Example of Physical Biology format
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Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format
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Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format Example of Physical Biology format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Physical Biology — Template for authors

Publisher: IOP Publishing
Categories Rank Trend in last 3 yrs
Biophysics #54 of 131 up up by 13 ranks
Structural Biology #29 of 48 up up by 5 ranks
Cell Biology #171 of 279 up up by 37 ranks
Molecular Biology #237 of 382 up up by 48 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 251 Published Papers | 1033 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 06/07/2020
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 9.6
SJR: 1.766
SNIP: 1.645
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 8.5
SJR: 3.189
SNIP: 1.342
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 5.5
SJR: 0.998
SNIP: 0.777
open access Open Access

Wiley

Quality:  
High
CiteRatio: 8.5
SJR: 2.677
SNIP: 1.204

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.0

10% from 2018

Impact factor for Physical Biology from 2016 - 2019
Year Value
2019 2.0
2018 1.818
2017 1.621
2016 1.494
graph view Graph view
table view Table view

4.1

24% from 2019

CiteRatio for Physical Biology from 2016 - 2020
Year Value
2020 4.1
2019 3.3
2018 3.4
2017 2.8
2016 3.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 10% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 24% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.137

9% from 2019

SJR for Physical Biology from 2016 - 2020
Year Value
2020 1.137
2019 1.041
2018 1.066
2017 0.756
2016 0.94
graph view Graph view
table view Table view

0.625

21% from 2019

SNIP for Physical Biology from 2016 - 2020
Year Value
2020 0.625
2019 0.517
2018 0.628
2017 0.495
2016 0.556
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 21% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Physical Biology

Guideline source: View

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IOP Publishing

Physical Biology

Physical Biology publishes research on the quantitative characterization and understanding of biological systems at different levels of complexity.... Read More

Biophysics

Structural Biology

Cell Biology

Molecular Biology

Biochemistry, Genetics and Molecular Biology

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Last updated on
06 Jul 2020
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ISSN
1478-3967
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Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
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Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
iopart-num
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker C W J 2006 Phys. Rev. Lett. 97 067007 URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1088/1478-3967/1/3/001
Stochastic simulation of chemical reactions with spatial resolution and single molecule detail
Steven S. Andrews1, Dennis Bray2
01 Sep 2004 - Physical Biology

Abstract:

Methods are presented for simulating chemical reaction networks with a spatial resolution that is accurate to nearly the size scale of individual molecules. Using an intuitive picture of chemical reaction systems, each molecule is treated as a point-like particle that diffuses freely in three-dimensional space. When a pair of... Methods are presented for simulating chemical reaction networks with a spatial resolution that is accurate to nearly the size scale of individual molecules. Using an intuitive picture of chemical reaction systems, each molecule is treated as a point-like particle that diffuses freely in three-dimensional space. When a pair of reactive molecules collide, such as an enzyme and its substrate, a reaction occurs and the simulated reactants are replaced by products. Achieving accurate bimolecular reaction kinetics is surprisingly difficult, requiring a careful consideration of reaction processes that are often overlooked. This includes whether the rate of a reaction is at steady-state and the probability that multiple reaction products collide with each other to yield a back reaction. Inputs to the simulation are experimental reaction rates, diffusion coefficients and the simulation time step. From these are calculated the simulation parameters, including the 'binding radius' and the 'unbinding radius', where the former defines the separation for a molecular collision and the latter is the initial separation between a pair of reaction products. Analytic solutions are presented for some simulation parameters while others are calculated using look-up tables. Capabilities of these methods are demonstrated with simulations of a simple bimolecular reaction and the Lotka-Volterra system. read more read less

Topics:

Reaction rate (63%)63% related to the paper, Transition state (59%)59% related to the paper, Rate equation (58%)58% related to the paper, Chemical reaction (54%)54% related to the paper, Chemical kinetics (53%)53% related to the paper
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607 Citations
Journal Article DOI: 10.1088/1478-3975/2/3/001
A single-cell-based model of tumor growth in vitro: monolayers and spheroids.
Dirk Drasdo1, Dirk Drasdo2, Stefan Höhme2
12 Jul 2005 - Physical Biology

Abstract:

