Laboratory for Cell Systems
Institute for Protein Research
Professor
Associate Professor
Assistant Professor

Mariko OKADA
Keita IIDA
Ayaka ICHIKAWAmokadakiidaa-ichikawa

URL: http://www.protein.osaka-u.ac.jp/cell̲systems/index.html
We study a gene interaction
network in the cell and focus on thedynamic changes to describe therelationship between inputs(signals) and outputs (cellphenotype). We use bioinformatics
and mathematical models to clarify
the gene network in the cell and its
regulatory mechanisms for cancer
and immune systems.
Cell proliferation andquiescence decisions - Cell cycle
regulation in signaling network
ErbB receptor signaling pathway is one of
the critical signaling pathways involved incell fate determination, and dysregulation ofthis pathway is known to cause various typesof cancer (Figure 1). Cancer progression isassociated with abnormal cell proliferation,
but how the ErbB pathway regulates the cell
cycle is unclear. There are two representative
features of cell cycle dynamics (oscillationand irreversibility). To elucidate thequantitative mechanisms of cell cycle
regulation, our lab conducts mathematicalmodeling, bioinformatics analysis of cell cycle
regulators, and live-cell imaging.

Disease data analysis using
mathematical models
Cancer can be understood as a dynamic
network of environmental and genetic
factors. Therefore, even if classified basedon the presence of a particular marker
gene at a specific time point, theprognosis varies from patient to patient.
Therefore, new classification methods areneeded to predict drug sensitivity for eachpatient. Our laboratory tries to build a
new disease classification method using insilico patient-specific model.
This method is based onmathematical models of the genetic
network combined with clinicalomics data and classifies patients
based on the simulation results.
(Figure 2). We also attempt to apply
various computational methods todisease classification, such as natural
language processing and deeplearning.

Omics analysis and development ofelemental technologies for omics
data analysis
We also conduct next-generation sequencing
analysis to study environmental responses incells comprehensively. Cellular responses andgene expression are examined by focusing on
enhancer regulation, a regulatory mechanismof gene expression. We also develop a new
method for informatics and mathematics toreveal principles underlying cellular
heterogeneity.

Figure 1. The ErbB signaling
network and the cell cycle.

We are promoting a new form of biologicalresearch that combines experiments withcomputational and mathematical modeling in ourlab. The ability to analyze genetic information
obtained by next-generation sequencing andother methods will become increasingly
necessary in society for basic research andunderstanding the mechanisms of disease
pathogenesis. Students interested inexperiments but also programming andmathematics are welcome to visit our laboratory.

Institute for Protein Research, Osaka University
3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
Figure 2. Overview of in-silico patient-specific model.
A method of mathematical modeling that allows us to construct mathematical models
from descriptions of biochemical reactions. By inputting gene expression data
derived from cancer patients, we can predict patient-specific drug responses.

TEL: +81-6-6879-8617
FAX : +81-6-6879-8619
Scan here for the lab‘s website >>