Artificial Intelligence in Medicine

Artificial Intelligence appeared in the 1950s as a term to designate a set of novel methods and philosophical attitudes toward problem solving. In the 1980s it had a serious falling in popularity, which was the period of methodologies such as Intellgent Control, fuzzy systems, and neural networks, nowadays composing computational intelligence, these are numerical.based methodolgies, so far artifiical intelligence was mainly worried about symbolic-based methodologies. 

Some says that artificial intelligence possesses too much Is, this is to highlight the problems faced by the same in the past. There are several definitions. Russel and Norvig (2010) categorizes them into: thinking humanly, thinking rationally, acting humanly, or acting rationally. Computational intelligence, a competitor for attention, is placed into acting rationally. 

Computer is without a doubt the revolution of the millennium. Medicine is not different from the other sciences, it is nowadays somehow slave of equipaments, some doctors will not move even an eye without the proper machinary. All these systems cannot be run and controlled just based on linear models or linearizations as it is done often by mathematician. Computer scientists and engineers are more practical and they have certainly been taking advantage of the changes so far.

According to Fieschi (1990),  artificial intelligence, a strange phrase in which the two words taken

separately conjure up opposite meanings. Intelligence seems to us to be intimately associated with human behaviour. The idea of 'artificial', on the other hand, conjures up the idea of objects characteristically not natural but 'man-made'. To call 'artificial' a prime component of human nature seems a paradox. This term is badly chosen, particularly as the aim of artificial intelligence systems is to represent behaviour comparable to human behaviour. The same observation was done on Poole et al (1998). Further, Fieschi (1990) still pinpoint, artificial intelligence in medicine is going to take a very important place in the science of medical informatics.

In summary, artificial intelligence is changing, and it seems for better. Medicine is a field rich on tough problems, problems which solution could benefit several people. This field certainly will occupy the minds of several researches on the future. I am quite sure that computational intelligence will not be left behind, see for example Lam et al (2012).

References cited

RUSSELL, Stuart; NORVIG, Peter. Artificial Intelligence: A modern approach. Third edition. Prentice Hall Series in Artificial Intelligence: 2010.
Fieschi, M, Artificial Intelligence in medicine: expert systems, Translated by D Cramp, Spring-Science + Business Media, 1990.
David Poole; Alan Mackworth; Randy Goebel. Computational Intelligence: A Logical Approach. Oxford University Press. 1998.
Lam, HK; Ling, SH; Nguyen, HT (eds) (2012). Computational Intelligence and its applications: evolutionary Computation, Fuzzy Logic, Neural Network, and Support Vector Machine Technique. Imperial College Press.

External Links

- Conference on Artificial Intelligence in Medicine (Italy)

- Artificial Intelligence in Medicine (Portugal)

Systems Identification vs. Model identification

"However, still the problem of system identification has not been completely solved.
Consequently, nowadays new ideas and methods to solve the system identification
problem or parts of it are introduced." Keesman 2011.

We can say that the ultimate goal of science is building models, mathematical modeling is one of the means to achieve this goal, the promising is to apply more symbolic methodologies once this is the way we think. However, as always, life is not easy. Behind this challenge, new ones come out. One of the is system identification, sometimes referred to as model identification, apparently model identification is used more in Italy, further, it seems to be a broader concept, having system identification as a special case. 

The challenge that arises is based upon the fact that besides we might know how a system model looks like, in general we do not know the parameter values, for instance, what demands methods to find them from experiments. Maybe a nice way to see it is if you know the basics of music theory. Besides a symphony is complex, it is built upon simple chords, in general 8, but some musicians rely their works on just three chords; e.g. I, IV, and V. If you hear a song, you suppose to identify the chords, given you know them. However in real life it does not happen so easily. If you ask ten guitar players to transcribe a song you like, this is highly possible they will disagree, especially on subtle differences such as G and G7. In this case we can say they have failed to identify the system model, this is so an identifiability problem, they know the model in general, but details cannot be filtered out. 

In general, system identification consists of three basic steps: experiment design and data acquisition, model structure selection and parameter estimation, and model validation [1]. System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system [2]. 

Traditionally system identification is based on mathematics, however new trends are applying what can be kept by the name of computational intelligence such as neural networks.  

References cited

[1] Karel J. Keesman, System Identification: an introduction, Advanced books in control and signal processing, Spring, 2011.

