42,044 research outputs found
Adaptive modeling of biochemical pathways
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 200
An Introduction to Rule-based Modeling of Immune Receptor Signaling
Cells process external and internal signals through chemical interactions.
Cells that constitute the immune system (e.g., antigen presenting cell, T-cell,
B-cell, mast cell) can have different functions (e.g., adaptive memory,
inflammatory response) depending on the type and number of receptor molecules
on the cell surface and the specific intracellular signaling pathways activated
by those receptors. Explicitly modeling and simulating kinetic interactions
between molecules allows us to pose questions about the dynamics of a signaling
network under various conditions. However, the application of chemical kinetics
to biochemical signaling systems has been limited by the complexity of the
systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune,
PySB) is an approach to address this complexity. In this chapter, by
application to the FcRI receptor system, we will explore the
origins of complexity in macromolecular interactions, show how rule-based
modeling can be used to address complexity, and demonstrate how to build a
model in the BioNetGen framework. Open source BioNetGen software and
documentation are available at http://bionetgen.org.Comment: 5 figure
The hidden impact of inter-individual genomic variations on cellular function
An analysis of the degree of genomic variation between two individual genomes suggests that there may be considerable biochemical differences among individuals. Examination of DNA sequence variations in 14 canonical signaling pathways and Monte-Carlo simulation modeling suggest that the kinetic and quantitative behavior of signaling pathways in many individuals may be significantly perturbed from the 'healthy' norm. Signal transduction pathways in some individuals may suffer context-specific failures, or they may function normally but fail easily in the face of additional environmental perturbations or somatic mutations. These findings argue for new systems biology approaches that can predict pathway status in individuals using personal genome sequences and biomarker data
Modeling and evolving biochemical networks: insights into communication and computation from the biological domain
This paper is concerned with the modeling and evolving
of Cell Signaling Networks (CSNs) in silico. CSNs are
complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first
provided, in which we describe the potential applications
of modeling and evolving these biochemical networks in
silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the
ESIGNET project. Results obtained with these methods
are summarized and discussed
Adaptive response and enlargement of dynamic range
Many membrane channels and receptors exhibit adaptive, or desensitized,
response to a strong sustained input stimulus, often supported by protein
activity-dependent inactivation. Adaptive response is thought to be related to
various cellular functions such as homeostasis and enlargement of dynamic range
by background compensation. Here we study the quantitative relation between
adaptive response and background compensation within a modeling framework. We
show that any particular type of adaptive response is neither sufficient nor
necessary for adaptive enlargement of dynamic range. In particular a precise
adaptive response, where system activity is maintained at a constant level at
steady state, does not ensure a large dynamic range neither in input signal nor
in system output. A general mechanism for input dynamic range enlargement can
come about from the activity-dependent modulation of protein responsiveness by
multiple biochemical modification, regardless of the type of adaptive response
it induces. Therefore hierarchical biochemical processes such as methylation
and phosphorylation are natural candidates to induce this property in signaling
systems.Comment: Corrected typos, minor text revision
Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism
Published in final edited form as:
Nat Ecol Evol. 2019 December ; 3(12): 1715–1724. doi:10.1038/s41559-019-1018-8.It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals before genetically encoded enzymes. A major challenge in unravelling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms with physico-chemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fuelled by a thioester- and redox-driven variant of the reductive tricarboxylic acid cycle that is capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors are not necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a protocell, and find that different combinations of carbon sources and electron donors can support the continuous production of a minimal ancient 'biomass' composed of putative early biopolymers and fatty acids.80NSSC17K0295 - Intramural NASA; 80NSSC17K0296 - Intramural NASA; T32 GM100842 - NIGMS NIH HHSAccepted manuscrip
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
Improved Network Performance via Antagonism: From Synthetic Rescues to Multi-drug Combinations
Recent research shows that a faulty or sub-optimally operating metabolic
network can often be rescued by the targeted removal of enzyme-coding
genes--the exact opposite of what traditional gene therapy would suggest.
Predictions go as far as to assert that certain gene knockouts can restore the
growth of otherwise nonviable gene-deficient cells. Many questions follow from
this discovery: What are the underlying mechanisms? How generalizable is this
effect? What are the potential applications? Here, I will approach these
questions from the perspective of compensatory perturbations on networks.
Relations will be drawn between such synthetic rescues and naturally occurring
cascades of reaction inactivation, as well as their analogues in physical and
other biological networks. I will specially discuss how rescue interactions can
lead to the rational design of antagonistic drug combinations that select
against resistance and how they can illuminate medical research on cancer,
antibiotics, and metabolic diseases.Comment: Online Open "Problems and Paradigms" articl
Identifying feasible operating regimes for early T-cell recognition: The speed, energy, accuracy trade-off in kinetic proofreading and adaptive sorting
In the immune system, T cells can quickly discriminate between foreign and
self ligands with high accuracy. There is evidence that T-cells achieve this
remarkable performance utilizing a network architecture based on a
generalization of kinetic proofreading (KPR). KPR-based mechanisms actively
consume energy to increase the specificity beyond what is possible in
equilibrium.An important theoretical question that arises is to understand the
trade-offs and fundamental limits on accuracy, speed, and dissipation (energy
consumption) in KPR and its generalization. Here, we revisit this question
through numerical simulations where we simultaneously measure the speed,
accuracy, and energy consumption of the KPR and adaptive sorting networks for
different parameter choices. Our simulations highlight the existence of a
'feasible operating regime' in the speed-energy-accuracy plane where T-cells
can quickly differentiate between foreign and self ligands at reasonable energy
expenditure. We give general arguments for why we expect this feasible
operating regime to be a generic property of all KPR-based biochemical networks
and discuss implications for our understanding of the T cell receptor circuit.Comment: 14 pages, 8 figure
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