Fumihide Shiraishi
Overview
Explore the profile of Fumihide Shiraishi including associated specialties, affiliations and a list of published articles.
Author names and details appear as published. Due to indexing inconsistencies, multiple individuals may share a name, and a single author may have variations. MedLuna displays this data as publicly available, without modification or verification
Snapshot
Snapshot
Articles
19
Citations
50
Followers
0
Related Specialties
Related Specialties
Top 10 Co-Authors
Top 10 Co-Authors
Published In
Published In
Affiliations
Affiliations
Soon will be listed here.
Recent Articles
1.
Hirai M, Shiraishi F
Curr Opin Biotechnol
. 2018 Sep;
54:138-144.
PMID: 30195121
Plant metabolism is characterized by a wide diversity of metabolites, with systems far more complicated than those of microorganisms. Mathematical modeling is useful for understanding dynamic behaviors of plant metabolic...
2.
Miyawaki-Kuwakado A, Komori S, Shiraishi F
IEEE/ACM Trans Comput Biol Bioinform
. 2018 Jul;
17(1):27-36.
PMID: 30004883
The calculation of steady-state metabolite concentrations in metabolic reaction network models is the first step in the sensitivity analysis of a metabolic reaction system described by differential equations. However, this...
3.
Iwata M, Miyawaki-Kuwakado A, Yoshida E, Komori S, Shiraishi F
Math Biosci
. 2018 Feb;
301:21-31.
PMID: 29410225
In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has...
4.
Yamada M, Iwanaga M, Sriyudthsak K, Hirai M, Shiraishi F
J Theor Biol
. 2016 Dec;
415:32-40.
PMID: 27939412
Kinetic-order sensitivity (the ratio of relative change in a dependent variable to the relative change in a kinetic order in a power-law-type differential equation) has recently become an important indicator...
5.
Miyawaki A, Sriyudthsak K, Hirai M, Shiraishi F
Math Biosci
. 2016 Nov;
282:21-33.
PMID: 27693302
Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent...
6.
Sriyudthsak K, Shiraishi F, Hirai M
Front Mol Biosci
. 2016 May;
3:15.
PMID: 27200361
The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale...
7.
Sriyudthsak K, Uno H, Gunawan R, Shiraishi F
Math Biosci
. 2015 Sep;
269:153-63.
PMID: 26384553
Metabolite concentrations in cells are governed by enzyme kinetics in the metabolic reaction system. One can analyze how these concentrations depend on system variables such as enzyme activities by computing...
8.
Shiraishi F, Yoshida E, Voit E
IEEE/ACM Trans Comput Biol Bioinform
. 2015 Sep;
11(6):1077-86.
PMID: 26357045
Stability and sensitivity analyses of biological systems require the ad hocwriting of computer code, which is highly dependent on the particular model and burdensome for large systems. We propose a...
9.
Sriyudthsak K, Sawada Y, Chiba Y, Yamashita Y, Kanaya S, Onouchi H, et al.
BMC Syst Biol
. 2015 Jan;
8 Suppl 5:S4.
PMID: 25559748
Background: Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values...
10.
Sriyudthsak K, Iwata M, Hirai M, Shiraishi F
Bull Math Biol
. 2014 May;
76(6):1333-51.
PMID: 24801819
The availability of large-scale datasets has led to more effort being made to understand characteristics of metabolic reaction networks. However, because the large-scale data are semi-quantitative, and may contain biological...