[2] Lennart Ljung, System Identification: theory for user, Second edition, Practice hall information znd system science series, 1999. 

Pharmacokinetics and pharmacodynamics

"Pharmacokinetics and pharmacodynamics are the important fields of pharmaceutical sciences for investigating disposition profiles and the pharmacological efficacy of drugs in the body under various experimental and clinical conditions."

(Caldwell et

al., 1995 and Cocchetto and Wargin, 1980, cited by Kwon 2002).

Source: Pires et al 2014.

Source: Pires et al 2014.

References cited

Younggil Kwon, Handbook of Essential Pharmacokinetics, Pharmacodynamics and Drug Metabolism for Industrial Scientists, Kluwer Academic Publishers, 2002.

Caldwell J. et al., An introduction to drug disposition: the basic principles of absorption, distribution,

Cocchetto D. M. and Wargin W. A., A bibliography for selected pharmacokinetic topics, Drug Intel. Clin. metabolism and excretion, Toxicol. Pathol. 23: 102-114, 1995.

Pharmacol. 14: 769-776,1980.

JG Pires, R Maggio, C Manes, P Palumbo, On the importance of pharmacokinetics and pharmacodynamics in engineering sciences as an inter- and multidisciplinary field: an introductory analysis. SIMPEP 2014,

Health care: past and future

Systems biology and the new demands

A lack of system-level understanding of cellular dynamics has prevented any substantial increase in the number of new drugs available to the public and any increase in drug efficacy or eradication of any specific disease. In contrast, pharmaceutical companies are currently lacking criteria for selecting the most valuable targets, research and development (R&D) expenses skyrocket, and new drugs rarely hit the market and often fail in clinical trials, while physicians face an increasing wealth of information that needs to be interpreted intelligently and holistically (Hood 2004;  Kriete and Eils, 2006).

Evolution of the Modern Healthcare System

Before 1900, medicine had little to offer the average citizen, since its resources consisted mainly of

the physician, his education, and his “little black bag.” In general, physicians seemed to be in short

supply, but the shortage had rather different causes than the current crisis in the availability of healthcare professionals. Although the costs of obtaining medical training were relatively low, the demand for doctors’ services also was very small, since many of the services provided by the physician also could be obtained from experienced amateurs in the community. The home was typically the site for treatment and recuperation, and relatives and neighbors constituted an able and willing nursing staff. Babies were delivered by midwives, and those illnesses not cured by home remedies were left to run their natural, albeit frequently fatal, course. The contrast with contemporary healthcare practices, in which specialized physicians and nurses located within the hospital provide critical diagnostic and treatment services, is dramatic (Bronzino, 2006).

References cited: 

Joseph D. Bronzino, Biomedical Engineering Fundamentals, The Biomedical Engineering Handbook, Third Edition, Taylor&Francis, 2006. 

Leroy Hood, James R. Heath, Michael E. Phelps, Biaoyang Lin, Systems Biology and New Technologies Enable Predictive and Preventative Medicine, Science Viewpoint, October, Vol. 306, 2004.

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

Stochastic models in medicine and life science (requirement for 2nd year, talk)

Whether we investigate the growth and interactions of an entire population, the evolution of DNA sequences, the inheritance of traits, or the spread of disease, biological systems are marked by change and adaptation [1]. It is often said that biology is going to be the science of the 21st  century as physics was the science of the 20th [3,4]. Computers, and computer science ideas and techniques, are of course an important part of all these scientific and engineering activities [4].

Indeed this represents the challenges in biological modeling compared to traditional branches such as physics. In general the adaptations and changes are much faster than physical systems, what makes the modeling and analysis in most of the cases a formidable task.

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  [3] .

In the pharmaceutical industry, the incorporation of the disciplines of pharmacokinetics, pharmacodynamics, and drug metabolism (PK/PD/DM) into various drug development processes has been recognized to be extremely important for appropriate compound selection and optimization [2].

Conerstones of my research

1. Introduction

The first year was dedicated to: 1) achieving the minimal requirements in terms of credits; 2) gathering the maximum amount of knowledge. The academic activities was divided into mathematical and biomedical; and master-level disciplines, summer schools, readings, and short courses. The activities was either suggested by the advisors, prof. Palumbo and prof. Manes, or chosen by me. Some of the disciplines was followed on the hope to increase my theoretical background.

