell populations within a consortium allows the genetic engineering in a particular cell to be reduced, increases the robustness, and makes it possible to reuse the synthesized modules for different control applications. Here, we analyze, in-silico, the design of a synthetic feedback controller implemented across two cell populations in a consortium. We study the effects of distributing the various functions required to build a control system across two populations, prove the robustness and modularity of the strategy described, and provide a computational proof-of-concept of its feasibility.

BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.

Large-scale collective behaviors such as synchronization and coordination spontaneously arise in many bacterial populations. With systems biology attempting to understand these phenomena, and synthetic biology opening up the possibility of engineering them for our own benefit, there is growing interest in how bacterial populations are best modeled. Here we introduce BSim, a highly flexible agent-based computational tool for analyzing the relationships between single-cell dynamics and population level features. BSim includes reference implementations of many bacterial traits to enable the quick development of new models partially built from existing ones. Unlike existing modeling tools, BSim fully considers spatial aspects of a model allowing for the description of intricate micro-scale structures, enabling the modeling of bacterial behavior in more realistic three-dimensional, complex environments. The new opportunities that BSim opens are illustrated through several diverse examples covering: spatial multicellular computing, modeling complex environments, population dynamics of the lac operon, and the synchronization of genetic oscillators. BSim is open source software that is freely available from http://bsim-bccs.sf.net and distributed under the Open Source Initiative (OSI) recognized MIT license. Developer documentation and a wide range of example simulations are also available from the website. BSim requires Java version 1.6 or higher.