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.
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.
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.
Living organisms employ endogenous negative feedback loops to maintain homeostasis despite environmental fluctuations. A pressing open challenge in Synthetic Biology * To whom correspondence should be addressed † Department of Engineering Mathematics, University of Bristol ‡ BrisSynBio, University of Bristol ¶ School of Biochemistry, University of Bristol § School of Biological Sciences, University of Bristol Department of Electrical Engineering and Information Technology, University of Naples ⊥ Contributed equally to this work as first authors # Contributed equally to this work 1 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.
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 Ca2+ 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00424-013-1251-y) contains supplementary material, which is available to authorized users.
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