How Life Can Be Annotated Publications

[1] Dundas, J, Ling, MHT. 2011. Higher Level Intelligence in Machines. Human-Level Intelligence 2: 2. (An editorial) [PDF]

There has been a large number of studies in neurological sciences on how human brain works, especially in reading and parallel information processing. So I think this statement is really sweeping. Perhaps it is better to knowledge the abilities of human brains and to comment on the limitations of the human brain. The book “Adapt” by Tim Hartford advocates micro-step changes. An important aspect in this area is to understand the processes involved behind the scenes so that it gives us a better formulation of the creativity algorithms involved. I will try to put in some pointers on the depths to which higher level intelligence has been simulated by AI in the past years. Some of the higher level intelligence mechanisms such as creativity, dreams and logical thinking have been implemented in machines in certain ways. However, they are still not implemented in the way humans do the same. The exact mechanism in which these intelligence mechanisms are used by human brains is still way ahead of that done by computers. However, we now look at other aspects in them as well as other functions which might be explored in the years ahead in AI.

[2] Ling, MHT. 2012. [Re-creating the Philosopher’s Mind: Artificial Life from Artificial Intelligence.](https://github.com/mauriceling/mauriceling.github.io/wiki/Re-creating-the-Philosopher%E2%80%99s-Mind-Artificial-Life-from-Artificial-Intelligence._ Human-Level Intelligence 2: 1.

The ultimate goal of artificial intelligence (AI) research is to create a system with human level intelligence. This manuscript suggests that AL may be a channel towards human level intelligence, and presents an overview of how high-level intelligence can be achieved from artificial life. It will be interesting when our simulated humans (such as the characters in a future version of Diablo) start to create their own artificial intelligence.

[3] Koh, YZ, Ling, MHT. 2013. On the Liveliness of Artificial Life. Human-Level Intelligence 3: 1.

There has been on-going philosophical debate on whether artificial life models, also known as digital organisms, are truly alive. The main difficulty appears to be finding an encompassing and definite definition of life. By examining similarities and differences in recent definitions of life, we define life as “any system with a boundary to confine the system within a definite volume and protect the system from external effects, consisting of a program that is capable of improvisation, able to react and adapt to the environment, able to regenerate parts of itself or its entirety, with energy system comprises of non-interference sets of secluded reactions for self-sustenance, is considered alive or a living system. Any incomplete system containing a program and can be re-assembled into a living system; thereby, converting the re-assembled system for the purpose of the incomplete system, are also considered alive.” Using this definition, we argue that digital organisms may not be the boundary case of life even though some digital organisms are not considered alive; thereby, taking the view that some form of digital organisms can be considered alive. In addition, we present an experimental framework based on continuity of the overall system and potential discontinuity of elements within the system for testing future definitions of life.

[4] Castillo, CFG, Ling, MHT. 2014. Resistant Traits in Digital Organisms Do Not Revert Preselection Status despite Extended Deselection: Implications to Microbial Antibiotics Resistance. BioMed Research International 2014, Article ID 648389.

We examined whether antibiotics resistance will decline after disuse of specific antibiotics under the assumption that there is no fitness cost for maintaining resistance. Our results show that during disuse of the specific antibiotics, a large initial loss and prolonged stabilization of resistance are observed but resistance is not lost to the stage of pre-resistance emergence. This suggests that a pool of partial resistant organisms persist long after withdrawal of selective pressure at a relatively constant proportion. Subsequent re-introduction of the same antibiotics results in rapid re-gain of resistance. Thus, our simulation results suggest that complete elimination of specific antibiotics resistance is unlikely after the disuse of antibiotics, once a resistant pool of micro-organism has been established.

[5] Ling, MHT. 2014. Applications of Artificial Life and Digital Organisms in the Study of Genetic Evolution. Advances in Computer Science: an International Journal 3(4): 107-112.

Testing evolutionary hypothesis in experimental setting is expensive, time consuming, and unlikely to recapitulate evolutionary history if evolution is repeated. Computer simulations of virtual organisms, also known as artificial life or digital organisms (DOs) can be used for in silico study of evolutionary processes. This mini-review focuses on the use of DOs in the study of genetic evolution. The three main areas focused in this review are (1) emergence of specialized cells, (2) chemical and environmental resistance, and (3) genetic adaptability. This review concludes with a discussion on the limitations on using DOs as a tool for studying genetic evolution.

[6] Castillo, CFG, Chay ZE, Ling, MHT. 2015. Resistance Maintained in Digital Organisms Despite Guanine/Cytosine-Based Fitness Cost and Extended De-Selection: Implications to Microbial Antibiotics Resistance. MOJ Proteomics & Bioinformatics 2(2): 00039.

