Mittwoch, 20. September 2017

Computational disease modeling – fact or fiction?

Tegnér et al. (2009): Computational disease modeling - fact or fiction?

In the Abstract, we can learn about the two main approaches towards computational systems biology:
There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity.
[T]he development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling.
A couple of more quotes from the paper:
Successful modeling of diseases is greatly facilitated by standards for data-collection and storage, interoperable representation, and computational tools enabling pattern/network analysis and modeling. There are several important initiatives in this direction, such as the ELIXIR program providing sustainable bioinformatics infrastructure for biomedical data in Europe. Similar initiatives are in progress in the USA and Asia.
Across different application areas, a key question concerns the handling of model uncertainty. This refers to the fact that for any biological system there are numerous competing models. Any discursive model of a biological system therefore involves uncertainty and incompleteness. Computational model selection has to cope systematically with the fact that there could be additional relevant interactions and components beyond those that are represented in the discursive model. For instance, there is often insufficient experimental determination of kinetic values for mechanisms contemplated in a verbal model, leading to serious indetermination of parameters in a computational model. Hence, biological models, unlike models describing physical laws, are as a rule highly over-parameterized with respect to the available data. This means that different regions of the parameter space can describe the available data equally well from a statistical point-of-view.
A successful strategy in computational neuroscience has been to identify minimal models that adequately describe and predict the biology, but at the potential price of selecting a too narrowly focused model. This approach is justified if adequate knowledge of the underlying mechanisms involved in a given condition exists.
An alternative approach, recently employed within the systems biology and computational neuroscience fields, is to search for parameter dimensions (as opposed to individual parameter sets) that are important for model performance. This concept of model ensembles represents a promising approach.
[A] mechanistic model is not very helpful unless there are experimental means to assess its predictive validity[.]
It appears that the systems biology community focuses on intracellular networks whereas computational neuroscience emphasizes top-down modeling.
It must also be recognized that top-down models of insufficient richness may excessively constrain model space and lose predictive ability.
There is a lack of theory for how to integrate model selection with constraint propagation across several layers of biological organization. Development of such a theory could be useful in modeling complex diseases even when only sparse data is available. One useful practical first approximation is the notion of disease networks – i.e. network representations of shared attributes among different diseases and their (potential) molecular underpinnings.
[In computational systems biology], much attention is given to formal methods of model selection and datadriven model construction. In contrast, in computational neuroscience (with the notable exception of computational neuroimaging), formal model selection methods are almost completely absent.

What it takes to understand and cure a living system

Swat et al. (2011): What it takes to understand and cure a living system: computational systems biology and a systems biology-driven pharmacokinetics-pharmacodynamics platform

This publication serves as a general introduction to Computational Systems Biology, as well as an introduction to the SBPKPD platform, more about which can be found at this website: http://www.sbpkpd.org/

