Systems Biology in simple terms: from experiments, computer simulations, and unthought-of insights

1. Introduction

Biology is indeed one of the youngest science; mathematics can be traced back to the "Greek Science," physics the way we see it today to Newton's time and renewed by Einstien; geography to the navegation times, chemistry to Lavoisier and so on. The term biology itself was used on the 19th century, but studies goes back to 1974 with the microorganisms by Anton Van Leeuwenhoek. Maybe what several people take as granted is the usage of reasoning in biology, until the turning of the 19th-20th-century we still had formally scientists that believed on the necessity of special laws to explain bio-systems; see that the existence of atoms was still in doubt as well, but settled by Einstein and his studies on the brownian motion. The question "what is life" is a tough one, Schrödinger itself pinpointed in a publication the complexity associated to bio-systems and foretold the emergence of new science, one that could finally address questions that physics and mathematics purely failed to reach such as the laws of biological systems; it is stable, isn't it? you walk, you breathe, you are miracle of miracles. 

A nice example of the shift of thinking and the enthusiam could be found on the words of a physicist that had shift to biology: "When I first read a biology textbook, it was like reading a thriller. Every page brought a new shock. As a physicist, I was used to studying matter that obeys precise mathematical laws. But cells are matter that dances. Structures spontaneously assemble, perform elaborate biochemical functions, and vanish effortlessly when their work is done. Molecules encode and process information virtually without errors, despite the fact that they are under strong thermal noise and embedded in a dense molecular soup. The main message is that biological systems contain an inherent simplicity ." Further, in the word of another scientist: "It is often said that biology is going to be the science of the 21st  century as physics was the science of the 20th."

2. Systems Biology

Nowadays the most beautiful and remarkable expression of biology is called systems biology, in Portuguese (Brazilian): Biologia Sistêmica. In general, the term is associated to a publication of a Japanese scientist called H Kitano in 2000, however the term was used much before by F Kapra in 1972 in the book called The Turning Point, in which Capra strives to highlight the need of a novel science, a science that is based upon holistic view rather than reductionist. On the current state of the art, one of the most prominant scientists is called Leroy Hood, an American scientist. He is responsible for the term P4 medicine, a medicine based on prediction rather than action; see that in the popular sayings we know that it is better foresee than remedy. A consequence of the enthusiam around systems biology gave rise to new terms in the scientific community such as systems pharmacology, systems medicine (sometimes called systems biomedicine), and P4 medicine; see that the term systems engineering has already been around for a while, but used in a different context.

A fast search on Google provides us the following definitions. 

"Systems biology (also known as Systeomics) is the computational and mathematical modeling of complex biological systems. An emerging engineering approach applied to biomedical and biological scientific research." [1]

"Systems Biology at a Glance

- Systems Biology is an information science.

- Systems biology is a holistic rather than reductionist approach to understanding and controlling biological complexity.

- Systems biology uses a collaborative, cross-disciplinary approach.

- Systems biology integrates many multi-scale types of biological information.

- Systems biology develops new experimental approaches to capture temporal and spatial dynamics of biological networks.

- Systems biology permits the development of predictive and actionable models of biology or disease.

- Systems biology creates many opportunities to transfer knowledge to society and to build strategic partnerships to leverage the power of ISB." [2]

"Systems biology is the study of systems of biological components, which may be molecules, cells, organisms or entire species. Living systems are dynamic and complex, and their behavior may be hard to predict from the properties of individual parts. To study them, we use quantitative measurements of the behavior of groups of interacting components, systematic measurement technologies such as genomics, bioinformatics and proteomics, and mathematical and computational models to describe and predict dynamical behavior. Systems problems are emerging as central to all areas of biology and medicine." [3]

"A Biologia Sistêmica é o ramo da ciência que busca entender os organismos biológicos em todos os seus níveis, desde a caracterização de suas partes constituintes (genes, RNAs, proteínas, metabólitos), a elucidação das interconexões entre os distintos membros dessas redes de interações, até a compreensão do organismo como um todo." [4]

   

More much can be found, given the rise of the field. 

Biology came so far from the "big", study of population with methodologies such as taxonony then anatomy and so on until molecular biology with genes and proteins. Now we are doing the opposity with systems biology, from gene to macro-propreties, from the genotype to the phenotype. And this is the grand challenge of modern science, the amount of data, the necessity of creating virtual systems for simulations and so on has occupied the mind of several scientists.  

