Modelling Biological Systems with Competitive Coherence

Many living systems, from cells to brains to governments, are controlled by the activity of a small subset of their constituents. It has been argued that coherence is of evolutionary advantage and that this active subset of constituents results from competition between two processes, a Next process that brings about coherence over time, and a Now process that brings about coherence between the interior and the exterior of the system at a particular time. This competition has been termed competitive coherence and has been implemented in a toy-learning program in order to clarify the concept and to generate—and ultimately test—new hypotheses covering subjects as diverse as complexity, emergence, DNA replication, global mutations, dreaming, bioputing (computing using either the parts of biological system or the entire biological system), and equilibrium and nonequilibrium structures. Here, we show that a program using competitive coherence, Coco, can learn to respond to a simple input sequence 1, 2, 3, 2, 3, with responses to inputs that differ according to the position of the input in the sequence and hence require competition between both Next and Now processes.

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The Beacon Calculus: A formal method for the flexible and concise modelling of biological systems

10.1101/579029 ◽

2019 ◽

Author(s):

Michael A. Boemo ◽

Luca Cardelli ◽

Conrad A. Nieduszynski

Keyword(s):

Dna Replication ◽

Open Source ◽

Biological System ◽

Genome Stability ◽

Formal Method ◽

Biological Systems ◽

Origin Firing ◽

Link Type ◽

Multisite Phosphorylation ◽

Large State Space

AbstractBiological systems are made up of components that change their actions (and interactions) over time and coordinate with other components nearby. Together with a large state space, the complexity of this behaviour can make it difficult to create concise mathematical models that can be easily extended or modified. This paper introduces the Beacon Calculus, a process algebra designed to simplify the task of modelling interacting biological components. Its breadth is demonstrated by creating models of DNA replication dynamics, the gene expression dynamics in response to DNA methylation damage, and a multisite phosphorylation switch. The flexibility of these models is shown by adapting the DNA replication model to further include two topics of interest from the literature: cooperative origin firing and replication fork barriers. The Beacon Calculus is supported with the open-source simulator bcs (https://github.com/MBoemo/bcs.git) to allow users to develop and simulate their own models.Author summarySimulating a model of a biological system can suggest ideas for future experiments and help ensure that conclusions about a mechanism are consistent with data. The Beacon Calculus is a new language that makes modelling simple by allowing users to simulate a biological system in only a few lines of code. This simplicity is critical as it allows users the freedom to come up with new ideas and rapidly test them. Models written in the Beacon Calculus are also easy to modify and extend, allowing users to add new features to the model or incorporate it into a larger biological system. We demonstrate the breadth of applications in this paper by applying the Beacon Calculus to DNA replication and DNA damage repair, both of which have implications for genome stability and cancer. We also apply it to multisite phosphorylation, which is important for cellular signalling. To enable users to create their own models, we created the open-source Beacon Calculus simulator bcs (https://github.com/MBoemo/bcs.git) which is easy to install and is well-supported by documentation and examples.

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Physical Constraints on Epistasis

Molecular Biology and Evolution ◽

10.1093/molbev/msaa124 ◽

2020 ◽

Vol 37 (10) ◽

pp. 2865-2874 ◽

Cited By ~ 4

Author(s):

Kabir Husain ◽

Arvind Murugan

Keyword(s):

Experimental Data ◽

Biological System ◽

Regulatory Networks ◽

Collective Mode ◽

Biological Systems ◽

Living Systems ◽

Single Mutation ◽

Physical Constraints ◽

Long Time

Abstract Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global nonlinearity applied to an underlying linear trait, that is, as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable.

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The Systemic Theory of Living Systems and Relevance to CAM

Evidence-based Complementary and Alternative Medicine ◽

10.1093/ecam/neh068 ◽

2005 ◽

Vol 2 (1) ◽

pp. 13-18 ◽

Cited By ~ 15

Author(s):

José A. Olalde Rangel

Keyword(s):

