Spatial Aspects in Biological System Simulations

Resat, Haluk; Costa, Michelle N.; Shankaran, Harish

doi:10.1016/b978-0-12-381270-4.00017-2

Cited by 10 publications

(10 citation statements)

References 64 publications

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“…The model used a hybrid approach where the soluble substrate and enzyme kinetics were described with continuous partial differential equations on a 3-D grid while the movement and position of each cell was individually tracked. Individual modeling of the cells made it possible to include physiological states into their dynamical description as each individual object (e.g., an agent) could have its own rules [42]. Our approach was similar to BacSim [26,27] except that the dynamics of individually tracked and continuum-based components of the system were propagated forward in time simultaneously for better accuracy.…”

Section: Methodsmentioning

confidence: 99%

“…Different organism types were assigned attributes with complementary functional mechanisms (e.g., secreted versus cell-associated polymer hydrolases). Similarly, since each individual cell could be associated with its own physiological state [22,26,27,42,52], individual-based modeling enabled representation of population diversity with respect to physiological state in response to the local microenvironment.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Microbial Dynamics in Heterogeneous Environments: Growth on Soil Carbon Sources

et al. 2011

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We have developed a new kinetic model to study how microbial dynamics are affected by the heterogeneity in the physical structure of the environment and by different strategies for hydrolysis of polymeric carbon. The hybrid model represented the dynamics of substrates and enzymes using a continuum representation and the dynamics of the cells were modeled individually. Individual-based biological model allowed us to explicitly simulate microbial diversity, and to model cell physiology as regulated via optimal allocation of cellular resources to enzyme synthesis, control of growth rate by protein synthesis capacity, and shifts to dormancy. This model was developed to study how microbial community functioning is influenced by local environmental conditions in heterogeneous media such as soil and by the functional attributes of individual microbes. Microbial community dynamics were simulated at two spatial scales: micro-pores that resemble 6-20-μm size portions of the soil physical structure and in 111-μm size soil aggregates with a random pore structure. Different strategies for acquisition of carbon from polymeric cellulose were investigated. Bacteria that express membrane-associated hydrolase had different growth and survival dynamics in soil pores than bacteria that release extracellular hydrolases. The kinetic differences suggested different functional niches for these two microbe types in cellulose utilization. Our model predicted an emergent behavior in which co-existence of membrane-associated hydrolase and extracellular hydrolases releasing organisms led to higher cellulose utilization efficiency and reduced stochasticity. Our analysis indicated that their co-existence mutually benefits these organisms, where basal cellulose degradation activity by membrane-associated hydrolase-expressing cells shortened the soluble hydrolase buildup time and, when enzyme buildup allowed for cellulose degradation to be fast enough to sustain exponential growth, all the organisms in the community shared the soluble carbon product and grew together. Although pore geometry affected the kinetics of cellulose degradation, the patterns observed for the bacterial community dynamics in the 6-20 μm-sized micro-pores were relevant to the dynamics in the more complex 111-μm-sized porous soil aggregates, implying that micro-scale studies can be useful approximations to aggregate scale studies when local effects on microbial dynamics are studied. As shown with examples in this study, various functional niches of the bacterial communities can be investigated using complex predictive mathematical models where the role of key environmental aspects such as the heterogeneous three-dimensional structure, functional niches of the community members, and environmental biochemical processes are directly connected to microbial metabolism and maintenance in an integrated model.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Microbial Dynamics in Heterogeneous Environments: Growth on Soil Carbon Sources

et al. 2011

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“…Unfortunately, most models neglect the significance of receptor spatial organization by assuming the well-mixed condition which is inherent to ordinary differential equation (ODE) models or their stochastic versions (Resat, Costa et al 2011). Here we have taken a different approach and developed a multiscale stochastic platform to study spatio-temporal properties of signal transduction networks in pseudo three dimensions.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Coarse Graining Method for Signal Transduction in Three Dimensions

Archuleta

McDermott

Edwards

et al. 2012

Fundamenta Informaticae

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The spatio-temporal landscape of the plasma membrane regulates activation and signal transduction of membrane bound receptors by restricting their two-dimensional mobility and by inducing receptor clustering. This regulation also extends to complex formation between receptors and adaptor proteins, which are the intermediate signaling molecules involved in cellular signaling that relay the received cues from cell surface to cytoplasm and eventually to the nucleus. Although their investigation poses challenging technical difficulties, there is a crucial need to understand the impact of the receptor diffusivity, clustering, and spatial heterogeneity, and of receptor-adaptor protein complex formation on the cellular signal transduction patterns. Building upon our earlier studies, we have developed an adaptive coarse-grained Monte Carlo method that can be used to investigate the role of diffusion, clustering and membrane corralling on receptor association and receptor-adaptor protein complex formation dynamics in three dimensions. The new Monte Carlo lattice based approach allowed us to introduce spatial resolution on the 2-D plasma membrane and to model the cytoplasm in three-dimensions. Being a multi-resolution approach, our new method makes it possible to represent various parts of the cellular system at different levels of detail and enabled us to utilize the locally homogeneous assumption when justified (e.g., cytoplasmic region away from the cell membrane) and avoid its use when high spatial resolution is needed (e.g., cell membrane and cytoplasmic region near the membrane) while keeping the required computational complexity manageable. Our results have shown that diffusion has a significant impact on receptor-receptor dimerization and receptor-adaptor protein complex formation kinetics. We have observed an “adaptor protein hopping” mechanism where the receptor binding proteins may hop between receptors to form short-lived transient complexes. This increased residence time of the adaptor proteins near cell membrane and their ability to frequently change signaling partners may explain the increase in signaling efficiency when receptors are clustered. We also hypothesize that the adaptor protein hopping mechanism can cause concurrent or sequential activation of multiple signaling pathways, thus leading to crosstalk between diverse biological functions.

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“…Despite the enormous advances in modeling and simulation of biological systems, the multiscale character of these systems still presents significant conceptual and computational challenges . Multiscale modeling is an emerging field aiming at a mechanistic understanding of biological interaction networks at the subcellular, cellular, intercellular, tissue, and organismic level.…”

mentioning

confidence: 99%

Bridging the gap between metabolic liver processes and functional tissue structure by integrated spatiotemporal modeling applied to hepatic ammonia detoxification

Wierling

2014

Hepatology

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Spatial Aspects in Biological System Simulations

Cited by 10 publications

References 64 publications

Modeling Microbial Dynamics in Heterogeneous Environments: Growth on Soil Carbon Sources

Modeling Microbial Dynamics in Heterogeneous Environments: Growth on Soil Carbon Sources

An Adaptive Coarse Graining Method for Signal Transduction in Three Dimensions

Bridging the gap between metabolic liver processes and functional tissue structure by integrated spatiotemporal modeling applied to hepatic ammonia detoxification

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