BioCAE: A New Strategy of Complex Biological Systems for Biofabrication of Tissues and Organs

Abstract

Biofabrication as an interdisciplinary area is fostering new knowledge and integration of areas like nanotechnology, chemistry, biology, physics, materials science, control systems, among many others, necessary to accomplish the challenge of bioengineering functional complex tissues. The emergence of integrated platforms and systems biology to understand complex biological systems in multiscale levels will enable the prediction and creation of biofabricated biological structures. This systematic analysis (meta-analysis) or integrated platforms for estimating biological process have been named as BioCAE, which will become the key for important steps of the biofabrication processes. Biological Computational Aided Engineering (BioCAE) is a new computational approach to understanding and bioengineer complex tissues (biofabrication) using a combination of different methods as multiscale modelling, computer simulations, data mining and systems biology. In addition, multi-agent systems (MAS), which are composed of different interacting computing entities called agents, also provide an interesting way to
design and implement simulations of biological systems, integrating them with all steps of the BioCAE. MAS as a part of computational science has become a growing area to manipulate and solve complex problems. This paper presents an approach that will allow predicting the development and behavior of different biological processes such as molecular networks, gene interactions, cells, tissues and organs due to its flexibility, beyond to provide a new outlook in the biofabrication of tissues and organs.

Full text

Volume 8 • Issue 2 • 1000200
J Tissue Sci Eng, an open access journal
ISSN: 2157-7552
Review Article OMICS International
Journal of
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ISSN: 2157-7552
Dernowsek et al., J Tissue Sci Eng 2017, 8:2
DOI: 10.4172/2157-7552.1000200
Keywords: Biological computational aided engineering (BioCAE);
Biofabrication; Information technology; Systems biology; Computer
simulations; Multi-agent system
Introduction
Biofabrication as an interdisciplinary area is fostering new knowledge
and integration of areas like nanotechnology, chemistry, biology, physics,
materials science, control systems, among many others, necessary
to accomplish the challenge of bioengineering functional complex
tissues. e term biofabrication was widely discussed recently and is
broadly used for tissue engineering and additive manufacturing as “the
automated generation of biologically functional products with structural
organization from living cells, bioactive molecules, biomaterials, cell
aggregates such as micro-tissues or hybrid cell-material constructs,
through Bioprinting or Bioassembly and subsequent tissue maturation
processes” [1]. Undoubtedly, the current biofabrication initiatives are
possible because of a set of technologies that are completely supported
by information technology (IT), such as imaging, database, bioprinting
technique [2], articial intelligence (AI), soware and hardware. ese
factors make the biofabrication not only a consistent promise but also a
reality for the automated production of human tissues and organs.
e bioengineering of functional tissues, functional vascular
networks, interfaces, structural hierarchy and complex features is
emerging as an unparalleled scientic and technical challenge for the
next generation of tissue engineers [3]. However, advances in molecular
and cellular biology, over the last decades, have triggered tremendous
growth in the availability of experimental data. On the other hand, the
computational approaches have allowed the multiparametric analyses
of various datasets and biological behaviors. is is, therefore, the rst
engine to leverage the main goal of bioengineering tissues and organs.
It begins with a deep knowledge of the behavior of the cell, mainly their
mechanisms to connect molecules and machinery, aiming at activating,
sustaining and modulating their functional metabolic networks
responsible for the survival, growth, proliferation, dierentiation and
death [4].
e large-scale high-throughput experimental techniques have
greatly increased overall knowledge and understanding of the global
organization of complex cross-talks among the dierent signaling
cascades and dierent cells. Recognizing this complexity, the concept
of complex biological systems or systems biology has emerged as a
powerful eld that requires the ability to mathematics, engineering
and information technology domains to analyze, integrate and use data
from dierent databanks for the creation of working models of entire
biological systems [5]. e ultimate outcome of this structural and
functional organization is the metabolism of the cells, tissues and organs,
important in all biofabrication processes. is technology is evolved
into a complex system composed of many processes as computer-aided
design (CAD), computer aided engineering (CAE) and computer aided
manufacturing (CAM) and biological processes, which depends on
the combination of dierent interrelated components as molecules,
genes, regulatory networks, cells, organoids and tissues, integrated
with computational approaches as design, modeling, simulation and
optimization, among others.