One of the notable features of this platform is its statistic capabilities:
An automated statistical analysis provides parameter estimates with their standard errors, covariance matrix, residual plots and goodness-of-fit measures, such as the Akaike Information and Schwartz criteria.
On the technical foundations of the platform:
To avoid typical problems of accessibility (owing to restriction to one platform or browser type), we based our SBPKPD on the platform-free Java-based Google Widget Toolkit technology. All models are implemented and run in R, a programming language for statistical computing (http://www.r-project.org/). [...] To our knowledge, no tool in this area has been designed so far for execution on an R-based cluster, and we would like to use this exciting possibility for computationally expensive tasks.
Further development will focus on the following things:
With its solid conceptual base and its mathematical background, our SBPKPD platform is suitable for further development into more specialized facilities. In a (semi)automatic in vitro–in vivo correlation system, existing models and approaches such as PK fitting supported by new processes like numerical deconvolution, could establish mathematical relations between the in vitro drug dissolution and its in vivo behaviour. Such an ‘IVIVC’ system could be quite useful for clinical and pharmaceutical research in the process of new drug admission, for which few tools exist, and which are all commercial: it is our goal to stimulate crossinstitutional cooperation in this area by providing an open-source simulation and modelling platform, the development of which will also be guided by clinical users informed best about current needs in daily medical practice.
Here are some more quotations from the paper, giving an introduction to Computational Systems Biology in general.
Genomics started from biochemistry and then molecular biology. It was paralleled by a development in physics and mathematics, which led to applications of non-equilibrium thermodynamics in biology, mathematical biology and ultimately to metabolic control analysis, flux balance analysis and dynamic network modelling. These two upward movements have since been combined into a scientific discipline called Systems Biology. Systems Biology (SB) aims at understanding how biological function emerges in the interactions between components of biological systems. Ultimately, SB should enable one to understand how improper networking of the macromolecules of living organisms leads to their diseases and how molecular interference may redirect those networks to their proper functioning. SB has progressed to new understanding of the organization and functioning of metabolic and signal transduction pathways in ways that had been impossible with molecular and cell biology, and indeed with functional genomics, alone. Moreover, not even SB has delivered yet the understanding of the functioning of entire organisms, such as in an understanding of disease or in actual drug discovery.
[T]he extreme bottom-up approach to whole organism SB that would describe the activity of every individual macromolecule, is not within the reach of the present computation methodologies, and, even worse, not within the reach of the necessary experimentation facilities.
The equations used in pharmacokinetics (PK) [...] use abstractions of physiological processes to fit equations to observed dynamics of the concentrations of drugs in the patient. Parameters again refer to abstractions of real components of the systems; they include ‘distribution volumes’, which often much exceed realistic volumes, as they comprise the effects of partition coefficients. This is fine for quasisteady states, but may not work well in dynamic situations, or when saturable kinetics determines distributions. Indeed, mechanistic PK is probably the most neglected field in the area of medically relevant biosimulations.
The lack of quantitative and standardized in vivo measurement techniques at the molecular level forces one to obtain in vitro data in artificial or cell line-derived constructs (e.g. Caco-2) or to interrogate animal models barely resembling the human. The accompanying hurdle is the in vitro-to-in vivo and/or inter-species extrapolation (often based on phenomenological and disputable allometric ‘laws’). Each of these steps is full of simplifications distorting the reality one thinks to observe.
There exist of course a number of excellent tools for physiologically based whole-body models like SIMCYP, GASTROPLUS and PKSIM or ADAPT II, WINNONLIN, NONMEM and KINETICA for compartmental (population) PK analysis. The tools in the first category suffer from their closed architecture making open source collaboration impossible. On the other hand, tools in the latter category are accessible as standalone applications, running to a large extent under Windows only. Their user-friendliness varies between very sophisticated but expensive, and disputable (e.g. Fortran syntax in NONMEM) but free or inexpensive. [...] In this paper we shall introduce SBPKPD, a platform for [...] an open-source collaboration.
A number of opensource model repositories exist, with a broad spectrum of models and simulation facilities (Java Web Simulation - JWS and Biomodels.net, or more specialized (e.g. CCDB, which contains cell cyclerelated models only)). Together, JWS online and Biomodels store hundreds of kinetic models for metabolic, signal transduction and gene-expression pathways.
JWS online also offers the possibility to run simulations and multiple analysis options (e.g. steady-state and metabolic-control analysis) for any of its models online, i.e. without downloading of software tools. This is what defines it as a ‘live’-model repository, i.e. the models are alive through the web. Through the web, one can change parameter values in any of the models and calculate the implications for model behaviour. One can also determine which steps in a modelled pathway most determine a specific flux or concentration. The view is to make mathematical models produced by SB useful to scientists who are ignorant of mathematics. The use of JWS online is close to experimentation. It may be important that quality control of models is disentangled from the application or validation of the models. If these important activities are mixed, internal inconsistency of modelling may cover up for lack of experimental validation.
JWS online also has the perspective of the silicon organism, also called the virtual biochemical organism (human) (http://vbhuman.org/). This means that it hopes that its models can be linked up with each other such that they grow, ultimately to cover significant parts of entire organisms. This may seem less efficient than the approach of genome-wide kinetic models for entire organisms, but it may not be. The automobile industry is using modular production lines to improve the robustness of the overall production flow to fluctuations in the activities in individual steps. Modularity also makes the quality control manageable. Checking the quality of a genome-wide model is impossible for any individual because of the great complexity. Scientific experts may still be able to check the quality of pathway models.
An important deliverable of the JWS online and Biomodels facilities will become the connecting of adjacent models into larger models of part of the whole cell. Such an activity could greatly reduce the total complexity of the modelling of whole organisms. Success is not guaranteed however; it will depend on whether the biological function is indeed modular and on advances in multi-scale modelling approaches. The organization of whole organisms into tissues, of tissues into cells, and of cells into organelles, as well as the separation between transcription, translation and metabolism, suggests that biology is indeed modular, perhaps because of the same robustness requirements as the automobile industry. At the same time, where such obvious modules are absent this may signal a functional reason, and the approach might not work.

Freitag, 15. September 2017

Model approach for stress induced steroidal hormone cascade changes in severe mental diseases

As I have been talking about it to a fellow Doctor of Medicine, I would like to point out that my most important scientific publication so far, "Model approach for stress induced steroidal hormone cascade changes in severe mental diseases", can be accessed free of charge here.