3. More about systems biology

The specific philosophical paradigm is exactly what differentiates systems biology from closely related sciences, e.g. from molecular biology and from computational biology. Some people might think that systems biology is just a computationally based version of molecular biology , or, on the contrary, that systems biology is just a bimolecular version of computational biology and consists of computation plus experiment. This view may serve the current needs, but certainly systems biology is more than that. Systems biology itself is a philosophy, an strong set of ideas that was created due to the limitation of the classical sciences, based mainly on reduced snapshots of reality. Systems biology is rather a new scientific paradigm aiming to understand how biological function, that is absent from macromolecules in isolation, emerges when they are components in the system. Systems biology deals with a strong emergence in the sense that systems properties cannot be reduced to the knowledge of the behaviour of components in isolation. For beginners this view might sound difficult to take in. A quite simple example is if you had a car that when it is working it behaves differently from when it is not turned on; for sure the workers on this area would require much more complex tools for fixing your car [5].

A novel concept largely used in systems biology is called emergent properties. The main idea is that the emergent properties are related to the intreconnection of many molecular/cellular players (holistic viewpoint), instead of merely the sum of single players' properties (reductionism viewpoint) [6]. As an example, consider one definition of systems biology and make use of the term emergent properties: "Systems biology is the analysis of the relationships among the elements in a system in response to genetic or environmental perturbations, with the goal of understanding the system or the emergent properties of the system" [7]. 

4. Final Remarks and comments

In a recent scientific meeting, Como Lake, 2014, it was clear to me the strenght of systems biology. For me what really will be respected about systems biology is its industrial side. Besides all the problematic created by the industry, it is here to stay. The best we can do is making it better, more efficient, more practical, more for the society it supposes to serve. Systems biology is applying techniques already used in the industrial community such as simulations, see for example in industrial engineering the software called Arena (R) or even the systemic-simulation-like software in Life Cycle Assessment in Enviromental sciences called Umberto (R). To have a glimpse on the closeness, see softwares such as Modelica (R) and Copasi (R) already in use by the community of systems biology. 

Cited references:

[1] Systems biology. (2014, December 13). In Wikipedia, The Free Encyclopedia. Retrieved 10:01, December 13, 2014, from http://en.wikipedia.org/w/index.php?title=Systems_biology&oldid=637883773

[2] About Systems Biology, Institute for systems biology, http://www.systemsbiology.org/about-systems-biology

[3] Welcome to the Department of Systems Biology, http://sysbio.med.harvard.edu/

[4] Biologia Sistêmica, Laboratório de Biologia Sistêmica de Microorganismos, http://labisismi.fmrp.usp.br/index.php/br/

[5] Systems Biology vs. Computational Biology, adapted from personal comunicaton by email with Alexey Kolodkin. 

[6] "Systems Biology" and "Emergent Properties", adapted from personal comunicaton by email with Pasquale Palumbo.
[7] WESTON, A. D. and L. Hood.  Systems Biology, Proteomics, and the Future of Health Care: Toward Predictive, Preventative, and Personalized Medicine. Journal of Proteome Research 2004, 3, 179-196 179.

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PS. this is the draft for book on systems biology in simple terms, aimed to a no-expertise audience interested on the new trends on science. 

Systems Biology in simple terms

Neural Computing

Besides the tracing of the pathway between genetic information and biological existence is not obvious and easy to see in a level of modeling aim, those relations are quite clear and confirmed in a qualitative  - theoretical – way. One may keep in mind that each bio-structure may be boiled down to genetic information, even though those are not simple enough for an easy-recipe implementation. The first bio-structure resulting from gene existence is called protein and its activity is modeled via transcription networks. Proteins have been found taking place in all biological processes, from DNA repair, protein synthesis to immune system. A quite important group of protein is called enzymes, they speed up reactions. A second quite important group is called transcription factors, they control gene activity.

            In the other hand, it has been studied intensively a group of mathematical models named Artificial Neural Networks, or simply, neural networks. They are models that have the brain as source for basic principles. In a short and not complete time line, the works have started in 1943 with McCulloch and Pitts proposing a general theory for learning machine; 1960, Rosenblatt and his collaborators studied the perceptron; 1961, Caianiello created a learning algorithm based on hebb’s learning rule; 1970, use of Evolutionary Algorithms by  John Holland; and 1985, recognition of the back-propagation by some group of scientists. In the current state of the art, those methods based on learning machine are applied intensively for modeling natural phenomena with high nonlinearity-level, nosy, but with some hidden law that are difficult or even impossible for the current mathematical theory captures. 

            Here is studied the application of neural networks for modeling gene expression. In a more precise way, gene expression is modeled via estimation, once the sharp value is not possible to be known, due to technological limitation and biological fluctuation.

From: Neural Computing.

Computational Systems Biology

Computational Systems Biology.
Source: Andres Kriete, Roland Eils, Computational Systems Biology, Elsevier Academic Press, 2006.

neural network

A neural network is a massively parallel distributed processor made up of single processing units that has a natural propensity for storing experimental knowledge and making it available for use. It resembles the brain in two respects:

1. knowledge is acquired by the network from its environment through a learning process:

2. Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge.

Source: Simon Haykin, Neural networks and learning machine, third edition, Pearson Education, Inc, 2009. 