Next sessions we expose a new approach, based on Information
*Corresponding author: Janaina de Andrea Dernowsek, Center for information
technology Renato Archer (CTI), 3D Technologies Research Group (NT3D), Campinas,
Brazil, Tel: +55-19-3746-6203; E-mail: janaina.dernowsek@cti.gov.br
Received April 29, 2017; Accepted June 07, 2017; Published June 17, 2017
Citation: Dernowsek JA, Rezende RA, da Silva JVL (2017) BioCAE: A New
Strategy of Complex Biological Systems for Biofabrication of Tissues and Organs. J
Tissue Sci Eng 8: 200. doi: 10.4172/2157-7552.1000200
Copyright: © 2017 Dernowsek JA, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are credited.
BioCAE: A New Strategy of Complex Biological Systems for Biofabrication
of Tissues and Organs
Dernowsek JA1,2*, Rezende RA1-3 and JVL da Silva1,2
1Center for Information Technology Renato Archer (CTI), 3D Technologies Research Group (NT3D) Rodovia D. Pedro I (SP - 65) Km 143,6, Amarais, Campinas, SP,
13.069-901, Brazil
2Brazilian Institute of Biofabrication (INCT-BIOFABRIS), School of Chemical Engineering, UNICAMP, Campinas, SP, Brazil
3CERTBIO, Federal University of Campina Grande (UFCG), Campina Grande, PB, Brazil
Abstract
Biofabrication as an interdisciplinary area is fostering new knowledge and integration of areas like
nanotechnology, chemistry, biology, physics, materials science, control systems, among many others, necessary
to accomplish the challenge of bioengineering functional complex tissues. The emergence of integrated platforms
and systems biology to understand complex biological systems in multiscale levels will enable the prediction and
creation of biofabricated biological structures. This systematic analysis (meta-analysis) or integrated platforms for
estimating biological process have been named as BioCAE, which will become the key for important steps of the
biofabrication processes. Biological Computational Aided Engineering (BioCAE) is a new computational approach to
understanding and bioengineer complex tissues (biofabrication) using a combination of different methods as multi-
scale modelling, computer simulations, data mining and systems biology. In addition, multi-agent systems (MAS),
which are composed of different interacting computing entities called agents, also provide an interesting way to
design and implement simulations of biological systems, integrating them with all steps of the BioCAE. MAS as a
part of computational science have become a growing area to manipulate and solve complex problems. This paper
presents an approach that will allow predicting the development and behavior of different biological processes such
as molecular networks, gene interactions, cells, tissues and organs due to its exibility, beyond to provide a new
outlook in the biofabrication of tissues and organs.

Citation: Dernowsek JA, Rezende RA, da Silva JVL (2017) BioCAE: A New Strategy of Complex Biological Systems for Biofabrication of Tissues and
Organs. J Tissue Sci Eng 8: 200. doi: 10.4172/2157-7552.1000200
Page 2 of 6
Volume 8 • Issue 2 • 1000200
J Tissue Sci Eng, an open access journal
ISSN: 2157-7552
Technology that potentially will help to improve understanding,
integration and consequently optimizing the output of the biofabrication
process.
Integrated Platform for Biofabrication
e emergence of platforms and systems biology to understand
biological systems in multiscale levels will enable the prediction and
the creation of biofabricated biological structures [6]. is systematic
analysis (meta-analysis) or integrated platforms for predicting the
biological behavior has been named BioCAE (Figure 1) and together
with BioCAD and BioCAM will become the key procedures for
establishing essential steps of the biofabrication processes.
Biological Computational Aided Engineering (BioCAE) is a new
computational approach to understanding and bioengineer complex
tissues (biofabrication) using a combination of dierent methods as
multi-scale modeling, computer simulations, data mining and systems
biology (Figure 1). In addition, multi-agent systems (MAS), which are
composed of dierent interacting computing entities called agents, also
provide an interesting way to design and implement simulations of
biological systems [7], integrating them with all steps of the BioCAE.
MAS, as a part of computational science, has become a growing area for
the manipulation and solving complex problems [8]. is paper, presents
a new integrated approach to predict the development and behavior of
dierent biological processes such as molecular networks, intracellular
interactions (including genes, mRNAs, microRNAs and proteins),
cell–cell interactions and tissues and organs, besides to provide a new
landscape in the design of functional organs and tissues for the next step
of their production by means of bioprinting processes.
