Detailed simulations of cell biology with Smoldyn 2.1.

Most cellular processes depend on intracellular locations and random collisions of individual protein molecules. To model these processes, we developed algorithms to simulate the diffusion, membrane interactions, and reactions of individual molecules, and implemented these in the Smoldyn program. Compared to the popular MCell and ChemCell simulators, we found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use. Using Smoldyn, we modeled pheromone response system signaling among yeast cells of opposite mating type. This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient. This model involved about 200,000 protein molecules, about 7000 cubic microns of volume, and about 75 minutes of simulated time; it took about 10 hours to run. Over the next several years, as faster computers become available, Smoldyn will allow researchers to model and explore systems the size of entire bacterial and smaller eukaryotic cells

Adaptive stochastic-deterministic chemical kinetic simulations

Motivation: Biochemical signaling pathways and genetic circuits often involve very small numbers of key signaling molecules. Computationally expensive stochastic methods are necessary to simulate such chemical situations. Single-molecule chemical events often co-exist with much larger numbers of signaling molecules where mass-action kinetics is a reasonable approximation. Here, we describe an adaptive stochastic method that dynamically chooses between deterministic and stochastic calculations depending on molecular count and propensity of forward reactions. The method is fixed timestep and has first order accuracy. We compare the efficiency of this method with exact stochastic methods. Results: We have implemented an adaptive stochastic-deterministic approximate simulation method for chemical kinetics. With an error margin of 5%, the method solves typical biologically constrained reaction schemes more rapidly than exact stochastic methods for reaction volumes >1-10 μm3. We have developed a test suite of reaction cases to test the accuracy of mixed simulation methods

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Artificial in its own right

Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman

1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time

Investigation of cellular and network dynamics in the brain by means of modeling & simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling & simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in level of detail to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing spatial aspects of the cells. For single cell or small-world networks, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the 3D morphology of cells and organelles into 3D space and time-dependent simulations. Every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. We present a hybrid simulation approach, that makes use of reduced 1D-models using e.g. the NEURON which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed 3D-morphology of neurons and organelles. To couple 1D- & 3D-simulations, we present a geometry and membrane potential mapping framework, with which graph-based morphologies, e.g. in swc-/hoc-format, are mapped to full surface and volume representations of the neuron; membrane potential data from 1D-simulations are used as boundary conditions for full 3D simulations. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved highly detailed 3D-modeling approaches. The new framework is applied to investigate electrically active neurons and their intracellular spatio-temporal Calcium Dynamics

Experimental and computational studies of calcium-triggered transmitter release

Calcium influx through presynaptic calcium channels triggers transmitter release, but many of the details that underlie calcium-triggered secretion are not well understood. In an attempt to increase our understanding of this process, synaptic transmission at the frog neuromuscular junction has been investigated using physiological experiments and computational modeling. Pharmacological manipulations ((R)-roscovitine and DAP) were used as tools to modulate presynaptic calcium influx and study effects on transmitter release. I showed that (R)-roscovitine predominately slowed deactivation kinetics of calcium current (by 427%), and as a result, increased the integral of calcium channel current evoked by a physiological action potential waveform (by 44%). (R)-roscovitine also increased the quantal content of acetylcholine released from the motor nerve terminals (by 149%) without changing paired-pulse facilitation under low calcium conditions. In contrast, exposure to 3,4-diaminopyridine (which affects transmitter release evoked by partially blocking potassium channels, altering the amplitude of the presynaptic action potential, and indirectly increasing calcium entry) increased paired-pulse facilitation (by 23%). In normal calcium conditions, both pharmacological treatments showed relatively similar effects on paired-pulse facilitation. I used a computational model, constrained by previous reports in the literature and my physiological measurements, to simulate my experimental data. This model faithfully reproduced calcium current with a single action potential, the average number of released synaptic vesicles, and the effects of (R)-roscovitine and DAP on calcium influx and vesicle release. Using this model, I made several predictions about the mechanisms underlying transmitter release. First, calcium ions originating from one or two voltage-gated calcium channels most often contributed to cause the fusion of each vesicle. Second, the calcium channel closest to a vesicle that fuses, provides 77% of calcium ions. My simulation of paired-pulse facilitation using the present model needed more adjustments, and in the process of adjusting the model parameters, various hypotheses that might explain observed short-term synaptic plasticity, including the effects of changes in buffer conditions, the effects of uneven calcium channel distribution, reducing terminal volume by adding vesicles to a storage pool, changes in the second action potential waveform, and possible persistent changes in vesicle release machinery were explored