Processes 2019, 7, 205
optimizing bioprocess applications. Norton et al. [2] study systems on the cellular and tissue scales and
examine the interactions between tumor cells, host immune cells and local microenvironments. Phalak
and Henson [7] study a multicellular scale system quantifying the dynamic interactions between
multiple microorganisms, including the exchange of metabolites, and the role of time and space on
microbial infections.
The Interface of Biotic and Abiotic Processes
Life occurs at the interface of biological and physical constraints. Biological processes, including
metabolism, are constrained by physical processes such as chemical transport to and from the cell.
Phalak and Henson [7] analyze how assemblages of different microorganisms can organize along
chemical gradients established by an imbalance between biological reaction rates and abiotic diffusion
rates. These gradients lead to spatial distributions of cell types and often enhanced system robustness.
Farzan and Ierapetritou [6] consider the interface of mammalian cells and convective transport
processes which ultimately influence the local chemical, thermal and mechanical environments.
The work also discusses computational optimization and selection of solvers for these types of
modeling applications.
Processing of Large Data Sets for Enhanced Analysis
Modern biology is rapidly becoming a study of large sets of data. Roberts et al [8] analyze tools
for extracting additional information from microRNA extracted from breast cancer by measuring
50,000 recurrent editing sites. The data identifies the presence of additional levels of complexity in
microRNAs which influences how the molecules interact with target mRNA.
Representing the output from computational biology efficiently, in a manner that facilitates
communication, is often difficult. Rose and Mazat [9] present software that enables the visualization
of metabolic flux data using a graphical user interface that permits rapid and simplified formatting
of data.
Parameters Optimization and Measurements
Computational representations of life require parameters. Parameter identification is a major
challenge and a focus of many studies. The Special Issue includes contributions which focus on
optimizing parameters required to represent biphasic systems, including generalized mass action
networks, relevant to gene signaling and metabolite networks [3], as well as calcium-induced oscillation
in cardiac cells [4]. Beck et al. [10] provide detailed methods for experimentally measuring key
parameters required for genome-scale metabolic models, including the biomass synthesis reaction.
The authors then demonstrate how different biomass parameters produce very different results based
on the interaction of electron balances and metabolism.
This Special Issue is coordinated with the Metabolic Pathway Analysis 2017 conference held in
Bozeman, MT and Interagency Modeling and Analysis Group (IMAG) MultiScale Modeling (MSM)
working groups (https://www.imagwiki.nibib.nih.gov/).
References
1.
Hunt, C.; Erdemir, A.; Lytton, W.; Mac Gabhann, F.; Sander, E.; Transtrum, M.; Mulugeta, L. The Spectrum of
Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena. Processes 2018, 6, 56.
[CrossRef]
2.
Norton, K.A.; Gong, C.; Jamalian, S.; Popel, A.S. Multiscale Agent-Based and Hybrid Modeling of the Tumor
Immune Microenvironment. Processes 2019, 7, 37. [CrossRef][PubMed]
3.
Porubsky, V.L.; Sauro, H.M. Application of Parameter Optimization to Search for Oscillatory Mass-Action
Networks Using Python. Processes 2019, 7, 163. [CrossRef]
2
optimizing bioprocess applications. Norton et al. [2] study systems on the cellular and tissue scales and
examine the interactions between tumor cells, host immune cells and local microenvironments. Phalak
and Henson [7] study a multicellular scale system quantifying the dynamic interactions between
multiple microorganisms, including the exchange of metabolites, and the role of time and space on
microbial infections.
The Interface of Biotic and Abiotic Processes
Life occurs at the interface of biological and physical constraints. Biological processes, including
metabolism, are constrained by physical processes such as chemical transport to and from the cell.
Phalak and Henson [7] analyze how assemblages of different microorganisms can organize along
chemical gradients established by an imbalance between biological reaction rates and abiotic diffusion
rates. These gradients lead to spatial distributions of cell types and often enhanced system robustness.
Farzan and Ierapetritou [6] consider the interface of mammalian cells and convective transport
processes which ultimately influence the local chemical, thermal and mechanical environments.
The work also discusses computational optimization and selection of solvers for these types of
modeling applications.
Processing of Large Data Sets for Enhanced Analysis
Modern biology is rapidly becoming a study of large sets of data. Roberts et al [8] analyze tools
for extracting additional information from microRNA extracted from breast cancer by measuring
50,000 recurrent editing sites. The data identifies the presence of additional levels of complexity in
microRNAs which influences how the molecules interact with target mRNA.
Representing the output from computational biology efficiently, in a manner that facilitates
communication, is often difficult. Rose and Mazat [9] present software that enables the visualization
of metabolic flux data using a graphical user interface that permits rapid and simplified formatting
of data.
Parameters Optimization and Measurements
Computational representations of life require parameters. Parameter identification is a major
challenge and a focus of many studies. The Special Issue includes contributions which focus on
optimizing parameters required to represent biphasic systems, including generalized mass action
networks, relevant to gene signaling and metabolite networks [3], as well as calcium-induced oscillation
in cardiac cells [4]. Beck et al. [10] provide detailed methods for experimentally measuring key
parameters required for genome-scale metabolic models, including the biomass synthesis reaction.
The authors then demonstrate how different biomass parameters produce very different results based
on the interaction of electron balances and metabolism.
This Special Issue is coordinated with the Metabolic Pathway Analysis 2017 conference held in
Bozeman, MT and Interagency Modeling and Analysis Group (IMAG) MultiScale Modeling (MSM)
working groups (https://www.imagwiki.nibib.nih.gov/).
References
1.
Hunt, C.; Erdemir, A.; Lytton, W.; Mac Gabhann, F.; Sander, E.; Transtrum, M.; Mulugeta, L. The Spectrum of
Mechanism-Oriented Models and Methods for Explanations of Biological Phenomena. Processes 2018, 6, 56.
[CrossRef]
2.
Norton, K.A.; Gong, C.; Jamalian, S.; Popel, A.S. Multiscale Agent-Based and Hybrid Modeling of the Tumor
Immune Microenvironment. Processes 2019, 7, 37. [CrossRef][PubMed]
3.
Porubsky, V.L.; Sauro, H.M. Application of Parameter Optimization to Search for Oscillatory Mass-Action
Networks Using Python. Processes 2019, 7, 163. [CrossRef]
2