Biological Computational Aided Engineering for
Biofabrication (BioCAE)
BioCAE will be an integrated environment where several modules
of modeling and simulation are interoperated to understand and predict
a suitable and functional organ design (BioCAD) for biofabrication
(BioCAM). In other words, today, great eorts and high costs are
required to, experimentally, manipulate cells and/or cell agglomerates
that will especially necessary for the biofabrication of organs and tissues.
erefore, the use and the development of computational approaches
to study complex biological systems in dierent levels (cells, tissues
and organs) in silico are fundamental for the progress of the unique
biofabrication eld.
e emergence of powerful soware platforms to study the
biological behaviors in dierent environments, as BioCAE, depends
on data handling/data mining, imaging, computer modeling, computer
simulation, integrated computational analysis, MAS and knowledge
integration, among many others. One of the main approaches of our
proposal is to create computational models that enable users to predict
the behaviors of biological systems at multi-scales, thereby helping
users to understand the mechanisms involved as well as to predict
the impacts that occur during the biofabrication process. It includes
a myriad of parameters such as pressures, pH, oxygenation, nutrition,
fusion, diusion, temperature, stresses, velocity and viscosity, among
many others that aect biological viability in the survival, growth,
proliferation and dierentiation.
Many papers highlight the importance of computational multi-scale
methods in systems biology [9-13] and recently, in the biofabrication
[14]. e biological systems are made up of many spatial and temporal
scales. ese diverse levels coupling intra and interscale interactions
make a biological system extremely complex, requiring advanced
mathematical and computational models for the integration of the
dierent scales [15,16]. ere is a range of multi-scale modelling
methods that could potentially be employed in systems biology [17-22]
and consequently in the biofabrication of tissues and organs.
Figure 1: Illustration of how BioCAD, BioCAE and BioCAM concepts, together with tissue engineering principles, systems biology and information technology (IT) can
synergically benet to establish the core for the biofabrication.

Citation: Dernowsek JA, Rezende RA, da Silva JVL (2017) BioCAE: A New Strategy of Complex Biological Systems for Biofabrication of Tissues and
Organs. J Tissue Sci Eng 8: 200. doi: 10.4172/2157-7552.1000200
Page 3 of 6
Volume 8 • Issue 2 • 1000200
J Tissue Sci Eng, an open access journal
ISSN: 2157-7552
en, in the next section, we will describe some multi-scale
approaches and/or soware which may be part of the BioCAE and
that are playing or will play important roles in multi-scale problems in
systems biology and tissues biofabricated, thereby preventing a large
amount of trial and error experiments in laboratories.
From Molecules, Cells and Tissues towards Organ
e aim of multi-scale modelling is to represent, predict and
understand the behaviour of complex systems across, not only in a wide
spatial scale (molecules, genes, mRNAs, proteins, metabolic networks,
cells, organoids, tissues and organs) as shown in Figure 2, but also in
temporal scale (seconds (s), minutes (min), hours (h), days, weeks,
months, years). Despite the signicant growth of this eld of knowledge,
there is a need to develop frameworks for the interoperability among
dierent simulation soware. In this context, it is possible to nd that
several toolkits, databases, pipelines, methods, integrated frameworks
and platforms have been developed for this purpose [23]. On the other
hand, the major drawback of these approaches is the high computational
requirements, particularly for higher eukaryotes, where even a single
organ is composed of a large number of cells. Many approaches share
mathematical models, especially when using integrated platforms or
soware are used. Accordingly, various methods are presented below,
which were developed for the study of complex biological systems that
can be adapted to compose the BioCAE concept.
For a better understanding, the following three sections describe a
multilevel approach, from molecules to organs that will be integrated
with relevant information and models using the BioCAE framework
proposal.
Cell Modelling Level: From Molecules to Cells
Because of the intrinsic nature of biological systems and their
processes (sequences, genes, proteins, signaling networks, regulatory
networks and metabolic networks), the unequivocal identication of
a molecule or sequence in a specic process is nontrivial. In contrast,
the vast amount of experimental data and high throughput analyses
(genomics, transcriptomics, miRNomics and proteomics) has identied
a myriad of key factors (genes, RNAs, microRNAs, proteins and
pathways). With this in mind, tissue bioengineers and other researchers
who wish to biofabricate tissues and organs should understand rstly
how molecular-scale mechanisms lead to complex functional structures
at the scale of tissues, organs and organisms.
e characterization of intracellular pathways and their behaviour
are the original focus of systems biology and it has a long history
of work and achievement in the development and in the use of
mathematical models of cellular signalling, metabolic control and MAS
to set up simulations for molecular and cellular biology [10]. Equally
important are the graphical diagrams of biological processes, which
are useful for visual presentation, mainly for biologists. In terms of
Figure 2: Computational and mathematical methods to study complex models at several levels of biological organization – BioCAE – (molecules, genes, mRNAs,
proteins, metabolic networks, cells, organoids, tissues, and organs), which can be adapted to predict the development of a tissue and/or organ in many steps of the
biofabrication.