Antibiotics resistance has caused much complication in the treatment of diseases, where the pathogen is no longer susceptible to specific antibiotics and the use of such antibiotics are no longer effective for treatment. A recent study that utilizes digital organisms suggests that complete elimination of specific antibiotic resistance is unlikely after the disuse of antibiotics, assuming that there are no fitness costs for maintaining resistance once resistance are established. Fitness cost are referred to as reaction to change in environment, where organism improves its’ abilities in one area at the expense of the other. Our goal in this study is to use digital organisms to examine the rate of gain and loss of resistance where fitness costs have incurred in maintaining resistance. Our results showed that GC-content based fitness cost during de-selection by removal of antibiotic-induced selective pressure portrayed similar trends in resistance compared to that of no fitness cost, at all stages of initial selection, repeated de-selection and re-introduction of selective pressure. Paired t-tests suggested that prolonged stabilization of resistance after initial loss is not statistically significant for its difference to that of no fitness cost. This suggests that complete elimination of specific antibiotics resistance is unlikely after the disuse of antibiotics despite presence of fitness cost in maintaining antibiotic resistance during the disuse of antibiotics, once a resistant pool of micro-organism has been established.

[7] Ling, MHT. 2016. Of (Biological) Models and Simulations. MOJ Proteomics & Bioinformatics 3(4): 00093.

Modeling and simulation are recognized as important aspects of the scientific method for more than 70 years but its adoption in biology has been slow. Debates on its representativeness, usefulness, and whether the effort spent on such endeavors is worthwhile, exist to this day. Here, I argue that most of learning is modeling; hence, arriving at a contradiction if models are not useful. Representing biological systems through mathematical models can be difficult but the modeling procedure is a process in itself that follows a semi-formal set of rules. Although seldom reported, failure in modeling is not a rare event but I argue that this is usually a result of erroneous underlying knowledge or mis-application of a model beyond its intended purpose. I argue that in many biological studies, simulation is the only experimental tool. In others, simulation is a means of reducing possible combinations of experimental work; thereby, presenting an economical case for simulation; thus, worthwhile to engage in this endeavor. The representativeness of simulation depends on the validation, verification, assumptions, and limitations of the underlying model. This will be illustrated using the inter-relationship between population, samples, probability theory, and statistics.

[8] Ang, DGY, Ling, MHT. 2021. Sudden and Steep Harsh Environment Results in Over-Compensation in Digital Organisms. EC Microbiology 17(7): 104-113.

Adaptation to external environment to produce viable offspring is an important aspect of evolution. Although experimental studies adapting bacteria to various ecological niches had been carried out, they are usually time-consuming, labour-intensive, and represents one instance of life - biological life; hence, unable to generalize to all forms of life. Digital organisms (DOs), which are computer-simulated organisms, presents alternative life forms. In this study, DOs were used to evaluate ecological niche adaptation by measuring the fitness of the DOs in two varying external parameters (resembling oxygen and carbon). Two oxygen adaptation schemes (gradual and sudden) were tested in the context of two carbon environments (high and no carbon) while fitness is determined as the energy availability as the amount of metabolite E. Our results suggest that external oxygen decline impacts on energy production but sudden oxygen decline increases energy production regardless of carbon environment. Hence, sudden harsh oxygen deprivation may result in over-compensation in adaptation compared to gradual oxygen deprivation in both carbon environments.

[9] Kannan, KSS, Patil, T, Vij, R, Loh, BJK, Ling, MHT. 2022. Nutrient Availability Impacts Intracellular Metabolic Profiles in Digital Organisms. Acta Scientific Microbiology 5(6): 18-25.

The ability of organisms to utilize environmental chemicals as nutrients and adapt to changes in nutrient availability is a hallmark of life. Yet despite different environments, the concentration and osmolarity of intracellular metabolites are relatively constant across different organism. Although adaptation experiments can be performed, they are usually labour intensive and must be carried out in stepwise or gradual manner. On the other hand, digital organisms or computer-simulated organisms can be used to study adaptations to extreme conditions. Here, we examine the effects of nutrient levels on the metabolic profiles of organisms. Our results show that nutrient availability results in significantly different average intracellular metabolite amounts (F = 5166, p-value < 1E-200) at 1500th generation despite the range within one order but there is significant decline of the impact of nutrient availability on the amounts of intracellular metabolites with increasing generations (r = -0.995, F = 385, p-value = 3.98E-05). However, mean intracellular amounts of specific metabolites are significantly different across all 12 nutrient availabilities (14 ≤ F ≤ 1927, 4.1E-304 ≤ p-value ≤ 1.6E-22). This suggests that the impact of nutrient availability is beyond the overall intercellular metabolite amounts but at the level of individual metabolites.