Biological System ◽

Treatment Strategy ◽

Living Systems ◽

State Of Health ◽

Total Entropy ◽

First Line ◽

Available Energy ◽

Systemic Theory ◽

Or Organization ◽

Normal Health

The Systemic Theory of Living Systems is being published in several parts in eCAM. The theory is axiomatic. It originates from the phenomenological idea that physiological health is based on three factors: integrity of its structure or organization,O, functional organic energy reserve,E, and level of active biological intelligence,I. From the theory is derived a treatment strategy called Systemic Medicine (SM). This is based on identifying and prescribing phytomedicines and/or other medications that strengthen each factor. Energy-stimulating phytomedicines increase available energy and decrease total entropy of an open biological system by providing negative entropy. The same occurs with phytomedicines that act as biological intelligence modulators. They should be used as the first line of treatment in all ailments, since all pathologies, by definition, imply a higher than normal organic entropy. SM postulates that the state of health,H, of an individual, is effectively equal to the product of the strength of each factorH=O×E×I. SM observes that when all three factors are brought back to ideal levels, patients' conditions begin the recovery to normal health.

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Classification of Chaotic Behaviors in Jerky Dynamical Systems

Complex Systems ◽

10.25088/complexsystems.30.1.93 ◽

2021 ◽

Vol 30 (1) ◽

pp. 93-110

Author(s):

Tianyi Wang ◽

Keyword(s):

Machine Learning ◽

Chaotic Systems ◽

Chaotic Behavior ◽

Learning Algorithm ◽

Model Systems ◽

Supervised Machine Learning ◽

Learning Program ◽

New Methods ◽

Over Time

Differential equations are widely used to model systems that change over time, some of which exhibit chaotic behaviors. This paper proposes two new methods to classify these behaviors that are utilized by a supervised machine learning algorithm. Dissipative chaotic systems, in contrast to conservative chaotic systems, seem to follow a certain visual pattern. Also, the machine learning program written in the Wolfram Language is utilized to classify chaotic behavior with an accuracy around 99.1±1.1%.

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Integrative Data Analysis for Biological Discovery

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch164 ◽

2011 ◽

pp. 1058-1065

Author(s):

Sai Moturu

Keyword(s):

Molecular Biology ◽

Biological System ◽

Molecular Level ◽

Biological Systems ◽

Integrated Analysis ◽

John Muir ◽

Complex Biological System ◽

Protein Levels ◽

Lofty Goal ◽

Complex Relationships

As John Muir noted, “When we try to pick out anything by itself, we find it hitched to everything else in the Universe” (Muir, 1911). In tune with Muir’s elegantly stated notion, research in molecular biology is progressing toward a systems level approach, with a goal of modeling biological systems at the molecular level. To achieve such a lofty goal, the analysis of multiple datasets is required to form a clearer picture of entire biological systems (Figure 1). Traditional molecular biology studies focus on a specific process in a complex biological system. The availability of high-throughput technologies allows us to sample tens of thousands of features of biological samples at the molecular level. Even so, these are limited to one particular view of a biological system governed by complex relationships and feedback mechanisms on a variety of levels. Integrated analysis of varied biological datasets from the genetic, translational, and protein levels promises more accurate and comprehensive results, which help discover concepts that cannot be found through separate, independent analyses. With this article, we attempt to provide a comprehensive review of the existing body of research in this domain.

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Bottom-Up Synthetic Biology: Engineering in a Tinkerer’s World

Science ◽

10.1126/science.1211701 ◽

2011 ◽

Vol 333 (6047) ◽

pp. 1252-1254 ◽

Cited By ~ 137

Author(s):

Petra Schwille

Keyword(s):

Synthetic Biology ◽

Life Science ◽

Biological Systems ◽

Living Systems ◽

Design Features ◽

Literal Interpretation ◽

Bottom Up ◽

Systematic Construction ◽

Science Discipline ◽

Recent Developments

How synthetic can “synthetic biology” be? A literal interpretation of the name of this new life science discipline invokes expectations of the systematic construction of biological systems with cells being built module by module—from the bottom up. But can this possibly be achieved, taking into account the enormous complexity and redundancy of living systems, which distinguish them quite remarkably from design features that characterize human inventions? There are several recent developments in biology, in tight conjunction with quantitative disciplines, that may bring this literal perspective into the realm of the possible. However, such bottom-up engineering requires tools that were originally designed by nature’s greatest tinkerer: evolution.