Citation: Dernowsek JA, Rezende RA, da Silva JVL (2017) BioCAE: A New Strategy of Complex Biological Systems for Biofabrication of Tissues and
Organs. J Tissue Sci Eng 8: 200. doi: 10.4172/2157-7552.1000200
Page 4 of 6
Volume 8 • Issue 2 • 1000200
J Tissue Sci Eng, an open access journal
ISSN: 2157-7552
soware, a dierent representation format is needed for quantifying
a model for simulation and analysis. is representation can use a
standard language called Systems Biology Markup Language (SBML)
[24]. SBML is a machine-readable format for representing models and
it is suitable for representing models commonly found in research
on a number of topics, including cell signalling pathways, metabolic
pathways, biochemical reactions, gene regulation and many others [25].
Another possibility to comprehend these phenomena would be
by means of specialized frameworks, for example, the CompuCell3D
(CC3D), which is multi-model soware that simulates interactions of
a gene regulatory network with cellular mechanisms, cell adhesion,
haptotaxis and chemotaxis, among others. e three main background
components of CC3D are a Cellular Potts Model (CPM), a Reaction–
Diusion (RD) module and a combined ODE/state model of genetic
regulatory networks and dierentiation [26].
e crucial step to the computational modeling is to cast the model
into a standardized, computable format that can be analyzed rigorously
using simulations and mathematical methods, whereas the dierent
representations of models are useful for dierent purposes. For this
reason, many research groups have developed approaches for studying
biological processes related to the intracellular and extracellular
environment, as secretion, absorption, diusion of chemicals with
the environment, cell proliferation, cell dierentiation, adhesion, cell
movement and cell internal dynamics. Below are described some of
these models found in the literature:
i) Chemical diusion: the process of molecule secretion and
absorption as facilitated diusion. e literature lacks
information about the transport mechanisms of such molecules
as genes, transcription factors microRNAs, proteins and about
the rate of diusion. However, the following approaches can
be used to study these processes: Quasi-continuum (GC),
Quantum Mechanics-Molecular Mechanics (QM-MM),
Computational Fluid Dynamic (CFD), Equation-free Multi-
scale (EFM), Kinetic Monte Carlo (KMC), Lattice Boltzmann
(LB) and Molecular Dynamics Codes (MDC).
ii) Regulatory network: the replication, transcription, translation
from the perspective of DNA and RNA begin with the
binding at the gene promoter of one or more transcription
factors. However, these intrinsic regulations might also be
repressed by transcription factors and microRNAs [27]. is
activation/inhibition is stochastic and highly depends on the
concentration of molecules and sequences. erefore, the
probability of molecular mechanisms is a sum of positive and
negative contributions from the concentration of enhancers and
silencers, respectively [28]. Logical models (Boolean models,
Probabilistic models and Bayesian Models) and Continuous
Models (Linear Model and Dierential equation based model)
are the mathematical and statistical methods most commonly
found in the regulatory networks prediction platforms [29].
iii) Migration and adhesion: e model of cell movement and
adhesion considers the chemotaxis phenomenon, which is
known to be responsible for cell sorting during morphogenesis
and this model component is inspired by a previous work that
considers chemotaxis as an important actor for the creation of
self-organized structures [30,31]. e latest models proposed
in the literature to describe dierent events in cellular adhesion
and migration are based on Cellular Automata (CA) and
Cellular Potts Models [32,33].
iv) Mitosis: cell division is fast and synchronous until cleavage,
then slows down and becomes asynchronous. e rate of
division is constant in the rst hours of cell development and
then decreases until a low value [34]. Mogilner et al. showed
some related models with mitosis, among them, stochastic
and transient microtubes - motor assemblies to drive steady,
accurate movements during the process [35].
Tissue Modelling Level: From Cells to Tissues
Tissue-scale models explain how local interactions within and
between cells lead to complex biological patterning where some
methods have already been mentioned before. However, the two main
approaches to tissue modeling are continuum models, which use cell-
density elds and partial dierential equations (PDEs) to model cell
interactions without explicit representations of cells and MAS, which
represent individual cells and interactions explicitly [17]. Furthermore,
the most promising family of mathematical models in tissue simulation
is those based on the Ising model, the Potts model and the CPM-
GGH model. e frameworks TSIM® (http://www.tsimsoware.com),
Morpheus (https://imc.zih.tu-dresden.de/wiki/morpheus/doku.
php?id=start), Simmune modeler (https://www.niaid.nih.gov/research/
simmune-project), CellSys (http://ms.izbi.uni-leipzig.de/index.php/
soware/cellsys2010) and CC3D (http://www.compucell3d.org/) are
some solutions that use this family of models.