2. Master-level disciplines

• Controllo Ottimo. Prof E De Santis (UAQ): in theory important for optimum regimen design in medical treatments. As result, a talk in the IASI-CNR in June and a paper in the Symposium SIMPEP 2014.
• Farmacologia Speciale. Prof. R, Maggio (UAQ): this theory supposes to support me next year, drug regimen design. As result, a talk given in the department of Medicine (UAQ) and an awarded paper in SIMPEP 2014, a journal extension was proposed by SIMPEP and a book proposal was submitted;

3. Summer Schools

• Mathematical Models and Methods for Living Systems: it was a one-intensive week of studies, see http://web.math.unifi.it/users/cime/. Presentation of talk: On the mathematical modeling in gene expression estimation: an initial discussion on PBM and BM;
• Systems Biology and Systems Medicine: precision Biotechnology and Therapies: this was a one-intensive week of studies and computer simulations, tutorial and lessons. See: http://ucbf.lakecomoschool.org/. Presentation of poster: On the mathematical modeling in gene expression estimation: an initial discussion on PBM and BM;

4. Short courses

• Software Architecture: theory of how to design better software;
• Convergence theory for observers: Necessary, and Sufficient conditions: theory on the design of state reconstruction systems;
• Others: other courses were followed in the hope to find insights and methodologies.

5. Main references used

• S Lenhart, J T Workman, Optimal Control Applied to biological models, Chapman & Hall/ CRC, Mathematical and Computational Biology Series, 2007;
• Sara E Rosenbaum, basic pharmacokinetics and pharmacodynamics: an integrated textbook and computer simulations, John Wiley & Sons, 2011.

6. Most significant publications

• JG Pires, R Maggio, C Manes, P Palumbo, On the importance of pharmacokinetics and pharmacodynamics in engineering sciences as an inter- and multidisciplinary field: an introductory analysis. SIMPEP 2014, Bauru (São Paulo, Brasil), Online: http://www.simpep.feb.unesp.br/anais_simpep.php?e=9
• JG Pires, C Manes, P Palumbo, On the importance of optimal control theory in engineering sciences as a complementary and supplementary methodology to Operations Research: a case-study analysis. SIMPEP 2014, Bauru (São Paulo, Brasil), Online: http://www.simpep.feb.unesp.br/anais_simpep.php?e=9 

1. Introduction

The year of 2015, second year of the PhD pathway of the abovementioned student, was agreed to be dedicated to researches on the IASI-CNR (Gemelli Ospedale)[1]. The researches will consist of reading literatures and testing models published or propose new ones. The main topics is what we have called "The Big Glucose Model," which boils down to an attempt to enhance already existing mathematical and computational models for studying glucose control, in general the models are based just on insulin, the idea is to gather several different models based on other hormones or bio- molecules considered significant on the alteration of glucose levels in the human blood.

2. Courses to enroll

• Identificazione dei Modelli e Analisi dei Dati: this is based on state space models;
• Complementi di Automatica: this is based on kalman filter models and parameter stimation;

3. References

The references were not defined yet, it will be taken from an archive offered by De Gaetano, from the IASI-CNR Gemelli Ospedale.

A starting point could be:

• P. Palumbo, S. Ditlevsen, A. Bertuzzi, A. De Gaetano, Mathematical modeling of the glucose-insulin system: A review, Mathematical Bioscience, 2013;
• Ludovic J. Chassin, Malgorzata E. Wilinska, Roman Hovorka. Evaluation of glucose controllers in virtual environment: methodology and sample application, Artificial Intelligence in Medicine (2004) 32, 171—181

4. Final Remarks

Unfortunately a precise agenda for next year is complex, once it depends on my response to the project proposed by Andrea De Gaetano and the success on the research. In the first year I have finished all the prerequisites - two master-level disciplines and 18 credits of ad hoc activities - for avoiding conflicts with this part of my academic cycle.

5. Extra

One paper partially accepted: Biologia Sistêmica: um novo paradigma para as ciências biológicas e exatas “ou” Biologia Sistêmica e Inteligência Computacional. One paper invited to a journal, and three textbooks proposed to publish under invitation.

---

[1] CNR-IASI - Laboratorio di Biomatematica, UCSC – Largo A. Gemelli 8, 00168, Roma, Italy, Ph: +39 06 30155389       Fax: +39 06 3057845

http://www.biomatematica.it

See also: Information Engineering, Engenharia da informação VS engenharia de produção

Suggestion: Access the video from Youbube, then you will see a set of correlated videos, which can be very helpful. See scheme below.

