9 research outputs found
Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells
Generation of induced pluripotent stem cells (iPSCs) opens a new avenue in regenerative medicine. One of the major hurdles for therapeutic applications is to improve the efficiency of generating iPSCs and also to avoid the tumorigenicity, which requires searching for new reprogramming recipes. We present a systems biology approach to efficiently evaluate a large number of possible recipes and find those that are most effective at generating iPSCs. We not only recovered several experimentally confirmed recipes but we also suggested new ones that may improve reprogramming efficiency and quality. In addition, our approach allows one to estimate the cell-state landscape, monitor the progress of reprogramming, identify important regulatory transition states, and ultimately understand the mechanisms of iPSC generation
Stem Cell Based Tissue Engineering and Regenerative Medicine: A Review Focusing on Adult Stem Cells
Tissue engineering has emerged as a field that attempts to harness the bodies\u27 own developmental and repair features to treat diseases and illnesses. Many of these illnesses are caused by necrosis or loss of functionality of complete organs or specific cell types. Early discoveries in embryonic stem cells fueled a wave of research that led to claims about possibly regenerating nonfunctioning organs. Although we are still far away from being able to grow functional organs in a Petri dish, the field continues to progress forward, and new clinical trials have been approved for using both embryonic and adult stem cell based solutions for regenerative medicine and tissue engineering. Current trends have moved towards adult stem cells for cell based therapies as they offer an autologous source and are less tumorigenic than their embryonic and induced-pluripotent stem cell counter parts. This review will begin with an outline of stem cell classes and then focus on current therapies in myocardial tissue repair, neural tissue repair, diabetes, as well as osteogenic and chondrogenic differentiation are also reviewed
Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development
Progress in cell type reprogramming has revived the interest in Waddington's
concept of the epigenetic landscape. Recently researchers developed the
quasi-potential theory to represent the Waddington's landscape. The
Quasi-potential U(x), derived from interactions in the gene regulatory network
(GRN) of a cell, quantifies the relative stability of network states, which
determine the effort required for state transitions in a multi-stable dynamical
system. However, quasi-potential landscapes, originally developed for
continuous systems, are not suitable for discrete-valued networks which are
important tools to study complex systems. In this paper, we provide a framework
to quantify the landscape for discrete Boolean networks (BNs). We apply our
framework to study pancreas cell differentiation where an ensemble of BN models
is considered based on the structure of a minimal GRN for pancreas development.
We impose biologically motivated structural constraints (corresponding to
specific type of Boolean functions) and dynamical constraints (corresponding to
stable attractor states) to limit the space of BN models for pancreas
development. In addition, we enforce a novel functional constraint
corresponding to the relative ordering of attractor states in BN models to
restrict the space of BN models to the biological relevant class. We find that
BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics
of pancreas cell differentiation. This framework can also determine the genes'
influence on cell state transitions, and thus can facilitate the rational
design of cell reprogramming protocols.Comment: 24 pages, 6 figures, 1 tabl
Ensembles, Dynamics, and Cell Types: Revisiting the Statistical Mechanics Perspective on Cellular Regulation
Genetic regulatory networks control ontogeny. For fifty years Boolean
networks have served as models of such systems, ranging from ensembles of
random Boolean networks as models for generic properties of gene regulation to
working dynamical models of a growing number of sub-networks of real cells. At
the same time, their statistical mechanics has been thoroughly studied. Here we
recapitulate their original motivation in the context of current theoretical
and empirical research. We discuss ensembles of random Boolean networks whose
dynamical attractors model cell types. A sub-ensemble is the critical ensemble.
There is now strong evidence that genetic regulatory networks are dynamically
critical, and that evolution is exploring the critical sub-ensemble. The
generic properties of this sub-ensemble predict essential features of cell
differentiation. In particular, the number of attractors in such networks
scales as the DNA content raised to the 0.63 power. Data on the number of cell
types as a function of the DNA content per cell shows a scaling relationship of
0.88. Thus, the theory correctly predicts a power law relationship between the
number of cell types and the DNA contents per cell, and a comparable slope. We
discuss these new scaling values and show prospects for new research lines for
Boolean networks as a base model for systems biology.Comment: 22 pages, article will be included in a special issue of J. Theor.
Biol. dedicated to the memory of Prof. Rene Thoma
Gene regulatory interactions limit the gene expression diversity
The diversity of expressed genes plays a critical role in cellular
specialization, adaptation to environmental changes, and overall cell
functionality. This diversity varies dramatically across cell types and is
orchestrated by intricate, dynamic, and cell type-specific gene regulatory
networks (GRNs). Despite extensive research on GRNs, their governing
principles, as well as the underlying forces that have shaped them, remain
largely unknown. Here, we investigated whether there is a tradeoff between the
diversity of expressed genes and the intensity of GRN interactions. We have
developed a computational framework that evaluates GRN interaction intensity
from scRNA-seq data and used it to analyze simulated and real scRNA-seq data
collected from different tissues in humans, mice, fruit flies, and C. elegans.