Organs Modelling Level: From Tissues to Organs
A biological organ is a group of tissues joined in structure unit
to perform a specic function or functions (e.g. heart, brain, kidney,
lungs, etc.). Various processes at the tissue, cellular, sub-cellular and
Model Strategy References
Modelling and simulation of diffusion process in tissue
spheroids CFD [14]
Agent-based virtual tissue simulations CC3D [17]
Cell compressibility, motility and contact inhibition on the
growth of tumor cell clusters CC3D [18]
Multiscale modeling of the early CD8 T cell immune
response in lymph nodes CC3D [19]
Multi-scale knowledge on cardiac development CC3D [20]
The cell behavior ontology CC3D [21]
Cell differentiation in the transition to multicellularity CC3D [22]
Dynamics of cell aggregates fusion CC3D [36]
A multi-cell model of tumor evolution CC3D [37]
Virtual tissues MAS [38]
Disruption of blood vessel development CC3D [39]
Tumor growth and angiogenesis CC3D [40]
Agent-oriented in silico liver (ILS) MAS [41]
Model of thrombus development MAS (TS) [42]
Three-dimensional multi-scale tumor model MAS [43]
A multi-scale model of dendritic cell education and
trafcking in the lung CM [44]
Multi-scale model of follicular development CM [45]
Multi-scale in silico leukocytes model MAS [46]
Multi-scale model of organogenesis CM [47]
Limitations of spheroids under inappropriate conditions FE [48]
Finite element modelling of the viscoelastic human
cranial cavity FE [49]
CM: Continuum Model; SH: Spatially Hierarchical; TS: Temporally Separated;
CC3D: CompuCell 3D; CP: Cellular Potts; FE: Finite element; CFD: Computational
Fluid Dynamics; MAS: Multi-Agent System
Table 1: Summary of models of the biological system and the strategies used.

Citation: Dernowsek JA, Rezende RA, da Silva JVL (2017) BioCAE: A New Strategy of Complex Biological Systems for Biofabrication of Tissues and
Organs. J Tissue Sci Eng 8: 200. doi: 10.4172/2157-7552.1000200
Page 5 of 6
Volume 8 • Issue 2 • 1000200
J Tissue Sci Eng, an open access journal
ISSN: 2157-7552
lower levels occur at multiple time scales inuencing the behavior of
the organ. Multi-scale models are usually based on the continuum
modeling and/or MAS approaches which can be decomposed into
N single-scale mathematical models and several physical processes.
Various works have already been done which apply many multi-scale
methods in several biological systems [14,17-22,36-49]: a good review
is proposed in Table 1.
Conclusion
Systems biology aims at describing and understands the complex
biological systems and ultimately to develop predictive models of
human disease, meanwhile, our long-term goal is to develop a new
strategy using the information technology to model, simulate and
predict behaviours of a biological system for biofabrication of tissues
and organs. Computational models can be used to explicitly represent
a set of entities with a complex internal behaviour, which interacts with
the others and with the environment generating an emergent behaviour
representing the system dynamics; under those circumstances, many
elds of knowledge will obtain essentials biological insights.
e BioCAE approach intends to integrate the various modeling levels
by means of relevant information among them. is accomplishment
requires integration of knowledge from diverse biological components,
data and methods that span several spatial and temporal scales into
models of the system as a whole, though an important diculty in
multi-scale modeling is their high computational costs and integration.
erefore, the development of functional tissues and organs will only
be possible with the emergence of a BioCAE framework that will take
advantage of the huge amount of available models.
Acknowledgement
Our sincere thanks to the National Council for Scientic and Technological
Development (CNPq) and FAPESP for the Brazilian Institute of Biofabrication
(INCT-BIOFABRIS process 2008/57860-3) for nancial support. The authors are
also thankful to CNPq for the “Regenerative Medicine” grant (process 467643/2014-
8). The authors are thankful to FAPESP for the Brazilian Research Institute for
Neuroscience and Neurotechnology - BRAINN (CEPID process 2013/07559-3), for
the Thematic Project (Grant 2011/22749-8). The authors are grateful to the whole
team for the cooperation in the creation of images and table which are original and
used for the rst time.
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Strategy of Complex Biological Systems for Biofabrication of Tissues and
Organs. J Tissue Sci Eng 8: 200. doi: 10.4172/2157-7552.1000200
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