Accessing Youtube from the current blog

Optimal Control in life sciences and medicine

Cover for the slides

Abstract: Optimal Control Applied to Life Sciences

Life Sciences might be seen as the connections between medicine, biology, mathematics, physics, and computer sciences. Further, optimal control might be defined as the extension of static optimization, or even as some comments, the new face of Variational Calculus.

In this talk we present several examples from life sciences analyzed with the support of optimal control. We might apply basically three approaches to optimal control problems, with their own weaknesses and strengthens: the Pontryagin’s Maximum Principle, Dynamic Programming, or Static Optimization. All the problems treated here were analyzed by the Pontryagin’s Maximum Principle.

The problems are solved using numerical schemes implemented in a computer. Topping the bill, we present two cases from a paper on process of publication: phototherapy for infants affected by neonatal jaundice and Feed-Forward Loop Network. We leave as future works comparisons with other approaches such as dynamic programming, or works on constraints on state space. Furthermore, we have concentrated on continuous-deterministic problems.  

 Keywords: Life Sciences, applied optimal control, numerical schemes, Runge-Kutta Method, forward-backward sweep method. 

Full PDF:

Optimal Control applied

 

General Scheme for the numerical simulations

Scheme for the several ways to tackle numerically problems in optimal control theory

flowchart for the algorithms used in the numerical simulations

numerical simulations for the neonatal jaundice photo-therapy using optimal control theory

PS. I was biased mainly by

LENHART, S.; WORKMAN, J. T, Optimal Control Applied to biological models, Chapman & Hall/ CRC, Mathematical and Computational Biology Series, 2007.

See that is also a nice reference: 

Sebastian Anița, Viorel Arnăutu, Vincenzo Capasso, An introduction to optimal control problems in life sciences and economics: from mathematical models to numerical simulation with Matlab®, Modeling and Simulation in Science, Engineering and Technology, Birkhäuser, 2011. 

See Suzanne Lenhart homepage for several codes in Matlab. See that the codes are built independently of Matlab, they can be easily adapted to other languages.

 See for a simplified version of the codes used, in Portugal: 

 

On the design of a molecular dynamics based model for studying receptor-receptor interactions: Systems Biology, Molecular Dynamics, and biomechanics

•Content

Introduction

•Methodological procedures

•Theoretical Background

•Receptors

•Dimerization and ligand binding

•Bioinformatics and mathematical modelling

•G-protein-coupled receptor dynamics

•The receptor–dimer cooperativity index

•Single-molecule imaging revealed dynamic GPCR dimerization

•Proposed model

•Conclusions and Final remarks

•References 

Download the full text: PDF.

Introduction

The pharmaceutical industries is likely to be amongst the most important and controversy ones. Issues present on the industries vary from misuse of advances such as not allowing drugs to reach the consumers or even manipulation of results. However, with no doubt , this is an extreme active area. Hot topics at the moment are: Systems Biology, Systems (bio) Medicine, P4 Medicine, and Systems Pharmacology.

The several scale to studying matter, from a physics viewpoint and from a biological viewpoint. For physics what matters is size rather organization. For physics the brain and a stone of the same size and mass is the same thing, but for biology one is a miracle of life and the second is a chunk of matter, useless.

 

The several levels of organization from human to genes, each level can be homeland for modeling, approaches that mixture them are in general called multiscales approaches

Scheme depicting the importance of receptors. A distal region can be controlled due to the concept of ligand and receptors



scheme for the methodology proposed for developing the software. the sphere supposes to represent the cell surface, whereas the dots are the dimers, or even monomers, that is, any clusters that can be considered a point, a node.

Videos used on the talk

  

This video was produced in 2012 in Gdansk as a warming up exercise in Continuous and Discrete simulations, taught by prof. Sergey Kshevetskii
Theoretical Physics Department of Immanuel Kant Russian
State University. The model is simple, just several particles in a box, with a potential between them, they are given an initial kick, there is no dissipation. 