We find a significant tradeoff between diversity and interaction intensity,
driven by stability constraints, where the GRN could be stable up to a critical
level of complexity - a product of gene expression diversity and interaction
intensity. Furthermore, we analyzed hematopoietic stem cell differentiation
data and find that the overall complexity of unstable transition states cells
is higher than that of stem cells and fully differentiated cells. Our results
suggest that GRNs are shaped by stability constraints which limit the diversity
of gene expression
Network resilience
Many systems on our planet are known to shift abruptly and irreversibly from
one state to another when they are forced across a "tipping point," such as
mass extinctions in ecological networks, cascading failures in infrastructure
systems, and social convention changes in human and animal networks. Such a
regime shift demonstrates a system's resilience that characterizes the ability
of a system to adjust its activity to retain its basic functionality in the
face of internal disturbances or external environmental changes. In the past 50
years, attention was almost exclusively given to low dimensional systems and
calibration of their resilience functions and indicators of early warning
signals without considerations for the interactions between the components.
Only in recent years, taking advantages of the network theory and lavish real
data sets, network scientists have directed their interest to the real-world
complex networked multidimensional systems and their resilience function and
early warning indicators. This report is devoted to a comprehensive review of
resilience function and regime shift of complex systems in different domains,
such as ecology, biology, social systems and infrastructure. We cover the
related research about empirical observations, experimental studies,
mathematical modeling, and theoretical analysis. We also discuss some ambiguous
definitions, such as robustness, resilience, and stability.Comment: Review chapter
Modeling and Analysis of Signal Transduction Networks
Biological pathways, such as signaling networks, are a key component of biological systems of each living cell. In fact, malfunctions of signaling pathways are linked to a number of diseases, and components of signaling pathways are used as potential drug targets. Elucidating the dynamic behavior of the components of pathways, and their interactions, is one of the key research areas of systems biology. Biological signaling networks are characterized by a large number of components and an even larger number of parameters describing the network. Furthermore, investigations of signaling networks are characterized by large uncertainties of the network as well as limited availability of data due to expensive and time-consuming experiments. As such, techniques derived from systems analysis, e.g., sensitivity analysis, experimental design, and parameter estimation, are important tools for elucidating the mechanisms involved in signaling networks. This Special Issue contains papers that investigate a variety of different signaling networks via established, as well as newly developed modeling and analysis techniques
Decoding biological gene regulatory networks by quantitative modeling
Gene regulatory network is essential to regulate the biological functions of cells.
With the rapid development of “omics” technologies, the network can be inferred for a
certain biological function. However, it still remains a challenge to understand the
complex network at a systematic level. In this thesis, we utilized quantitative modeling
approaches to study the nonlinear dynamics and the design principles of these biological
gene regulatory networks. We aim to explain the existing experimental observations with
the model, and further propose reasonable hypothesis for future experimental designs.
More importantly, the understanding of the circuit’s regulatory mechanism would benefit
the design of a de novo gene circuit for a new biological function.
We first studied the plasticity of cell migration phenotypes during cancer
metastasis, which contains two key cellular plasticity mechanisms - epithelial-tomesenchymal
transition (EMT) and mesenchymal-to-amoeboid transition (MAT). In this
study, we quantitatively modeled the core Rac1/RhoA gene regulatory circuit for MAT
and later connected it with the core regulatory circuit for EMT. We found four different
stable states, consistent with the amoeboid (A), mesenchymal (M), the hybrid
amoeboid/mesenchymal (A/M), and the hybrid epithelial/mesenchymal (E/M)
phenotypes that are observed in the experiment. We also explored the effects of
microRNAs and EMT-inducing signals like Hepatocyte Growth Factor (HGF), and
provided a new insight for the transitions among these phenotypes.
To improve the traditional modeling approaches, we developed a new
computational modeling method called Random Circuit Perturbation (RACIPE) to
explore the dynamic behavior of gene regulatory circuits without the requirement of
detailed kinetic parameters. We applied RACIPE on several gene circuits, and found the
existence of robust gene expression patterns even though the model parameters are wildly
perturbed. We also showed the powerful aspect of RACIPE to decipher the operating
principles of the circuits.
This kind of quantitative models not only works for gene regulatory network, but
also is capable to be extended to study the cell-cell interactions among cancer and
immune cells. The results shown the co-occurrence of three cancer states: low risk
cancer with intermediate immunity (L), intermediate risk cancer with high immunity (I) and high risk cancer with low immunity state (H). We further used the model to assess the different combinations of cancer therapies