52,069 research outputs found
Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling
We propose a method for finding metabolic parameters of cells, organs and
whole organisms, which is based on the earlier discovered general growth law.
Based on the obtained results and analysis of available biological models, we
propose a general framework for modeling biological phenomena and discuss how
it can be used in Virtual Liver Network project. The foundational idea of the
study is that growth of cells, organs, systems and whole organisms, besides
biomolecular machinery, is influenced by biophysical mechanisms acting at
different scale levels. In particular, the general growth law uniquely defines
distribution of nutritional resources between maintenance needs and biomass
synthesis at each phase of growth and at each scale level. We exemplify the
approach considering metabolic properties of growing human and dog livers and
liver transplants. A procedure for verification of obtained results has been
introduced too. We found that two examined dogs have high metabolic rates
consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per
day, and verified this using the proposed verification procedure. We also
evaluated consumption rate of nutrients in human livers, determining it to be
about 0.088 gram of nutrients per cubic centimeter of liver per day for males,
and about 0.098 for females. This noticeable difference can be explained by
evolutionary development, which required females to have greater liver
processing capacity to support pregnancy. We also found how much nutrients go
to biomass synthesis and maintenance at each phase of liver and liver
transplant growth. Obtained results demonstrate that the proposed approach can
be used for finding metabolic characteristics of cells, organs, and whole
organisms, which can further serve as important inputs for many applications in
biology (protein expression), biotechnology (synthesis of substances), and
medicine.Comment: 20 pages, 6 figures, 4 table
Boolean network model predicts cell cycle sequence of fission yeast
A Boolean network model of the cell-cycle regulatory network of fission yeast
(Schizosaccharomyces Pombe) is constructed solely on the basis of the known
biochemical interaction topology. Simulating the model in the computer,
faithfully reproduces the known sequence of regulatory activity patterns along
the cell cycle of the living cell. Contrary to existing differential equation
models, no parameters enter the model except the structure of the regulatory
circuitry. The dynamical properties of the model indicate that the biological
dynamical sequence is robustly implemented in the regulatory network, with the
biological stationary state G1 corresponding to the dominant attractor in state
space, and with the biological regulatory sequence being a strongly attractive
trajectory. Comparing the fission yeast cell-cycle model to a similar model of
the corresponding network in S. cerevisiae, a remarkable difference in
circuitry, as well as dynamics is observed. While the latter operates in a
strongly damped mode, driven by external excitation, the S. pombe network
represents an auto-excited system with external damping.Comment: 10 pages, 3 figure
Formal executable descriptions of biological systems
The similarities between systems of living entities and systems of concurrent processes may support biological experiments in silico. Process calculi offer a formal framework to describe biological systems, as well as to analyse their behaviour, both from a qualitative and a quantitative point of view. A couple of little examples help us in showing how this can be done. We mainly focus our attention on the qualitative and quantitative aspects of the considered biological systems, and briefly illustrate which kinds of analysis are possible. We use a known stochastic calculus for the first example. We then present some statistics collected by repeatedly running the specification, that turn out to agree with those obtained by experiments in vivo. Our second example motivates a richer calculus. Its stochastic extension requires a non trivial machinery to faithfully reflect the real dynamic behaviour of biological systems
Towards modular verification of pathways: fairness and assumptions
Modular verification is a technique used to face the state explosion problem
often encountered in the verification of properties of complex systems such as
concurrent interactive systems. The modular approach is based on the
observation that properties of interest often concern a rather small portion of
the system. As a consequence, reduced models can be constructed which
approximate the overall system behaviour thus allowing more efficient
verification.
Biochemical pathways can be seen as complex concurrent interactive systems.
Consequently, verification of their properties is often computationally very
expensive and could take advantage of the modular approach.
In this paper we report preliminary results on the development of a modular
verification framework for biochemical pathways. We view biochemical pathways
as concurrent systems of reactions competing for molecular resources. A modular
verification technique could be based on reduced models containing only
reactions involving molecular resources of interest.
For a proper description of the system behaviour we argue that it is
essential to consider a suitable notion of fairness, which is a
well-established notion in concurrency theory but novel in the field of pathway
modelling. We propose a modelling approach that includes fairness and we
identify the assumptions under which verification of properties can be done in
a modular way.
We prove the correctness of the approach and demonstrate it on the model of
the EGF receptor-induced MAP kinase cascade by Schoeberl et al.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347
Designer Gene Networks: Towards Fundamental Cellular Control
The engineered control of cellular function through the design of synthetic
genetic networks is becoming plausible. Here we show how a naturally occurring
network can be used as a parts list for artificial network design, and how
model formulation leads to computational and analytical approaches relevant to
nonlinear dynamics and statistical physics.Comment: 35 pages, 8 figure
A Taxonomy of Causality-Based Biological Properties
We formally characterize a set of causality-based properties of metabolic
networks. This set of properties aims at making precise several notions on the
production of metabolites, which are familiar in the biologists' terminology.
From a theoretical point of view, biochemical reactions are abstractly
represented as causal implications and the produced metabolites as causal
consequences of the implication representing the corresponding reaction. The
fact that a reactant is produced is represented by means of the chain of
reactions that have made it exist. Such representation abstracts away from
quantities, stoichiometric and thermodynamic parameters and constitutes the
basis for the characterization of our properties. Moreover, we propose an
effective method for verifying our properties based on an abstract model of
system dynamics. This consists of a new abstract semantics for the system seen
as a concurrent network and expressed using the Chemical Ground Form calculus.
We illustrate an application of this framework to a portion of a real
metabolic pathway
Structural Kinetic Modeling of Metabolic Networks
To develop and investigate detailed mathematical models of cellular metabolic
processes is one of the primary challenges in systems biology. However, despite
considerable advance in the topological analysis of metabolic networks,
explicit kinetic modeling based on differential equations is still often
severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and
their associated parameter values. Here we propose a method that aims to give a
detailed and quantitative account of the dynamical capabilities of metabolic
systems, without requiring any explicit information about the particular
functional form of the rate equations. Our approach is based on constructing a
local linear model at each point in parameter space, such that each element of
the model is either directly experimentally accessible, or amenable to a
straightforward biochemical interpretation. This ensemble of local linear
models, encompassing all possible explicit kinetic models, then allows for a
systematic statistical exploration of the comprehensive parameter space. The
method is applied to two paradigmatic examples: The glycolytic pathway of yeast
and a realistic-scale representation of the photosynthetic Calvin cycle.Comment: 14 pages, 8 figures (color
On the emergence and evolution of artificial cell signaling networks
This PhD project is concerned with the evolution of Cell
Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We are investigating the possibility to build an evolutionary simulation platform that would allow the spontaneous emergence and evolution of Artificial Cell Signaling Networks (ACSNs). From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. This work may also contribute to the biological understanding of the origins and evolution of real CSNs
Where Do You Get Your Protein? Or: Biochemical Realization
Biochemical kinds such as proteins pose interesting problems for philosophers of science, as they can be studied from the points of view of both biology and chemistry. The relationship between the biological functions of biochemical kinds and the microstructures that they are related to is the key question. This leads us to a more general discussion about ontological reductionism, microstructuralism, and multiple realization at the biology-chemistry interface. On the face of it, biochemical kinds seem to pose a challenge for ontological reductionism and hence motivate a dual theory of chemical and biological kinds, a type of pluralism about natural kinds. But it will be argued that the challenge, which is based on multiple realization, can be addressed. The upshot is that there are reasonable prospects for ontological reductionism about biochemical kinds, which corroborates natural kind monism
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