This video was produced in Gdansk, as part of the lesson in classical simulations, by Winczewski Szymon, see Necking (nanowire)

 

ON THE BOARD (publication submitted, ACCEPTED)

On the importance of pharmacokinetics and pharmacodynamics in engineering sciences as an inter- and multidisciplinary field: an introductory analysis

This paper was awarded by the commission of SIMPEP a position of highlight, amongst the best papers of 2014. The authors are honored and pretty gratified by the gesture of recognition of importance and impact of the research. Thanks SIMPEP for the kindness!  

 Jorge Guerra Pires

Department of Information Engineering, Computer Science and Mathematics
University of L'Aquila, Italy
Institute of Systems Analysis and Computer Science (IASI)
Cosiglio Nazionale  delle Ricerche (CNR) Rome, Italy
CAPES Foundation, Ministry of Education of Brazil

Email: jorgeguerrapires@yahoo.com.br

Roberto Maggio

Department of Biotechnological and Applied Clinical Sciences
University of L'Aquila (UAQ)

Email: roberto.maggio@univaq.it 

Pasquale Palumbo

Institute of Systems Analysis and Computer Science (IASI)
Cosiglio Nazionale  delle Ricerche (CNR) Rome, Italy
Email: pasquale.palumbo@iasi.cnr.it

Costanzo Manes
Department of Information Engineering, Computer Science and Mathematics
University of L'Aquila, Italy
Institute of Systems Analysis and Computer Science (IASI)
Cosiglio Nazionale  delle Ricerche (CNR) Rome, Italy
Email: costanzo.manes@univaq.it

Submitted to  SIMPEP

Language: English

Area: Engineering Teachings

Abstract:

Amongst the entire potential spectrum of expansion of the engineering sciences as an omnipresent field, we might point out the studies of drugs, applied pharmacology, as a good candidate for receiving attention. The common problematic encountered in the literature faced by the industry of drugs is the high cost for drug development and systematic approaches are demanded. Based upon the authors’ viewpoint, by no means it interferes with the natural and important chase of identity by (industrial) production engineering. On the hope of bringing the discussions to engineering’s territory, we borrow several insights from mathematical modeling; it is presented a simple case study, tumor treatment using optimal control, we shall see that it is possible with simple tools already standard in engineering to design a optimal regimen for the tumor therapy, given that the tumor respects our model. On the example presented, we shall see that tools already part of (industrial) production engineering, with exception of optimal control theory, is enough for getting insights. The ideas discussed herein could diminish the cost of drug development if properly extended. As any endeavor, we have challenges, such as to gain the credibility necessary for really using those models in the academy and industry.

Key-words: Teachings of Engineering Sciences; Simulations; Pharmacology; Optimal Control; Cancer Therapy;

On the importance of optimal control theory in engineering sciences as a complementary and supplementary methodology to Operations Research: a case-study analysis

Jorge Guerra Pires

Department of Information Engineering, Computer Science and Mathematics
University of L'Aquila, Italy
Institute of Systems Analysis and Computer Science (IASI)
Cosiglio Nazionale  delle Ricerche (CNR) Rome, Italy
CAPES Foundation, Ministry of Education of Brazil

Email: jorgeguerrapires@yahoo.com.br

Costanzo Manes
Department of Information Engineering, Computer Science and Mathematics
University of L'Aquila, Italy
Institute of Systems Analysis and Computer Science (IASI)
Cosiglio Nazionale  delle Ricerche (CNR) Rome, Italy
Email: costanzo.manes@univaq.it

Pasquale Palumbo

Institute of Systems Analysis and Computer Science (IASI)
Cosiglio Nazionale  delle Ricerche (CNR) Rome, ItalyEmail: pasquale.palumbo@iasi.cnr.it

Submitted to  SIMPEP

Language: English

Area: Mathematical Programming

Abstract:

On this work it is discussed on the optimal control theory as supplementary and complementary methodological procedure to operations researches. Accordingly, it is disserted on the importance of optimal control theory as an essential tool for supporting operations research on its chases for enhancing processes by means of optimization. Here two case studies are presented taken from the literature of production (industrial) engineering: phototherapy applied to neonatal affected by the syndrome called jaundice and optimal production of protein. Furthermore, we have made use of numerical schemes for solving ordinary differential equations to achieve the optimal controls: the forward-backward sweep method and the Runge-Kutta 4. The paper is organized in an introduction, methodological procedures, the case studies, the conclusions and final remarks, and finally, the references. It was strived to achieve a paper in a layman terms, for a general audience. 