The publication has not gained the attention it deserves yet. Basically it proposes a model how changes to the steroidal hormone cascade might be the cause or at least a symptom of several mental illness. What this publication does not mention is that we have found out that applying high doses of isoflavones alters the steroidal hormone cascade, which has a beneficial effect in severe mental illness.

Direct link to the PDF file

Artificial Life: An Introduction

In addition to the subjects I listed in my blog post "New Year's Resolutions", I would like to learn more about the emerging field of artificial life. I chose the paper "Open Problems in Artificial Life" (published in 2001) as an introduction to the matter. Here are some crucial quotes from that paper:
In contrast with mathematics, artificial life is quite young and essentially interdisciplinary. The phrase “artificial life” was coined by C. Langton (1986), who envisaged an investigation of life as it is in the context of life as it could be. [...] This broadly based area of study embraces the possibility of discovering lifelike behavior in unfamiliar settings and creating new and unfamiliar forms of life, and its major aim is to develop a coherent theory of life in all its manifestations, rather than an historically contingent documentation bifurcated by discipline. [...] Artificial life is foremost a scientific rather than an engineering endeavor. Given how ignorant we still are about the emergence and evolution of living systems, artificial life should emphasize understanding first and applications second, so the challenges we list below focus on the former.
The challenges that sound most intriguing to me are:
Achieve the transition to life in an artificial chemistry in silico.
Artificial chemistries are computer-based model systems composed of objects (abstractions of molecules), which are generated by collision between existing objects according to a predefined interaction law. [...][...] Bimolecular chemistry is assumed to be sufficient to display the transition to life, but this may involve complex structures. The chemistry may be stochastic rather than deterministic, but should be constructive rather than descriptive; that is, an interaction law should predict (like an algorithm) the product molecules for colliding objects of arbitrary complexity. [...] Artificial chemistries have been investigated by many authors in spaces of various dimensionalities, with deterministic and probabilistic interaction laws. Molecules have been abstracted using cellular automata, secondary structure folding algorithms, finite state automata, Turing machines, von Neumann machines, and the lambda calculus.
Simulate a unicellular organism over its entire lifecycle.
The artificial organism should exhibit virtually its complete spectrum of behavior, including its ability to evolve. [...] The integration of the simulation of many thousands of proteins, and genetic as well as regulatory networks, at the level of deterministic kinetics would already provide important novel quantitative understanding of cell cycle dynamics. However, for moderate completeness, simulating the folding of all biopolymers and their reactions and supramolecular interactions is still a formidable challenge, since current successes in folding are statistical rather than ab initio, and vast progress in integrating molecular dynamics on time scales of minutes to hours is needed. [...] [C]ombinations of (for example) reaction kinetics, molecular dynamics simulations, and lattice gas simulations would be more powerful than any single simulation approach.
Determine what is inevitable in the open-ended evolution of life.
In different historical unfoldings of the evolutionary process and in evolution in other media, two related questions arise: (a) What are the features common to all evolutionary processes, or to broad classes of evolutionary processes? (b) Do different evolutionary processes contain fundamentally different evolutionary potential?
Determine the predictability of evolutionary consequences of manipulating organisms and ecosystems.
The ecosystems of interest include those as different as the entire global biosphere and individual human immune systems, and ecological manipulations range from industrial pollution, climate change, and large-scale mono-crop agriculture to the introduction of genetically engineered organisms. [...] How far can one rationally redesign or rapidly select organisms to fulfill multiple novel criteria without disturbing the viability of the organisms’ organization and defense systems? Is there a tradeoff between utility and viability, or between size of modification and duration of organism utilization? [...] With increasing understanding of the genetic control of development, it will be possible to create novel multicellular organisms through sequential genetic reprogramming. Do we need long-term evolutionary optimization to support or perfect such major changes to organisms?
Develop a theory of information processing, information flow, and information generation for evolving systems.
Firstly, there appear to be two complementary kinds of information transmission in living systems. One is the conservative hereditary transmission of information through evolutionary time. The other is transmission of information specified in a system’s physical environment to components of the system, possibly mediated by the components themselves, with the concomitant possibility of a combination of information processing and transmission. The latter is clearly also linked with the generation of information[.] [...] Secondly, the challenge is to unify evolution with information processing. One starting point is the observation that components of evolving systems (organisms or groups of organisms) seem to solve problems as part of their existence. More generally, theory must address what the capacity of an evolving system’s information processing is, and how it changes with evolution. Are there thresholds between levels of information processing during evolution that match the levels identified in automata theory—for example, from finite state machines to universal computation? How do the algorithms employed by organisms classify in terms of their problem solving efficiency? The third and least-understood role of information is its generation during evolution. As evolution takes place, evolving systems seem to become more complex; successfully quantifying complexity and its increase during evolution is one important part of understanding information generation. Another problem in this area is that of understanding how complexity in an evolving system’s environment can affect the complexity of the organisms that are evolving within the environment.
Demonstrate the emergence of intelligence and mind in an artificial living system.
Two deep issues in this area arise for artificial life. The first is substantive: whether and, if so, how the natures of life and mind are intrinsically connected. The second is methodological: whether it is most profitable to study mind and intelligence only when embodied in living systems. Both issues motivate artificial life’s existing attention to autonomous agents and embodied cognition, and they bear on artificial life’s relation to its elder sister, artificial intelligence. Progress on this challenge will shed new light on many current controversies in both fields, such as the extent to which life and mind should be viewed as “computational.” A constructive approach to all these concerns is to try to demonstrate the emergence of intelligence and mind in an artificial living system.
I am looking for people with similar interests, and perhaps even experience in these fields, in order to discuss the state of this emerging branch of science and possibly cooperate on projects.

