Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion.

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions.

Minimal intervening control of biomolecular networks leading to a desired cellular state.

A cell phenotype can be represented by an attractor state of the underlying molecular regulatory network, to which other network states eventually converge. Here, the set of states converging to each attractor is called its basin of attraction. A central question is how to drive a particular cell state toward a desired attractor with minimal interventions on the network system.

The phenotype control kernel of a biomolecular regulatory network.

Background: Controlling complex molecular regulatory networks is getting a growing attention as it can provide a systematic way of driving any cellular state to a desired cell phenotypic state. A number of recent studies suggested various control methods, but there is still deficiency in finding out practically useful control targets that ensure convergence of any initial network state to one of attractor states corresponding to a desired cell phenotype.

Results: To find out practically useful control targets, we introduce a new concept of phenotype control kernel (PCK) for a Boolean network, defined as the collection of all minimal sets of control nodes having their fixed state values that can generate all possible control sets which eventually drive any initial state to one of attractor states corresponding to a particular cell phenotype of interest.

An efficient algorithm for identifying primary phenotype attractors of a large-scale Boolean network.

Background: Boolean network modeling has been widely used to model large-scale biomolecular regulatory networks as it can describe the essential dynamical characteristics of complicated networks in a relatively simple way. When we analyze such Boolean network models, we often need to find out attractor states to investigate the converging state features that represent particular cell phenotypes. This is, however, very difficult (often impossible) for a large network due to computational complexity.

Identification of Gene Networks with Time Delayed Regulation Based on Temporal Expression Profiles.

There are fundamental limitations in inferring the functional interaction structure of a gene (regulatory) network only from sequence information such as binding motifs. To overcome such limitations, various approaches have been developed to infer the functional interaction structure from expression profiles. However, most of them have not been so successful due to the experimental limitations and computational complexity.

Functional roles of multiple feedback loops in extracellular signal-regulated kinase and Wnt signaling pathways that regulate epithelial-mesenchymal transition.

Epithelial-mesenchymal transition (EMT) is a key event in the generation of invasive tumor cells. A hallmark of EMT is the repression of E-cadherin expression, which is regulated by various signal transduction pathways including extracellular signal-regulated kinase (ERK) and Wnt. These pathways are highly interconnected via multiple coupled feedback loops (CFL).

Positive- and negative-feedback regulations coordinate the dynamic behavior of the Ras-Raf-MEK-ERK signal transduction pathway.

The Ras-Raf-MEK-ERK pathway (or ERK pathway) is an important signal transduction system involved in the control of cell proliferation, survival and differentiation. However, the dynamic regulation of the pathway by positive- and negative-feedback mechanisms, in particular the functional role of Raf kinase inhibitor protein (RKIP) are still incompletely understood. RKIP is a physiological endogenous inhibitor of MEK phosphorylation by Raf kinases, but also participates in a positive-feedback loop in which ERK can inactivate RKIP.

Cardiac systems biology and parameter sensitivity analysis: intracellular Ca2+ regulatory mechanisms in mouse ventricular myocytes.

Intracellular Ca(2+) dynamics of cardiac myocytes are regulated by complex mechanisms of a variety of ion channels, transporters, and exchangers. Alterations of these Ca(2+) regulatory components might lead to development of cardiac diseases. To investigate the regulatory mechanisms and hidden Ca(2+) dynamics we use integrative systems analysis.

System-level investigation into the regulatory mechanism of the calcineurin/NFAT signaling pathway.

Calcineurn/nuclear factor of the activated T cell (CaN/NFAT) signaling pathway plays crucial roles in the development of cardiac hypertrophy, Down's syndrome, and autoimmune diseases in response to pathological stimuli. The aim of the present study is to get a system-level understanding on the regulatory mechanism of CaN/NFAT signaling pathway in consideration of the controversial roles of myocyte-enriched calcineurin interacting protein1 (MCIP1) for varying stress stimuli. To this end, we have developed an experimentally validated mathematical model and carried out computer simulations as well as cell-based experiments.

Dynamical analysis of the calcium signaling pathway in cardiac myocytes based on logarithmic sensitivity analysis.

Many cellular functions are regulated by the Ca(2+) signal which contains specific information in the form of frequency, amplitude, and duration of the oscillatory dynamics. Any alterations or dysfunctions of components in the calcium signaling pathway of cardiac myocytes may lead to a diverse range of cardiac diseases including hypertrophy and heart failure. In this study, we have investigated the hidden dynamics of the intracellular Ca(2+) signaling and the functional roles of its regulatory mechanism through in silico simulations and parameter sensitivity analysis based on an experimentally verified mathematical model.

Unraveling the functional interaction structure of a biomolecular network through alternate perturbation of initial conditions.

Various approaches attempting to infer the functional interaction structure of a hidden biomolecular network from experimental time-series measurements have been developed; however, due to both experimental limitations and methodological complexities, a large majority of these approaches have been unsuccessful. In particular, with respect to the elucidation of such networks, there are (i) a dimensionality problem: too many network nodes with too few available sampling points, (ii) a computational complexity problem: exponential complexity if a priori information is unavailable for regulatory nodes, and (iii) an experimental measurement problem: no guidelines for an appropriate experimental design for distinguishing direct and indirect influences among network nodes. Here, we sought to develop a new methodology capable of identifying the correct functional interaction structure with only a few sampling points through relatively simple computations.

Switching feedback mechanisms realize the dual role of MCIP in the regulation of calcineurin activity.

Calcineurin (CaN) assists T-cell activation, growth and differentiation of skeletal and cardiac myocytes, memory, and apoptosis. It also activates transcription of the nuclear factor of activated T-cells (NFAT) family including hypertrophic target genes. It has been reported that the modulatory calcineurin-interacting protein (MCIP) inhibits the CaN activity and thereby reduces the hypertrophic response.

Inferring biomolecular regulatory networks from phase portraits of time-series expression profiles.

Reverse engineering of biomolecular regulatory networks such as gene regulatory networks, protein interaction networks, and metabolic networks has received an increasing attention as more high-throughput time-series measurements become available. In spite of various approaches developed from this motivation, it still remains as a challenging subject to develop a new reverse engineering scheme that can effectively uncover the functional interaction structure of a biomolecular network from given time-series expression profiles (TSEPs). We propose a new reverse engineering scheme that makes use of phase portraits constructed by projection of every two TSEPs into respective phase planes.

A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data.

We propose a unified framework for the identification of functional interaction structures of biomolecular networks in a way that leads to a new experimental design procedure. In developing our approach, we have built upon previous work. Thus we begin by pointing out some of the restrictions associated with existing structure identification methods and point out how these restrictions may be eased.

Unravelling the functional interaction structure of a cellular network from temporal slope information of experimental data.

Due to the unavoidable nonbiological variations accompanying many experiments, it is imperative to consider a way of unravelling the functional interaction structure of a cellular network (e.g. signalling cascades or gene networks) by using the qualitative information of time-series experimental data instead of computation through the measured absolute values.