Keywords: Optimal Control Theory; Operations Research; Numerical Schemes; Pontryagin’s Maximum Principle.   

Incoming (Publication)

Poster Section: Como Lake Summer School

On the mathematical modeling in gene expression estimation: an initial discussion on PBM and BM

Jorge Guerra Pires , jorgeguerrapires@yahoo.com.br

Department of Information Engineering, Computer Science and Mathematics
University of L'Aquila, Italy
Institute of Systems Analysis and Computer Science (IASI)
Cosiglio Nazionale  delle Ricerche (CNR) Rome, Italy

CAPES Foundation, Ministry of Education of Brazil 

Abstract— with the advent of computers, new technologies could finally rise up. Maybe one of the most polemic and prominent ones are the methods from artificial intelligence such as neural networks or even fuzzy logic. In this short paper we discuss a set of insights generated by the authors after studying the two-communities currently predominant in applied mathematical modeling. We conclude that applied mathematical models, here focused on biological systems, might beneath considerably by recognizing middle-way models between classical (traditional) mathematics and knowledge-based models. 

Keywords—systems biology; pharmacology; biomathematics; knowledge-based modeling; state-space-based models.

  Download files: PDF 1, PDF 2.

Comments

PBM stands for Partial Blind Models and BM for Blind Models. This idea came to me by accident when I was trying to express myself during the pathway of my thesis of master of science, I have used those terms for the very first time on Pires (2013b), then replicated on Pires (2013a). The idea sounds trivial and without any innovation, nonetheless recently I have taken it serious and based upon my readings, this is really interesting for the upcoming generation of scientists.

====

Pires JG (2013b). 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. 

Pires JG (2013a). 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.

====

Jorge Pires

Stochastic Modelling in the biomedical sciences

Physiological processes at several scales (subcellular, cellular, tissue, organ and even population) are inherently stochastic, due to a great variety of noisy factors affecting the phenomenon under investigation.

In fact, deterministic models (representing the vast majority of formalizations hitherto employed in biomedicine) are not realistic, unless the random fluctuations remain small. An incorrect representation, omitting substantial system noise where this is in fact present, leads to poor model identification, biased parameter estimations and inconsistent conclusions.

In recent years there has been an increased emphasis into modeling the randomness inherent in many physiological phenomena, and tools like Stochastic Differential Equations (SDE), or Equações Diferenciais Estocásticas , so far mainly utilized in finance, have found initial application. 

Connected with the use of these techniques in representing biomedical processes, are issues such as identifiability, stability or periodicity, which are typically more complicated to study then their deterministic counterparts.

==

Source: JG. Pires; P Palumbo; A De Gaetano; C. Manes. ‘Stochastic models in medicine and life sciences: mathematical analysis, model identification, validation and stability properties’. Proposal for PhD programme. 2014. Unpublished.

==
Jorge Pires

Think about it! Is it reality ‘deterministic’ or ‘stochastic’?

Maybe one of the most famous men in the battle against the “random world” as one of the truths was Einstein. He believed firmly until his death on a reality so deterministic that we could predict the future [1]. However, it is kind of fun that even his world played against him, with the famous work on Brownian Motion, one of his famous five papers [2]. Maybe if Julius Caeser had met Einstein, perhaps he would never had said that “his luck has been thrown to the dice.” This is irony maybe that the works on Brownian motion led to the current theory of stochastic differential equations, the “random” counterpart of the differential equations. 

What about the current state of the art?

“The message that keeps being repeated is that the kinetics of biological processes at the intra-cellular level are stochastic, and that cellular function cannot be properly understood without building that stochasticity into in silico models. ” [4]

 “I have reasons to expect that ‘quantum mechanics’ in ‘biological systems’ is not the only source of noise (randomness), and neither it is lack of knowledge. Those factors, mainly lack of knowledge, might be currently predominant, but it will perhaps be clear that biological systems are not so predictable as it is physical systems. It does not mean that they possess special set of laws. The matter might be boiled down to ‘flexibility’ and ‘variability’ in the cell level. The probability of finding true homogeneity in a cell population is almost zero, no matter the size. For instance, Brownian motion is significant in many real cases in biology, and it has nothing to do with quantum mechanics. “[5]

The problem of the observer in physics.

References:

[1] Clark, R. W. Einstein: the life and times. Públicado em acordos com World Publishing Company, 1971.