[10] Naing, SY, Thia, EWJ, Roh, D, Chew, C, Tun, SK, Wai, MK, Ling, MHT. 2023. Novel Populations from Simulated Admixed Populations. Medicon Medical Sciences 4(1): 9-15.

Admixtures of two relatively distinct populations; as a result of clashing, mixing, and merging; can drastically affect its population genetics. Studies have suggested that admixed populations are instrumental in establishing novel populations. Computer simulations is a common method to study population admixtures. Although it is plausible to study the emergence of novel populations from simulated admixed populations, studies in this area have been sparse. Here, we attempt to demonstrate the emergence of novel populations from admixed populations using simulation. Our results show that all admixed populations have the potential to result in the emergence of novel populations despite large majority (up to 90%) of one of the two source populations. The null hypothesis of no significant allelic changes can be rejected with a p-value of 5.3E-05. Therefore, our simulation study supports current studies suggesting that admixed populations are instrumental in establishing novel populations.

[11] Dundas, JB, Ling, MHT. 2023. A Computational Approach to Understand the Human Thought Process. 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS).

The aim of this paper is to understand the human mind functions such that we can simulate them into a computational model for making cognitive systems more intelligent. We then present this model with formulae using a top-down approach. We propose that the human thought process, which is the fundamental unit for the human mind and all the different emotions coming from it, can be implemented as a mathematical formulae to a reasonable extent. Furthermore, we argue that our model has the ability to derive mind functions such as dreams and thinking. The model utilizes a top-down approach for understanding the factors involved in the thought process while explaining the factors involved. The authors are intending to make continuous improvements in the formulae as more scientific advances happen in the understanding of the human cognitive functions.

[12] Teo, W, Kwan ZJ, Lum, AKY, Ng, SMH, Ling, MHT. 2024. Independent Genic-Encoded Enzymatic Reactions May Randomly Link into Multi-Step Biochemical Pathways in the Absence of Large Cell Selective Pressure. EC Microbiology 20(2): 01-07.

Origin of metabolic pathways is an important milestone in evolution, linking pre-biotic / abiotic era to biotic era, and genic-encoded enzymatic reactions is at the core. Studies imply that de novo genic origin of early enzymes is plausible. There are also suggestions that large cells may be at the origin of life. However, the questions of whether unlinked enzymatic reactions link to multi-step biochemical pathways and whether large cells are necessary remain. Here, we use digital organisms to examine the emergence of multi-step biochemical pathways from independently genic-encoded enzymatic reactions. Our simulation results suggest that independently genic-encoded enzymatic reactions can randomly link into multi-step biochemical pathways in the absence of large cell selective pressure (p-value > 0.05). This suggests that genic-encoded multi-step biochemical pathways may arise randomly once enzymes are prevalent.

[13] Low, KKM, Ling, MHT. 2024. ODE Versus Petri Net Implementation of Identical SEIRS Model. Acta Scientific Medical Sciences 8(6): 100-104.

Differential equation; more commonly, ordinary differential equation (ODE); and Petri Net are complementary methods commonly used in dynamic systems modelling. However, the differences between ODE models and Petri Net models have not been adequately studied. In this study, we implement a closed 4-compartment SEIRS infectious disease model in both ODE and Petri Net, to examine the differences by comparing their simulation results. Our simulation results suggest that although there are differences between the simulation results across various ODE solvers, the differences between ODE or Petri Net implementations are significant differences (t ≥ 15.34, p-value ≤ 1.59E-12) as a whole; but these differences may not be significant across all compartments. This suggests that ODE model and Petri Net model may reveal different insights into the same problem; hence, supporting the view that ODE model and Petri Net model are complementary.

[14] Maitra, A, Lim, JJH, Ho, CJY, Tang, AY, Teo, W, Alejado, ELC, Ling, MHT. 2024. Experimenting the Unexperimentable with Digital Organisms. To appear in Encyclopedia of Bioinformatics and Computational Biology, 2nd edition.

Digital organisms (DOs) are computer programs with digital genetics that possess similarities to biological life-forms, which can be valuable tools in confirming evolutionary hypotheses and are considered model organisms and instances of evolution rather than computational simulations of evolution. Hence, DOs can be an avenue to study areas that cannot be studied in wet laboratory settings due to various reasons; for example, the non-feasibility of experimental setup (such as, origins of multicellularity from unicellularity, or the emergence of specialized cells), and potential risks or ethical issues (such as, gain-of-function in pathogens and its effects on population, or effects of extinction events). In this article, we describe the origins of DOs, and its use in biology to examine experimentally impossible or ethically challenging areas. The advantages and disadvantages of using DOs, as well as the relevance of DO studies on biological life will also be discussed.