Montag, 11. September 2017

Survival of the Fittest!

There has just been a TV debate between the leaders of the classical-liberal party (which I belong to) and the right-wing nationalist party of Austria. Most of the people who were asked to rate the performance of the two contestants said they preferred the right-wing politician. This may be due to his strong anti-immigration stance, which many people in Austria seem to feel sympathy with.

I do not.

The reason is probably that I do not just think about myself or my family. Rather than that, I have still kept my idealism and altruism. I view the world as a place where human beings live, and what interests me is not just my own well-being but more importantly the future of mankind. And in my opinion, the best should prevail, no matter what ancestry or skin color they have. The best should come to Austria and populate the country. I do not regret if they will eventually replace the indigenous population.

The world is for the best of humanity. School, university and professional life test who is good and who is bad. The good ones shall prevail and procreate. The bad ones may perish, I do not feel any sympathies with them.

Just because somebody is Austrian or has a white skin does not mean that he or she is worth surviving.

"Survival of the fittest", as Charles Darwin said!

Note: As the discussion about this statement on Facebook has shown, this statement can be misunderstood. To clarify: Having a chronic illness does not make you "unfit"! But unwillingness to learn and lack of work ethics do constitute "unfitness".

Freitag, 1. September 2017

"New Year's" Resolutions

The year 2017 started a long time ago, and it is still plenty of time until 2018, but I feel like writing a summary of achievements of the past months and kind of "resolutions". As the Jewish New Year 5778 starts on September 20th, which is quite soon, one might argue that these are resolutions for the Jewish New Year.

Professional Life
My greatest achievement in the past year has been the development and implementation of a mesh voxelizer, that is a routine that converts a three-dimensional model specified by polygons to a set of voxels (cuboids). We need this routine so that we can further process the model and make computations using the solver of our program, which employs the finite differences method to solve certain partial differential equations that occur in physics. Coupled with the ability to import DXF and IFC files, on which I previously worked, we now have a powerful functionality which is unique in our market segment - no other program in the segment has it. This makes us stand out from the competition.
All in all there is no reason why I should have to worry about keeping my workplace, so I am happy to enjoy a regular income and look into a reasonably bright future.

Continuing Education
I completed Andrew Ng's course on Machine Learning at Coursera earlier this year, and I have started Geoffrey Hinton's course (which is far more difficult). All in all I am no longer completely clueless about this hot emerging branch of artificial intelligence.
Recently I have bought books on ordinary and partial differential equations and I plan to study them thoroughly.

Websites
I keep modifying my personal homepage all the time. For the website "21st Century Headlines", I did a redesign using a photo I had taken at the Danube river, so now it looks far more professional than before. I also have a new project idea in my mind, which I have called "Find Common Interests", and purchased the related domain. Let's see when I will be in the mood to implement a prototype.

So, the "New Year's" Resolutions are primarily about continuing to study differential equations and machine learning. In addition I shall see to it that I implement a prototype of my new website idea soon.

Mittwoch, 23. August 2017

Warum ich hier wenig über Politik schreibe

Wenn man diesen Blog so liest, könnte man meinen, dass ich sehr mit mir selbst beschäftigt sei.

In Wirklichkeit denke ich aber mehr über die Probleme der Menschheit und Österreichs nach und überlege, wie man das Ärgste verhindern könnte.

Da ich aber nur ein normaler Bürger und kein hochrangiger Politiker bin, steht mir nur ein eingeschränktes Maß an glaubwürdiger Information zur Verfügung. Bevor ich mich in wilde Spekulationen stürze, halte ich mich daher mit öffentlichen Äußerungen zurück.