[2] Stachel, J (1998). Einstein’s miraculous year: five papers that changed the face of physics. Princeton University Press. New Jersey.

[3] Russell, Stuart; Norvig, Peter. Artificial Intelligence: A modern approach. Second edition. Prentice Hall Series in Artificial Intelligence: 2003.

[4] Wilkinson, DJ (2012). Stochastic modelling for systems biology. Second Edition. Chapman and Hall Book. CBC press. Online: http://www.staff.ncl.ac.uk/d.j.wilkinson/smfsb/2e/. last access: July 2014.

[5] Pires J G (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.

[6] Rome: The Rise and Fall of an Empire. Documentary.

[7] Ruffino, P R C. Uma iniciação aos sistemas dinâmicos estocásticos. Publicações Matemáticas.  IMPA, 2009.

===

Jorge Pires

NEWS

I have just created two new blogs: a counterpart for this one in Portuguese and a page for miscellaneous topics.

Jorge Pires

Upcoming Events (Summer Schools)

"Mathematical Models and Methods for Living Systems”
CIME-Foundation and CIRM
CIME
Levico Terme (Trento) from Sept. 1  to  Sept. 5, 2014

Status (Accepted, permission to travel given)

Mathematical Models and Methods for Living Systems

- Modelling the formation of vascular networks
Mark Chaplain (Univ. Dundee, Great Britain)
- Mathematical modeling of morphogenesis in living materials
Pasquale Ciarletta (CNRS, France)
- Cell movement in non-isotropic environments and the modeling of cancer spread
Thomas Hillen (Univ. Alberta, Canada)
- Cell-based, continuum and hybrid models of tissue dynamics
Hans G. Othmer (Univ. Minnesota, USA)
- Modelling cell migration in fiber networks
Luigi Preziosi (Politecnico Torino, Italy)
Il CIME ed il CIRM finanzieranno le spese di soggiorno per circa 30 partecipanti.
Per iscriversi collegatevi al sito
http://web.math.unifi.it/users/cime/

Summer School 2014 Participants (Levico Terme, Trento, Hotel Bellavista. Photo by  Augusto Micheletti)

“How To Understand Complex Biological Functions”

1st SyBSyM Como School

Lake Como School of Advanced Studies

September 21 to September 27,

Como, Italy.

School wesite

Status (Accepted, permission to travel given)

Poster accepted. 

Lectures

Sep 21, Sun

·                       Jens Nielsen (1), Lecture 1: Systems biology of yeast metabolism and evolution (Chalmers University of Technology, Göteborg)

Sep 22, Mon

·                       Jonathan Karr, Integrative computational models (Icahn School of Medicine at Mount Sinai, NYC)

·                       Lilia Alberghina, Systems biology of growth and the cell cycle, SYSBIO Italy (Univ. of Milano-Bicocca)

Sep 23, Tue

·                       Dina Petranovic, Yeast as a model for cell aging and death (Chalmers University of Technology, Göteborg)

·                       James Sharpe, Image-driven modelling of multicellular systems (CRG Barcelona)

Sep 24, Wed

·                       Jens Nielsen (2) Lecture 2: Systems biology of cancer metabolism (Chalmers University of Technology, Göteborg)

·                       Richard Kitney, ISBE: an infrastructure for systems biology and systems medicine ISBE Coordinator (Imperial College London)

·                       Walter Kolch CASyM, How to shape systems medicine? (CASyM Steering Committee, UCD Dublin)

Sep 25, Thu

·                       Marco Vanoni, Signaling and metabolism (SYSBIO Italy, Univ. of Milano-Bicocca)

·                       Matteo Barberis, Cell cycling and chromatin dynamics (University of Amsterdam)

Sep 26, Fri

·                       Damjana Rozman, From healthy to fatty liver: a systems approach (University of Ljubljana)

·                       Sampsa Hautaniemi, Analysis and Integration of Large-scale Molecular and Clinical Data in Cancers (University of Helsinki)

Sep 27, Sat

·                       Rudi Balling, Systems biology of Parkinson’s disease (Luxembourg Centre for Systems Biomedicine)

·                       Mikael Benson, Complex disease, omics maps and their clinical implementations (CASyM Steering Committee, University of Göteborg)

·                       Hans Westerhoff, Mapping the systems biology of multifactorial diseases (Univ. of Amsterdam, Univ. of Manchester)