NEURAL NETWORK FOR TRANSCRIPTION NETWORKS

Short View:

Neural Networks have been applied widely to real problem modeling. The applications have been concentrated mainly on image processing, classification of patterns, and function approximation. The best characteristic of neural networks from the point of view for function approximation is the parallel processing power. Besides neural networks by its own are just linear models, their local structure for calculation, named neurons, may be selected properly in order to those behave as a nonlinear model. In the other hand, Transcription Networks has been applied as a gene expression modeling tool. The principle behind those is that nature solves problems in “local machines” termed network motifs. Here is claimed that the theory of neural networks is a powerful tool for transcription network modeling based on experimental data from gene expression.

   All the living organism physical state is function in a production and replication level of the genetic code, including all the biological fluids and internal structures. One may use applied genetics to explain biological components, such as the blood cells or different biological liquid separators, which in wrong development or repairing processing may be the cause of significant physical diseases. This justifies the study of genetics in the gene-level. 

Key-words: Neural Networks; Transcription Networks; Gene Expression Estimation; Systems Biology. 

Schematic view of how a typical supervised neural network handles the data for learning

Schematic view of the backpropagation, two waves of information, backward and forward

Typical challenges faced in neural network design

On the tracks of Systems Biology

Abstract— Systems biology is a potential new branch of biology with roots in many other fields such as biology, physics, mathematics, and computer science. Accordingly, this is a manifestation of mathematical biology and endeavors to understand complex systems, such as networks of genes, as interacting systems’ components.  In this paper, we analyze the field from a literature review framework; the author is passive in the review. Further, we conclude that systems biology is a potential field for contributing to the new horizons of science, especially biomedical sciences; also the weakness of systems biology is that it is not commonly mentioned in traditional books in biology, even recently published. Nonetheless, as the literature claims, this field might be the bridge for making biology, especially genetics, a source of ideas and principles for other fields, such as biomechanics; and simultaneously an example for other fields of biomedical sciences that mathematics, physics and other branch indeed can enhance those with new insights and methods.  

Keywords—Systems Biology; Biological Systems; Biological Networks; Gene sharing;

Go on: On the tracks of Systems Biology: An interim look

On the Applicability of Computational Intelligence in Transcription Network Modelling

On the Applicability of Computational Intelligence in Transcription Network Modelling

Abstract

On this manuscript, we report the proposal of the use of computational intelligence methods as a powerful and valuable source of mathematical tools for modelling gene expression networks. Gene expression networks are modelled via transcription networks. Transcription networks are graph-oriented mathematical-computational models that attempt to understand gene expression as simpler and smaller systems named network motifs. Each type of transcription network presents a peculiar set of network motifs. On the other hand, neural networks are graph-oriented mathematical models with roots on philosophy, physiology, neuroscience, physics, computer science and other scientific branches that and they have been promising as mathematical models for modeling nonparametric data and systems with hidden laws. On the presented manuscript, we run some simulations, discuss some literatures and finish out with some discussions on the promises for possible future achievements on the field. This work is novel on the sense that it proposes on a single methodology the junction of the fields of gene expression networks and neural networks and at the same time, we give the directions for possible intelligence-based systems; called on the literature “intelligent agents”. We do not solve simple examples or stop on some specific cases; those are left for personal achievements.

Key-words: Systems Biology; Neural Networks; Computational Intelligence; software engineering; bioinformatics; gene expression modelling; transcription networks.

Introduction

Systems Biology and learning machine share a commonplace on their endeavors. My main motivation for creating this blog was the decision of Wikipedia to delete my article Systems Biology and Machine Learning. Further, I have spent some time on this direction  for a while. 

As highlights Hamid (2008)

"If we could start with complete knowledge of a system, we might construct a very detailed model and then find approximation that hold true under conditions of interest. However, in GRN modeling, we are often faced with the opposite situation. We do not know the mechanisms that cause an observed outcome. In such cases, we typically construct models in three steps: 1) use biological knowledge to hypothesize the nature of the interactions in the system; 2) propose an approximate model (e.g a rate law) for each interaction; and 3) find parameters values that minimize the error between the model and the data. " This and other statements surely pinpoint the natural sinergy between systems biology and machine learning based models.

Reference

PIRES J. G. On the applicability of Computational Intelligence in Transcription Network Modeling. Thesis of master of science. Faculty of Applied Physics and Mathematics, Gdansk University of Technology, Poland. 74:1:46. 2012.

Pires JG (2013). On the mathematical modelling in gene expression estimation. II Workshop and School on Dynamics, Transport and Control in Complex Networks  (ComplexNet), Ribeirão Preto, SP, Brazil. 21-26/October. Poster.

Pires JG (2013). Neural Networks in Transcription Networks: An alternative and complementary approach for the observer-based method. 1st BRICS Countries  & 11th Brazilian Congress on Computational Intelligence. Brazil. 2: 1-2.

Jorge Pires

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