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Characterizing Autopoiesis in the Game of Life

Artificial Life ◽

10.1162/artl_a_00143 ◽

2015 ◽

Vol 21 (1) ◽

pp. 1-19 ◽

Cited By ~ 7

Author(s):

Randall D. Beer

Keyword(s):

Cellular Automaton ◽

Cognitive Science ◽

Formal Analysis ◽

Initial Step ◽

Spatiotemporal Patterns ◽

Living Systems ◽

Game Of Life ◽

Enactive Approach ◽

Conway’S Game Of Life ◽

Over Time

Maturana and Varela's concept of autopoiesis defines the essential organization of living systems and serves as a foundation for their biology of cognition and the enactive approach to cognitive science. As an initial step toward a more formal analysis of autopoiesis, this article investigates its application to the compact, recurrent spatiotemporal patterns that arise in Conway's Game-of-Life cellular automaton. In particular, we demonstrate how such entities can be formulated as self-constructing networks of interdependent processes that maintain their own boundaries. We then characterize the specific organizations of several such entities, suggest a way to simplify the descriptions of these organizations, and briefly consider the transformation of such organizations over time.

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REGULATED BIOLOGICAL SYSTEMS

Journal of Biological System ◽

10.1142/s0218339000000092 ◽

2000 ◽

Vol 08 (02) ◽

pp. 141-149 ◽

Cited By ~ 5

Author(s):

MICHEL CABANAC ◽

MAURICIO RUSSEK

Keyword(s):

Control Theory ◽

Open Systems ◽

Block Diagram ◽

Biological Systems ◽

Fundamental Difference ◽

Living Systems ◽

Regulation Process

Control theory is concerned mainly with the treatment of signals. This article takes into account that living beings not only treat information, but they are open systems traversed by flows of energy and mass. A new block diagram of the regulation process is proposed, taking into account this fundamental difference between engineered and living systems. This new diagram possesses both didactic and heuristic advantages.

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Evaluating the impact of Idea-Inspire 4.0 on analogical transfer of concepts

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060418000136 ◽

2018 ◽

Vol 32 (4) ◽

pp. 431-448 ◽

Cited By ~ 4

Author(s):

L. Siddharth ◽

Amaresh Chakrabarti

Keyword(s):

Engineering Design ◽

Biological System ◽

Biological Systems ◽

Knowledge Bases ◽

Design Problems ◽

Function Decomposition ◽

Engineering Design Problems ◽

Biological Domain ◽

Biological Concepts ◽

The Impact

AbstractThe biological domain has the potential to offer a rich source of analogies to solve engineering design problems. However, due to the complexity embedded in biological systems, adding to the lack of structured, detailed, and searchable knowledge bases, engineering designers find it hard to access the knowledge in the biological domain, which therefore poses challenges in understanding the biological concepts in order to apply these concepts to engineering design problems. In order to assist the engineering designers in problem-solving, we report, in this paper, a web-based tool called Idea-Inspire 4.0 that supports analogical design using two broad features. First, the tool provides access to a number of biological systems using a searchable knowledge base. Second, it explains each one of these biological systems using a multi-modal representation: that is, using function decomposition model, text, function model, image, video, and audio. In this paper, we report two experiments that test how well the multi-modal representation in Idea-Inspire 4.0 supports understanding and application of biological concepts in engineering design problems. In one experiment, we use Bloom's method to test “analysis” and “synthesis” levels of understanding of a biological system. In the next experiment, we provide an engineering design problem along with a biological-analogous system and examine the novelty and requirement-satisfaction (two major indicators of creativity) of resulting design solutions. In both the experiments, the biological system (analogue) was provided using Idea-Inspire 4.0 as well as using a conventional text-image representation so that the efficacy of Idea-Inspire 4.0 is tested using a benchmark.

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Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100433 ◽

2019 ◽

Vol 8 (10) ◽

pp. 433 ◽

Cited By ~ 4

Author(s):

Jianfeng Huang ◽

Yuefeng Liu ◽

Yue Chen ◽

Chen Jia

Keyword(s):

Deep Neural Network ◽

Rapid Development ◽

Input Sequence ◽

Learning Task ◽

Point Of Interest ◽

Poi Recommendation ◽

Recommendation Accuracy ◽

Location Based Social Networks ◽

Historical Trajectory ◽

Over Time

Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.

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