To what extent the growth dynamics of tumors is controlled by nutrients, biomechanical forces and other factors at different stages and in different environments is still largely unknown. Here we present a biophysical model to study the spatio-temporal growth dynamics of two-dimensional tumor monolayers and three-dimensional ... To what extent the growth dynamics of tumors is controlled by nutrients, biomechanical forces and other factors at different stages and in different environments is still largely unknown. Here we present a biophysical model to study the spatio-temporal growth dynamics of two-dimensional tumor monolayers and three-dimensional tumor spheroids as a complementary tool to in vitro experiments. Within our model each cell is represented as an individual object and parametrized by cell-biophysical and cell-kinetic parameters that can all be experimentally determined. Hence our modeling strategy allows us to study which mechanisms on the microscopic level of individual cells may affect the macroscopic properties of a growing tumor. We find the qualitative growth kinetics and patterns at early growth stages to be remarkably robust. Quantitative comparisons between computer simulations using our model and published experimental observations on monolayer cultures suggest a biomechanically-mediated form of growth inhibition during the experimentally observed transition from exponential to sub-exponential growth at sufficiently large tumor sizes. Our simulations show that the same transition during the growth of avascular tumor spheroids can be explained largely by the same mechanism. Glucose (or oxygen) depletion seems to determine mainly the size of the necrotic core but not the size of the tumor. We explore the consequences of the suggested biomechanical form of contact inhibition, in order to permit an experimental test of our model. Based on our findings we propose a phenomenological growth law in early expansion phases in which specific biological small-scale processes are subsumed in a small number of effective parameters. read more read less

Topics:

Cellular pathology (51%)51% related to the paper
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439 Citations
Journal Article DOI: 10.1088/1478-3975/9/5/055001
Boolean modeling in systems biology: An overview of methodology and applications
Rui-Sheng Wang1, Assieh Saadatpour1, Réka Albert1
25 Sep 2012 - Physical Biology

Abstract:

Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand,... Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand, a wealth of molecular level qualitative data on individual components and interactions can be obtained from the experimental literature and high-throughput technologies, making qualitative approaches such as Boolean network modeling extremely useful. In this paper, we build on our research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models. We finally demonstrate how Boolean models can be applied to perform the structural analysis of cellular networks. This overview aims to acquaint life science researchers with the basic steps of Boolean modeling and its applications in several areas of systems biology. read more read less

Topics:

Boolean network (70%)70% related to the paper, Systems biology (52%)52% related to the paper, Gene regulatory network (50%)50% related to the paper
View PDF
406 Citations
open accessOpen access Journal Article DOI: 10.1088/1478-3975/6/4/046001
Stochastic modelling of reaction-diffusion processes: algorithms for bimolecular reactions.
Radek Erban1, S. Jonathan Chapman1
21 Aug 2009 - Physical Biology

Abstract:

Several stochastic simulation algorithms (SSAs) have recently been proposed for modelling reaction–diffusion processes in cellular and molecular biology. In this paper, two commonly used SSAs are studied. The first SSA is an on-lattice model described by the reaction–diffusion master equation. The second SSA is an off-lattice... Several stochastic simulation algorithms (SSAs) have recently been proposed for modelling reaction–diffusion processes in cellular and molecular biology. In this paper, two commonly used SSAs are studied. The first SSA is an on-lattice model described by the reaction–diffusion master equation. The second SSA is an off-lattice model based on the simulation of Brownian motion of individual molecules and their reactive collisions. In both cases, it is shown that the commonly used implementation of bimolecular reactions (i.e. the reactions of the form A + B → C or A + A → C) might lead to incorrect results. Improvements of both SSAs are suggested which overcome the difficulties highlighted. In particular, a formula is presented for the smallest possible compartment size (lattice spacing) which can be correctly implemented in the first model. This implementation uses a new formula for the rate of bimolecular reactions per compartment (lattice site). read more read less

Topics:

Reaction–diffusion system (55%)55% related to the paper, Stochastic modelling (50%)50% related to the paper
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315 Citations
open accessOpen access Journal Article DOI: 10.1088/1478-3967/1/3/006
The statistical mechanics of complex signaling networks: nerve growth factor signaling.
01 Oct 2004 - Physical Biology

Abstract:

The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function.... The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.' read more read less

Topics:

Cell signaling (52%)52% related to the paper
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291 Citations
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With SciSpace, you do not need a word template for Physical Biology.

It automatically formats your research paper to IOP Publishing formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

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Time taken to format a paper and Compliance with guidelines

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Physical Biology format uses iopart-num citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Physical Biology in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Physical Biology guidelines and auto format it.

2. Do you follow the Physical Biology guidelines?

Yes, the template is compliant with the Physical Biology guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Physical Biology?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Physical Biology citation style.

4. Can I use the Physical Biology templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Physical Biology.

5. Can I use a manuscript in Physical Biology that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Physical Biology that you can download at the end.

6. How long does it usually take you to format my papers in Physical Biology?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Physical Biology.

7. Where can I find the template for the Physical Biology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Physical Biology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Physical Biology's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Physical Biology an online tool or is there a desktop version?

SciSpace's Physical Biology is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Physical Biology?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Physical Biology?”

11. What is the output that I would get after using Physical Biology?

After writing your paper autoformatting in Physical Biology, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Physical Biology's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Physical Biology?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Physical Biology. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Physical Biology?

The 5 most common citation types in order of usage for Physical Biology are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Physical Biology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Physical Biology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Physical Biology in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Physical Biology Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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