182 research outputs found
Evolving Cellular Automata Schemes for Protein Folding Modeling Using the Rosetta Atomic Representation
Financiado para publicaciĂłn en acceso aberto: Universidade da Coruña/CISUG [Abstract] Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.This study was funded by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014-2020 Program), with grants CITIC (ED431G 2019/01), GPC ED431B 2019/03 and IN845D-02 (funded by the âAgencia Gallega de InnovaciĂłnâ, co-financed by Feder funds), and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431B 2019/03Xunta de Galicia; IN845D-0
Prédiction structurale de biomolécules à l'aide d'une construction d'automates cellulaires simulant la dynamique moléculaire
ThÚse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal
Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]
An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u
Stepping Beyond the Newtonian Paradigm in Biology. Towards an Integrable Model of Life: Accelerating Discovery in the Biological Foundations of Science
The INBIOSA project brings together a group of experts across many disciplines
who believe that science requires a revolutionary transformative
step in order to address many of the vexing challenges presented by the
world. It is INBIOSAâs purpose to enable the focused collaboration of an
interdisciplinary community of original thinkers.
This paper sets out the case for support for this effort. The focus of the
transformative research program proposal is biology-centric. We admit
that biology to date has been more fact-oriented and less theoretical than
physics. However, the key leverageable idea is that careful extension of the
science of living systems can be more effectively applied to some of our
most vexing modern problems than the prevailing scheme, derived from
abstractions in physics. While these have some universal application and
demonstrate computational advantages, they are not theoretically mandated
for the living. A new set of mathematical abstractions derived from biology
can now be similarly extended. This is made possible by leveraging
new formal tools to understand abstraction and enable computability. [The
latter has a much expanded meaning in our context from the one known
and used in computer science and biology today, that is "by rote algorithmic
means", since it is not known if a living system is computable in this
sense (Mossio et al., 2009).] Two major challenges constitute the effort.
The first challenge is to design an original general system of abstractions
within the biological domain. The initial issue is descriptive leading to the
explanatory. There has not yet been a serious formal examination of the
abstractions of the biological domain. What is used today is an amalgam;
much is inherited from physics (via the bridging abstractions of chemistry)
and there are many new abstractions from advances in mathematics (incentivized
by the need for more capable computational analyses). Interspersed
are abstractions, concepts and underlying assumptions ânativeâ to biology
and distinct from the mechanical language of physics and computation as
we know them. A pressing agenda should be to single out the most concrete
and at the same time the most fundamental process-units in biology
and to recruit them into the descriptive domain. Therefore, the first challenge
is to build a coherent formal system of abstractions and operations
that is truly native to living systems.
Nothing will be thrown away, but many common methods will be philosophically
recast, just as in physics relativity subsumed and reinterpreted
Newtonian mechanics.
This step is required because we need a comprehensible, formal system to
apply in many domains. Emphasis should be placed on the distinction between
multi-perspective analysis and synthesis and on what could be the
basic terms or tools needed.
The second challenge is relatively simple: the actual application of this set
of biology-centric ways and means to cross-disciplinary problems. In its
early stages, this will seem to be a ânew scienceâ.
This White Paper sets out the case of continuing support of Information
and Communication Technology (ICT) for transformative research in biology
and information processing centered on paradigm changes in the epistemological,
ontological, mathematical and computational bases of the science
of living systems. Today, curiously, living systems cannot be said to
be anything more than dissipative structures organized internally by genetic
information. There is not anything substantially different from abiotic
systems other than the empirical nature of their robustness. We believe that
there are other new and unique properties and patterns comprehensible at
this bio-logical level. The report lays out a fundamental set of approaches
to articulate these properties and patterns, and is composed as follows.
Sections 1 through 4 (preamble, introduction, motivation and major biomathematical
problems) are incipient. Section 5 describes the issues affecting
Integral Biomathics and Section 6 -- the aspects of the Grand Challenge
we face with this project. Section 7 contemplates the effort to
formalize a General Theory of Living Systems (GTLS) from what we have
today. The goal is to have a formal system, equivalent to that which exists
in the physics community. Here we define how to perceive the role of time
in biology. Section 8 describes the initial efforts to apply this general theory
of living systems in many domains, with special emphasis on crossdisciplinary
problems and multiple domains spanning both âhardâ and
âsoftâ sciences. The expected result is a coherent collection of integrated
mathematical techniques. Section 9 discusses the first two test cases, project
proposals, of our approach. They are designed to demonstrate the ability
of our approach to address âwicked problemsâ which span across physics,
chemistry, biology, societies and societal dynamics. The solutions
require integrated measurable results at multiple levels known as âgrand
challengesâ to existing methods. Finally, Section 10 adheres to an appeal
for action, advocating the necessity for further long-term support of the
INBIOSA program.
The report is concluded with preliminary non-exclusive list of challenging
research themes to address, as well as required administrative actions. The
efforts described in the ten sections of this White Paper will proceed concurrently.
Collectively, they describe a program that can be managed and
measured as it progresses
Adaptive Multi-Functional Space Systems for Micro-Climate Control
This report summarizes the work done during the Adaptive Multifunctional Systems for Microclimate
Control Study held at the Caltech Keck Institute for Space Studies (KISS) in 2014-2015.
Dr. Marco Quadrelli (JPL), Dr. James Lyke (AFRL), and Prof. Sergio Pellegrino (Caltech) led
the Study, which included two workshops: the first in May of 2014, and another in February
of 2015. The Final Report of the Study presented here describes the potential relevance of
adaptive multifunctional systems for microclimate control to the missions outlined in the 2010
NRC Decadal Survey.
The objective of the Study was to adapt the most recent advances in multifunctional reconfigurable
and adaptive structures to enable a microenvironment control to support space exploration in
extreme environments (EE). The technical goal was to identify the most efficient materials,
architectures, structures and means of deployment/reconfiguration, system autonomy and energy
management solutions needed to optimally project/generate a micro-environment around space
assets. For example, compact packed thin-layer reflective structures unfolding to large areas
can reflect solar energy, warming and illuminating assets such as exploration rovers on Mars or
human habitats on the Moon. This novel solution is called an energy-projecting multifunctional
system (EPMFS), which are composed of Multifunctional Systems (MFS) and Energy-Projecting
Systems (EPS)
Introduction to the Modeling and Analysis of Complex Systems
Keep up to date on Introduction to Modeling and Analysis of Complex Systems at http://bingweb.binghamton.edu/~sayama/textbook/!
Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.
This textbook is available for purchase in both grayscale and color via Amazon.com and CreateSpace.com.https://knightscholar.geneseo.edu/oer-ost/1013/thumbnail.jp
A Multi-Dimensional Width-Bounded Geometric Separator and its Applications to Protein Folding
We used a divide-and-conquer algorithm to recursively solve the two-dimensional problem of protein folding of an HP sequence with the maximum number of H-H contacts. We derived both lower and upper bounds for the algorithmic complexity by using the newly introduced concept of multi-directional width-bounded geometric separator. We proved that for a grid graph G with n grid points P, there exists a balanced separator A subseteq P$ such that A has less than or equal to 1.02074 sqrt{n} points, and G-A has two disconnected subgraphs with less than or equal to {2over 3}n nodes on each subgraph. We also derive a 0.7555sqrt {n} lower bound for our balanced separator. Based on our multidirectional width-bounded geometric separator, we found that there is an O(n^{5.563sqrt{n}}) time algorithm for the 2D protein folding problem in the HP model. We also extended the upper bound results to rectangular and triangular lattices
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