136 research outputs found
Identification of control targets in Boolean molecular network models via computational algebra
Motivation: Many problems in biomedicine and other areas of the life sciences
can be characterized as control problems, with the goal of finding strategies
to change a disease or otherwise undesirable state of a biological system into
another, more desirable, state through an intervention, such as a drug or other
therapeutic treatment. The identification of such strategies is typically based
on a mathematical model of the process to be altered through targeted control
inputs. This paper focuses on processes at the molecular level that determine
the state of an individual cell, involving signaling or gene regulation. The
mathematical model type considered is that of Boolean networks. The potential
control targets can be represented by a set of nodes and edges that can be
manipulated to produce a desired effect on the system. Experimentally, node
manipulation requires technology to completely repress or fully activate a
particular gene product while edge manipulations only require a drug that
inactivates the interaction between two gene products. Results: This paper
presents a method for the identification of potential intervention targets in
Boolean molecular network models using algebraic techniques. The approach
exploits an algebraic representation of Boolean networks to encode the control
candidates in the network wiring diagram as the solutions of a system of
polynomials equations, and then uses computational algebra techniques to find
such controllers. The control methods in this paper are validated through the
identification of combinatorial interventions in the signaling pathways of
previously reported control targets in two well studied systems, a p53-mdm2
network and a blood T cell lymphocyte granular leukemia survival signaling
network.Comment: 12 pages, 4 figures, 2 table
Some Perspectives on Network Modeling in Therapeutic Target Prediction
Drug target identification is of significant commercial interest to
pharmaceutical companies, and there is a vast amount of research done related
to the topic of therapeutic target identification. Interdisciplinary research
in this area involves both the biological network community and the graph
algorithms community. Key steps of a typical therapeutic target identification
problem include synthesizing or inferring the complex network of interactions
relevant to the disease, connecting this network to the disease-specific
behavior, and predicting which components are key mediators of the behavior.
All of these steps involve graph theoretical or graph algorithmic aspects. In
this perspective, we provide modelling and algorithmic perspectives for
therapeutic target identification and highlight a number of algorithmic
advances, which have gotten relatively little attention so far, with the hope
of strengthening the ties between these two research communities
A comparative study of qualitative and quantitative dynamic models of biological regulatory networks
Cell fate reprogramming by control of intracellular network dynamics
Identifying control strategies for biological networks is paramount for
practical applications that involve reprogramming a cell's fate, such as
disease therapeutics and stem cell reprogramming. Here we develop a novel
network control framework that integrates the structural and functional
information available for intracellular networks to predict control targets.
Formulated in a logical dynamic scheme, our approach drives any initial state
to the target state with 100% effectiveness and needs to be applied only
transiently for the network to reach and stay in the desired state. We
illustrate our method's potential to find intervention targets for cancer
treatment and cell differentiation by applying it to a leukemia signaling
network and to the network controlling the differentiation of helper T cells.
We find that the predicted control targets are effective in a broad dynamic
framework. Moreover, several of the predicted interventions are supported by
experiments.Comment: 61 pages (main text, 15 pages; supporting information, 46 pages) and
12 figures (main text, 6 figures; supporting information, 6 figures). In
revie
Boolean Modeling of Biochemical Networks
The use of modeling to observe and analyze the mechanisms of complex biochemical network function is becoming an important methodological tool in the systems biology era. Number of different approaches to model these networks have been utilized-- they range from analysis of static connection graphs to dynamical models based on kinetic interaction data. Dynamical models have a distinct appeal in that they make it possible to observe these networks in action, but they also pose a distinct challenge in that they require detailed information describing how the individual components of these networks interact in living cells. Because this level of detail is generally not known, dynamic modeling requires simplifying assumptions in order to make it practical. In this review Boolean modeling will be discussed, a modeling method that depends on the simplifying assumption that all elements of a network exist only in one of two states
Control of stochastic and induced switching in biophysical networks
Noise caused by fluctuations at the molecular level is a fundamental part of
intracellular processes. While the response of biological systems to noise has
been studied extensively, there has been limited understanding of how to
exploit it to induce a desired cell state. Here we present a scalable,
quantitative method based on the Freidlin-Wentzell action to predict and
control noise-induced switching between different states in genetic networks
that, conveniently, can also control transitions between stable states in the
absence of noise. We apply this methodology to models of cell differentiation
and show how predicted manipulations of tunable factors can induce lineage
changes, and further utilize it to identify new candidate strategies for cancer
therapy in a cell death pathway model. This framework offers a systems approach
to identifying the key factors for rationally manipulating biophysical
dynamics, and should also find use in controlling other classes of noisy
complex networks.Comment: A ready-to-use code package implementing the method described here is
available from the authors upon reques
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